{ "cells": [ { "cell_type": "markdown", "id": "4a867836", "metadata": {}, "source": [ "Bayesian Neural Network model traning and prediction data generation." ] }, { "cell_type": "code", "execution_count": 1, "id": "210da263", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.neural_network import MLPRegressor\n", "import matplotlib.pyplot as plt\n", "from sklearn.metrics import r2_score\n", "\n", "import pickle" ] }, { "cell_type": "code", "execution_count": 2, "id": "edbf3b27", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "from tensorflow.keras import layers\n", "import tensorflow_datasets as tfds\n", "import tensorflow_probability as tfp\n", "\n", "tfk = tf.keras\n", "tf.keras.backend.set_floatx(\"float32\")\n", "import tensorflow_probability as tfp\n", "tfd = tfp.distributions\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.ensemble import IsolationForest\n", "\n", "from scipy.stats import norm" ] }, { "cell_type": "markdown", "id": "9dadf6ec", "metadata": {}, "source": [ "Load the training databases, generated in player_match_database_creation" ] }, { "cell_type": "code", "execution_count": 3, "id": "fa098fa4", "metadata": {}, "outputs": [], "source": [ "db1 = pd.read_excel('mid_outputs/database_entries.xlsx', index_col = 0) \n", "db2 = pd.read_excel('mid_outputs/season2021/database_entries.xlsx', index_col = 0) \n", "db3 = pd.read_excel('mid_outputs/season2122/database_entries.xlsx', index_col = 0) " ] }, { "cell_type": "code", "execution_count": 4, "id": "f71fa9a4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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matchdayplayerteamoppteamhomevotegoalsassistscards_malusfantavote...miscontrolsdispossessedfoulsfouledaerials_wonaerials_lostcarriesprogressive_carriescarries_into_final_thirdcarries_into_penalty_area
01ToloiAtalantaSampdoria07.0100.010.0...0.0069060.0013810.0075970.0069060.0138120.0103590.3563540.0082870.0158840.000000
11DjimsitiAtalantaSampdoria06.0000.06.0...0.0032100.0064210.0048150.0048150.0224720.0144460.4622790.0048150.0048150.000000
21HateboerAtalantaSampdoria06.0000.55.5...0.0057270.0050110.0136010.0021470.0171800.0100210.2827490.0114530.0093060.000716
31OkoliAtalantaSampdoria05.5000.55.0...0.0130180.0035500.0153850.0082840.0473370.0272190.2698220.0023670.0047340.000000
41ZorteaAtalantaSampdoria06.0000.55.5...0.0111110.0055560.0166670.0222220.0166670.0277780.5611110.0722220.0555560.000000
..................................................................
2480638TamezeVeronaLazio05.5000.05.5...0.0198140.0101010.0128210.0132090.0236990.0213680.3372180.0213680.0128210.003885
2480738HonglaVeronaLazio07.0100.59.5...0.0184330.0122890.0230410.0076800.0230410.0261140.3410140.0092170.0153610.003072
2480838LasagnaVeronaLazio07.0100.59.5...0.0464530.0228040.0160470.0084460.0261820.0413850.1908780.0219590.0109800.005912
2480938CaprariVeronaLazio06.0000.06.0...0.0339540.0197150.0153340.0270170.0029210.0098580.3913840.0427160.0277470.018620
2481038SimeoneVeronaLazio07.0100.010.0...0.0512630.0301550.0211080.0218620.0226160.0407090.2461360.0150770.0128160.006031
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24811 rows × 122 columns

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" ], "text/plain": [ " matchday player team oppteam home vote goals assists \\\n", "0 1 Toloi Atalanta Sampdoria 0 7.0 1 0 \n", "1 1 Djimsiti Atalanta Sampdoria 0 6.0 0 0 \n", "2 1 Hateboer Atalanta Sampdoria 0 6.0 0 0 \n", "3 1 Okoli Atalanta Sampdoria 0 5.5 0 0 \n", "4 1 Zortea Atalanta Sampdoria 0 6.0 0 0 \n", "... ... ... ... ... ... ... ... ... \n", "24806 38 Tameze Verona Lazio 0 5.5 0 0 \n", "24807 38 Hongla Verona Lazio 0 7.0 1 0 \n", "24808 38 Lasagna Verona Lazio 0 7.0 1 0 \n", "24809 38 Caprari Verona Lazio 0 6.0 0 0 \n", "24810 38 Simeone Verona Lazio 0 7.0 1 0 \n", "\n", " cards_malus fantavote ... miscontrols dispossessed fouls \\\n", "0 0.0 10.0 ... 0.006906 0.001381 0.007597 \n", "1 0.0 6.0 ... 0.003210 0.006421 0.004815 \n", "2 0.5 5.5 ... 0.005727 0.005011 0.013601 \n", "3 0.5 5.0 ... 0.013018 0.003550 0.015385 \n", "4 0.5 5.5 ... 0.011111 0.005556 0.016667 \n", "... ... ... ... ... ... ... \n", "24806 0.0 5.5 ... 0.019814 0.010101 0.012821 \n", "24807 0.5 9.5 ... 0.018433 0.012289 0.023041 \n", "24808 0.5 9.5 ... 0.046453 0.022804 0.016047 \n", "24809 0.0 6.0 ... 0.033954 0.019715 0.015334 \n", "24810 0.0 10.0 ... 0.051263 0.030155 0.021108 \n", "\n", " fouled aerials_won aerials_lost carries progressive_carries \\\n", "0 0.006906 0.013812 0.010359 0.356354 0.008287 \n", "1 0.004815 0.022472 0.014446 0.462279 0.004815 \n", "2 0.002147 0.017180 0.010021 0.282749 0.011453 \n", "3 0.008284 0.047337 0.027219 0.269822 0.002367 \n", "4 0.022222 0.016667 0.027778 0.561111 0.072222 \n", "... ... ... ... ... ... \n", "24806 0.013209 0.023699 0.021368 0.337218 0.021368 \n", "24807 0.007680 0.023041 0.026114 0.341014 0.009217 \n", "24808 0.008446 0.026182 0.041385 0.190878 0.021959 \n", "24809 0.027017 0.002921 0.009858 0.391384 0.042716 \n", "24810 0.021862 0.022616 0.040709 0.246136 0.015077 \n", "\n", " carries_into_final_third carries_into_penalty_area \n", "0 0.015884 0.000000 \n", "1 0.004815 0.000000 \n", "2 0.009306 0.000716 \n", "3 0.004734 0.000000 \n", "4 0.055556 0.000000 \n", "... ... ... \n", "24806 0.012821 0.003885 \n", "24807 0.015361 0.003072 \n", "24808 0.010980 0.005912 \n", "24809 0.027747 0.018620 \n", "24810 0.012816 0.006031 \n", "\n", "[24811 rows x 122 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db = pd.concat([db1, db2, db3], ignore_index = True) \n", "\n", "db" ] }, { "cell_type": "code", "execution_count": 5, "id": "1d024554", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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matchdayplayerteamoppteamhomevotegoalsassistscards_malusfantavote...gk_psxggk_psnpxg_per_shot_on_target_againstgk_psxg_netgk_passes_completed_launchedgk_passes_launchedgk_passesgk_passes_throwsgk_goal_kicksgk_crossesgk_crosses_stopped
01MussoAtalantaSampdoria06.0000.55.5...13.000.20-1.0088.0201.0397.0112.0101.0186.010.0
11SkorupskiBolognaLazio06.5-200.04.5...32.500.292.50101.0242.0576.0117.0159.0304.018.0
21VicarioEmpoliSpezia05.5-100.04.5...27.800.26-0.20102.0313.0806.0117.0114.0431.025.0
31GolliniFiorentinaCremonese15.0-200.03.0...9.450.240.9524.068.0299.548.572.5128.03.5
41HandanovicInterLecce06.5-100.05.5...10.600.30-1.4027.052.0266.046.045.079.02.0
..................................................................
130022ConsigliSassuoloUdinese07.0-200.05.0...24.400.33-5.60113.0277.0711.0108.0179.0276.017.0
130122DragowskiSpeziaEmpoli06.5-200.04.5...29.500.27-4.5093.0285.0528.097.0122.0270.09.0
130222Milinkovic-Savic V.TorinoMilan06.5-100.05.5...23.100.240.10150.0540.0817.097.0160.0271.019.0
130322SilvestriUdineseSassuolo16.0-200.04.0...24.000.311.0091.0227.0470.080.0175.0293.05.0
130422Montipo'VeronaSalernitana16.5000.06.5...28.700.26-3.30208.0433.0513.067.0175.0305.015.0
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1305 rows × 102 columns

\n", "
" ], "text/plain": [ " matchday player team oppteam home vote \\\n", "0 1 Musso Atalanta Sampdoria 0 6.0 \n", "1 1 Skorupski Bologna Lazio 0 6.5 \n", "2 1 Vicario Empoli Spezia 0 5.5 \n", "3 1 Gollini Fiorentina Cremonese 1 5.0 \n", "4 1 Handanovic Inter Lecce 0 6.5 \n", "... ... ... ... ... ... ... \n", "1300 22 Consigli Sassuolo Udinese 0 7.0 \n", "1301 22 Dragowski Spezia Empoli 0 6.5 \n", "1302 22 Milinkovic-Savic V. Torino Milan 0 6.5 \n", "1303 22 Silvestri Udinese Sassuolo 1 6.0 \n", "1304 22 Montipo' Verona Salernitana 1 6.5 \n", "\n", " goals assists cards_malus fantavote ... gk_psxg \\\n", "0 0 0 0.5 5.5 ... 13.00 \n", "1 -2 0 0.0 4.5 ... 32.50 \n", "2 -1 0 0.0 4.5 ... 27.80 \n", "3 -2 0 0.0 3.0 ... 9.45 \n", "4 -1 0 0.0 5.5 ... 10.60 \n", "... ... ... ... ... ... ... \n", "1300 -2 0 0.0 5.0 ... 24.40 \n", "1301 -2 0 0.0 4.5 ... 29.50 \n", "1302 -1 0 0.0 5.5 ... 23.10 \n", "1303 -2 0 0.0 4.0 ... 24.00 \n", "1304 0 0 0.0 6.5 ... 28.70 \n", "\n", " gk_psnpxg_per_shot_on_target_against gk_psxg_net \\\n", "0 0.20 -1.00 \n", "1 0.29 2.50 \n", "2 0.26 -0.20 \n", "3 0.24 0.95 \n", "4 0.30 -1.40 \n", "... ... ... \n", "1300 0.33 -5.60 \n", "1301 0.27 -4.50 \n", "1302 0.24 0.10 \n", "1303 0.31 1.00 \n", "1304 0.26 -3.30 \n", "\n", " gk_passes_completed_launched gk_passes_launched gk_passes \\\n", "0 88.0 201.0 397.0 \n", "1 101.0 242.0 576.0 \n", "2 102.0 313.0 806.0 \n", "3 24.0 68.0 299.5 \n", "4 27.0 52.0 266.0 \n", "... ... ... ... \n", "1300 113.0 277.0 711.0 \n", "1301 93.0 285.0 528.0 \n", "1302 150.0 540.0 817.0 \n", "1303 91.0 227.0 470.0 \n", "1304 208.0 433.0 513.0 \n", "\n", " gk_passes_throws gk_goal_kicks gk_crosses gk_crosses_stopped \n", "0 112.0 101.0 186.0 10.0 \n", "1 117.0 159.0 304.0 18.0 \n", "2 117.0 114.0 431.0 25.0 \n", "3 48.5 72.5 128.0 3.5 \n", "4 46.0 45.0 79.0 2.0 \n", "... ... ... ... ... \n", "1300 108.0 179.0 276.0 17.0 \n", "1301 97.0 122.0 270.0 9.0 \n", "1302 97.0 160.0 271.0 19.0 \n", "1303 80.0 175.0 293.0 5.0 \n", "1304 67.0 175.0 305.0 15.0 \n", "\n", "[1305 rows x 102 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db_gk1 = pd.read_excel('mid_outputs/database_entries_gk.xlsx', index_col = 0) \n", "db_gk2 = pd.read_excel('mid_outputs/season2021/database_entries_gk.xlsx', index_col = 0) \n", "db_gk3 = pd.read_excel('mid_outputs/season2122/database_entries_gk.xlsx', index_col = 0) \n", "\n", "db_gk = pd.concat([db_gk1, db_gk1, db_gk1], ignore_index = True) \n", "\n", "db_gk" ] }, { "cell_type": "markdown", "id": "04df0936", "metadata": {}, "source": [ "Load player stats from current season and past seasons" ] }, { "cell_type": "code", "execution_count": 6, "id": "bc9dae87", "metadata": {}, "outputs": [], "source": [ "players_orig = pd.read_excel('mid_outputs/players_stats.xlsx', index_col = 3)\n", "#players = pd.read_excel('mid_outputs/players_stats_rwk.xlsx', index_col = 3) # reworked stats to account for past season\n", "\n", "players_old = pd.read_excel('mid_outputs/season2122/players_stats.xlsx', index_col = 3)\n", "players_old_2 = pd.read_excel('mid_outputs/season2021/players_stats.xlsx', index_col = 3)\n", "\n", "players = players_orig" ] }, { "cell_type": "markdown", "id": "397babf2", "metadata": {}, "source": [ "Load team data from current season and add an average Serie A team row" ] }, { "cell_type": "code", "execution_count": 7, "id": "493b0495", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\nicol\\AppData\\Local\\Temp\\ipykernel_1568\\661405348.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", " avg_row = pd.DataFrame(index = ['Avg'], data = [team_data.mean()], columns = team_data.columns)\n" ] }, { "data": { "text/html": [ "
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teamteam_players_usedteam_possessionteam_gamesteam_games_startsteam_minutesteam_goalsteam_assiststeam_pens_madeteam_pens_att...vs_team_foulsvs_team_fouledvs_team_offsidesvs_team_pens_wonvs_team_pens_concededvs_team_own_goalsvs_team_ball_recoveriesvs_team_aerials_wonvs_team_aerials_lostvs_team_aerials_won_pct
AtalantaAtalanta24.0048.60022.0242.01980.040.0028.006.008.0...244.00256.0026.01.08.001.01335.00273.00328.0045.40
BolognaBologna25.0052.40022.0242.01980.027.0020.004.004.0...280.00268.0038.03.04.001.01204.00250.00210.0054.30
CremoneseCremonese31.0043.80022.0242.01980.015.007.002.004.0...239.00271.0036.03.04.000.01229.00409.00314.0056.60
EmpoliEmpoli28.0047.50022.0242.01980.021.0011.000.000.0...283.00253.0036.02.00.000.01148.00263.00217.0054.80
FiorentinaFiorentina28.0057.20022.0242.01980.023.0018.002.004.0...307.00269.0059.01.04.000.01130.00289.00328.0046.80
VeronaHellas Verona34.0042.90022.0242.01980.018.0015.000.000.0...234.00315.0025.01.00.002.01231.00423.00445.0048.70
InterInter23.0054.50022.0242.01980.040.0027.002.002.0...276.00248.0019.02.02.001.01007.00231.00288.0044.50
JuventusJuventus26.0049.00022.0242.01980.034.0026.003.004.0...246.00242.0029.00.04.000.01134.00258.00272.0048.70
LazioLazio21.0051.80022.0242.01980.036.0026.003.004.0...308.00218.0044.01.04.001.01233.00218.00229.0048.80
LecceLecce26.0042.40022.0242.01980.020.0014.001.002.0...283.00300.0045.03.02.002.01200.00417.00332.0055.70
MilanMilan27.0053.50022.0242.01980.036.0031.002.002.0...275.00261.0025.04.02.002.01125.00263.00325.0044.70
MonzaMonza29.0055.00022.0242.01980.027.0017.004.004.0...318.00281.0037.00.04.001.01145.00242.00253.0048.90
NapoliNapoli24.0061.60022.0242.01980.054.0042.005.006.0...298.00199.0028.01.05.000.01100.00232.00280.0045.30
RomaRoma26.0049.30022.0242.01980.029.0019.004.006.0...316.00246.0012.00.06.000.01156.00221.00266.0045.40
SalernitanaSalernitana28.0046.00022.0242.01980.024.0016.001.001.0...252.00259.0057.08.01.001.01213.00291.00285.0050.50
SampdoriaSampdoria31.0047.30022.0242.01980.010.008.000.000.0...339.00300.0059.04.00.000.01189.00362.00349.0050.90
SassuoloSassuolo29.0048.70022.0242.01980.025.0018.004.005.0...287.00206.0072.02.05.001.01131.00256.00206.0055.40
SpeziaSpezia33.0045.50022.0242.01980.017.0010.003.003.0...235.00291.0053.01.03.002.01275.00343.00306.0052.90
TorinoTorino27.0053.00022.0242.01980.022.0017.001.001.0...239.00306.0024.03.01.000.01155.00367.00333.0052.40
UdineseUdinese25.0049.90022.0242.01980.029.0025.000.000.0...282.00252.0034.02.00.001.01101.00233.00277.0045.70
AvgAvg27.2549.99522.0242.01980.027.3519.752.353.0...277.05262.0537.92.12.950.81172.05292.05292.1549.82
\n", "

21 rows × 303 columns

\n", "
" ], "text/plain": [ " team team_players_used team_possession team_games \\\n", "Atalanta Atalanta 24.00 48.600 22.0 \n", "Bologna Bologna 25.00 52.400 22.0 \n", "Cremonese Cremonese 31.00 43.800 22.0 \n", "Empoli Empoli 28.00 47.500 22.0 \n", "Fiorentina Fiorentina 28.00 57.200 22.0 \n", "Verona Hellas Verona 34.00 42.900 22.0 \n", "Inter Inter 23.00 54.500 22.0 \n", "Juventus Juventus 26.00 49.000 22.0 \n", "Lazio Lazio 21.00 51.800 22.0 \n", "Lecce Lecce 26.00 42.400 22.0 \n", "Milan Milan 27.00 53.500 22.0 \n", "Monza Monza 29.00 55.000 22.0 \n", "Napoli Napoli 24.00 61.600 22.0 \n", "Roma Roma 26.00 49.300 22.0 \n", "Salernitana Salernitana 28.00 46.000 22.0 \n", "Sampdoria Sampdoria 31.00 47.300 22.0 \n", "Sassuolo Sassuolo 29.00 48.700 22.0 \n", "Spezia Spezia 33.00 45.500 22.0 \n", "Torino Torino 27.00 53.000 22.0 \n", "Udinese Udinese 25.00 49.900 22.0 \n", "Avg Avg 27.25 49.995 22.0 \n", "\n", " team_games_starts team_minutes team_goals team_assists \\\n", "Atalanta 242.0 1980.0 40.00 28.00 \n", "Bologna 242.0 1980.0 27.00 20.00 \n", "Cremonese 242.0 1980.0 15.00 7.00 \n", "Empoli 242.0 1980.0 21.00 11.00 \n", "Fiorentina 242.0 1980.0 23.00 18.00 \n", "Verona 242.0 1980.0 18.00 15.00 \n", "Inter 242.0 1980.0 40.00 27.00 \n", "Juventus 242.0 1980.0 34.00 26.00 \n", "Lazio 242.0 1980.0 36.00 26.00 \n", "Lecce 242.0 1980.0 20.00 14.00 \n", "Milan 242.0 1980.0 36.00 31.00 \n", "Monza 242.0 1980.0 27.00 17.00 \n", "Napoli 242.0 1980.0 54.00 42.00 \n", "Roma 242.0 1980.0 29.00 19.00 \n", "Salernitana 242.0 1980.0 24.00 16.00 \n", "Sampdoria 242.0 1980.0 10.00 8.00 \n", "Sassuolo 242.0 1980.0 25.00 18.00 \n", "Spezia 242.0 1980.0 17.00 10.00 \n", "Torino 242.0 1980.0 22.00 17.00 \n", "Udinese 242.0 1980.0 29.00 25.00 \n", "Avg 242.0 1980.0 27.35 19.75 \n", "\n", " team_pens_made team_pens_att ... vs_team_fouls \\\n", "Atalanta 6.00 8.0 ... 244.00 \n", "Bologna 4.00 4.0 ... 280.00 \n", "Cremonese 2.00 4.0 ... 239.00 \n", "Empoli 0.00 0.0 ... 283.00 \n", "Fiorentina 2.00 4.0 ... 307.00 \n", "Verona 0.00 0.0 ... 234.00 \n", "Inter 2.00 2.0 ... 276.00 \n", "Juventus 3.00 4.0 ... 246.00 \n", "Lazio 3.00 4.0 ... 308.00 \n", "Lecce 1.00 2.0 ... 283.00 \n", "Milan 2.00 2.0 ... 275.00 \n", "Monza 4.00 4.0 ... 318.00 \n", "Napoli 5.00 6.0 ... 298.00 \n", "Roma 4.00 6.0 ... 316.00 \n", "Salernitana 1.00 1.0 ... 252.00 \n", "Sampdoria 0.00 0.0 ... 339.00 \n", "Sassuolo 4.00 5.0 ... 287.00 \n", "Spezia 3.00 3.0 ... 235.00 \n", "Torino 1.00 1.0 ... 239.00 \n", "Udinese 0.00 0.0 ... 282.00 \n", "Avg 2.35 3.0 ... 277.05 \n", "\n", " vs_team_fouled vs_team_offsides vs_team_pens_won \\\n", "Atalanta 256.00 26.0 1.0 \n", "Bologna 268.00 38.0 3.0 \n", "Cremonese 271.00 36.0 3.0 \n", "Empoli 253.00 36.0 2.0 \n", "Fiorentina 269.00 59.0 1.0 \n", "Verona 315.00 25.0 1.0 \n", "Inter 248.00 19.0 2.0 \n", "Juventus 242.00 29.0 0.0 \n", "Lazio 218.00 44.0 1.0 \n", "Lecce 300.00 45.0 3.0 \n", "Milan 261.00 25.0 4.0 \n", "Monza 281.00 37.0 0.0 \n", "Napoli 199.00 28.0 1.0 \n", "Roma 246.00 12.0 0.0 \n", "Salernitana 259.00 57.0 8.0 \n", "Sampdoria 300.00 59.0 4.0 \n", "Sassuolo 206.00 72.0 2.0 \n", "Spezia 291.00 53.0 1.0 \n", "Torino 306.00 24.0 3.0 \n", "Udinese 252.00 34.0 2.0 \n", "Avg 262.05 37.9 2.1 \n", "\n", " vs_team_pens_conceded vs_team_own_goals \\\n", "Atalanta 8.00 1.0 \n", "Bologna 4.00 1.0 \n", "Cremonese 4.00 0.0 \n", "Empoli 0.00 0.0 \n", "Fiorentina 4.00 0.0 \n", "Verona 0.00 2.0 \n", "Inter 2.00 1.0 \n", "Juventus 4.00 0.0 \n", "Lazio 4.00 1.0 \n", "Lecce 2.00 2.0 \n", "Milan 2.00 2.0 \n", "Monza 4.00 1.0 \n", "Napoli 5.00 0.0 \n", "Roma 6.00 0.0 \n", "Salernitana 1.00 1.0 \n", "Sampdoria 0.00 0.0 \n", "Sassuolo 5.00 1.0 \n", "Spezia 3.00 2.0 \n", "Torino 1.00 0.0 \n", "Udinese 0.00 1.0 \n", "Avg 2.95 0.8 \n", "\n", " vs_team_ball_recoveries vs_team_aerials_won \\\n", "Atalanta 1335.00 273.00 \n", "Bologna 1204.00 250.00 \n", "Cremonese 1229.00 409.00 \n", "Empoli 1148.00 263.00 \n", "Fiorentina 1130.00 289.00 \n", "Verona 1231.00 423.00 \n", "Inter 1007.00 231.00 \n", "Juventus 1134.00 258.00 \n", "Lazio 1233.00 218.00 \n", "Lecce 1200.00 417.00 \n", "Milan 1125.00 263.00 \n", "Monza 1145.00 242.00 \n", "Napoli 1100.00 232.00 \n", "Roma 1156.00 221.00 \n", "Salernitana 1213.00 291.00 \n", "Sampdoria 1189.00 362.00 \n", "Sassuolo 1131.00 256.00 \n", "Spezia 1275.00 343.00 \n", "Torino 1155.00 367.00 \n", "Udinese 1101.00 233.00 \n", "Avg 1172.05 292.05 \n", "\n", " vs_team_aerials_lost vs_team_aerials_won_pct \n", "Atalanta 328.00 45.40 \n", "Bologna 210.00 54.30 \n", "Cremonese 314.00 56.60 \n", "Empoli 217.00 54.80 \n", "Fiorentina 328.00 46.80 \n", "Verona 445.00 48.70 \n", "Inter 288.00 44.50 \n", "Juventus 272.00 48.70 \n", "Lazio 229.00 48.80 \n", "Lecce 332.00 55.70 \n", "Milan 325.00 44.70 \n", "Monza 253.00 48.90 \n", "Napoli 280.00 45.30 \n", "Roma 266.00 45.40 \n", "Salernitana 285.00 50.50 \n", "Sampdoria 349.00 50.90 \n", "Sassuolo 206.00 55.40 \n", "Spezia 306.00 52.90 \n", "Torino 333.00 52.40 \n", "Udinese 277.00 45.70 \n", "Avg 292.15 49.82 \n", "\n", "[21 rows x 303 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "team_data = pd.read_excel('mid_outputs/team_data.xlsx', index_col = 0)\n", "\n", "avg_row = pd.DataFrame(index = ['Avg'], data = [team_data.mean()], columns = team_data.columns)\n", "avg_row['team']['Avg'] = 'Avg'\n", "\n", "team_data = pd.concat([team_data, avg_row])\n", "\n", "team_data" ] }, { "cell_type": "markdown", "id": "cc1cd13d", "metadata": {}, "source": [ "Data processing functions copied from player_match_dataset_creation" ] }, { "cell_type": "code", "execution_count": 8, "id": "32f56138", "metadata": {}, "outputs": [], "source": [ "features_abs = ['r',\n", " 'games',\n", " 'games_starts', \n", " 'minutes',\n", " 'shots_on_target_pct',\n", " 'goals_per_shot',\n", " 'goals_per_shot_on_target',\n", " 'passes_pct',\n", " #'dribble_tackles_pct',\n", " #'dribbles_completed_pct',\n", " 'aerials_won_pct',\n", " 'team_possession',\n", " 'team_goals_assists_per90',\n", " 'team_goals_pens_per90',\n", " 'team_goals_assists_pens_per90',\n", " 'team_xg_per90',\n", " 'team_gk_goals_against_per90',\n", " 'team_gk_save_pct',\n", " 'team_gk_clean_sheets_pct',\n", " 'team_passes_pct',\n", " 'team_passes_pct_medium',\n", " 'team_passes_pct_long',\n", " 'team_sca_per90',\n", " 'team_gca_per90',\n", " #'team_dribble_tackles_pct',\n", " 'team_aerials_won_pct',\n", " 'vs_team_possession',\n", " 'vs_team_goals_per90',\n", " 'vs_team_assists_per90',\n", " 'vs_team_xg_per90',\n", " 'vs_team_gk_save_pct',\n", " 'vs_team_gk_clean_sheets_pct',\n", " 'vs_team_gk_pct_passes_launched',\n", " 'vs_team_gk_crosses_stopped_pct',\n", " 'vs_team_shots_on_target_per90',\n", " 'vs_team_passes_pct',\n", " 'vs_team_passes_pct_short',\n", " 'vs_team_passes_pct_medium',\n", " 'vs_team_passes_pct_long',\n", " 'vs_team_sca_per90',\n", " 'vs_team_gca_per90',\n", " #'vs_team_dribble_tackles_pct',\n", " #'vs_team_dribbles_completed_pct',\n", " 'vs_team_aerials_won_pct',\n", " 'opp_team_possession',\n", " 'opp_team_goals_assists_per90',\n", " 'opp_team_goals_pens_per90',\n", " 'opp_team_goals_assists_pens_per90',\n", " 'opp_team_xg_per90',\n", " 'opp_team_gk_goals_against_per90',\n", " 'opp_team_gk_save_pct',\n", " 'opp_team_gk_clean_sheets_pct',\n", " 'opp_team_passes_pct',\n", " 'opp_team_passes_pct_medium',\n", " 'opp_team_passes_pct_long',\n", " 'opp_team_sca_per90',\n", " 'opp_team_gca_per90',\n", " #'opp_team_dribble_tackles_pct',\n", " 'opp_team_aerials_won_pct',\n", " 'opp_vs_team_possession',\n", " 'opp_vs_team_goals_per90',\n", " 'opp_vs_team_assists_per90',\n", " 'opp_vs_team_xg_per90',\n", " 'opp_vs_team_gk_save_pct',\n", " 'opp_vs_team_gk_clean_sheets_pct',\n", " 'opp_vs_team_gk_pct_passes_launched',\n", " 'opp_vs_team_gk_crosses_stopped_pct',\n", " 'opp_vs_team_shots_on_target_per90',\n", " 'opp_vs_team_passes_pct',\n", " 'opp_vs_team_passes_pct_short',\n", " 'opp_vs_team_passes_pct_medium',\n", " 'opp_vs_team_passes_pct_long',\n", " 'opp_vs_team_sca_per90',\n", " 'opp_vs_team_gca_per90',\n", " #'opp_vs_team_dribble_tackles_pct',\n", " #'opp_vs_team_dribbles_completed_pct',\n", " 'opp_vs_team_aerials_won_pct',\n", " \n", " 'vote_avg',\n", " 'vote_std']\n", "\n", "features_rel = [\n", " 'goals',\n", " 'assists',\n", " 'cards_yellow',\n", " 'cards_red',\n", " 'xg',\n", " 'npxg',\n", " 'shots_on_target',\n", " 'passes_completed',\n", " 'passes_into_final_third',\n", " 'passes_into_penalty_area',\n", " 'progressive_passes',\n", " 'passes_live',\n", " 'passes_dead',\n", " 'through_balls',\n", " 'passes_switches',\n", " 'crosses',\n", " 'corner_kicks',\n", " #'dribble_tackles',\n", " #'dribbles_vs',\n", " #'dribbled_past',\n", " 'blocks',\n", " 'blocked_shots',\n", " 'blocked_passes',\n", " 'interceptions',\n", " 'clearances',\n", " 'errors',\n", " 'touches',\n", " 'touches_def_pen_area',\n", " 'touches_def_3rd',\n", " 'touches_mid_3rd',\n", " 'touches_att_3rd',\n", " 'touches_att_pen_area',\n", " 'touches_live_ball',\n", " #'dribbles_completed',\n", " #'dribbles',\n", " 'passes_received',\n", " 'miscontrols',\n", " 'dispossessed',\n", " 'fouls',\n", " 'fouled',\n", " 'aerials_won',\n", " 'aerials_lost',\n", " 'carries',\n", " 'progressive_carries',\n", " 'carries_into_final_third',\n", " 'carries_into_penalty_area']\n", "\n", "features_rel_gamecorr = [\n", " 'goals',\n", " 'assists',\n", " 'xg',\n", " 'npxg',\n", " 'cards_yellow',\n", " 'cards_red'\n", "]" ] }, { "cell_type": "code", "execution_count": 9, "id": "4001f3f8", "metadata": {}, "outputs": [], "source": [ "features_abs_gk = [\n", " 'gk_games',\n", " 'gk_games_starts',\n", " 'gk_minutes',\n", " 'gk_goals_against_per90', \n", " 'gk_save_pct',\n", " 'gk_clean_sheets_pct',\n", " 'gk_psxg_net_per90',\n", " 'gk_passes_pct_launched',\n", " 'gk_pct_passes_launched',\n", " 'gk_passes_length_avg',\n", " 'gk_pct_goal_kicks_launched',\n", " 'gk_goal_kick_length_avg',\n", " 'gk_crosses_stopped_pct',\n", " 'gk_def_actions_outside_pen_area_per90',\n", " 'gk_avg_distance_def_actions',\n", " \n", " 'team_possession',\n", " 'team_goals_assists_per90',\n", " 'team_goals_pens_per90',\n", " 'team_goals_assists_pens_per90',\n", " 'team_xg_per90',\n", " 'team_gk_goals_against_per90',\n", " 'team_gk_save_pct',\n", " 'team_gk_clean_sheets_pct',\n", " 'team_passes_pct',\n", " 'team_passes_pct_medium',\n", " 'team_passes_pct_long',\n", " 'team_sca_per90',\n", " 'team_gca_per90',\n", " #'team_dribble_tackles_pct',\n", " 'team_aerials_won_pct',\n", " 'vs_team_possession',\n", " 'vs_team_goals_per90',\n", " 'vs_team_assists_per90',\n", " 'vs_team_xg_per90',\n", " 'vs_team_gk_save_pct',\n", " 'vs_team_gk_clean_sheets_pct',\n", " 'vs_team_gk_pct_passes_launched',\n", " 'vs_team_gk_crosses_stopped_pct',\n", " 'vs_team_shots_on_target_per90',\n", " 'vs_team_passes_pct',\n", " 'vs_team_passes_pct_short',\n", " 'vs_team_passes_pct_medium',\n", " 'vs_team_passes_pct_long',\n", " 'vs_team_sca_per90',\n", " 'vs_team_gca_per90',\n", " #'vs_team_dribble_tackles_pct',\n", " #'vs_team_dribbles_completed_pct',\n", " 'vs_team_aerials_won_pct',\n", " 'opp_team_possession',\n", " 'opp_team_goals_assists_per90',\n", " 'opp_team_goals_pens_per90',\n", " 'opp_team_goals_assists_pens_per90',\n", " 'opp_team_xg_per90',\n", " 'opp_team_gk_goals_against_per90',\n", " 'opp_team_gk_save_pct',\n", " 'opp_team_gk_clean_sheets_pct',\n", " 'opp_team_passes_pct',\n", " 'opp_team_passes_pct_medium',\n", " 'opp_team_passes_pct_long',\n", " 'opp_team_sca_per90',\n", " 'opp_team_gca_per90',\n", " #'opp_team_dribble_tackles_pct',\n", " 'opp_team_aerials_won_pct',\n", " 'opp_vs_team_possession',\n", " 'opp_vs_team_goals_per90',\n", " 'opp_vs_team_assists_per90',\n", " 'opp_vs_team_xg_per90',\n", " 'opp_vs_team_gk_save_pct',\n", " 'opp_vs_team_gk_clean_sheets_pct',\n", " 'opp_vs_team_gk_pct_passes_launched',\n", " 'opp_vs_team_gk_crosses_stopped_pct',\n", " 'opp_vs_team_shots_on_target_per90',\n", " 'opp_vs_team_passes_pct',\n", " 'opp_vs_team_passes_pct_short',\n", " 'opp_vs_team_passes_pct_medium',\n", " 'opp_vs_team_passes_pct_long',\n", " 'opp_vs_team_sca_per90',\n", " 'opp_vs_team_gca_per90',\n", " #'opp_vs_team_dribble_tackles_pct',\n", " #'opp_vs_team_dribbles_completed_pct',\n", " 'opp_vs_team_aerials_won_pct',\n", " \n", " 'vote_avg',\n", " 'vote_std']\n", "\n", "features_rel_gk = [\n", " 'gk_shots_on_target_against',\n", " 'gk_saves',\n", " 'gk_free_kick_goals_against',\n", " 'gk_corner_kick_goals_against',\n", " 'gk_own_goals_against',\n", " 'gk_psxg',\n", " 'gk_psnpxg_per_shot_on_target_against',\n", " 'gk_psxg_net',\n", " 'gk_passes_completed_launched',\n", " 'gk_passes_launched',\n", " 'gk_passes',\n", " 'gk_passes_throws',\n", " 'gk_goal_kicks',\n", " 'gk_crosses',\n", " 'gk_crosses_stopped',\n", "]" ] }, { "cell_type": "code", "execution_count": 10, "id": "f4017b2f", "metadata": {}, "outputs": [], "source": [ "DEL_G = False\n", "\n", "features_to_del = [\n", " 'goals',\n", " 'assists',\n", " 'xg',\n", " 'npxg'\n", "]\n", "\n", "def player_match_data(player, pteam, oppteam, oldseason = False):\n", " if(not(player in players.index)):\n", " return None\n", " \n", " if(oldseason):\n", " pdata = players_old.loc[[player]]\n", " else:\n", " pdata = players.loc[[player]]\n", " \n", " pteam_stats = team_data.loc[[pteam]].rename(index = {pteam : player})\n", " \n", " oppteam_stats = team_data.loc[[oppteam]].rename(index = {oppteam : player})\n", " \n", " oppteam_stats = oppteam_stats.rename(lambda x: 'opp_' + x, axis='columns')\n", " \n", " out = pd.concat([pdata, pteam_stats, oppteam_stats], axis = 1)\n", " \n", " return(out)\n", "\n", "def player_match_data_ext(player, pteam, oppteam, oldseason = False):\n", " pdata = player_match_data(player, pteam, oppteam, oldseason = oldseason)\n", " \n", " if(not isinstance(pdata, pd.DataFrame)):\n", " return None\n", " \n", " assert pdata['games'][0] > 0\n", " \n", " out = pd.concat([pdata[features_abs], pdata[features_rel]], axis = 1)\n", " \n", " out[features_rel] = out[features_rel] / max(pdata['minutes'][0], 1)\n", " \n", " out[features_rel_gamecorr] = out[features_rel_gamecorr] * (pdata['minutes'][0] / max(pdata['games'][0], 1) / 90)\n", " \n", " if(DEL_G):\n", " out[features_to_del] = 0\n", " \n", " return out\n", "\n", "def player_match_data_ext_gk(player, pteam, oppteam, oldseason = False):\n", " pdata = player_match_data(player, pteam, oppteam, oldseason = oldseason)\n", " \n", " if(not isinstance(pdata, pd.DataFrame)):\n", " return None\n", " \n", " if(pdata['gk_games'][0] <= 0):\n", " return None\n", " \n", " out = pd.concat([pdata[features_abs_gk], pdata[features_rel_gk]], axis = 1)\n", " \n", " out[features_rel_gk] = out[features_rel_gk] / max(pdata['minutes'][0], 1)\n", "\n", " return out\n", " " ] }, { "cell_type": "markdown", "id": "21fef3ae", "metadata": {}, "source": [ "Players stats rework:\n", "the current season stats are averaged (according to a calculated weight) with the past season data.\n", "In case a player doesn't have past season data, a config file (affine_players) can be used to load the data from an affine player (past season), e.g. Doig affine to Lazovic.\n", "In case, after this process, the player doesn't result in having a minimum amount of games, its stats are averaged with the average Serie A (defensive) player stat, depending on the games remaining to reach the minimum amount. This allows to use players who still haven't played a single game.\n", "\n", "These modified stats are used only for prediction, not for model traning.\n", "\n", "WEIGHT_0 = weight given to the current season in respect to the previous; if the player has a low amount of games this season, the weight is lowered\n", "min_games = minimum games so that the players stats are not averaged with the avg Serie A player stats" ] }, { "cell_type": "code", "execution_count": 11, "id": "6f8707b8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " \n", "Averaging players stats with past seasons:\n", "Meret 1\n", "Provedel 1\n", "Vicario 1\n", "Szczesny 1\n", "Falcone 1\n", "Silvestri 1\n", "Rui Patricio 1\n", "Sepe 1\n", "Milinkovic-Savic V. 1\n", "Musso 1\n", "Maignan 1\n", "Audero 1\n", "Montipo' 1\n", "Skorupski 1\n", "Consigli 1\n", "Dragowski 1\n", "Terracciano 1\n", "Tatarusanu 1\n", "Handanovic 0.7372677888806921\n", "Sportiello 1\n", "Perin 1\n", "Zoet 1\n", "Pegolo 1\n", "Mirante 0.0\n", "Ujkani 0.0\n", "Berisha 0.0\n", "Marchetti 1\n", "Padelli 0.0\n", "Bardi 1\n", "Cordaz 0.0\n", "Pinsoglio 0.0\n", "Fiorillo 0.0\n", "Cragno 0.07071960297766748\n", "Sirigu 0.0668969217356314\n", "Rossi F. 0.0\n", "Berardi A. 0.0\n", "Gemello 0.0\n", "Ravaglia 1\n", "Boer 0.0\n", "Adamonis 0.0\n", "Marfella 0.0\n", "Zovko 1\n", "Piana 0.0\n", "Dimarco 1\n", "Smalling 1\n", "Di Lorenzo 1\n", "Danilo 1\n", "Hernandez T. 1\n", "Udogie 0.9565331442750797\n", "Parisi 1\n", "Mario Rui 0.7658517004816814\n", "Romagnoli 1\n", "Bastoni S. 0.6399743856559673\n", "Mazzocchi 1\n", "Tomori 1\n", "Scalvini 1\n", "Toloi 1\n", "Demiral 1\n", "Maehle 1\n", "Dumfries 1\n", "Juan Jesus 0.649497814013943\n", "Depaoli 1\n", "Mancini 1\n", "Ibanez 0.9846664720478762\n", "Rodrigo Becao 0.8502516838000708\n", "Ebuehi 1\n", "Gosens 1\n", "Darmian 1\n", "Reca 1\n", "Bremer 0.9750733137829912\n", "Rrahmani 0.8642078351755771\n", "Vojvoda 0.9834089158894498\n", "Bastoni 0.9199631793804531\n", "Milenkovic 0.8387899576704131\n", "Kalulu 1\n", "Martinez Quarta 1\n", "Casale 0.756306865177833\n", "Perez N. 1\n", "Izzo 1\n", "Luperto 1\n", "Skriniar 0.743970223325062\n", "Rodriguez R. 1\n", "Marusic 1\n", "Lazzari 0.8799647802769551\n", "Kyriakopoulos 0.4694318473517583\n", "Ampadu 1\n", "Ismajli 1\n", "Llorente D. affine to Ibanez\n", "Llorente D. 0.2188147715661947\n", "Cambiaso 1\n", "Hysaj 1\n", "Biraghi 0.9718529944336394\n", "Medel 0.9017820888788629\n", "Bonucci 0.6716397849462366\n", "Calabria 0.858427180759687\n", "Acerbi 0.9900744416873448\n", "Spinazzola 1\n", "Lykogiannis 0.8397952853598015\n", "Pellegrini Lu. 0.13751033912324234\n", "Djidji 1\n", "Augello 1\n", "Singo 0.8856788372917405\n", "Mari' 1\n", "De Vrij 0.826633581472291\n", "Patric 0.8266335814722912\n", "Faraoni 0.658723635235732\n", "Ceccherini 0.6564443146985842\n", "Hateboer 1\n", "Rogerio 1\n", "Aina 0.9447240931111898\n", "Ferrari G. 0.8955197132616488\n", "Fazio 1\n", "Buongiorno 1\n", "Gunter 0.1650124069478908\n", "Troost-Ekong affine to Fazio\n", "Troost-Ekong 0.46498138957816376\n", "Soumaoro 0.9299627791563275\n", "Ceccaroni 0.03535980148883374\n", "Soppy 0.5303970223325062\n", "Ferrari A. 0.5918923292696083\n", "Zappacosta 0.510567800321121\n", "Gyomber 0.9199631793804531\n", "Alex Sandro 0.9742467210209147\n", "Pezzella Giu. 0.7071960297766748\n", "Bereszynski 0.5303970223325062\n", "Venuti 0.593019743230122\n", "Palomino 0.47409867172675524\n", "Nuytinck 0.6417149159084642\n", "Magnani 1\n", "Colley 0.6199751861042183\n", "Nikolaou 0.9988489109456851\n", "Terzic 1\n", "Igor 0.9092969396195203\n", "Toljan 1\n", "Zortea 0.2560537349191409\n", "Dawidowicz 1\n", "Bellanova 0.518990634755463\n", "Erlic 0.9075682382133995\n", "Ballo-Toure' affine to Calabria\n", "Ballo-Toure' 0.23845199465546857\n", "Stojanovic 0.8266335814722912\n", "Amian 0.9299627791563275\n", "Zima 0.5579776674937966\n", "De Winter 1\n", "Romagnoli S. 0.195409429280397\n", "Ghiglione 1\n", "Rugani 0.6199751861042183\n", "De Sciglio 0.9919602977667494\n", "Djimsiti 0.6799727847594653\n", "Caldara 0.7984471303930201\n", "Karsdorp 0.4477598566308244\n", "Marchizza 0.521091811414392\n", "Kjaer 1\n", "Ruggeri 1\n", "Zanoli 1\n", "Radovanovic 0.8856788372917405\n", "D'ambrosio 0.6199751861042183\n", "De Silvestri 0.39998399103497956\n", "Chiriches 0.4694318473517583\n", "Murru 0.901782088878863\n", "Bonifazi 0.39452966388450256\n", "Walukiewicz 1\n", "Ranieri L. 0.22918389853873725\n", "Gabbia 1\n", "Kumbulla 0.4376295431323894\n", "Lovato 0.9281947890818858\n", "Ferrer 0.13777226357871517\n", "Vasquez 0.7955955334987593\n", "Ruan 1\n", "Ostigard 1\n", "Coppola D. 1\n", "Cacace 1\n", "Conti 0.1771357674583481\n", "Conti 0.6679835812950357\n", "Marrone 0.11786600496277913\n", "Tonelli 0.08856788372917405\n", "Radu 1\n", "Florenzi 0.258322994210091\n", "Sala 0.4133167907361455\n", "Fares 0.0\n", "Fares 0.0\n", "Romagna 0.0\n", "Romagna 0.0\n", "Muldur 0.03999839910349796\n", "Amey 0.0\n", "Zaccagni 1\n", "Milinkovic-Savic 0.9718529944336394\n", "Barella 1\n", "Zielinski 1\n", "Luis Alberto 1\n", "Felipe Anderson 1\n", "Koopmeiners 1\n", "Calhanoglu 1\n", "Frattesi 1\n", "Diaz B. 1\n", "Zambo Anguissa 1\n", "Elmas 1\n", "Miranchuk 1\n", "Samardzic 1\n", "Pereyra 1\n", "Politano 0.9393563425821488\n", "Rabiot 1\n", "Lazovic 0.8752590862647788\n", "Lobotka 1\n", "Bonaventura 1\n", "Pessina 1\n", "Tonali 0.9299627791563276\n", "Pellegrini Lo. 1\n", "El Shaarawy 1\n", "Orsolini 1\n", "Ikone' 1\n", "Candreva 1\n", "Bennacer 0.9999599775874489\n", "Pasalic 0.8378043055462409\n", "Mkhitaryan 1\n", "Chiesa 1\n", "Bandinelli 1\n", "Fagioli affine to Henderson L.\n", "Fagioli 0.7178660049627792\n", "Messias 0.9538079786218743\n", "Arslan 1\n", "Ricci S. 1\n", "Verdi 1\n", "Sensi 1\n", "Barak 1\n", "Soriano 0.9565331442750797\n", "Dominguez 1\n", "Brozovic 0.743970223325062\n", "Cristante 1\n", "Saponara 1\n", "Vecino 1\n", "Locatelli 1\n", "Zaniolo 0.5756912442396314\n", "Maldini 1\n", "Marin 0.996949958643507\n", "Zalewski 1\n", "Bajrami 0.3889578163771712\n", "Coulibaly L. 1\n", "De Roon 1\n", "Mandragora 1\n", "Bourabia 1\n", "Sottil 0.6716397849462366\n", "Aebischer 1\n", "Ederson D.s. 1\n", "Miretti 1\n", "Cataldi 1\n", "Djuricic 1\n", "Linetty 1\n", "Haas 1\n", "Walace 1\n", "Agudelo 1\n", "Pobega 0.5625422964132641\n", "Rovella 1\n", "Amrabat 1\n", "Tameze 0.9789081885856079\n", "Gyasi 0.9644058450510063\n", "Ilic 0.27072348014888337\n", "Matheus Henrique 1\n", "Harroui 1\n", "Volpato 1\n", "Duncan 0.6763365666591473\n", "Cuadrado 0.9393563425821488\n", "Ekdal 1\n", "Schouten 1\n", "Obiang 1\n", "Kovalenko 0.7153559839664058\n", "Crnigoj 0.2547985695518902\n", "Basic 0.8551381877299562\n", "Asllani 0.8609342971194303\n", "Sabiri 1\n", "Grassi 0.7425558312655087\n", "Krunic 0.7528270116979794\n", "Rincon 1\n", "Miguel Veloso 1\n", "Henderson L. 0.6199751861042183\n", "Lopez M. 0.8502516838000708\n", "Cuisance 0.8567951899217408\n", "Saelemaekers 0.7921905155776124\n", "Maggiore 0.45967741935483875\n", "Akpa Akpro 0.0\n", "Akpa Akpro 0.0\n", "Maleh 0.5745967741935485\n", "Romero L. 0.9299627791563275\n", "Ceide 1\n", "Benassi 1\n", "Gagliardini 0.9644058450510063\n", "Vieira 0.08839950372208435\n", "Bianco 1\n", "Galdames 0.0\n", "Kastanos 0.9644058450510062\n", "Vignato 0.2062655086848635\n", "Askildsen 1\n", "Bove 1\n", "Bohinen 1\n", "Bakayoko 0.26570365118752215\n", "Zurkowski 0.21215880893300246\n", "Castrovilli 0.5391088574819289\n", "Demme 0.32630272952853595\n", "Darboe 0.0\n", "Darboe 0.24799007444168736\n", "Urbanski 0.0\n", "Yepes 1\n", "Osimhen 1\n", "Martinez L. 1\n", "Dybala 0.9815393171900401\n", "Rafael Leao 1\n", "Immobile 0.9599615784839509\n", "Vlahovic 1\n", "Arnautovic 0.6011880592525753\n", "Dzeko 1\n", "Nzola 1\n", "Beto 1\n", "Giroud 1\n", "Abraham 1\n", "Deulofeu 0.5835060575098525\n", "Simeone 0.6718362282878412\n", "Lozano 1\n", "Correa 1\n", "Berardi 0.7139108203624331\n", "Pedro 1\n", "Sanabria 1\n", "Thauvin affine to Deulofeu\n", "Thauvin 0.36469128594365785\n", "Cabral 1\n", "Caprari 1\n", "Piatek 1\n", "Rebic 1\n", "Bonazzoli 0.9299627791563275\n", "Zapata D. 1\n", "Gonzalez N. 0.7139108203624331\n", "Brekalo 0.15469913151364761\n", "Kean 0.9299627791563275\n", "Okereke 1\n", "Muriel 1\n", "Pinamonti 0.9281947890818859\n", "Di Francesco F. 1\n", "Caputo 0.5500413564929694\n", "Boga 1\n", "Alvarez A. affine to Raspadori\n", "Alvarez A. 0.688861317893576\n", "Petagna 1\n", "Barrow 0.9117282148591446\n", "Djuric 1\n", "Henry 0.6000451161741484\n", "Success 1\n", "Gabbiadini 1\n", "Kallon 1\n", "Nestorovski 1\n", "Raspadori 0.6531741108354012\n", "Lasagna 1\n", "Belotti 1\n", "Pellegri 1\n", "Verde 0.7514850740657192\n", "Destro 0.5500413564929694\n", "Seck 1\n", "Sansone 0.688861317893576\n", "Quagliarella 0.6763365666591473\n", "Defrel 0.6526054590570719\n", "Pjaca 0.6703629032258065\n", "Piccoli 1\n", "Shomurodov 0.44199751861042186\n", "Afena-Gyan 1\n", "Ibrahimovic 0.2156435429927716\n", "Pussetto 0.22099875930521093\n", "Cancellieri 1\n", "Oddei 0.0\n", "Oddei 0.4959801488833747\n", "Braaf 1\n", "Raimondo 1\n", "Kaio Jorge 0.0\n", "Players with low quantity of games:\n", "Aiwu 0.0\n", "Zeefuik 0.16666666666666663\n", "Dermaku 0.16666666666666663\n", "Ostigard 0.8333333333333334\n", "Gila 0.6666666666666667\n", "Bayeye 0.16666666666666663\n", "Moutinho J. 0.6666666666666667\n", "Paletta 0.0\n", "Romagna 0.0\n", "Cassandro 0.16666666666666663\n", "Amey 0.16666666666666663\n", "Zanotti 0.16666666666666663\n", "Buta 0.0\n", "Abankwah 0.16666666666666663\n", "Guessand A. 0.0\n", "Guarino 0.0\n", "Carboni F. 0.33333333333333337\n", "Pogba 0.5\n", "Machin 0.0\n", "Akpa Akpro 0.0\n", "Bianco 0.6666666666666667\n", "Galdames 0.0\n", "D'andrea 0.8333333333333334\n", "Cipot 0.8333333333333334\n", "Gaetano 0.8333333333333334\n", "Darboe 0.668006617038875\n", "Urbanski 0.16666666666666663\n", "Bertini 0.0\n", "Yepes 0.8333333333333334\n", "Pyyhtia 0.6666666666666667\n", "Trimboli 0.0\n", "Adli 0.8333333333333334\n", "Vignato S. 0.5\n", "Samek 0.0\n", "Ilkhan 0.5\n", "Degli Innocenti 0.0\n", "Acella 0.16666666666666663\n", "Carboni V. 0.8333333333333334\n", "Malagrida 0.6666666666666667\n", "Faticanti 0.0\n", "Oddei 0.5853432588916461\n", "Braaf 0.6666666666666667\n", "Raimondo 0.33333333333333337\n", "De Luca 0.33333333333333337\n", "Krollis 0.16666666666666663\n", "Vivaldo 0.0\n" ] } ], "source": [ "#for i in range(players.columns.shape[0]):\n", "# print(str(i) + ' - ' + players.columns[i])\n", "\n", "cols_toadapt = players.columns[9:]\n", "\n", "players = players_orig.copy()\n", "\n", "min_games = 6\n", "\n", "current_season_games = max(players_orig['games'])\n", "\n", "# weight_0 as function of current_season_games --> 1 as match day reachs 30 ? \n", "WEIGHT_0_same_team = (1 - (1 - 0.7) * (30 - current_season_games) / (38 - 12)) # 0.7\n", "WEIGHT_0_different_team = (1 - (1 - 0.75) * (30 - current_season_games) / (38 - 12)) # 0.75\n", "WEIGHT_mul_gk = 2\n", "\n", "rcsv = pd.read_csv('config/affine_players.txt') \n", "affine_players = pd.DataFrame(rcsv)\n", "affine_players = affine_players.set_index('player')\n", "\n", "\n", "def calc_weight(games_curr, games_old, same_team = 1, maxgames = current_season_games):\n", " if(same_team):\n", " weight_0 = WEIGHT_0_same_team\n", " else:\n", " weight_0 = WEIGHT_0_different_team\n", "\n", " weight = weight_0 * (games_curr / maxgames) / (max(games_old, 1) / 38)\n", " weight = min(weight, 1)\n", "\n", " return abs(weight)\n", "\n", "print(' ')\n", "print('Averaging players stats with past seasons:')\n", "\n", "for i in range(players.shape[0]):\n", " p = players.index[i]\n", " \n", "\n", " if(p in players_old.index or p in affine_players.index):\n", " p_ = p\n", " affine = 0\n", " \n", " if(p in affine_players.index):\n", " affine = 1\n", " p_ = affine_players.loc[p]['alike']\n", " \n", " print(p + ' affine to ' + p_)\n", " \n", " if(players.loc[p]['r'] == 'P'):\n", " weight = calc_weight(players.loc[p]['gk_games'], players_old.loc[p_]['gk_games'], affine == 1 or players.loc[p]['team'] == players_old.loc[p]['team'])\n", " weight *= WEIGHT_mul_gk\n", " weight = min(weight, 1)\n", " else:\n", " weight = calc_weight(players.loc[p]['games'], players_old.loc[p_]['games'], affine == 1 or players.loc[p]['team'] == players_old.loc[p]['team'])\n", "\n", " players.at[p, cols_toadapt] = (players.loc[p][cols_toadapt] * weight + (1-weight) * players_old.loc[p_][cols_toadapt])\n", " \n", " print(p + ' ' + str(weight)) \n", " \n", " # to handle players like Lukaku, who only played 2 seasons ago; only outfield players\n", " if(players.loc[p]['r'] != 'P' and players.loc[p]['games'] < min_games and p in players_old_2.index): \n", " weight = calc_weight(players.loc[p]['games'], players_old_2.loc[p]['games'], players.loc[p]['team'] == players_old_2.loc[p]['team'])\n", " \n", " players.at[p, cols_toadapt] = (players.loc[p][cols_toadapt] * weight + (1-weight) * players_old_2.loc[p][cols_toadapt])\n", " \n", " print(p + ' ' + str(weight))\n", " \n", " \n", "# handle players with low quantitites of games\n", "\n", "print('Players with low quantity of games:')\n", "\n", "def calc_weight_low(current_games, min_games = min_games):\n", " weight = 1 - (min_games - current_games)/min_games\n", " \n", " weight = min(weight, 1)\n", "\n", " return abs(weight)\n", "\n", "#mean_players_stats = players_orig[players_orig['games'] >= min_games][cols_toadapt].mean()\n", "\n", "mean_players_stats = players_orig.loc[players_orig.index[0]][cols_toadapt] * 0\n", "count = 0\n", "\n", "for i in range(players_orig.shape[0]):\n", " if(players_orig['games'][i] >= min_games and (players_orig['r'][i] == 'D')): # counting only defenders, to add a penalty\n", " mean_players_stats += players_orig.loc[players_orig.index[i]][cols_toadapt]\n", " count = count + 1\n", " \n", "mean_players_stats /= count\n", "\n", "for i in range(players.shape[0]):\n", " p = players.index[i]\n", " \n", " if(players.loc[p]['games'] < min_games and players.loc[p]['r'] != 'P'):\n", " weight = calc_weight_low(players.loc[p]['games'])\n", " \n", " players.at[p, cols_toadapt] = players.loc[p][cols_toadapt] * weight + (1-weight) * mean_players_stats\n", " \n", " print(p + ' ' + str(weight))\n", " \n", " \n", "players_out = players.copy()\n", "players_out = players_out.set_index(players_out.columns[0])\n", "players_out.insert(2, 'name', players_out.index)\n", "players_out.to_excel('mid_outputs/players_stats_rwk.xlsx')\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "49c28b07", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['games', 'games_starts', 'minutes', 'goals', 'assists', 'pens_made',\n", " 'pens_att', 'cards_yellow', 'cards_red', 'goals_per90',\n", " ...\n", " 'gk_pct_goal_kicks_launched', 'gk_goal_kick_length_avg', 'gk_crosses',\n", " 'gk_crosses_stopped', 'gk_crosses_stopped_pct',\n", " 'gk_def_actions_outside_pen_area',\n", " 'gk_def_actions_outside_pen_area_per90', 'gk_avg_distance_def_actions',\n", " 'vote_avg', 'vote_std'],\n", " dtype='object', length=151)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "players.columns[9:]" ] }, { "cell_type": "code", "execution_count": 13, "id": "d29102e5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 - matchday\n", "1 - player\n", "2 - team\n", "3 - oppteam\n", "4 - home\n", "5 - vote\n", "6 - goals\n", "7 - assists\n", "8 - cards_malus\n", "9 - fantavote\n", "10 - r\n", "11 - games\n", "12 - games_starts\n", "13 - minutes\n", "14 - shots_on_target_pct\n", "15 - goals_per_shot\n", "16 - goals_per_shot_on_target\n", "17 - passes_pct\n", "18 - aerials_won_pct\n", "19 - team_possession\n", "20 - team_goals_assists_per90\n", "21 - team_goals_pens_per90\n", "22 - team_goals_assists_pens_per90\n", "23 - team_xg_per90\n", "24 - team_gk_goals_against_per90\n", "25 - team_gk_save_pct\n", "26 - team_gk_clean_sheets_pct\n", "27 - team_passes_pct\n", "28 - team_passes_pct_medium\n", "29 - team_passes_pct_long\n", "30 - team_sca_per90\n", "31 - team_gca_per90\n", "32 - team_aerials_won_pct\n", "33 - vs_team_possession\n", "34 - vs_team_goals_per90\n", "35 - vs_team_assists_per90\n", "36 - vs_team_xg_per90\n", "37 - vs_team_gk_save_pct\n", "38 - vs_team_gk_clean_sheets_pct\n", "39 - vs_team_gk_pct_passes_launched\n", "40 - vs_team_gk_crosses_stopped_pct\n", "41 - vs_team_shots_on_target_per90\n", "42 - vs_team_passes_pct\n", "43 - vs_team_passes_pct_short\n", "44 - vs_team_passes_pct_medium\n", "45 - vs_team_passes_pct_long\n", "46 - vs_team_sca_per90\n", "47 - vs_team_gca_per90\n", "48 - vs_team_aerials_won_pct\n", "49 - opp_team_possession\n", "50 - opp_team_goals_assists_per90\n", "51 - opp_team_goals_pens_per90\n", "52 - opp_team_goals_assists_pens_per90\n", "53 - opp_team_xg_per90\n", "54 - opp_team_gk_goals_against_per90\n", "55 - opp_team_gk_save_pct\n", "56 - opp_team_gk_clean_sheets_pct\n", "57 - opp_team_passes_pct\n", "58 - opp_team_passes_pct_medium\n", "59 - opp_team_passes_pct_long\n", "60 - opp_team_sca_per90\n", "61 - opp_team_gca_per90\n", "62 - opp_team_aerials_won_pct\n", "63 - opp_vs_team_possession\n", "64 - opp_vs_team_goals_per90\n", "65 - opp_vs_team_assists_per90\n", "66 - opp_vs_team_xg_per90\n", "67 - opp_vs_team_gk_save_pct\n", "68 - opp_vs_team_gk_clean_sheets_pct\n", "69 - opp_vs_team_gk_pct_passes_launched\n", "70 - opp_vs_team_gk_crosses_stopped_pct\n", "71 - opp_vs_team_shots_on_target_per90\n", "72 - opp_vs_team_passes_pct\n", "73 - opp_vs_team_passes_pct_short\n", "74 - opp_vs_team_passes_pct_medium\n", "75 - opp_vs_team_passes_pct_long\n", "76 - opp_vs_team_sca_per90\n", "77 - opp_vs_team_gca_per90\n", "78 - opp_vs_team_aerials_won_pct\n", "79 - vote_avg\n", "80 - vote_std\n", "81 - goals.1\n", "82 - assists.1\n", "83 - cards_yellow\n", "84 - cards_red\n", "85 - xg\n", "86 - npxg\n", "87 - shots_on_target\n", "88 - passes_completed\n", "89 - passes_into_final_third\n", "90 - passes_into_penalty_area\n", "91 - progressive_passes\n", "92 - passes_live\n", "93 - passes_dead\n", "94 - through_balls\n", "95 - passes_switches\n", "96 - crosses\n", "97 - corner_kicks\n", "98 - blocks\n", "99 - blocked_shots\n", "100 - blocked_passes\n", "101 - interceptions\n", "102 - clearances\n", "103 - errors\n", "104 - touches\n", "105 - touches_def_pen_area\n", "106 - touches_def_3rd\n", "107 - touches_mid_3rd\n", "108 - touches_att_3rd\n", "109 - touches_att_pen_area\n", "110 - touches_live_ball\n", "111 - passes_received\n", "112 - miscontrols\n", "113 - dispossessed\n", "114 - fouls\n", "115 - fouled\n", "116 - aerials_won\n", "117 - aerials_lost\n", "118 - carries\n", "119 - progressive_carries\n", "120 - carries_into_final_third\n", "121 - carries_into_penalty_area\n" ] } ], "source": [ "for i in range(db.columns.shape[0]):\n", " print(str(i) + \" - \" + str(db.columns[i]))" ] }, { "cell_type": "markdown", "id": "089690d6", "metadata": {}, "source": [ "Elaborate databases data to have X and y for training, and split into a train test and a validation test.\n", "\n", "For outfield players: X -> y = [vote, fantavote]\n", "\n", "For goalkeepers: X -> y = [vote, fantavote, clean sheet probability]" ] }, { "cell_type": "code", "execution_count": 14, "id": "f19304f6", "metadata": {}, "outputs": [], "source": [ "npdb = np.array(db)\n", "\n", "y = npdb[:, [5,9]] # vote, fantavote\n", "\n", "#y[:, 1] = y[:, 1] - y[:, 0] # target = difference between fantavote and vote\n", "\n", "f_start = 14\n", "\n", "X = npdb[:, f_start:]\n", "\n", "if(DEL_G): \n", " del_g_idx = [\n", " list(db.columns).index('goals.1') - f_start,\n", " list(db.columns).index('assists.1') - f_start,\n", " list(db.columns).index('xg') - f_start,\n", " list(db.columns).index('npxg') - f_start,\n", " list(db.columns).index('shots_on_target') - f_start]\n", " \n", " X[:, del_g_idx] = 0\n", "\n", "\n", "# add role and home factor\n", "toadd = np.zeros((X.shape[0], 4))\n", "toadd[:, 0] = npdb[:, 4] # home\n", "\n", "toadd[:, 1] = npdb[:, 10] == 'D'\n", "toadd[:, 2] = npdb[:, 10] == 'C'\n", "toadd[:, 3] = npdb[:, 10] == 'A'\n", "\n", "X = np.concatenate((X, toadd), axis = 1)\n", "\n" ] }, { "cell_type": "code", "execution_count": 15, "id": "370d41d2", "metadata": {}, "outputs": [], "source": [ "scaler = StandardScaler()\n", "scaler.fit(X)\n", "\n", "X_train_, X_test_, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 12)\n", "\n", "X_train = scaler.transform(X_train_)\n", "X_test = scaler.transform(X_test_)" ] }, { "cell_type": "code", "execution_count": 16, "id": "a7b1fb52", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 - matchday\n", "1 - player\n", "2 - team\n", "3 - oppteam\n", "4 - home\n", "5 - vote\n", "6 - goals\n", "7 - assists\n", "8 - cards_malus\n", "9 - fantavote\n", "10 - gk_games\n", "11 - gk_games_starts\n", "12 - gk_minutes\n", "13 - gk_goals_against_per90\n", "14 - gk_save_pct\n", "15 - gk_clean_sheets_pct\n", "16 - gk_psxg_net_per90\n", "17 - gk_passes_pct_launched\n", "18 - gk_pct_passes_launched\n", "19 - gk_passes_length_avg\n", "20 - gk_pct_goal_kicks_launched\n", "21 - gk_goal_kick_length_avg\n", "22 - gk_crosses_stopped_pct\n", "23 - gk_def_actions_outside_pen_area_per90\n", "24 - gk_avg_distance_def_actions\n", "25 - team_possession\n", "26 - team_goals_assists_per90\n", "27 - team_goals_pens_per90\n", "28 - team_goals_assists_pens_per90\n", "29 - team_xg_per90\n", "30 - team_gk_goals_against_per90\n", "31 - team_gk_save_pct\n", "32 - team_gk_clean_sheets_pct\n", "33 - team_passes_pct\n", "34 - team_passes_pct_medium\n", "35 - team_passes_pct_long\n", "36 - team_sca_per90\n", "37 - team_gca_per90\n", "38 - team_aerials_won_pct\n", "39 - vs_team_possession\n", "40 - vs_team_goals_per90\n", "41 - vs_team_assists_per90\n", "42 - vs_team_xg_per90\n", "43 - vs_team_gk_save_pct\n", "44 - vs_team_gk_clean_sheets_pct\n", "45 - vs_team_gk_pct_passes_launched\n", "46 - vs_team_gk_crosses_stopped_pct\n", "47 - vs_team_shots_on_target_per90\n", "48 - vs_team_passes_pct\n", "49 - vs_team_passes_pct_short\n", "50 - vs_team_passes_pct_medium\n", "51 - vs_team_passes_pct_long\n", "52 - vs_team_sca_per90\n", "53 - vs_team_gca_per90\n", "54 - vs_team_aerials_won_pct\n", "55 - opp_team_possession\n", "56 - opp_team_goals_assists_per90\n", "57 - opp_team_goals_pens_per90\n", "58 - opp_team_goals_assists_pens_per90\n", "59 - opp_team_xg_per90\n", "60 - opp_team_gk_goals_against_per90\n", "61 - opp_team_gk_save_pct\n", "62 - opp_team_gk_clean_sheets_pct\n", "63 - opp_team_passes_pct\n", "64 - opp_team_passes_pct_medium\n", "65 - opp_team_passes_pct_long\n", "66 - opp_team_sca_per90\n", "67 - opp_team_gca_per90\n", "68 - opp_team_aerials_won_pct\n", "69 - opp_vs_team_possession\n", "70 - opp_vs_team_goals_per90\n", "71 - opp_vs_team_assists_per90\n", "72 - opp_vs_team_xg_per90\n", "73 - opp_vs_team_gk_save_pct\n", "74 - opp_vs_team_gk_clean_sheets_pct\n", "75 - opp_vs_team_gk_pct_passes_launched\n", "76 - opp_vs_team_gk_crosses_stopped_pct\n", "77 - opp_vs_team_shots_on_target_per90\n", "78 - opp_vs_team_passes_pct\n", "79 - opp_vs_team_passes_pct_short\n", "80 - opp_vs_team_passes_pct_medium\n", "81 - opp_vs_team_passes_pct_long\n", "82 - opp_vs_team_sca_per90\n", "83 - opp_vs_team_gca_per90\n", "84 - opp_vs_team_aerials_won_pct\n", "85 - vote_avg\n", "86 - vote_std\n", "87 - gk_shots_on_target_against\n", "88 - gk_saves\n", "89 - gk_free_kick_goals_against\n", "90 - gk_corner_kick_goals_against\n", "91 - gk_own_goals_against\n", "92 - gk_psxg\n", "93 - gk_psnpxg_per_shot_on_target_against\n", "94 - gk_psxg_net\n", "95 - gk_passes_completed_launched\n", "96 - gk_passes_launched\n", "97 - gk_passes\n", "98 - gk_passes_throws\n", "99 - gk_goal_kicks\n", "100 - gk_crosses\n", "101 - gk_crosses_stopped\n" ] } ], "source": [ "for i in range(db_gk.columns.shape[0]):\n", " print(str(i) + \" - \" + str(db_gk.columns[i]))" ] }, { "cell_type": "code", "execution_count": 17, "id": "5a7cf079", "metadata": {}, "outputs": [], "source": [ "npdb_gk= np.array(db_gk)\n", "\n", "y_gk = npdb_gk[:, [5,9,6]] # vote, fantavote, goals == 0 (clean sheet)\n", "y_gk[:, 2] = (y_gk[:, 2] == 0) * 1\n", "\n", "f_start_gk = 13\n", "\n", "X_gk = npdb_gk[:, f_start_gk:]\n", "\n", "# add home factor\n", "toadd_gk = np.zeros((X_gk.shape[0], 1))\n", "toadd_gk[:, 0] = npdb_gk[:, 4] # home\n", "\n", "X_gk = np.concatenate((X_gk, toadd_gk), axis = 1)\n", "\n" ] }, { "cell_type": "code", "execution_count": 18, "id": "0bc0568b", "metadata": {}, "outputs": [], "source": [ "scaler_gk = StandardScaler()\n", "scaler_gk.fit(X_gk)\n", "\n", "X_gk_train_, X_gk_test_, y_gk_train, y_gk_test = train_test_split(X_gk, y_gk, test_size = 0.2, random_state = 18)\n", "\n", "X_gk_train = scaler_gk.transform(X_gk_train_)\n", "X_gk_test = scaler_gk.transform(X_gk_test_)" ] }, { "cell_type": "markdown", "id": "ebd27493", "metadata": {}, "source": [ "MLP Regressor , to see performance of a simple neural network" ] }, { "cell_type": "code", "execution_count": 17, "id": "04564bee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.1569906306642661\n", "0.19152381866355805\n" ] }, { "data": { "image/png": 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", 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iYWIackqq4OyoxFtT+2DWkDB2AdkhBixERNTmqNUCViVfxsdJGVCpBXTzd8fKRwbirhAujmuvGLAQEVGbkl9ejefXnsDBi/kAgD/EdMI/pveFuwsvefaMZ4+IiNqMlMsFeO77NOSVVcPVSYm/3d8XD93dmV1AbQADFiIisnsqtYAVey/h0z0ZUAtAZGBHrHxkIKKCPKxdNWohDFiIiMiu5ZVVYdH3J3D4cgEA4KFBnfH2tD7o4MxLXFvCs0lERHbr0MV8LFqbhvzyGnRwdsA/pvfFAwM7W7taZAEMWIiIyO7UqdT4dM9FrNh3CYIA9Ar2wIrZA9EjsKO1q0YWwoCFiIjsSm5JFRZ+n4ZfMwsBALOGdMGbU3vD1cnByjUjS2LAQkREdmP/hTws/iEdhRU1cHd2wLIZ/XF/dKi1q0WtgAELERHZvFqVGh/tysAXyZcBAH1CPbFi9kBE+LtbuWbUWhiwEBGRTbtRfBsLEtPw27UiAMCjw8Px6pS72AXUzjBgISIim7X77E28+FM6iitr4eHiiPce7I8p/UKsXS2yAgYsRERkc2rq1Hh/x3n851AmAKB/Zy+smDUQXfw6WLlmZC0MWIiIyKZkF1ZifmIa0rOLAQCP3xOBJfG94OyotG7FyKoYsBARkc3YcToXL/2UjrKqOni6OuLDh6IR2yfY2tUiG8CAhYiIrK66ToVl287jq8NXAQAxXbzx2awYdPZhFxBpMGAhIiKrulZQgflr0nDqegkA4OnR3fBiXE84ObALiBoxYCEiIqvZcvIGlqw7hfLqOvh0cMJHCdEY3yvI2tUiG8SAhYiIWl1VrQp/33IW3x3NAgAM7uqD5bNiEOLlZuWaka0yu73twIEDmDp1KkJDQ6FQKLBx40bJ63PnzoVCoZB8DRs2rMn9rlu3Dr1794aLiwt69+6NDRs2mFs1IiKyA1duleMPnx/Gd0ezoFAA88Z1R+JTwxiskFFmBywVFRWIjo7GihUrZMtMnjwZOTk54te2bduM7jMlJQUzZ87EnDlzkJ6ejjlz5iAhIQFHjx41t3pERGTDNqZdx32fHcK5nFL4uTvj6z8NwUtxveDIfBVqgkIQBKHZGysU2LBhA6ZPny4+N3fuXBQXF+u1vBgzc+ZMlJaWYvv27eJzkydPho+PDxITE03aR2lpKby8vFBSUgJPT0+TfzcREVne7RoV3tp8BmuPZwMAhnXzxacPxyDI09XKNSNrM/X6bZGQdv/+/QgMDERUVBSeeuop5OXlGS2fkpKC2NhYyXNxcXE4fPiw7DbV1dUoLS2VfBERke25eLMM01Yewtrj2VAogOcmROK7J4cxWCGztHjSbXx8PB566CGEh4cjMzMTb7zxBsaPH4/ffvsNLi4uBrfJzc1FUJA0KzwoKAi5ubmyv2fZsmV4++23W7TuRETUsn48no2/bjqD27UqBHi44NOZAzCih7+1q0V2qMUDlpkzZ4qP+/bti7vvvhvh4eHYunUrHnjgAdntFAqF5GdBEPSe07Z06VIsXrxY/Lm0tBRhYWF3UHMiImopFdV1eGPTaaxPvQ4AGNnDH5/MHIAAD8M3rkRNsfiw5pCQEISHh+PixYuyZYKDg/VaU/Ly8vRaXbS5uLjIttgQEZH1nM8txbzvUnH5VgWUCmDxpCj8ZWwPOCjlb0KJmmLxtOyCggJkZ2cjJER+OfDhw4cjKSlJ8tyuXbswYsQIS1ePiIhaiCAI+P7XLExb8Qsu36pAkKcLEp8ahvnjIxms0B0zu4WlvLwcly5dEn/OzMzEiRMn4OvrC19fX7z11luYMWMGQkJCcPXqVbz66qvw9/fHH/7wB3GbRx99FJ06dcKyZcsAAM899xxGjx6N9957D9OmTcOmTZuwe/duHDp0qAUOkYiILK28ug6vrj+Fzek3AABjogLwcUI0/DqyJZxahtkBy/HjxzFu3Djx54Y8ksceewyrVq3CqVOn8M0336C4uBghISEYN24c1q5dCw8PD3GbrKwsKJWNjTsjRozA999/j9dffx1vvPEGunfvjrVr12Lo0KF3cmxERNQKztwowfw1acjMr4CDUoGX4nriz6O6QclWFWpBdzQPiy3hPCxERK1LEAR8ezQLf99yFjV1aoR6ueKz2TEYFO5r7aqRHTH1+s21hIiIyGylVbVYuu4Utp7KAQBMvCsQHzwYDR93ZyvXjNoqBixERGSWk78XY/6aNGQVVsJRqcCS+F54YmSE0akoiO4UAxYiIjKJIAhY/ctVLNt+DrUqAZ193LBi9kAMCPO2dtWoHWDAQkRETSqprMVLP6Vj19mbAIC4PkF4/8FoeLk5Wblm1F4wYCEiIqPSsoowf00arhffhrODEq/dexceHR7OLiBqVQxYiIjIILVawJeHMvHejvOoUwsI9+uAFbMGol9nL2tXjdohBixERKSnqKIGL/yYjr3n8wAA9/YPwbIH+sHTlV1AZB0MWIiISOL41UIsSExDTkkVnB2VeHNqb8we0oVdQGRVDFiIiAiApgvoiwOX8dGuDKjUArr5u2PF7IHoHcrJOMn6GLAQERHyy6ux+Id0HMi4BQCYPiAU//hDP3R04WWCbAM/iURE7dyRKwVYmJiGvLJquDop8bf7++KhuzuzC4hsCgMWIqJ2SqUWsHLfJfxzdwbUAtAjsCNWzh6InsEeTW9M1MoYsBARtUN5ZVV4fu0J/HKpAADw4KDO+Nu0PujgzMsC2SZ+MomI2plfLuXjue9PIL+8Gm5ODvjH9L6YMaiztatFZBQDFiKidqJOpcbyPRfx2b5LEASgZ5AHVj4yED0CO1q7akRNYsBCRNQO3CytwoLENPyaWQgAmDUkDG9O7QNXJwcr14zINAxYiIjauP0X8rD4h3QUVtTA3dkB7zzQD9MGdLJ2tYjMwoCFiKiNqlOp8VFSBlbtvwwA6B3iiZWPDESEv7uVa0ZkPgYsRERt0I3i21iYmIbj14oAAHOGheO1e+9iFxDZLQYsRERtzJ5zN/HCj+korqyFh4sj3nuwP6b0C7F2tYjuCAMWIqI2oqZOjQ92nse/D2YCAPp39sKKWQPRxa+DlWtGdOcYsBARtQHZhZVYkJiGE9nFAIA/3dMVS+J7wcWRXUDUNjBgISKyczvP5OKlH9NRWlUHT1dHfPBQNOL6BFu7WkQtigELEZGdqq5TYdm28/jq8FUAwIAwb6yYHYPOPuwCoraHAQsRkR26VlCB+WvScOp6CQDgz6O74aW4nnByUFq5ZkSWwYCFiMjObD2ZgyXrTqKsug7eHZzwcUI0xvcKsna1iCyKAQsRkZ2oqlXhH1vP4tsjWQCAu8N9sHxWDEK93axcMyLLY8BCRGQHrtwqx7w1aTiXUwoAeHZsdyyeFAVHdgFRO8GAhYjIxm06cR2vrj+FihoV/Nyd8fHMARgTFWDtahG1KgYsREQ26naNCm//fAbfH8sGAAzr5otPH45BkKerlWtG1PoYsBAR2aBLeWWY910aLtwsg0IBLBgfiecmRMJBqbB21YiswuzOzwMHDmDq1KkIDQ2FQqHAxo0bxddqa2vxyiuvoF+/fnB3d0doaCgeffRR3Lhxw+g+v/rqKygUCr2vqqoqsw+IiMje/fTb75j62S+4cLMM/h1d8O0TQ7F4UhSDFWrXzA5YKioqEB0djRUrVui9VllZidTUVLzxxhtITU3F+vXrkZGRgfvvv7/J/Xp6eiInJ0fy5erKZk8iaj8qa+rwwg/pePHHdNyuVeGeHn7Y9txI3NPD39pVI7I6s7uE4uPjER8fb/A1Ly8vJCUlSZ777LPPMGTIEGRlZaFLly6y+1UoFAgO5lTSRNQ+Xcgtw7Pf/YbLtyqgVADPT4zCs+N6sFWFqJ7Fc1hKSkqgUCjg7e1ttFx5eTnCw8OhUqkwYMAA/P3vf0dMTIxs+erqalRXV4s/l5aWtlSViYhajSAIWHssG29uPoPqOjWCPF3w6cMxGNbNz9pVI7IpFh3AX1VVhSVLlmD27Nnw9PSULderVy989dVX2Lx5MxITE+Hq6op77rkHFy9elN1m2bJl8PLyEr/CwsIscQhERBZTXl2HRWtPYMn6U6iuU2NMVAC2LRzFYIXIAIUgCEKzN1YosGHDBkyfPl3vtdraWjz00EPIysrC/v37jQYsutRqNQYOHIjRo0dj+fLlBssYamEJCwtDSUmJWb+LiMgaztwowYI1abiSXwEHpQIvxvbE06O7QckuIGpnSktL4eXl1eT12yJdQrW1tUhISEBmZib27t1rdgChVCoxePBgoy0sLi4ucHFxudOqEhG1KkEQ8O3RLPx9y1nU1KkR4uWKz2bF4O6uvtauGpFNa/GApSFYuXjxIvbt2wc/P/ObNgVBwIkTJ9CvX7+Wrh4RkdWUVtVi6fpT2HoyBwAwoVcgPnwoGj7uzlauGZHtMztgKS8vx6VLl8SfMzMzceLECfj6+iI0NBQPPvggUlNTsWXLFqhUKuTm5gIAfH194eys+aN89NFH0alTJyxbtgwA8Pbbb2PYsGGIjIxEaWkpli9fjhMnTmDlypUtcYxERFZ36vcSzFuTiqzCSjgqFVgS3wtPjIyAQsEuICJTmB2wHD9+HOPGjRN/Xrx4MQDgsccew1tvvYXNmzcDAAYMGCDZbt++fRg7diwAICsrC0plY75vcXEx/vznPyM3NxdeXl6IiYnBgQMHMGTIEHOrR0RkUwRBwNeHr+KdbedRo1Kjk7cbVsyOQUwXH2tXjciu3FHSrS0xNWmHiKi1lFTW4uV16dh55iYAILZ3ED54MBpeHZysXDMi22HVpFsiovYuLasI89ek4XrxbTg7KPHqlF54bERXdgERNRMDFiKiFiQIAv5zMBPv7TiPOrWALr4dsHL2QPTr7GXtqhHZNQYsREQtpKiiBi/+mI495/MAAPf2C8GyGf3g6couIKI7xYCFiKgFHL9aiIWJabhRUgVnRyX+el9vPDK0C7uAiFoIAxYiojugVgv44sBlfLQrAyq1gAh/d6yYHYM+oewCImpJDFiIiJqpoLwai39IR3LGLQDAtAGh+H9/6IeOLvzXStTS+FdFRNQMR68UYOH3abhZWg0XRyX+Nq0PEu4OYxcQkYUwYCEiMoNKLeDzfZfwye4MqAWgR2BHrJw9ED2DPaxdNaI2jQELEZGJbpVVY9HaNPxyqQAAMGNgZ/x9eh90cOa/UiJL418ZEZEJfrmUj+e+P4H88mq4OTng79P74sFBna1dLaJ2gwELEZERKrWAT/dcxGd7L0IQgJ5BHlj5SAx6BLILiKg1MWAhIpJxs7QKCxPTcDSzEADw8OAwvDm1D9ycHaxcM6L2hwELEZEByRm3sHjtCRRU1MDd2QHvPNAP0wZ0sna1iNotBixERFrqVGp8lJSBVfsvAwDuCvHEytkx6BbQ0co1I2rfGLAQEdW7UXwbCxPTcPxaEQBgzrBwvHbvXXB1YhcQkbUxYCEiArD3/E0s/iEdxZW18HBxxLsz+uPe/iHWrhYR1WPAQkTtWq1KjQ92XsC/DlwBAPTr5IUVs2MQ7udu5ZoRkTYGLETUbv1eVIn5a9JwIrsYADB3RFcsndILLo7sAiKyNQxYiKhd2nkmFy/9mI7Sqjp4ujrig4eiEdcn2NrVIiIZDFiIqF2prlPh3e3nsfqXqwCAAWHe+GxWDMJ8O1i3YkRkFAMWImo3sgoqMW9NKk5dLwEAPDUqAi/F9YKzo9LKNSOipjBgIaJ2YdupHLzy00mUVdfBu4MTPnooGhPuCrJ2tYjIRAxYiKhNq6pV4f9tPYf/HbkGALg73AfLZ8Ug1NvNyjUjInMwYCGiNiszvwLzvkvF2ZxSAMCzY7vj+UlRcHJgFxCRvWHAQkRt0qYT1/Hq+lOoqFHB190Zn8wcgDFRAdauFhE1EwMWImpTqmpVePvnM0j8NRsAMDTCF8tnxSDI09XKNSOiO8GAhYjajEt55Zj3XSou3CyDQgEsGNcDCydEwpFdQER2jwELEbUJ6377Ha9vPI3btSr4d3TBP2cOwMhIf2tXi4haCAMWIrJrlTV1+OumM/jpt98BAPf08MMnMwcg0INdQERtCQMWIrJbGTfLMO+7VFzMK4dSASyaGIV543rAQamwdtWIqIUxYCEiuyMIAn44no03N59BVa0agR4uWD4rBsO6+Vm7akRkIWZnoh04cABTp05FaGgoFAoFNm7cKHldEAS89dZbCA0NhZubG8aOHYszZ840ud9169ahd+/ecHFxQe/evbFhwwZzq0ZE7UB5dR2eX3sCr6w7hapaNUZHBWDbc6MYrBC1cWYHLBUVFYiOjsaKFSsMvv7+++/j448/xooVK3Ds2DEEBwdj0qRJKCsrk91nSkoKZs6ciTlz5iA9PR1z5sxBQkICjh49am71iKgNO3ujFPd/dggbT9yAg1KBlyf3xFdzB8O/o4u1q0ZEFqYQBEFo9sYKBTZs2IDp06cD0LSuhIaGYtGiRXjllVcAANXV1QgKCsJ7772Hp59+2uB+Zs6cidLSUmzfvl18bvLkyfDx8UFiYqJJdSktLYWXlxdKSkrg6enZ3EMiIhskCAK+O5qFv205i5o6NUK8XLF8VgwGd/W1dtWI6A6Zev1u0ckJMjMzkZubi9jYWPE5FxcXjBkzBocPH5bdLiUlRbINAMTFxRndprq6GqWlpZIvImp7yqpqMT8xDa9vPI2aOjUm9ArEtoWjGKwQtTMtGrDk5uYCAIKCpCugBgUFia/JbWfuNsuWLYOXl5f4FRYWdgc1JyJbdOr3Etz32SFsPZkDR6UCr025C/957G74uDtbu2pE1MosMv2jQiEdUigIgt5zd7rN0qVLUVJSIn5lZ2c3v8JEZFMEQcBXv2RixqrDuFZQiU7ebvjhmeF4anS3Jv+XEFHb1KLDmoODgwFoWkxCQkLE5/Py8vRaUHS3021NaWobFxcXuLgw0Y6orSm5XYtXfjqJHWc0/xNiewfhgwej4dXByco1IyJratEWloiICAQHByMpKUl8rqamBsnJyRgxYoTsdsOHD5dsAwC7du0yug0RtT0nsotx7/KD2HEmF04OCrw5tTf+b84gBitEZH4LS3l5OS5duiT+nJmZiRMnTsDX1xddunTBokWL8M477yAyMhKRkZF455130KFDB8yePVvc5tFHH0WnTp2wbNkyAMBzzz2H0aNH47333sO0adOwadMm7N69G4cOHWqBQyQiWycIAr48lIl3t59HnVpAF98OWDE7Bv07e1u7akRkI8wOWI4fP45x48aJPy9evBgA8Nhjj+Grr77Cyy+/jNu3b+PZZ59FUVERhg4dil27dsHDw0PcJisrC0plY+POiBEj8P333+P111/HG2+8ge7du2Pt2rUYOnTonRwbEdmB4soavPhjOnafywMATOkXjHdn9IenK1tViKjRHc3DYks4DwuR/fntWiEWrEnDjZIqODsq8cZ9vfHHoV3aRGJtRkYGLl++jB49eiAyMtLa1SGyWaZev7mWEJEWXmRah1ot4P8OXMGHuy5ApRYQ4e+OFbNj0CfUy9pVu2OFhYWY/cfZ2Ll9p/hcXHwcEr9LhI+PjxVrRmTfLDKsmcjeFBYWYvKUyejZsyemTJmCqKgoTJ4yGUVFRdauWptTUF6Nx78+hvd2nIdKLWDagFD8vGBkmwhWAGD2H2dj94HdwAMAngfwALD7wG7MemSWVeqTkZGB7du34+LFi1b5/UQthV1CRAAmT5mM3Qd2QxWnAsIBXAMcdjpg4uiJ2LFth7Wr12YcvVKAhd+n4WZpNVwclXj7/j6YOTisTXQBAZrgoGfPnppgpb/WC+kANmheb62WO7b0kL2wytT8RPYoIyMDO7fv1AQr/QF4AegPqGJV2Ll9J+9MW4BKLeCzPRcx699HcLO0Gt0D3LFp/j14eEjbyFdpcPnyZc2DcJ0Xumq+aY+wtDRba+khulMMWKjda62LTHttmr9VVo3H/vsrPkrKgFoAZgzsjJ8XjESv4LbXEtq9e3fNg2s6L1zVfOvRo0er1KO5QXh7/YySfWDAQu2epS8y7Tk/5vClfExZfhCHLuXDzckBHz4UjY8SotHB2bx8f3u5kEZFRSEuPg4OOx003UAlANIBh10OiIuPa7XuIHODcEt/Ru3l/JGNE9qIkpISAYBQUlJi7aqQHYqLjxMc3B0ETISA6RAwCYKDu4MQFx/Xcvt+AAKeh4AHWm7ftqpOpRY+2nVB6LpkixD+yhZh0sf7hYzcUrP3U1BQIMTFxwkAxK+4+DihsLDQArVuGYWFhVav84ULFzS/+wEIeEvr6w+a+mRkZEjKW+ozao/nj1qfqddvJt1Sm2fKUOXU1FQMGzEMtdW14nNOLk749civGDBgwB39bltJwmwtN0ur8Nz3aThypRAA8PDgMLw5tQ/cnB3EMqYOHxeToUeoAHcAlYDDL/aRDH3x4kVcunTJakPkxfcuVqVpWbmqaenRfe8s+RltT8nsnBKh+Zh0S+2eOc3cE2MnorauVvJcbV0txk8cb/R3NNXUbUtJmK3hQMYtTPn0II5cKYS7swM+fXgA3p3RXwxWzDknYh6GhwpIArARwC5A1dE+kqEjIyMRHx9vtYtX4neJmDh6IrABwCcANgATR09E4neJknKW+oy2l2T29tzl29oYsFCbZeooiZ07d6KosAhwgqQsnICiwiK9hTkB0/9J2UoSpqXVqdR4f8d5PPrfX1FQUYO7Qjzx84KRmDagk6ScOSNXLl++DCigyQPRPi+lABRtL9hraT4+PtixbQcyMjKwbds2ZGRkYMe2HXpDmi31GW0vwTpHY7UeBizUJplzd7d161ZN7/oUSMoiHoAAbNmyRW//4j+pSQCmA4g1/E/KVpIwLSmn5DZm/fsIPt+vuUD9cVgXbHh2BLoFdJSUM/eOW6lUas5LPIBQAHkAOgGYDEAAHB05Ubcpmur1t9RntD0E6+2lFclWMGChNsmcu7vAwECjZcXX65nbVWFq07w92nc+D1M+PYhjV4vQ0cURK2bH4B/T+8HVyUGvrLl33Gq1WvMgDcAKAN8B+AzACc3TdXV1LXIM5jBntIu1R8aY01Vhic9oewjW20srkq3gLQq1SZK7O+1Ewquab9p3d4MHDzZadsiQIZJ9i10VxdA0A9cnE2IbxK4K7X/GDU3z1k7CbEm1KjU+3HkB/3fgCgCgXycvrJgdg3A/d9ltzDknkvJZOjvKMlzeksyZNdZWZpiVtALWJyw3tALqJrxa6jOa+F0iZj0yCzs3NL4XE+PbRrAOmP+ZpjvDgIXapIa7u907d0NVqgI6AqgAHA47YGL8RMk/Y7VarQlAtkLTBdEVmn849QGI7p282FXR0IWE+u8CgA3yXRWRkZF2H6gAwO9FlViQmIa0rGIAwNwRXbF0Si+4OOq3quhRQPO+ar/P2+uf15GZmal53gnANEgDQzVw9erVVns/JXkK9fXYvdPwxd+cspbS0AqIYGhaAeupghpbAQ29dy39GW2Lwbo2yf8ZQWc0ls7/GbpzDFiozfp8xecYMmwICnYXiM95B3hj1cpVknI3btzQXEB9oGkSbxAE4CZw8+ZNSfmsrPpbfJlm4GvXdDvtNdrCsMddZ3Lx0k8nUXK7Fp6ujnj/wWhM7hts0raXL1/WvM8hkL7PEQAy9Vum9HKLAElguGXLFkyaNKkFjso48eL/ACS5NKpYFXZukF78JWW16qwS9MveSX2a+hyZ2wpoaW0lWDekrbci2RIGLNRmPfnnJ1FQVCB5rqCoAE/++UnsSdojPnf9+nXNg1kA6gAUAvCF5q/jE60Apd6hQ4c0D2SagQ8dOoQnn3xSfNpWugjuRE2dGsu2n8PqX64CAKLDvLFiVgzCfDuYvA+x+TwGwH1ofJ9/B5Cp33yuVNan2MkEhg4OJrTotAAxTyENwHqtFyI037Qv/qbkNDT3wm3O50gMwmWCPd0gnJqvrbci2RIm3VKblJGRgX1792mCDu0hsY7A3j17JYmQQ4cO1Ty4BsAPQGT996uap4cPHy7Zd1FRUWPXhlYyYUPXhm5So70Pe8wqqMSDXxwWg5WnRkXgx6eHmxWsiBret98BBNZ/l+kSEsmMMmmtOS/NyaWx5MgYcz5H6enpmgcygVNaWlqz60GGWXvenfaAAQu1ScnJyUaHKicnJ4tlIyIiGnNYtAOQ+ubzrl27SvY9dOhQzb5rIRlVgVrNvrUDHMmwR93uBDsY9rjtVA7uXX4QJ38vgXcHJ/zn0bvx2r294exo/r8OvS6hhvctBIBgeH0bY+elsLDwTg7NPA25NDrz9OgGWpYaGWPu8FlxZJtM4KQ78o3IHjBgobZN5g5Tm3ghlQlAdC+k4j/7AJ0d+dd/8/eX7huQHZprq8Meq2pVeGPjaTz7XSrKquswKNwHWxeOwsTeQc3ep9j6cFvnhUrNN93Wh5ycHM15UUB6XgBAAHJzc5tdF3OYE/wClhkibO7w2YSEBKOtgAkJCc2uC5G1MIeF2qQxY8ZoHlyDpmWjCI35Etqvo76/H9Dvlqj/2WB/v/YMrA0JjQa6Nrp37655LgcGkx+tMeyxqaTNzPwKzF+TijM3SgEAz4zpjhdio+DkcGf3N1FRUXBycUJtUa1kqC2SNes26daltLRU894pAMQC6FBf/qDmuZKSkmYdX7OFA8hH42epq+FilshpMHf4bFRUFLx9vFFcUixNcHYAvH282W1BdokBC7VJUVFRGDVmFA5uOgiotF5wAEaNGSX5h52enm50+GxaWhrmzp0rls/Ly2ucgdVAQmN+fr60MkaSH1uCqRdoU5I2N6ffwNJ1J1FRo4KvuzM+TojG2J6mdR80VY+dO3dqFpfUGWqLIKD2Zi2SkpIko348PT3132dAE+hsALy8vMw+vuYQg9tEANqNOkE6r+toyZEx5g6fzcjIQHFhsea91q6zP1B8s7hFRisRtTZ2CZFdMmUW0draWoPP19VK51UpKSkx2uRfWloqKX/+/HnNg4Y77osACiDecZ85c0Ysa8mZMM1ddO2hhIewa98uSR7Grn278GDCg6iqVWHp+lNYmJiGihoVhkT4YtvCUSYFK6bW4+jRo0bXBkpJSZGUz8vL0zyQee90W74sldwcFRUFT29P4JbOC/mAl7dXq134zelqEj93bjov1OdJ22pXJJExbGEhu2LqXXRGRgaOpBwBXKDXapKSkiK5wxRHm8g0+YtTxNfr1auX5oHMHXefPn3Ep8ShuTJN+XeyHs6DCQ9iX/I+yXM7k3biwYQHJcO2Ac37sXfPXr35QQRBwIHkDMR/vBeZRTVQKID543rguQmRcDSxC8jUidI6depktGWqS5cukv26u9fPmivTrSe+DvPmSjFXRkYGSktKDX6WSkpKWq21wpyuJklX5Aho3mMlgN9gta5Ic7SFOYuo5TFgIbti6sXxhx9+MNoV88MPP+C1114DAPj6+mpelwlAxNfrDRw4UPPAwB03AERHR4tPmTuLrqkyMjKwb88+QHcqEgHYu3uv3kVUTAzVaa1w7zYOvl2fRWZRDfw7uuCfMwdgZKQ/TGVOoBAaGmqwDg2BYVCQNKE3MjJS0+qyCXrdeoCm5aOBOXOlmMucz1JrMLmrqWHU92Gt51y0nrdBbWHOIrIcBixkN8yZRbSp7gTxdWjNq1IEaWLsVhicV0WpVBrNedFuNRGn8a+DXvLjnaw4nJycbLQOycnJkoua2H1S39KjEFzgW/sMOjpqckZCHcqw8bkJCPRwNase5gQK5rY26eao6PL09BQfWzK52ZzPkq0QZ7oVYPAzLRfAWbtlwxaWNSDbxRwWshvm5IPce++9mgfXoMkx2Q/gMsSL43333SeWPXTokOYf+72Q5rBMASBozWxb79ixY0ZzXn799VexrDhLrswQaLlp/Jty8+bNxjpotWw01EE3vyMoKEhzAdsOOJ3rguDKj9FRNQmCWoXiQ99ibtdys4MVwECg0JCXkgO9QEFsbZIZaqvb2iS2VOm2CNT/HBMTo/+8zDm5E5LPkrarmm/anyVbIc50K/OZ1v18mJsPZQnmzjVD7Q9bWMhumDO0My4uTvNgIwDtFJT6EF17NIr4T1kmENKdoCwjI8NoefF11F8YjAyBvuMp0mVaNnSNGTMGEAD3QZPg2+lpKJWuqCsrQP4vH6I6/RTGrf5b8+tg4iioXbt2See7aVDf2rRnzx7Ex8eLT586daqxFeleSFsJarRmc4V8l1fDOdFtcTJHXFwcfP19Ubi1UK9bz9fft1XWMzKXuNyEzPuhu9yEpVs2TF7/yEidW3v9I3NZu3WqPWALC9kNc2YR/fLLL43OTvrVV1+JZSXJndqjfq5qnu7YsaOkHjk5OY3ltV3Veb2BdqJpw53uZBi9829qFFR+fr7Rlg3dodXnLl6B330vwP+e56B0csXtilTkZC5E9QVNUHD16lX5yhhhTquXOApoGoAFAB6p/36/5mndlqyff/7ZaCvBzz//rF8hmXNyp47/ehx+nn6SETp+nn44/utx2W1MGclmKZLlJrRd1XyTnY25hVs2zGm5seSyBpZkC61T7QUDFrIrpg7tXLNmjdGL3f/+9z+xbFhYmObBJkhno92sebpz586SfYt3/jJdGydPnhTLioGDzAW9oEC6OKOp//wKCgqMdoFotwqdvVGKNw5VoGOfcRDUKhQlf428FW9Cvb1EnBZfd0ixqcy5yAQH16/qHA7pmk1dNU936tRJsgvxGExo+RozZozRcyI3V4qpIiIikJ+Xj127duHtt9/Grl27kJ+Xr1nWQYctXMDMWW7CkkPvzRlqbqllDSzN3tcKsyfsEiK7YurQToWifspZmX/CYgIogA4djC/ipz18FgDKy8sNJ9I6AhCAiooK8SlxSLRMN5ZKpT38xfSmeT8/P6PH5+fnB0EQsObXLLz981nUCK6oK8tHfvH7qO5xFnAFEAagHECm/gKPZmkIFLS7SwzM+nvvvfdi06ZNsu+FdncQoLUas0x57dWao6KiMHLUSE0rjfY5UQKjRo+SvdiZ24wfHh6Ouro6vfWltDWne6WluxPE5QR8IH0/ggDclHaRmTuLrqnMSZJvkPhdImY9Mgs7NzSOEpoYf2fLGlhSc46Rmo8BC9mlpoZ2enh4aB7I/BMWX29gZMSNLjEIkUkG1Q5CxGTXbdBMkNYRQAXE6eVDQkLEsuYMERbXK5I7Pt8ALEhMw5aTmu6p8b0Csem1v6D6ZrbeEGEfP59m52HoLWjYIAJApoFRQkbeC91RQiqVyuiQcN1gz9nZGQpnBYTRgjiNv+KAAk5OTnr1Nnf4rDnz/5hzAbP4MN5Z0ATWhdDMYeMITcukFnNn0TVVc3JSLLGsgSXZe96NvWnxLqGuXbtCoVDofc2bN89g+f379xssL84mSu1CS/f3Z2ZmGm0Sz8zMFMtev37daPeKmMBYr66uzmh+jPZoF3E0jxLAbmiSgJPqfxak+S5mL5Qoc3zOwd2xoaIntpzMgaNSgVen9MJ/Hr0bPSM6ay5Y2nV21JoIz4Cmzot4dx4DaV7KAM3T2nfn6enpmveiRue9qNG8F2lpaZJ9C4JgdFFKccK/+nru3b0XwhRBM1HaAAAjACFeEOel0WZuM76p5c3tXrFUd4I4Cd81SLvfrtZXL1xawXf+8Q6UdUrJ+6ysU+Ldd95tdh3uJCclMjIS8fHxNn+xt9e8G3vV4i0sx44dk9z5nD59GpMmTcJDDz1kdLsLFy5I5lUICNAdB0ptkaXuMKuqqozOf1JdXS0+detW/Qxw5syzYeLImJKSEk1goYbB+UG0F/AThwjfgMG5M7T/+QUFBRk8Po+774PPmCdQKjihk7cbPpsdg4FdfJCRkYGUX1L07vwhACkbUpp95y+5Ox+u0rSa5AEOh/XvzsVVrnW6ihp+Fl+vFxISonl/ptcfZyaAbtAEexukrVPm3Oma2wpiTnlzulcs2Z1g7oSFEyZNQG2ddCmL2rpajJswDkUFzcu9sVTLjS1pD8doS1q8hSUgIADBwcHi15YtW9C9e/cmk94CAwMl22n3T1PbZak7TN1EWV3irKsA3NzqF1yRuUvSzXERuy5kLo7aXRsnT540Ol+KdoIuAKOJwtrEO+j6ETeKP7rD/4Wl8J3wDBSOThjgr8C2haMwsIsmuDBl2K82c87L5ys+h3cHb0mriXcHb6xauUpSbvDgwUZbpoYMGSIp37NnT82DNGgSoE9Bkxh9QvO09ky3kkDBwLw72oGCua0g5pQ3J3HUksmu4oSFDTksDa1T3tCbsHDnzp0oLio2eF6Ki4qRlJSE5hI/G1p1MPTZsGfmrPFEd8aio4Rqamrw7bff4vHHH29MgpQRExODkJAQTJgwAfv27Wty39XV1SgtLZV8kX2x5HDKvn37Gr049uvXTyzr7OxstPvI2dlZsm8x4VUmwBHzSwBkZ2drHsh082jPhyEJKgwsqqgdVIjbhQPOZVEI9fsU7o73QBBqUbj7X7jX6wa8OjTmbkhmujVQZ+35YMw9L8/OfxbFlcXAJGhaQ2KB4spi/GXeXyTlzJlwD6hv2WoYuj0CwHAA90Acui22jEETKHj5eGkCpu+gCVj+B2AT4OUjXaDQ3GZ8yQy9Bsrr5t6YegGzZHeCmGc1C9Kuutmap7VbWL799luj5+Xbb79tdj3Ez0YsjH427FlD3k1GRga2bduGjIwM7Ni2g0sJWIBFk243btyI4uJizJ07V7ZMSEgI/vWvf2HQoEGorq7G//73P0yYMAH79+/H6NGjZbdbtmwZ3n77bQvUmlrLnSSs7dy5E0ePHsXw4cMNJoz++uuvRrtttC+OOTk5mudDYTBx9MaNG/oVaAhwtJNHD0Cvu6OmpqbxojsJgDuAysayBleUllnTSFvDitAeR6bB5565UDg4obY4F/nb30NN1kWcPdvNcJ23Q9pFsEO/zs3qXgmGJh+lniqoMbhpKLt//36j+923b59kTZ7U1FSj6+H89ttv4lMZGRkoKS4BnKE3yVxJsXSBwqioKKMTwel+5sTuFZn3Trd7xdTEUUt2J+h1TdXH2EjXfNMOhpoaUddcBru8AKjc2+YIGpPXeKJms2jA8uWXXyI+Pl7S/K6rZ8+ejU2/0AyvzM7Oxocffmg0YFm6dCkWL14s/lxaWto4nwbZheYMp7x8+TKGDh+KgluN85f4Bfjh2NFjkjkxzp49q3kg809YfB1a+SwxAO5D44iK3wFkSvNdgPo7fwGai9hurRfqL6TarRV1dXWast6QXNAbhpdqX+zEuUSKIA1ukqE3l0huYRkCHngdHSKHAQAq1IdQULAcws1KQKE/O6+Y8+IF/WGut6ULD5pzXsQ1a0pgcCZf7eDm999/N7pf3cCwtrbW6Ho42sGeuEBhQ3caILtAYUZGBgrzCzVBls57UXizUO9CKnav6OYo1Z9vufWgTLmANWcYrylDoCXBUE59TqECcEjXD4YeeeQRzbxEMuflj3/8o9FjkGOLI2g4G619s1jAcu3aNezevRvr169vurCOYcOGNdkM6eLiAhcXl+ZWj2xAc+4whw4fioLSAskFrGBrAQYPHYz8vMbZXcWLiMw/Ye2LjKOjI2pqazQXwinQS1DUvSCJrSYKGLxI19TUiGXFobkyF3TtgCUzM7Mx78BAcHP16lVERkbit2uFOO4/ER1CPSGoa1Ho8m+UO2wD+kIzZHkDkJur3USjFezIrDCtHQyZc17Ei3nDTL6AJFDQfu/EAESmpUI3MBRHCckEIYJWYo85CxSKXWsyQ351p/EXW1gEaLo26odMNwSSzV1xGzBvGK+5Cerv/OMd7B2xF6qUxkEQShf9kT9xcXHw9vVG8dZivRYnb1/vZg95t9T8Ls3BVaDbBosFLKtXr0ZgYGDjwmFmSEtLk4wAoLbLnDvMnTt3alpWDIx0KdhQgKSkJPGfq7+/PwoKC2S7bbRHoYkXRpkRRdrDZxs3guxFWptarTZaVsw1ALB161ajwc3PW7bgsnM3fLDzAlTOnqgtvIFbHu+i1vFK4y/sqvmmu1YMoNkHHAGMh7T1xsBcM6aeF7H+MoGC9sU8ICAAV65ckW3l0R0lJL7vMvvWPi/iQonXoOnaK0JjKxkMLJTYUNZAd4kuMSi7V2vfYdC8hxvkW1jMuZs3pTXG3AnpYifHoha1kvK1W2sxMXaiJLgHgL2792Lo8KGo3dDYauXk4oR9e5rOJ5RjSyNouAp022CRgEWtVmP16tV47LHH9P6Yly5diuvXr+Obb74BAPzzn/9E165d0adPHzFJd926dVi3bp0lqkY2xmAwIOPo0aOaBzIXsJSUFDFg6dy5My5cuKCZs0O726Z+/hPtaeAl86pEQ3Mh7wDgLAC1kTtoE/r8zbnolpSUyAY3yh2eOKTsh83bNfMTdSw4j7PfvAHhvtsG7151R0mJXSYyrTfaXSaA6Xf+krtoA4GC9l301KlTcfTXo5oyMQBuA3CD5n1WANOmTdPbv7hvA8eoTex23gS9ifEAaZeXZBp/7RYFmWn8xaAsDQYXmtT9fFjibt7cIdDmBPcAsPS1pVA7qoExEFuQ1L+oseTVJXd0QbeFmWs5G23bYZGAZffu3cjKysLjjz+u91pOTo7k7q+mpgYvvvgirl+/Djc3N/Tp0wdbt27FlClTLFE1sjHm3PlIFnQzcAHTnl7ew8PD6Eq/2jPdSoKmVK391ucoaHc9SJhwIRXnJJIpqz1nkdwKuy4RfeD/p5dR6OIHF0cl3rq/D3Z8/iPO1N6WnWdDHPZcTxxxI9N6Y3CuGTR95x8VFQUfPx8UbSoyOIOu9ra3bt1qnAguTatsfRCpu3K1v78/8gvyZY/R369xNJY4h40jpLMVbwWglgZOUVFR8PH1QVFxkV6Lmo+vj97xivuWSZzW7dqwxN28ufkg5gT3lkyOtYWZa20xl4aaxyIBS2xsrOyds/YquQDw8ssv4+WXX7ZENcjGmXvnI1nQzcAFTHt9l/PnzxvNf9CbSdlIcqfBeKUhF0O7u+mQTHljo3O0yorDdBuCG0EBz7qH4O34CBQeDlCW52HT6zPQK9gTny48L50FtkH9xb9hFFEDMelWpmtKrgu2qW6NjIwMFBUWaUbn6AQKRYVFknN48uRJo0HkqVOnJPvWm+lW5xj1/scYOd96dS4o0iTdaqf6+ANFN4sMX6AFyCZO6+7bEnfz5uaDmBPct8YF3ZojaGwpl4buDFdrJqsxd+IscUE3B0gnw6rPNdGep0Sc/0Rm3+LrDbQvdl6QnbBNUl5minmDZRvyNhrq7KlfVgzItgPK094IrHwbPnWPQqFwQPnpPehzfSt6BWtmgxYTVKVTxGiCAegPlxZbL8JhcI4X7SUCANNXHBbPicx7p31OioqKjJbV3Xdpaanm/XCGZB4POANQQDL3kjmfJbGszBwlBj93CgDFkM7pU6Kph/YxWmoyOHNXMm5IpDU0t5CPr3TtqLY+vby9rgJN+rj4IVmNZEKuJkbySEyGpvvhCiRTtWsTL+gy+66qqtLfrwk5KSIj3Q8GW1iK0didoLXgn3bZqVOnYtOmTXDt2R9+QS/CUekLdU0VCn9dhYpf9mDG6tVi2du3b2se6E7jUp9OUVlZKXlaDNBk5njRDeDEbg2tLpCGmW4NdmuY8N6Jw5Zlyhoc1gw0zhKsk+yqHZSZcxdtzhwlgNZ6UDJz+mh3ZVnybt7cfBBBEAy2TqkFaZa1JDm2VCW2GBpaXsFe2UIuDd05BixkNeZOyCUmQ2onVp6CmFipnSzp4OCAOlWd7Cghg0s/mJCTIjKx+8HR0VFzHLWQdifUtwpJpvE/dQpeI2fDa8TDUCiUqKm4hvzz76L2t2xAUb94YL2KigrNA90RPmqd1+vl5OQYneNFexi0OZPBie+5zHunfU46duyoyZWRKau7BIJSqdR8RmSSXbXPodkjUowk3erKz68fUSMTaBUUNM4JZMmRMebkg+zcuRMlRSWaliB3ANnQBHvlQMmGEr2k289XfI4hw4agYHfjsXgHtJ0p9G0hl4buHAMWspim8h+6d+9udDIz3btRcQVmmZaNhnlKAK3hxAoYnNxNL//BSG6MbLeQCa0KTc3R0fB6XmkVtlZ0g/c9motIWfpOFO3+F4S6as2oDUFrpljodJcYyAfRXlQR0FlbxkAehvbSGeZMBmfOrLH9+/fHlcwrsu+zODS5npOTE6prqjXJrgYWjtRtgRMvuhuMX3QvX76s+f0hMDizsW7OhjhKSGYklHbiNGD5u3lTRtZJkm69ANQ3/KD+Y6GddAsAT/35KRSWFUre58JthXjyz09iT9KeFqm3LeBstPaNAQu1OLNX+j2wG6pJKrG7RK4peuvWrUZbNrZs2SL+ExYnKVOgycndAEgXimtgIKlSwpwWmWnQLHqoPYtu/e86kHELz689gTq/7lDX3Ebh3pWo6L5fk1ehdYHWTqStrKw0+l7odgn16NEDKUdSZIMQ7eDwxo0bRhN0ddcdMnXWWDGJVqVT1hEGg0iVSmW0K0Y3UBDXrRlRX0YJFJ/QrFuj3Y0ldtvchlRl43ulTRwSLTNkWjdh2VJ38+YMlzYn6TYjIwN79+zVSxQWBAF7N+zlsF+yGUy6pRb3YMKD2Jm0U/LczqSdeDDhQb2y4kJxSRCTV+VWOhVbDWRaNvQWwNS+6DYkeE6GfIuJm87PHQyW0mjoytJOaDSwLo8oXOf3dgWgUMJ71Bw8tvpXFFTUoCYvEzlfL9IEKwYWoTO47pDMe6G72OiYMWOMvh/jxo0Ty8oNrzY0KZ05Cazl5eWNLWTaSbSOABT1r2sRW6dk6qHdeiUu2Oih0qw7lALgF0DVUX/BxqioKPgF+GlaSrSTaIs1yzzoXpxv3rzZWG/t8vX11k1YbhAZGYn4+PgWu9ibs4J2XFyc5hgNJN36BfhJWlfMXcmbyFrYwkItKiMjA/v27tN0vdwPSTP+3j36d2vm3I16eXlpHsjcNXp6eupvZGoibcM8Gwa6Hpoc+dOgvivLIJ1kV4cIP/jPegmuYX0hCMAjQ7tgWcIDEOpqTKqzk5OT5oJ9DZpA6zrEHAVAv7vkwIEDmgdGLkoNi5Sac3eulzitk8CqXQ+xhUW7xQQQk2hlmdCSJenG0mphQSr0urEyMjIaJ1ULBZAHTevXFM2karqfUXFCP5nWLN3uN0toznDppJ1JBmev3b1rt6ScZCVvA++z7vw4RNbCgIValDjMVaYZX3edlgam9C0HBQUZXSVZu2new8MDZWVlpgc3DXXWvoDFQ/5CauLIH5HWGj6u3e6G/73Pw6GDF9TVlVg59x5MjQ7FO3X13VQmXKDFLq2NkCbe1scPuq0x4urUMvs+cuSI+FRcXBycXZxQs7VWL9fE2cVJcncuJk7LJLBqt4KICzKakLwK1CcsNyROG8h5cXRo/PcldmMJMLiys/ZFV2wVkknm1c1hMWe2Yktpzlwpps5eK/5dyZxD7VmCiayJAQtZhqktG2bYt2+f0UTaPXv2YMmSJQDqR5AYSaQ1OEpI5gJmkADZkT96xERhB3h3exReDjMAANW5l5C/6T1M/eSGtKzc6BWtfYuLKspMwqab7Ovt7W30/fD19RXLZmRkoKa6Fo6OQJ1WwOboCNTU1Uru5sXEad3hs/XvhXY+iDiUXCZoEodq13Nzc9MEnQqdfdefb+1RRdevX298jwyMgtLuxlIqlUZb1HRbp6KiohrrbSDpVnzdgswdLi1pkdEaEm5o9lqxu1DmHOouVUBkLQxYqEWZM8zVXL///rvRGWm15xIpLi7WPJC52OlOUgYA0F0v0MD6gRJdAGQa+bmBADhMC4B/95fhqr4LAFBatBlF3/4XUNXplZUbvWJov6YMrQbqgwUBsgs8as9Lc/nyZdlUHAVkZj41klTcwMfHx+SgCagPYBoqor1Kcn2LmnZisTgpncwoKO38pqysLKOtgNeuXZPUIyEhAW+88YZs0m1CQgIszdzh0ua0IkVFRWH8xPHYu3+v9JcqgPETxzPhlmwGAxZqUeYMczXXXXfdpVnQUOYi3bt3b+kGDaOEtC92xrptzKWba3nDYCm49RgCv67Pw0HtATXKke/8KW67p0gvftpiANwH6cXfUMACmNySJV7AZAIL7aRUpVIJQQGonSDJQ1JvBwSVtAVCkrDphcYcFsfG1xvOeW5urtHRWLq5EmIrkUzOi3YrkjhXilbXm+YFzTftfUtm/dXWVb8soPlMe/t6o7iiWG84vbeXd6td0M0ZLi1Z/8hAK5Jui8y//+/fmiHhtxq75fx8/PCff/3HEodC1CwMWKhFmTPM1Vxi3onMhUZMym2gPSqmgVyCp9htg6Znrm0oL9PS01C+pk4Nn/FPwnPwdABAtSID+c7voU55U34IdEMLxBSYNh+MOUOrAdnAQntUUUMLhFpnWLNapgXC1HqIo49mQRNY/A5NorA/gE8MLJegXWdtXfWL9OrVq7GLTPscbgOgBvr06SOWFXMyZOqsm7ORkZGB4sJigysfF28obrVhv2YPlzbSiqRLHBKu9Xku3qk/JJzImhiwUIuSDHOtQ+OdvCOAT+QXUtu5cyeOHj2K4cOHS5I6tYndPDIXGjGpU5upuTRmdK+YUj67sBLz16SKwUpp6gYUdfwa6Fonm5ci7lem20aPke4V3fKSyc8MvHe6c5oAMOm969Kli9G8m/Dwxp2IicAyywPITrJnQjAUGBho9ALt79+4svOYMWOM1lm329LWVvs1JUHdnDpbasFGopbGgIValLnrtFy+fBlDhw+VNkUH+OHY0WOaxQC1dOvWzeiFxuA/VXNaIMxNFJYp7xY1HFOWH0RZVR1Ut8tQsPUT3M78VX80j1y3lAn5IAAaF2DUfk2mJaaurs5ogKMdLJiThyTOKCyTd6O9X3G5hCIYbJkSh0hrMzEJWWTCOYyKisL4CfU5G9p1dgTGT9DP2bDH1X7NqbOtBWREchiwUIsyNzlw8NDBKCqWJsAWFBZg0OBBmq4lLVeuXDF8ka6/+Iv/eBs0XKBztH7+DS3XvaI7auS6E3wmPg7PQVNRVlWHgV28sXnJn6Aqu2XeFI0y3TYG/QFNLgQJaNbxqS6olm298fDwkG6gAJTbAbXW8HHlIUCtk41rzqyx4rpKMi1TTk5O+hUXIDt6RZu5yd7m5GxYcn0gSzGnzvYYkFH7xJluqcWJs9duAPAJgA2GZ6/duXMnigqLNHkH2jOIOgFFhUVISkqSlBdzJ3SHsNT/nJmpk5na0L2SUv91uP5nue4Vc2auBTSjRlYA+A5w/F8Igj0/gOegqQCAp8d0w9qnh2uClYbcCp1jlN23borIVSN1CIcmSXdG/feuhouJLR0BOi/467yOxrV2vByhGT6+EUBS/c+CdPZac2aNFbtlZO7ktbttzNUw0kWxTSE5h4rtCoMjXcScDa0Zd4srNTkbhpj6mbYlpta5Ibhx2Okgee8cdjkgLj7OJgMyap/YwkItztTkQHFtoCnQzNaaBk0SZv2EbdprAwFaE4sZyvuATA6LqQSYN3OtVpJuh4jR8FPMh9KhA1SVJcjf+jGWvntcum9T82PMyEsBIDs3iK6amhqjCxpWV1eLZRtGmJTUQTKnSUn9CCvtO27JrLE6x6c7a+z48ePxzTffyN7Jjx8/3nDlZQJUXT/98JPeKJrY+Fi9C7TBnA0YnqOkQcNneteuXThy5IjRXCtbYU6SrqUXbCRqCQxYyCxNrcCsrakZQAMDAzUPdkDsQgAgruEjvl4vJyfH6GRpemu6GBk1onfxV8C8mWsFQHGfM3x6PgUPVTwAoKryNPJXfwBVeYF+eXOSf01NugVk5wbR5ejoaHRBQ93J0iAAai9I5jRRGwjgJPkP+WgMnLpqntbLf2hoydIOyOpbsnTXP1IqlVALas0xjYN0MjgVoFRIG4hNvUA3J2fDnIUHbY0pSbqWWrCRqCUxYCGTmPMP29SyCQkJeOOvb2guuAZmJ9WdkEscySLTWqE3ysSMYZ1iQKDdC+UC2UDB0bcTArotgbMqAgLUKHH8ASWOa4ByteENzMmPMTXp1gz+/v6aWWNlLtIBAY19RZJ1eQy0xmhf0MX8B5mRP9qtMdnZ2UZbsrRnowU0M91WVFTITgbn1kF3tUqNpi7QzcnZkCw8WP9+7N6pWXiwLQ37NSW4IbIWBixkEvEftlZg0bBSrO4/7OkPTMfBIwclZXfu3Ynpf5iO5P3JYrnMzEyjs5NevXrV8D9Pmbt5g0xt2WhoSYmBphWhA4AzMNjC4t57LHzj5kGpdIMKRch3/ghVDieanlvF1G6ecGgSTY3U2cHBASq1SrYFSXudHQAYPXq05v2WuUiPHTtWfEqpVJrcGtOQw1JQVKA38kc3h6WqqkpzzEWQtmTVz1yr3S0FaI1skgmcZIdBN8HcJFoO+yWyDQxYqEniP+xgSAILVZAKO7dL/2FnZGTgYPJBvbIIAg4kH5CUXbVqldEL0ueff244T0Dmbt4gU1s2BM3vRJrWczotLLdrVPjrptPwn/oiAKAqOx35NR9CFVZkPAgxt5vHhONTKpWaFieZFiTdIcKenp5Gu2M6duwolhXnbJEJ9rQDBXNyWMQh0KGQfjYMDIFuOEZjgZPBYdAwrdvSnJwNDvslsg0MWKhJ5nQR/PDDD41ltbt56vNBfvjhB7z22msA6lcINnJB0l5BWNRwh25khllJWVNbNhqek9lvxs0yzPsuFRfzyiGoVSj5JRElv/4A1Gl1ARmb4M0ZwChIlwiokqmHCcfn6OiomYhN5iKqu7ijOLGaTHeMdr6QpebwGD58OI4ePSq79MDIkSMlu3BxcdGsJySzb1dXV8nT5nRbmpOzwWG/RLaBw5qpSXp3ul713ycDEKRdBOKKyg0JmxsB7ALgqSm7f/9+saw4c63MBUl8XZv2iJuGekyBbGKsuG7NJ/XfvY2UvReau/88aHJI6vfr3m8i7l9xCBfzyhHg4YKb37+OksPfA/ergQUAHoHm+/0G9tuw73gAIwAMqP8+uYl6NHF8YmvENWi6xy4CKIB4EdVtrUhISJAmFk+v/14CvXwhc4a5Si7m2urroX0xj42NbQwifwcQWP+9PoicMGGCZBd33XWX0X2Lr9eT5JnUD69u6LaUExkZifj4eKMtJBz2S2Qb2MJCTTKniwCA0dYY7ZFDYhKtzJ2rbI6COTkseTo/6y6Opy0NkpVtFZGu8L33WXTsOx5VtWqMivTHJzMHIOD1U4318IJpE7yZmktjYlmxhUVmlJDeqB+gcRK2JJ3yBgInU7tMzMkHSU1NNbr4YXp6OuLj48WnhwwZgpQjKbKtZEOHDhXLWjrPhMN+iayPAQs1yZwm8fHjx2Pv3r2y3TwTJ04UyyoUCqPdNrrDXEWm5rA0DGseDekIpBoY7orRWtnWKacrAjxegZNvGAS1Ci/H98ZfxnSHUqlVJ3NG/rRw2fDwcJy/cF52wcauXbtKyotdN10gXfm5/mfdPAxLzOFx/vx5zQOZdabOnDkjKS9JyjYQ4GhPFGjpPBMO+yWyPgYs1CRz7qIHDhyoeSBz4YiOjhafElsJZC5IBlsJAP1WknyZihsZgWSw7BQA/YCOqjj4hj8NBZxRV5aP/M0fYN77p6Xlzc2PkUl2NSfvRqE1Y1pISIgmAJAZ4h0SEiLZrRh0yuSPyOVhtOQcHmPHjsW3334ru86UbpeQuPq2TIAjrt6N1ssz4bBfIuthwEIm+XzF55q1VzY0TormHeCNVStXScqZc+EQE0NlLkgGAxZzJ4OT6Zoy1A2iCHeDX+18uKvGAAAq1cdQsPoTqG+X6hc2Z+SPkWRXbR4eHpq5UhQ6ZetHK3l4Nq73079/f02+kAmBIaATdMZaZj2cpi7mTzzxBJ7885OGAzIlMHfuXEn5Rx55BP/73/9kA5w//vGPho/PTtb7ISLzMOmWTGLW2isKaC5CWgmKYqCgRRyZcg2ai1Fk/ferOq9ra2gJ0U5KjYd8oCCTKKzLKbAbQvAp3FVjIKAORY7/xa1LfzMcrDSQWZdHj/bcI9PrvxdD7/3o27dvYzA1AsDw+u/1++jTp49Y9tlnn9U8kElI/ctf9M+LtdfD2blzpyawbAjIGhKhAUANvbWj4uLi4O3rrQlwtD9L2wBvX2+9Ie/WPj4isiy2sFCTzFl7JTk5WXPBDYG0laB+ro3k5GSxbNeuXXEl84omuNG+464PbiIiIgxXqAUTWAVBQMeYe+E7/kkoHJ1QV5uHW3gfNRnnjbbGGJsATbu82GpSB4PJrtrdGlOmTEFKSopmH4e1yta3sNx77736dZDrljLA2nkYR48e1Tz4CzTdeNnQrB3lD+ATICUlRS8IST2eisFDB0ta9vwC/HDs6DG9/Vv7+IjIshiwUJOaldAokyuhzcPDw2hw4+7uLinv4OCgGVkk092kO/cIAKMJrCW3a7Fk3Un4xWpaIyqvH0HBT/+EuqpcUg+DBMhOgKZtyJAh2LN3j+YvrT803UBuAM4CUGtebyAuVdDQwiJAE3z8Br2hxw0rKiMUBt87Y0mm1srDEEf1NJyT+t7Dhi6e4cOH620TERGB/Lx8JCUlISUlxaRFB5lnQtQ2MWChJpmTlzJmzJjGLqGRADpCM7S4fuK4MWPGiGXFkSwywY1uC8vYsWM1F3+ZFplx48aJZRUKBQQIsi0QriFRuO+zg8guvA1BVYuifatRFr4ZeEq/Hrrc3d01a9zI1Fs70Bo7diz27NmjCSzStHZSH1hoT4kPoDE3RruFxUBuTHOTaK0pLi5OM43/1gK9c+IX4Gc0EJk0aZLNr45MRJbV4jksb731FhQKheQrODjY6DbJyckYNGgQXF1d0a1bN3zxxRctXS26A+ZMnBUVFYVRo0dphg7vhmbiuCQANcCo0aMkZf39/RuDG+2JxOoDEH9/aVLIuHHjNBc6b0hzILwACNJRJuLqxLU6ZWsBj0H3I3D2e8guvI0wXzfc/O4VlKVulq2H7vDqGTNmGJ0A7cEHHxTLiq0iMZBOMjdA53XUd6cBmoRiAxPSia/rnhOtOtj6ZGbHjh6Dn6ef5Jz4eRru4iEi0maRFpY+ffpg9+7d4s8Gm+rrZWZmYsqUKXjqqafw7bff4pdffsGzzz6LgIAAzYWBbII5E2c5OTlB4ayAMEUQR+coting5OQkKRcUFCQNKhrUtygEBUknWFGpVEZzR2pqasSy4joz0+vLZQPKLh3h57EIHToOAwDE9w3GuzP6o8v/y0W1ka4p7dE5gCZYELtrDIzm6dWrl6Sst683ircWa5KFu0JsVfD29ZZf3NELTU5IZ4+TmTWni4eICLBQwOLo6Nhkq0qDL774Al26dME///lPAJrpto8fP44PP/yQAYsNMTWhMSMjA3t379WbcVQQBOzdsFeSoKvdPWSI7utDhw5tDBS0c0fqAwXtHIhOnTrhypUr4sXfuWsvBNS+DEchEEJdLZQnN+LzZauhUCjw+OOPaz5/Mt0rTz31lKQe4gRoMsmj2hOgZWRkoLiwWLMYpM6w5uKbxYbfD5muN933w56TTNnFQ0Tmssiw5osXLyI0NBQRERF4+OGHNRcOGSkpKZo1RrTExcXh+PHjmknFZFRXV6O0tFTyRZbX1NorpiToNoiKisL4ieP1R7UogPETx+v9joYcCEPDfnVzIF599VXNg2sKeNY+gOCad+EoBKK25gZy/vcC3pw9Vuzq+eSTT4x28Xz44YeSeoh5J9egSRwdW//9quZp7a4p8f2YBWk3z2z590OxTSHpelNsVxh8PxqYsh4OEZG9a/GAZejQofjmm2+wc+dO/Pvf/0Zubi5GjBiBgoICg+Vzc3P1mv6DgoJQV1eH/Hy5KUyBZcuWwcvLS/wKCwtr0eOg5jFnMTwA+OmHnxA3KU7yXNykOPz0w08G9y/mQBwGkALgsOEciCeeeAJOHt4IcPwrfOoehwKOqChNRs5/noO68JreJGVbft5iMN9ly89b9OrwxBNPwMnFyeD8IE4uTpJ9S94PA3PNGHo/YsfFSuoROy5W9v0gImo3BAsrLy8XgoKChI8++sjg65GRkcI777wjee7QoUMCACEnJ0d2v1VVVUJJSYn4lZ2dLQAQSkpKWrT+ZL64+DjBwd1BwB8g4HkI+AMEB3cHIS4+TnabjIwMYdu2bUJGRoZJv2PXrl3C22+/Lezatcvg60evFAgD39omhL+yRQhbvE7oGB0nABCcXJyEtLQ02f2+8MILQv/+/YUXXnjB6O9PS0sTnFycBGjae4zuuzXeDyIie1VSUmLS9VshCFrL51rIpEmT0KNHD6xatUrvtdGjRyMmJgaffvqp+NyGDRuQkJCAyspKvURNOaWlpfDy8kJJSYlkMi5qfUVFRZpk0O2NyaBx8XFI/C4RPj4+Fv3darWAVcmX8XFSBlRqAd0C3BHrno0zv+zChAkT9FpW7tRXX32FPXv2GN23Nd8PIiJbZ+r12+LzsFRXV+PcuXMYNWqUwdeHDx+On3/+WfLcrl27cPfdd5scrJBtsVYyaH55NZ5fewIHL2q6Eh+I6YS/T+8LdxdH4Jk5Fvmdc+fObTIIsufkWCIiW9HiLSwvvvgipk6dii5duiAvLw//+Mc/kJycjFOnTiE8PBxLly7F9evX8c033wDQDGvu27cvnn76aTz11FNISUnBM888g8TERLNGCbGFpX07fDkfz31/ArfKquHqpMTfpvXFQ4M6682hQkREtsVqLSy///47Zs2ahfz8fAQEBGDYsGE4cuQIwsM1w0ZycnKQlZUllo+IiMC2bdvw/PPPY+XKlQgNDcXy5cs5pJlMolIL+GzvRSzfcxFqAYgM7IjPHxmIyCCPpjcmIiK70So5LK2BLSztT15pFRatPYHDlzUj0BLu7oy37+8LN2f5iQqJiMi22EwOC5ElHLx4C8+vPYH88hp0cHbA//tDX/whprO1q0VERBbCgIXsSp1KjX/uvoiV+y9BEIBewR5YMXsgegR2tHbViIjIghiwkN3IKbmN5xJP4NerhQCA2UO74K/39YarE7uAiIjaOgYsZBf2XcjD4rUnUFRZi44ujnjngX64PzrU2tUiIqJWwoCFbFqtSo0Pd13A/yVr1qPq28kTK2YNRFd/dyvXjIiIWhMDFrJZ14tvY8GaVKRmFQMAHhsejlfvvQsujuwCIiJqbxiwkE1KOnsTL/6YjpLbtfBwdcT7M/ojvl+ItatFRERWwoCFbEpNnRrv7TiPLw9lAgCiO3thxeyBCPPtYOWaERGRNTFgIZuRXViJ+YlpSM8uBgA8MTICr0zuBWdHpXUrRkREVseAhWzCjtM5eOmnkyirqoOXmxM+fCgak3oHWbtaRERkIxiwkFVV16nwztZz+DrlGgBgYBdvLJ8Vg84+7AIiIqJGDFjIaq7mV2B+YipOXy8FADw9phtejO0JJwd2ARERkRQDFrKKLSdvYMm6UyivroNPByd8nDAA43oFWrtaRERkoxiwUKuqqlXhb1vOYs3RLADA4K4+WD4rBiFeblauGRER2TIGLNRqLt8qx7zvUnE+twwKBTBvbA8smhgJR3YBERFRExiwUKvYkPY7XttwGpU1Kvh3dMYnMwdgVGSAtatFRER2ggELWdTtGhXe3HwaPxz/HQAwvJsfPn14AAI9Xa1cMyIisicMWMhiLt4sw7w1qci4WQ6FAnhuQiQWjI+Eg1Jh7aoREZGdYcBCFvHj8Wy8sek0qmrVCPBwwacPD8CI7v7WrhYREdkpBizUoiqq6/DGptNYn3odADAq0h8fJwxAgIeLlWtGRET2jAELtZjzuaWY910qLt+qgFIBvBDbE38Z0x1KdgEREdEdYsBCd0wQBHx/LBtvbT6D6jo1gj1dsXxWDIZE+Fq7akRE1EYwYKE7UlZVi1c3nMbP6TcAAGN7BuDjhAHwdXe2cs2IiKgtYcBCzXb6egnmr0nF1YJKOCgVeDmuJ54a1Y1dQERE1OIYsJDZBEHAt0eu4e9bzqFGpUYnbzcsnxWDQeE+1q4aERG1UQxYyCylVbVYsu4ktp3KBQBMvCsIHz7UH94d2AVERESWw4CFTJaeXYz5ianILrwNJwcFlsTfhcfv6QqFgl1ARERkWQxYqEmCIGD1L1exbPs51KoEdPZxw8rZAxEd5m3tqhERUTvBgIWMKq6swUs/nUTS2ZsAgMl9gvHeg/3h5eZk5ZoREVF7woCFZKVmFWHBmjRcL74NZwclXr/vLswZFs4uICIianUMWEiPWi3gP4eu4P0dF1CnFhDu1wErZw9E305e1q4aERG1U8qW3uGyZcswePBgeHh4IDAwENOnT8eFCxeMbrN//34oFAq9r/Pnz7d09agJhRU1ePKb43hn23nUqQXc1z8EWxaMZLBCRERW1eItLMnJyZg3bx4GDx6Muro6vPbaa4iNjcXZs2fh7u5udNsLFy7A09NT/DkgIKClq0dGHLtaiIWJacgpqYKzoxJvTe2DWUPC2AVERERW1+IBy44dOyQ/r169GoGBgfjtt98wevRoo9sGBgbC29u7patETVCrBaxKvoyPkzKgUgvoFuCOlbMH4q4Qz6Y3JiIiagUWz2EpKSkBAPj6Nr0QXkxMDKqqqtC7d2+8/vrrGDdunGzZ6upqVFdXiz+XlpbeeWXbofzyajy/9gQOXswHAPwhphP+Mb0v3F2Y3kRERLajxXNYtAmCgMWLF2PkyJHo27evbLmQkBD861//wrp167B+/Xr07NkTEyZMwIEDB2S3WbZsGby8vMSvsLAwSxxCm5ZyuQBTPj2Igxfz4eqkxPsP9sfHCdEMVoiIyOYoBEEQLLXzefPmYevWrTh06BA6d+5s1rZTp06FQqHA5s2bDb5uqIUlLCwMJSUlkjwY0qdSC1ix9xI+3ZMBtQBEBnbEykcGIirIw9pVIyKidqa0tBReXl5NXr8tdiu9YMECbN68GQcOHDA7WAGAYcOG4dtvv5V93cXFBS4uLndSxXYpr6wKi74/gcOXCwAADw3qjLen9UEHZ7aqEBGR7Wrxq5QgCFiwYAE2bNiA/fv3IyIioln7SUtLQ0hISAvXrn07dDEfi9amIb+8Bh2cHfCP6X3xwEDzg0kiIqLW1uIBy7x587BmzRps2rQJHh4eyM3VrOrr5eUFNzc3AMDSpUtx/fp1fPPNNwCAf/7zn+jatSv69OmDmpoafPvtt1i3bh3WrVvX0tVrl+pUany65yJW7LsEQQB6BXtgxeyB6BHY0dpVIyIiMkmLByyrVq0CAIwdO1by/OrVqzF37lwAQE5ODrKyssTXampq8OKLL+L69etwc3NDnz59sHXrVkyZMqWlq9fu5JZUYeH3afg1sxAAMGtIF7w5tTdcnRysXDMiIiLTWTTptjWZmrTTnuy/kIfFP6SjsKIG7s4OWDajP+6PDrV2tYiIiERWT7ol66lVqfHRrgx8kXwZANAn1BMrZg9EhL/xmYaJiIhsFQOWNuZ68W0sTEzDb9eKAACPDg/Hq1PuYhcQERHZNQYsbcjuszfx4k/pKK6shYerI96f0R/x/TjSioiI7B8Dljagpk6N93ecx38OZQIAojt74bNZA9HFr4OVa0ZERNQyGLDYuezCSsxPTEN6djEA4PF7IrAkvhecHS266gIREVGrYsBix3aczsVLP6WjrKoOnq6O+PChaMT2CbZ2tYiIiFocAxY7VF2nwrJt5/HV4asAgJgu3vhsVgw6+7ALiIiI2iYGLHbmWkEF5q9Jw6nrJQCAp0d3w4txPeHkwC4gIiJquxiw2JEtJ29gybpTKK+ug08HJ3yUEI3xvYKsXS0iIiKLY8BiB6pqVfj7lrP47qhmOYPBXX2wfFYMQrzcrFwzIiKi1sGAxcZduVWOeWvScC6nFAoF8OzY7nh+YhQc2QVERETtCAMWG7Yx7Tpe3XAKlTUq+Lk745OZAzA6KsDa1SIiImp1DFhs0O0aFd7afAZrj2cDAIZ188Xyh2MQ6Olq5ZoRERFZBwMWG3PxZhnmrUlFxs1yKBTAwvGRWDghEg5KhbWrRkREZDUMWGzIj8ez8ddNZ3C7VoUADxd8OnMARvTwt3a1iIiIrI4Biw2oqK7DG5tOY33qdQDAyB7++GTmAAR4uFi5ZkRERLaBAYuVnc8txbzvUnH5VgWUCmDxpCg8O7YHlOwCIiIiEjFgsRJBELD2WDbe3HwG1XVqBHm6YPnDMRjazc/aVSMiIrI5DFisoLy6Dq+uP4XN6TcAAGOiAvBxQjT8OrILiIiIyBAGLK3szI0SzF+Thsz8CjgoFXgprif+PKobu4CIiIiMYMDSSgRBwLdHruHvW8+hpk6NUC9XfDY7BoPCfa1dNSIiIpvHgKUVlFbVYsm6k9h2KhcAMPGuQHzwYDR83J2tXDMiIiL7wIDFwk7+Xoz5a9KQVVgJJwcFXpncC0+MjIBCwS4gIiIiUzFgsRBBELD6l6tYtv0calUCOvu4YcXsgRgQ5m3tqhEREdkdBiwWUFJZi5d+SseuszcBAJP7BOO9B/vDy83JyjUjIiKyTwxYWlhaVhHmr0nD9eLbcHZQ4rV778Kjw8PZBURERHQHGLC0ELVawJeHMvHejvOoUwsI9+uAFbMGol9nL2tXjYiIyO4xYGkBRRU1eOHHdOw9nwcAuLd/CN59oB88XNkFRERE1BIYsNyh41cLsSAxDTklVXB2VOLNqb0xe0gXdgERERG1IAYszaRWC/jiwGV8tCsDKrWAbv7uWDF7IHqHelq7akRERG0OA5ZmyC+vxuIf0nEg4xYAYPqAUPzjD/3Q0YVvJxERkSUoLbXjzz//HBEREXB1dcWgQYNw8OBBo+WTk5MxaNAguLq6olu3bvjiiy8sVbU7cuRKAaZ8ehAHMm7B1UmJ92f0xyczBzBYISIisiCLBCxr167FokWL8NprryEtLQ2jRo1CfHw8srKyDJbPzMzElClTMGrUKKSlpeHVV1/FwoULsW7dOktUr1lUagGf7r6I2f8+gryyavQI7IjN80ciYXAY81WIiIgsTCEIgtDSOx06dCgGDhyIVatWic/dddddmD59OpYtW6ZX/pVXXsHmzZtx7tw58blnnnkG6enpSElJMel3lpaWwsvLCyUlJfD0bNk8kryyKjy/9gR+uVQAAHhoUGe8Pa0POjizVYWIiOhOmHr9bvEWlpqaGvz222+IjY2VPB8bG4vDhw8b3CYlJUWvfFxcHI4fP47a2lqD21RXV6O0tFTyZQm/XMrHlE8P4ZdLBXBzcsDHCdH44KFoBitEREStqMUDlvz8fKhUKgQFBUmeDwoKQm5ursFtcnNzDZavq6tDfn6+wW2WLVsGLy8v8SssLKxlDkDL7RoVnvv+BPLLq9Er2AM/LxiJBwZ2bvHfQ0RERMZZLOlWN69DEASjuR6Gyht6vsHSpUtRUlIifmVnZ99hjfW5OTvgo4RozBrSBRvn3YMegR1b/HcQERFR01q8X8Pf3x8ODg56rSl5eXl6rSgNgoODDZZ3dHSEn5+fwW1cXFzg4uLSMpU2YkxUAMZEBVj89xAREZG8Fm9hcXZ2xqBBg5CUlCR5PikpCSNGjDC4zfDhw/XK79q1C3fffTecnDi9PRERUXtnkS6hxYsX4z//+Q/++9//4ty5c3j++eeRlZWFZ555BoCmO+fRRx8Vyz/zzDO4du0aFi9ejHPnzuG///0vvvzyS7z44ouWqB4RERHZGYsMdZk5cyYKCgrwt7/9DTk5Oejbty+2bduG8PBwAEBOTo5kTpaIiAhs27YNzz//PFauXInQ0FAsX74cM2bMsET1iIiIyM5YZB4Wa7DkPCxERERkGVabh4WIiIiopTFgISIiIpvHgIWIiIhsHgMWIiIisnkMWIiIiMjmMWAhIiIim8eAhYiIiGweAxYiIiKyeQxYiIiIyOZZZGp+a2iYsLe0tNTKNSEiIiJTNVy3m5p4v80ELGVlZQCAsLAwK9eEiIiIzFVWVgYvLy/Z19vMWkJqtRo3btyAh4cHFApFi+23tLQUYWFhyM7ObrNrFLX1Y+Tx2b+2fow8PvvX1o/RkscnCALKysoQGhoKpVI+U6XNtLAolUp07tzZYvv39PRskx9CbW39GHl89q+tHyOPz/619WO01PEZa1lpwKRbIiIisnkMWIiIiMjmMWBpgouLC9588024uLhYuyoW09aPkcdn/9r6MfL47F9bP0ZbOL42k3RLREREbRdbWIiIiMjmMWAhIiIim8eAhYiIiGweAxYiIiKyeQxYAHz++eeIiIiAq6srBg0ahIMHDxotn5ycjEGDBsHV1RXdunXDF1980Uo1Nd+yZcswePBgeHh4IDAwENOnT8eFCxeMbrN//34oFAq9r/Pnz7dSrU331ltv6dUzODjY6Db2dP66du1q8FzMmzfPYHl7OHcHDhzA1KlTERoaCoVCgY0bN0peFwQBb731FkJDQ+Hm5oaxY8fizJkzTe533bp16N27N1xcXNC7d29s2LDBQkdgnLHjq62txSuvvIJ+/frB3d0doaGhePTRR3Hjxg2j+/zqq68MnteqqioLH41hTZ3DuXPn6tV12LBhTe7XHs4hAIPnQqFQ4IMPPpDdpy2dQ1OuC7b4d9juA5a1a9di0aJFeO2115CWloZRo0YhPj4eWVlZBstnZmZiypQpGDVqFNLS0vDqq69i4cKFWLduXSvX3DTJycmYN28ejhw5gqSkJNTV1SE2NhYVFRVNbnvhwgXk5OSIX5GRka1QY/P16dNHUs9Tp07JlrW383fs2DHJsSUlJQEAHnroIaPb2fK5q6ioQHR0NFasWGHw9ffffx8ff/wxVqxYgWPHjiE4OBiTJk0S1wszJCUlBTNnzsScOXOQnp6OOXPmICEhAUePHrXUYcgydnyVlZVITU3FG2+8gdTUVKxfvx4ZGRm4//77m9yvp6en5Jzm5OTA1dXVEofQpKbOIQBMnjxZUtdt27YZ3ae9nEMAeufhv//9LxQKBWbMmGF0v7ZyDk25Ltjk36HQzg0ZMkR45plnJM/16tVLWLJkicHyL7/8stCrVy/Jc08//bQwbNgwi9WxJeXl5QkAhOTkZNky+/btEwAIRUVFrVexZnrzzTeF6Ohok8vb+/l77rnnhO7duwtqtdrg6/Z07gRBEAAIGzZsEH9Wq9VCcHCw8O6774rPVVVVCV5eXsIXX3whu5+EhARh8uTJkufi4uKEhx9+uMXrbA7d4zPk119/FQAI165dky2zevVqwcvLq2Ur10IMHeNjjz0mTJs2zaz92PM5nDZtmjB+/HijZWz5HOpeF2z177Bdt7DU1NTgt99+Q2xsrOT52NhYHD582OA2KSkpeuXj4uJw/Phx1NbWWqyuLaWkpAQA4Ovr22TZmJgYhISEYMKECdi3b5+lq9ZsFy9eRGhoKCIiIvDwww/jypUrsmXt+fzV1NTg22+/xeOPP97kAp/2cu50ZWZmIjc3V3KOXFxcMGbMGNm/SUD+vBrbxlaUlJRAoVDA29vbaLny8nKEh4ejc+fOuO+++5CWltY6FWym/fv3IzAwEFFRUXjqqaeQl5dntLy9nsObN29i69ateOKJJ5osa6vnUPe6YKt/h+06YMnPz4dKpUJQUJDk+aCgIOTm5hrcJjc312D5uro65OfnW6yuLUEQBCxevBgjR45E3759ZcuFhITgX//6F9atW4f169ejZ8+emDBhAg4cONCKtTXN0KFD8c0332Dnzp3497//jdzcXIwYMQIFBQUGy9vz+du4cSOKi4sxd+5c2TL2dO4Mafi7M+dvsmE7c7exBVVVVViyZAlmz55tdEG5Xr164auvvsLmzZuRmJgIV1dX3HPPPbh48WIr1tZ08fHx+O6777B371589NFHOHbsGMaPH4/q6mrZbez1HH799dfw8PDAAw88YLScrZ5DQ9cFW/07bDOrNd8J3btVQRCM3sEaKm/oeVszf/58nDx5EocOHTJarmfPnujZs6f48/Dhw5GdnY0PP/wQo0ePtnQ1zRIfHy8+7tevH4YPH47u3bvj66+/xuLFiw1uY6/n78svv0R8fDxCQ0Nly9jTuTPG3L/J5m5jTbW1tXj44YehVqvx+eefGy07bNgwSdLqPffcg4EDB+Kzzz7D8uXLLV1Vs82cOVN83LdvX9x9990IDw/H1q1bjV7Y7e0cAsB///tfPPLII03motjqOTR2XbC1v8N23cLi7+8PBwcHvegvLy9PL0psEBwcbLC8o6Mj/Pz8LFbXO7VgwQJs3rwZ+/btQ+fOnc3eftiwYVa/EzCFu7s7+vXrJ1tXez1/165dw+7du/Hkk0+ava29nDsA4ggvc/4mG7Yzdxtrqq2tRUJCAjIzM5GUlGS0dcUQpVKJwYMH2815DQkJQXh4uNH62ts5BICDBw/iwoULzfq7tIVzKHddsNW/w3YdsDg7O2PQoEHiyIsGSUlJGDFihMFthg8frld+165duPvuu+Hk5GSxujaXIAiYP38+1q9fj7179yIiIqJZ+0lLS0NISEgL167lVVdX49y5c7J1tbfz12D16tUIDAzEvffea/a29nLuACAiIgLBwcGSc1RTU4Pk5GTZv0lA/rwa28ZaGoKVixcvYvfu3c0KlAVBwIkTJ+zmvBYUFCA7O9tofe3pHDb48ssvMWjQIERHR5u9rTXPYVPXBZv9O2yR1F079v333wtOTk7Cl19+KZw9e1ZYtGiR4O7uLly9elUQBEFYsmSJMGfOHLH8lStXhA4dOgjPP/+8cPbsWeHLL78UnJychJ9++slah2DUX/7yF8HLy0vYv3+/kJOTI35VVlaKZXSP8ZNPPhE2bNggZGRkCKdPnxaWLFkiABDWrVtnjUMw6oUXXhD2798vXLlyRThy5Ihw3333CR4eHm3m/AmCIKhUKqFLly7CK6+8oveaPZ67srIyIS0tTUhLSxMACB9//LGQlpYmjpJ59913BS8vL2H9+vXCqVOnhFmzZgkhISFCaWmpuI85c+ZIRvL98ssvgoODg/Duu+8K586dE959913B0dFROHLkiE0dX21trXD//fcLnTt3Fk6cOCH5m6yurpY9vrfeekvYsWOHcPnyZSEtLU3405/+JDg6OgpHjx5t9eMTBOPHWFZWJrzwwgvC4cOHhczMTGHfvn3C8OHDhU6dOrWJc9igpKRE6NChg7Bq1SqD+7Dlc2jKdcEW/w7bfcAiCIKwcuVKITw8XHB2dhYGDhwoGfL72GOPCWPGjJGU379/vxATEyM4OzsLXbt2lf3A2gIABr9Wr14tltE9xvfee0/o3r274OrqKvj4+AgjR44Utm7d2vqVN8HMmTOFkJAQwcnJSQgNDRUeeOAB4cyZM+Lr9n7+BEEQdu7cKQAQLly4oPeaPZ67hqHXul+PPfaYIAiaIZVvvvmmEBwcLLi4uAijR48WTp06JdnHmDFjxPINfvzxR6Fnz56Ck5OT0KtXL6sFacaOLzMzU/Zvct++feI+dI9v0aJFQpcuXQRnZ2chICBAiI2NFQ4fPtz6B1fP2DFWVlYKsbGxQkBAgODk5CR06dJFeOyxx4SsrCzJPuz1HDb4v//7P8HNzU0oLi42uA9bPoemXBds8e9QUV95IiIiIpvVrnNYiIiIyD4wYCEiIiKbx4CFiIiIbB4DFiIiIrJ5DFiIiIjI5jFgISIiIpvHgIWIiIhsHgMWIiIisnkMWIiIiMjmMWAhIiIim8eAhYiIiGweAxYiIiKyef8fer4G6mBOqusAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "regr = MLPRegressor(max_iter = 400000, solver = 'lbfgs', hidden_layer_sizes = (8, 8), alpha = 500, verbose = True)\n", "\n", "regr.fit(X_train, y_train)\n", "\n", "\n", "y_train_predict = regr.predict(X_train)\n", "\n", "plt.plot([0, 20], [0, 20])\n", "\n", "plt.scatter(y_train[:, 0], y_train_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", "plt.scatter(y_train[:, 1], y_train_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", "\n", "print(r2_score(y_train[:, 0], y_train_predict[:, 0]))\n", "print(r2_score(y_train[:, 1], y_train_predict[:, 1]))\n", "\n", "\n", "plt.show()\n", "\n", "y_test_predict = regr.predict(X_test)\n", "\n", "plt.plot([0, 20], [0, 20])\n", "\n", "plt.scatter(y_test[:, 0], y_test_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", "plt.scatter(y_test[:, 1], y_test_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", "\n", "print(r2_score(y_test[:, 0], y_test_predict[:, 0]))\n", "print(r2_score(y_test[:, 1], y_test_predict[:, 1]))\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "f9513b25", "metadata": {}, "source": [ "Train neural network for outfield players.\n", "\n", "The outputs of the NN are probability distribution of SinhArcsinh type (a skewed distribution, which is a generalization of Gaussian)" ] }, { "cell_type": "code", "execution_count": 19, "id": "8aad9652", "metadata": {}, "outputs": [], "source": [ "load_model_of = True# load scaler and model weights for outfield player predictor\n", "refit_model_of = False\n", "\n", "if(load_model_of):\n", " scaler = pickle.load(open('saves/scaler.pkl', 'rb'))\n", " \n", " X_train = scaler.transform(X_train_)\n", " X_test = scaler.transform(X_test_)\n", "\n", "\n", "n_epochs = 1000\n", "\n", "n_samples = X_train.shape[0]\n", "\n", "batch_size = 256\n", "\n", "X_len = X_train.shape[1]\n", "y_len = y_train.shape[1]\n", "\n", "\n", "#tailweight_param = 1.1\n", "\n", "tailweight_min = 0.5\n", "tailweight_range = 1.2\n", "\n", "\n", "callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience = 10)\n", "neg_log_likelihood = lambda x, rv_x: -rv_x.log_prob(x)\n", "\n", "\n", "inputs = tfk.layers.Input(shape=(X_len,), name=\"input\")\n", "x = tfk.layers.Dropout(0.2)(inputs)\n", "x = tfk.layers.Dense(16, activation=\"relu\") (x)\n", "x = tfk.layers.Dropout(0.2)(x)\n", "x = tfk.layers.Dense(16, activation=\"relu\") (x)\n", "\n", "\n", "prob_dist_params = 4\n", "\n", "def prob_dist(t): \n", " return tfp.distributions.SinhArcsinh(loc=t[..., 0], scale=1e-3 + tf.math.softplus(t[..., 1]), skewness = t[..., 2], \n", " tailweight = tailweight_min + tailweight_range * tf.math.sigmoid(t[..., 3]),\n", " allow_nan_stats = False)\n", "\n", "x1 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", "x1 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x1)\n", "out_1 = tfp.layers.DistributionLambda(prob_dist)(x1)\n", "\n", "x2 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", "x2 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x2)\n", "out_2 = tfp.layers.DistributionLambda(prob_dist)(x2)\n", "\n", "\n", "modelb = tf.keras.Model(inputs, [out_1, out_2])\n", "\n", "modelb.compile(optimizer=tf.keras.optimizers.Nadam(learning_rate = 0.001), \n", " loss=neg_log_likelihood)\n", "\n", "if(load_model_of):\n", " modelb.load_weights('saves/modelb')\n", " \n", "if( (not load_model_of) or refit_model_of):\n", " modelb.fit(X_train.astype('float32'), [y_train[:, 0].astype('float32'), y_train[:, 1].astype('float32')], \n", " validation_data = (X_test.astype('float32'), [y_test[:, 0].astype('float32'), y_test[:, 1].astype('float32')]),\n", " batch_size = batch_size, shuffle = True, epochs=n_epochs, verbose=True, callbacks = [callback])" ] }, { "cell_type": "code", "execution_count": 20, "id": "4e2bf9dc", "metadata": {}, "outputs": [], "source": [ "def sample_predict(X, iterations = 100):\n", " y = np.zeros((2, X.shape[0]))\n", " \n", " dist = modelb(X)\n", " \n", " for i in range(iterations):\n", " y[0, :] += dist[0].sample()\n", " y[1, :] += dist[1].sample()\n", " \n", " return y.transpose() / iterations\n", " " ] }, { "cell_type": "code", "execution_count": 56, "id": "c2674211", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.14539483185579238\n", "0.16229047232752447\n" ] }, { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y_train_predict = sample_predict(X_train)\n", "\n", "plt.plot([0, 20], [0, 20])\n", "\n", "plt.scatter(y_train[:, 0], y_train_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", "plt.scatter(y_train[:, 1], y_train_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", "\n", "print(r2_score(y_train[:, 0], y_train_predict[:, 0]))\n", "print(r2_score(y_train[:, 1], y_train_predict[:, 1]))\n", "\n", "plt.show()\n", "\n", "y_test_predict = sample_predict(X_test)\n", "\n", "plt.plot([0, 20], [0, 20])\n", "\n", "plt.scatter(y_test[:, 0], y_test_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", "plt.scatter(y_test[:, 1], y_test_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", "\n", "print(r2_score(y_test[:, 0], y_test_predict[:, 0]))\n", "print(r2_score(y_test[:, 1], y_test_predict[:, 1]))\n", "\n", "\n" ] }, { "cell_type": "markdown", "id": "f574ffdb", "metadata": {}, "source": [ "Train neural network for goalkeepers.\n", "\n", "For clean sheet probability prediction, a Bernoulli distribution is used." ] }, { "cell_type": "code", "execution_count": 48, "id": "41e7e1ee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/2500\n", "9/9 [==============================] - 4s 92ms/step - loss: 105.9979 - distribution_lambda_21_loss: 99.1596 - distribution_lambda_22_loss: 5.7268 - distribution_lambda_23_loss: 1.1114 - val_loss: 85.6839 - val_distribution_lambda_21_loss: 79.7444 - val_distribution_lambda_22_loss: 4.8681 - val_distribution_lambda_23_loss: 1.0713\n", "Epoch 2/2500\n", "9/9 [==============================] - 0s 6ms/step - loss: 78.7734 - distribution_lambda_21_loss: 72.8187 - distribution_lambda_22_loss: 4.8669 - distribution_lambda_23_loss: 1.0879 - val_loss: 63.5727 - val_distribution_lambda_21_loss: 58.3870 - val_distribution_lambda_22_loss: 4.1357 - val_distribution_lambda_23_loss: 1.0500\n", "Epoch 3/2500\n", "9/9 [==============================] - 0s 6ms/step - loss: 59.3165 - distribution_lambda_21_loss: 54.1085 - distribution_lambda_22_loss: 4.1424 - distribution_lambda_23_loss: 1.0655 - val_loss: 48.0409 - val_distribution_lambda_21_loss: 43.4830 - val_distribution_lambda_22_loss: 3.5318 - val_distribution_lambda_23_loss: 1.0260\n", "Epoch 4/2500\n", "9/9 [==============================] - 0s 6ms/step - loss: 45.1089 - distribution_lambda_21_loss: 40.5643 - distribution_lambda_22_loss: 3.4981 - distribution_lambda_23_loss: 1.0466 - val_loss: 37.6652 - val_distribution_lambda_21_loss: 33.5340 - val_distribution_lambda_22_loss: 3.1345 - val_distribution_lambda_23_loss: 0.9967\n", "Epoch 5/2500\n", "9/9 [==============================] - 0s 6ms/step - loss: 36.3207 - distribution_lambda_21_loss: 32.1626 - distribution_lambda_22_loss: 3.1592 - distribution_lambda_23_loss: 0.9990 - val_loss: 30.6755 - val_distribution_lambda_21_loss: 26.8368 - val_distribution_lambda_22_loss: 2.8735 - val_distribution_lambda_23_loss: 0.9652\n", "Epoch 6/2500\n", "9/9 [==============================] - 0s 7ms/step - loss: 30.0433 - distribution_lambda_21_loss: 26.1435 - distribution_lambda_22_loss: 2.9074 - distribution_lambda_23_loss: 0.9924 - val_loss: 25.8243 - val_distribution_lambda_21_loss: 22.1859 - val_distribution_lambda_22_loss: 2.7075 - val_distribution_lambda_23_loss: 0.9309\n", "Epoch 7/2500\n", "9/9 [==============================] - 0s 6ms/step - loss: 25.5853 - distribution_lambda_21_loss: 21.8961 - distribution_lambda_22_loss: 2.7424 - distribution_lambda_23_loss: 0.9468 - val_loss: 22.3435 - val_distribution_lambda_21_loss: 18.8484 - val_distribution_lambda_22_loss: 2.5991 - val_distribution_lambda_23_loss: 0.8960\n", "Epoch 8/2500\n", "9/9 [==============================] - 0s 5ms/step - loss: 22.2391 - distribution_lambda_21_loss: 18.7085 - distribution_lambda_22_loss: 2.6146 - distribution_lambda_23_loss: 0.9160 - val_loss: 19.7279 - val_distribution_lambda_21_loss: 16.3430 - val_distribution_lambda_22_loss: 2.5234 - val_distribution_lambda_23_loss: 0.8615\n", "Epoch 9/2500\n", "9/9 [==============================] - 0s 5ms/step - loss: 19.7891 - distribution_lambda_21_loss: 16.3677 - distribution_lambda_22_loss: 2.5346 - distribution_lambda_23_loss: 0.8867 - val_loss: 17.6639 - val_distribution_lambda_21_loss: 14.3678 - val_distribution_lambda_22_loss: 2.4684 - val_distribution_lambda_23_loss: 0.8277\n", "Epoch 10/2500\n", "9/9 [==============================] - 0s 6ms/step - loss: 17.8097 - distribution_lambda_21_loss: 14.4868 - distribution_lambda_22_loss: 2.4627 - distribution_lambda_23_loss: 0.8602 - val_loss: 16.0307 - val_distribution_lambda_21_loss: 12.8070 - val_distribution_lambda_22_loss: 2.4279 - val_distribution_lambda_23_loss: 0.7958\n", "Epoch 11/2500\n", "9/9 [==============================] - 0s 5ms/step - loss: 16.3632 - distribution_lambda_21_loss: 13.1148 - 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[==============================] - 0s 6ms/step - loss: 2.1012 - distribution_lambda_21_loss: 0.3907 - distribution_lambda_22_loss: 1.2847 - distribution_lambda_23_loss: 0.4257 - val_loss: 2.5832 - val_distribution_lambda_21_loss: 0.6258 - val_distribution_lambda_22_loss: 1.5785 - val_distribution_lambda_23_loss: 0.3789\n", "Epoch 806/2500\n", "9/9 [==============================] - 0s 5ms/step - loss: 2.1202 - distribution_lambda_21_loss: 0.3934 - distribution_lambda_22_loss: 1.2893 - distribution_lambda_23_loss: 0.4375 - val_loss: 2.5492 - val_distribution_lambda_21_loss: 0.6279 - val_distribution_lambda_22_loss: 1.5441 - val_distribution_lambda_23_loss: 0.3772\n", "Epoch 807/2500\n", "9/9 [==============================] - 0s 5ms/step - loss: 2.0868 - distribution_lambda_21_loss: 0.3804 - distribution_lambda_22_loss: 1.2706 - distribution_lambda_23_loss: 0.4359 - val_loss: 2.5646 - val_distribution_lambda_21_loss: 0.6407 - val_distribution_lambda_22_loss: 1.5464 - val_distribution_lambda_23_loss: 0.3775\n", "Epoch 808/2500\n", "9/9 [==============================] - 0s 5ms/step - loss: 2.1279 - distribution_lambda_21_loss: 0.3954 - distribution_lambda_22_loss: 1.2927 - distribution_lambda_23_loss: 0.4398 - val_loss: 2.5454 - val_distribution_lambda_21_loss: 0.6195 - val_distribution_lambda_22_loss: 1.5467 - val_distribution_lambda_23_loss: 0.3792\n", "Epoch 809/2500\n", "9/9 [==============================] - 0s 5ms/step - loss: 2.0938 - distribution_lambda_21_loss: 0.3961 - distribution_lambda_22_loss: 1.2694 - distribution_lambda_23_loss: 0.4283 - val_loss: 2.5654 - val_distribution_lambda_21_loss: 0.6194 - val_distribution_lambda_22_loss: 1.5646 - val_distribution_lambda_23_loss: 0.3815\n", "Epoch 810/2500\n", "9/9 [==============================] - 0s 5ms/step - loss: 2.1311 - distribution_lambda_21_loss: 0.4111 - distribution_lambda_22_loss: 1.2903 - distribution_lambda_23_loss: 0.4297 - val_loss: 2.6188 - val_distribution_lambda_21_loss: 0.6488 - val_distribution_lambda_22_loss: 1.5877 - val_distribution_lambda_23_loss: 0.3823\n", "Epoch 811/2500\n", "9/9 [==============================] - 0s 5ms/step - loss: 2.1009 - distribution_lambda_21_loss: 0.4019 - distribution_lambda_22_loss: 1.2632 - distribution_lambda_23_loss: 0.4358 - val_loss: 2.6334 - val_distribution_lambda_21_loss: 0.6452 - val_distribution_lambda_22_loss: 1.6066 - val_distribution_lambda_23_loss: 0.3816\n", "Epoch 812/2500\n", "9/9 [==============================] - 0s 5ms/step - loss: 2.0880 - distribution_lambda_21_loss: 0.3785 - distribution_lambda_22_loss: 1.2761 - distribution_lambda_23_loss: 0.4334 - val_loss: 2.5874 - val_distribution_lambda_21_loss: 0.6287 - val_distribution_lambda_22_loss: 1.5783 - val_distribution_lambda_23_loss: 0.3804\n", "Epoch 813/2500\n", "9/9 [==============================] - 0s 5ms/step - loss: 2.0805 - distribution_lambda_21_loss: 0.3868 - distribution_lambda_22_loss: 1.2677 - distribution_lambda_23_loss: 0.4259 - val_loss: 2.5655 - val_distribution_lambda_21_loss: 0.6276 - val_distribution_lambda_22_loss: 1.5591 - val_distribution_lambda_23_loss: 0.3788\n", "Epoch 814/2500\n", "9/9 [==============================] - 0s 5ms/step - loss: 2.0828 - distribution_lambda_21_loss: 0.3872 - distribution_lambda_22_loss: 1.2669 - distribution_lambda_23_loss: 0.4287 - val_loss: 2.5787 - val_distribution_lambda_21_loss: 0.6367 - val_distribution_lambda_22_loss: 1.5628 - val_distribution_lambda_23_loss: 0.3793\n", "Epoch 815/2500\n", "9/9 [==============================] - 0s 5ms/step - loss: 2.0688 - distribution_lambda_21_loss: 0.3822 - distribution_lambda_22_loss: 1.2501 - distribution_lambda_23_loss: 0.4366 - val_loss: 2.6174 - val_distribution_lambda_21_loss: 0.6476 - val_distribution_lambda_22_loss: 1.5888 - val_distribution_lambda_23_loss: 0.3810\n", "Epoch 816/2500\n", "9/9 [==============================] - 0s 5ms/step - loss: 2.0728 - distribution_lambda_21_loss: 0.3944 - distribution_lambda_22_loss: 1.2538 - distribution_lambda_23_loss: 0.4246 - val_loss: 2.6198 - val_distribution_lambda_21_loss: 0.6442 - val_distribution_lambda_22_loss: 1.5957 - val_distribution_lambda_23_loss: 0.3798\n", "Epoch 817/2500\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "9/9 [==============================] - 0s 6ms/step - loss: 2.0625 - distribution_lambda_21_loss: 0.3847 - distribution_lambda_22_loss: 1.2416 - distribution_lambda_23_loss: 0.4363 - val_loss: 2.6079 - val_distribution_lambda_21_loss: 0.6247 - val_distribution_lambda_22_loss: 1.6053 - val_distribution_lambda_23_loss: 0.3779\n", "Epoch 818/2500\n", "9/9 [==============================] - 0s 5ms/step - loss: 2.0822 - distribution_lambda_21_loss: 0.3917 - distribution_lambda_22_loss: 1.2572 - distribution_lambda_23_loss: 0.4333 - val_loss: 2.6130 - val_distribution_lambda_21_loss: 0.6191 - val_distribution_lambda_22_loss: 1.6167 - val_distribution_lambda_23_loss: 0.3771\n", "Epoch 819/2500\n", "9/9 [==============================] - 0s 5ms/step - loss: 2.0498 - distribution_lambda_21_loss: 0.3793 - distribution_lambda_22_loss: 1.2450 - distribution_lambda_23_loss: 0.4255 - val_loss: 2.6328 - val_distribution_lambda_21_loss: 0.6270 - val_distribution_lambda_22_loss: 1.6274 - val_distribution_lambda_23_loss: 0.3783\n", "Epoch 820/2500\n", "9/9 [==============================] - 0s 5ms/step - loss: 2.0865 - distribution_lambda_21_loss: 0.3950 - distribution_lambda_22_loss: 1.2662 - distribution_lambda_23_loss: 0.4253 - val_loss: 2.6273 - val_distribution_lambda_21_loss: 0.6253 - val_distribution_lambda_22_loss: 1.6256 - val_distribution_lambda_23_loss: 0.3764\n", "Epoch 821/2500\n", "9/9 [==============================] - 0s 5ms/step - loss: 2.0850 - distribution_lambda_21_loss: 0.3877 - distribution_lambda_22_loss: 1.2640 - distribution_lambda_23_loss: 0.4333 - val_loss: 2.6110 - val_distribution_lambda_21_loss: 0.6186 - val_distribution_lambda_22_loss: 1.6148 - val_distribution_lambda_23_loss: 0.3776\n", "Epoch 822/2500\n", "9/9 [==============================] - 0s 5ms/step - loss: 2.0699 - distribution_lambda_21_loss: 0.3884 - distribution_lambda_22_loss: 1.2563 - distribution_lambda_23_loss: 0.4253 - val_loss: 2.6213 - val_distribution_lambda_21_loss: 0.6317 - val_distribution_lambda_22_loss: 1.6136 - val_distribution_lambda_23_loss: 0.3760\n" ] } ], "source": [ "load_model_gk = True# load scaler and model weights for goalkeeper player predictor\n", "refit_model_gk = False\n", "\n", "if(load_model_gk):\n", " scaler_gk = pickle.load(open('saves/scaler_gk.pkl', 'rb'))\n", " \n", " X_gk_train = scaler_gk.transform(X_gk_train_)\n", " X_gk_test = scaler_gk.transform(X_gk_test_)\n", " \n", " \n", "n_epochs = 2500\n", "\n", "n_samples = X_gk_train.shape[0]\n", "\n", "batch_size = 128\n", "\n", "X_gk_len = X_gk_train.shape[1]\n", "y_gk_len = y_gk_train.shape[1]\n", "\n", "\n", "#tailweight_param = 1.1\n", "\n", "tailweight_min = 0.5\n", "tailweight_range = 1.1\n", "\n", "\n", "callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience = 50)\n", "neg_log_likelihood = lambda x, rv_x: -rv_x.log_prob(x)\n", "\n", "\n", "inputs = tfk.layers.Input(shape=(X_gk_len,), name=\"input\")\n", "x = tfk.layers.Dense(16, activation=\"relu\") (inputs)\n", "x = tfk.layers.Dropout(0.2)(x)\n", "x = tfk.layers.Dense(16, activation=\"relu\") (x)\n", "\n", "\n", "prob_dist_params = 4\n", "\n", "def prob_dist(t): \n", " return tfp.distributions.SinhArcsinh(loc=t[..., 0], scale=1e-3 + tf.math.softplus(t[..., 1]), skewness = t[..., 2], \n", " tailweight = tailweight_min + tailweight_range * tf.math.sigmoid(t[..., 3]),\n", " allow_nan_stats = False)\n", "\n", "x1 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", "x1 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x1)\n", "out_1 = tfp.layers.DistributionLambda(prob_dist)(x1)\n", "\n", "x2 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", "\n", "x2 = tfk.layers.Dense(prob_dist_params, activation=\"linear\")(x2)\n", "out_2 = tfp.layers.DistributionLambda(prob_dist)(x2)\n", "\n", "x3 = tfk.layers.Dense(8, activation=\"sigmoid\")(x)\n", "x3 = tfk.layers.Dropout(0.5)(x3)\n", "x3 = tfk.layers.Dense(1, activation=\"sigmoid\")(x3)\n", "out_3 = tfp.layers.DistributionLambda(lambda t: tfp.distributions.Bernoulli(probs = t[..., 0]))(x3)\n", "\n", "modelb_gk = tf.keras.Model(inputs, [out_1, out_2, out_3])\n", "\n", "modelb_gk.compile(optimizer=tf.keras.optimizers.Nadam(learning_rate = 0.001), \n", " loss=neg_log_likelihood)\n", "\n", "if(load_model_gk):\n", " modelb_gk.load_weights('saves/modelb_gk')\n", "\n", "if( (not load_model_gk) or refit_model_gk): \n", " modelb_gk.fit(X_gk_train.astype('float32'), [y_gk_train[:, 0].astype('float32'), y_gk_train[:, 1].astype('float32'), y_gk_train[:, 2].astype('int')], \n", " validation_data = (X_gk_test.astype('float32'), [y_gk_test[:, 0].astype('float32'), y_gk_test[:, 1].astype('float32'), y_gk_test[:, 2].astype('int')]),\n", " batch_size = batch_size, shuffle = True, epochs=n_epochs, verbose=True, callbacks = [callback])" ] }, { "cell_type": "code", "execution_count": 34, "id": "39a9bdc6", "metadata": {}, "outputs": [], "source": [ "def sample_predict_gk(X, iterations = 100):\n", " y = np.zeros((3, X.shape[0]))\n", " \n", " dist = modelb_gk(X)\n", " \n", " for i in range(iterations):\n", " y[0, :] += dist[0].sample()\n", " y[1, :] += dist[1].sample()\n", " y[2, :] += dist[2].sample()\n", " \n", " return y.transpose() / iterations\n" ] }, { "cell_type": "code", "execution_count": 49, "id": "c41cf448", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.29228994199557035\n", "0.5457850368866328\n" ] }, { "data": { "image/png": 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\n", 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y_gk_train_predict = sample_predict_gk(X_gk_train)\n", "\n", "plt.plot([0, 20], [0, 20])\n", "\n", "plt.scatter(y_gk_train[:, 0], y_gk_train_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", "plt.scatter(y_gk_train[:, 1], y_gk_train_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", "\n", "print(r2_score(y_gk_train[:, 0], y_gk_train_predict[:, 0]))\n", "print(r2_score(y_gk_train[:, 1], y_gk_train_predict[:, 1]))\n", "\n", "plt.show()\n", "\n", "y_gk_test_predict = sample_predict_gk(X_gk_test)\n", "\n", "plt.plot([0, 20], [0, 20])\n", "\n", "plt.scatter(y_gk_test[:, 0], y_gk_test_predict[:, 0], color = 'orange', edgecolors = 'black', s = 20)\n", "plt.scatter(y_gk_test[:, 1], y_gk_test_predict[:, 1], color = 'green', edgecolors = 'black', s = 20)\n", "\n", "print(r2_score(y_gk_test[:, 0], y_gk_test_predict[:, 0]))\n", "print(r2_score(y_gk_test[:, 1], y_gk_test_predict[:, 1]))\n", "\n", "\n" ] }, { "cell_type": "markdown", "id": "91869883", "metadata": {}, "source": [ "Use the following codes to save the scalers and the model weights" ] }, { "cell_type": "code", "execution_count": 51, "id": "cecf5392", "metadata": {}, "outputs": [], "source": [ "save_model_of = True\n", "save_model_gk = True\n", "\n", "if(save_model_of):\n", " pickle.dump(scaler, open('saves/scaler.pkl', 'wb'))\n", " modelb.save_weights('saves/modelb')\n", " \n", "if(save_model_gk):\n", " pickle.dump(scaler_gk, open('saves/scaler_gk.pkl', 'wb'))\n", " modelb_gk.save_weights('saves/modelb_gk')\n", " " ] }, { "cell_type": "markdown", "id": "32635a0e", "metadata": {}, "source": [ "Generalized prediction function for a player (playing for team against opp_team, at home or not)\n", "\n", "Estimate prediction mean and sigma (using a custom definitions).\n", "\n", "Generate a plot.\n" ] }, { "cell_type": "code", "execution_count": 23, "id": "ddf433f9", "metadata": {}, "outputs": [], "source": [ "def vote_predict_NNb(player, team, opp_team, home = 1, plot = 0, log = 0, oldseason = False):\n", " if(players['r'][player] == 'P'):\n", " ptest = player_match_data_ext_gk(player, team, opp_team, oldseason = oldseason)\n", "\n", " x_ptest = np.array(ptest)[:, 3:]\n", " r = np.array(ptest)[0, 0]\n", "\n", " # add home and role\n", " xadd = np.zeros((1, 1))\n", " xadd[0, 0] = home\n", "\n", " x_ptest = np.concatenate((x_ptest, xadd), axis = 1)\n", "\n", " x_scaled = scaler_gk.transform(x_ptest)\n", "\n", " dist = modelb_gk(x_scaled)\n", " \n", " clean_shoot_prob = dist[2].probs.numpy()[0]\n", " else:\n", " ptest = player_match_data_ext(player, team, opp_team, oldseason = oldseason)\n", "\n", " x_ptest = np.array(ptest)[:, 4:]\n", " r = np.array(ptest)[0, 0]\n", "\n", " # add home and role\n", " xadd = np.zeros((1, 4))\n", " xadd[0, 0] = home\n", " xadd[0, 1] = r == 'D'\n", " xadd[0, 2] = r == 'C'\n", " xadd[0, 3] = r == 'A'\n", "\n", " x_ptest = np.concatenate((x_ptest, xadd), axis = 1)\n", "\n", " x_scaled = scaler.transform(x_ptest)\n", "\n", " dist = modelb(x_scaled)\n", " \n", " \n", " x = np.arange(0, 40, 0.002)\n", "\n", " px1 = dist[0].prob(x);\n", " px2 = dist[1].prob(x);\n", "\n", " \n", " #sample1 = dist[0].sample(10000)\n", " #sample2 = dist[1].sample(10000)\n", " \n", " m1 = np.average(x, weights = px1)\n", " m2 = np.average(x, weights = px2)\n", " \n", " #m1 = np.mean(sample1)\n", " #m2 = np.mean(sample2)\n", " \n", " #s1 = np.std(sample1)\n", " #s2 = np.std(sample2)\n", " \n", " # not standard deviation, but expected range extimated by quantile \n", " \n", " if(players['r'][player] == 'P'):\n", " s1 = ( dist[0].quantile(0.9545) - m1 ) / 2\n", " s2 = -( dist[1].quantile(1 - 0.9) - m2 ) / 2\n", " else:\n", " s1 = ( dist[0].quantile(0.9545) - m1 ) / 2\n", " s2 = ( dist[1].quantile(0.9) - m2 ) / 2\n", " \n", "\n", " \n", " #y_pred_m = np.array([dist[0].loc, dist[1].loc]).flatten()\n", " y_pred_m = np.array([m1, m2]).flatten()\n", " #y_pred_s = np.array([dist[0].scale, dist[1].scale]).flatten()\n", " y_pred_s = np.array([s1, s2]).flatten()\n", " \n", " clean_sheet_text = ''\n", " if(players['r'][player] == 'P'):\n", " clean_sheet_text = ' (' + \"{:.1f}\".format(clean_shoot_prob*100) + '% cs)'\n", " \n", " if(plot):\n", " ax = plt.gca()\n", " \n", " plt.plot(x, px1, \n", " label = 'MV ' + \"{:.2f}\".format(y_pred_m[0]) + ' ± ' + \"{:.2f}\".format(2 * y_pred_s[0]),\n", " color = 'b')\n", " plt.plot(x, px2, \n", " label = 'FV ' + \"{:.2f}\".format(y_pred_m[1]) + ' + ' + \"{:.2f}\".format(2 * y_pred_s[1]) + clean_sheet_text,\n", " color = 'g')\n", " \n", " plt.fill_between(x, px1, color = 'lightblue')\n", " plt.fill_between(x, px2, color = 'lightgreen')\n", " \n", " plt.legend()\n", " \n", " plt.vlines(x = y_pred_m[0], color = 'b', ymin = 0, ymax = 3, linestyle = 'dashed')\n", " plt.vlines(x = y_pred_m[1], color = 'g', ymin = 0, ymax = 3, linestyle = 'dashed')\n", " \n", " plt.title(player + ' (' + team + ' vs ' + opp_team + ')')\n", " \n", " plt.xlim([0, 15])\n", " \n", " if(players['r'][player] == 'P'): \n", " plt.ylim([0, 2.5])\n", " else:\n", " plt.ylim([0, 1.5])\n", " \n", " plt.show()\n", " \n", " if(log):\n", " print(player + ': ' + \n", " 'MV ' + \"{:.2f}\".format(y_pred_m[0]) + ' ± ' + \"{:.2f}\".format(2 * y_pred_s[0]) +\n", " '; FV ' + \"{:.2f}\".format(y_pred_m[1]) + ' + ' + \"{:.2f}\".format(2 * y_pred_s[1]) + clean_sheet_text);\n", " return [y_pred_m, y_pred_s, dist]\n" ] }, { "cell_type": "markdown", "id": "98817aa0", "metadata": {}, "source": [ "Load Serie A calendar. " ] }, { "cell_type": "code", "execution_count": 24, "id": "d31bad38", "metadata": {}, "outputs": [], "source": [ "cal = np.array(pd.read_excel('fantacalcio/seriea_calendar.xlsx', header = None))\n", "\n", "cal_df = pd.DataFrame(columns = ['matchday', 'team1', 'team2'])\n", "\n", "matchday = 0\n", "\n", "for i in range(cal.shape[0]):\n", " if(cal[i, 0][0].isnumeric()):\n", " matchday = matchday + 1\n", " continue\n", " \n", " teams = cal[i, 0].split('-')\n", " \n", " frame = pd.DataFrame([[matchday, teams[0], teams[1]]], columns = cal_df.columns)\n", "\n", " cal_df = pd.concat([cal_df, frame], ignore_index = True)\n", " " ] }, { "cell_type": "code", "execution_count": 25, "id": "62b9f588", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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380 rows × 3 columns

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" ], "text/plain": [ " matchday team1 team2\n", "0 1 Fiorentina Cremonese\n", "1 1 Verona Napoli\n", "2 1 Juventus Sassuolo\n", "3 1 Lazio Bologna\n", "4 1 Lecce Inter\n", ".. ... ... ...\n", "375 38 Lecce Bologna\n", "376 38 Sassuolo Fiorentina\n", "377 38 Milan Verona\n", "378 38 Torino Inter\n", "379 38 Udinese Juventus\n", "\n", "[380 rows x 3 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cal_df" ] }, { "cell_type": "markdown", "id": "62872819", "metadata": {}, "source": [ "Function for generating a prediction for a player, taking match data from a given matchday, according to Serie A calendar." ] }, { "cell_type": "code", "execution_count": 26, "id": "c58ba41d", "metadata": {}, "outputs": [], "source": [ "def PlayerMatch(player, match = 0):\n", " team = players.loc[player]['team']\n", " \n", " if(match == 0):\n", " oppteam = 'Avg'\n", " home = 1\n", " else:\n", " for i in range (cal_df.shape[0]):\n", " if(cal_df['matchday'][i] == match):\n", " if(cal_df['team1'][i] == team):\n", " home = 1\n", " oppteam = cal_df['team2'][i]\n", " elif(cal_df['team2'][i] == team):\n", " home = 0\n", " oppteam = cal_df['team1'][i]\n", " \n", " return [player, team, oppteam, home]\n", "\n", "def predict_player(player, match = 0, plot = 0, log = 0, oldseason = False):\n", " [player, team, oppteam, home] = PlayerMatch(player, match)\n", " return vote_predict_NNb(player, team, oppteam, home = home, plot = plot, log = log, oldseason = oldseason)" ] }, { "cell_type": "markdown", "id": "e8b63a98", "metadata": {}, "source": [ "Load current matchday playing probabilities for Serie A players." ] }, { "cell_type": "code", "execution_count": 27, "id": "f79792b6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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starterpercentage
player
Falcone1.0090
Gendrey1.0090
Baschirotto1.0090
Umtiti1.0090
Gallo0.6565
.........
Barrenechea0.0020
Pogba0.0050
Chiesa0.4055
Soule'0.0020
Milik0.4060
\n", "

474 rows × 2 columns

\n", "
" ], "text/plain": [ " starter percentage\n", "player \n", "Falcone 1.00 90\n", "Gendrey 1.00 90\n", "Baschirotto 1.00 90\n", "Umtiti 1.00 90\n", "Gallo 0.65 65\n", "... ... ...\n", "Barrenechea 0.00 20\n", "Pogba 0.00 50\n", "Chiesa 0.40 55\n", "Soule' 0.00 20\n", "Milik 0.40 60\n", "\n", "[474 rows x 2 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "probables = pd.read_excel('mid_outputs/match_probable_players.xlsx', index_col = 0) \n", "\n", "probables" ] }, { "cell_type": "markdown", "id": "e4751014", "metadata": {}, "source": [ "Generate prediction data for each Serie A player for the current matchday.\n", "\n", "Output to excel file, using a template made for data elaboration." ] }, { "cell_type": "code", "execution_count": 28, "id": "5e63c2b7", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Meret: MV 6.24 ± 0.83; FV 5.13 + 1.01 (14.5% cs)\n", "Provedel: MV 6.24 ± 0.83; FV 6.62 + 0.71 (96.3% cs)\n", "Vicario: MV 6.24 ± 0.83; FV 5.09 + 0.90 (17.2% cs)\n", "Szczesny: MV 6.24 ± 0.83; FV 6.39 + 0.69 (92.6% cs)\n", "Falcone: MV 6.24 ± 0.83; FV 4.83 + 0.90 (1.1% cs)\n", "Silvestri: MV 6.24 ± 0.83; FV 4.39 + 1.17 (0.9% cs)\n", "Rui Patricio: MV 6.24 ± 0.83; FV 4.75 + 0.91 (3.2% cs)\n", "Onana: MV 6.24 ± 0.83; FV 4.67 + 0.91 (3.5% cs)\n", "Sepe: MV 6.24 ± 0.83; FV 5.77 + 0.83 (58.3% cs)\n", "Milinkovic-Savic V.: MV 6.24 ± 0.83; FV 4.84 + 0.87 (6.9% cs)\n", "Musso: MV 6.25 ± 0.82; FV 5.42 + 0.84 (25.0% cs)\n", "Maignan: MV 6.24 ± 0.83; FV 5.36 + 0.82 (14.1% cs)\n", "Carnesecchi: MV 6.24 ± 0.83; FV 4.39 + 1.15 (0.1% cs)\n", "Di Gregorio: MV 6.24 ± 0.83; FV 4.55 + 1.07 (1.9% cs)\n", "Audero: MV 6.24 ± 0.83; FV 5.17 + 0.83 (18.3% cs)\n", "Montipo': MV 6.24 ± 0.83; FV 4.62 + 1.04 (0.1% cs)\n", "Skorupski: MV 6.24 ± 0.83; FV 4.27 + 1.07 (1.9% cs)\n", "Consigli: MV 6.24 ± 0.83; FV 4.27 + 1.30 (0.1% cs)\n", "Dragowski: MV 6.24 ± 0.83; FV 4.49 + 1.03 (0.4% cs)\n", "Terracciano: MV 6.24 ± 0.83; FV 5.35 + 0.85 (30.5% cs)\n", "Tatarusanu: MV 6.24 ± 0.83; FV 5.44 + 0.97 (19.7% cs)\n", "Handanovic: MV 6.24 ± 0.83; FV 4.94 + 0.89 (4.0% cs)\n", "Sportiello: MV 6.18 ± 0.86; FV 5.72 + 1.09 (40.4% cs)\n", "Perin: MV 6.24 ± 0.83; FV 5.31 + 0.79 (24.2% cs)\n", "Zoet: MV 6.24 ± 0.83; FV 5.52 + 0.75 (18.9% cs)\n", "Ochoa: MV 6.24 ± 0.83; FV 5.22 + 0.82 (15.7% cs)\n", "Pegolo: MV 6.24 ± 0.83; FV 4.16 + 1.34 (0.1% cs)\n", "Gollini: MV 6.25 ± 0.83; FV 4.93 + 1.34 (43.4% cs)\n", "Mirante no data\n", "Sarr M. no data\n", "Lamanna no data\n", "Ujkani no data\n", "Berisha: MV 6.24 ± 0.83; FV 5.94 + 0.88 (65.1% cs)\n", "Marchetti: MV 6.24 ± 0.83; FV 4.18 + 1.24 (0.1% cs)\n", "Perilli: MV 6.24 ± 0.83; FV 4.29 + 1.29 (1.1% cs)\n", "Padelli: MV 6.23 ± 0.83; FV 4.02 + 1.38 (0.3% cs)\n", "Perisan: MV 6.26 ± 0.81; FV 5.53 + 0.94 (32.5% cs)\n", "Bardi: MV 6.24 ± 0.83; FV 6.04 + 0.78 (78.7% cs)\n", "Cordaz no data\n", "Pinsoglio: MV 6.24 ± 0.83; FV 6.19 + 0.70 (86.7% cs)\n", "Fiorillo: MV 6.24 ± 0.83; FV 4.96 + 0.82 (0.3% cs)\n", "Cragno: MV 6.24 ± 0.83; FV 4.65 + 1.12 (0.1% cs)\n", "Sirigu: MV 6.24 ± 0.83; FV 4.35 + 1.30 (0.0% cs)\n", "Cerofolini no data\n", "Rossi F.: MV 6.24 ± 0.83; FV 4.53 + 1.05 (0.8% cs)\n", "Ravaglia F.: MV 6.24 ± 0.83; FV 4.70 + 0.92 (1.4% cs)\n", "Brancolini no data\n", "Bleve no data\n", "Berardi A.: MV 6.23 ± 0.83; FV 3.86 + 1.53 (0.1% cs)\n", "Russo A. no data\n", "Gemello: MV 6.24 ± 0.83; FV 6.15 + 0.84 (78.0% cs)\n", "Ravaglia: MV 6.24 ± 0.83; FV 5.89 + 0.71 (59.6% cs)\n", "Boer no data\n", "Adamonis no data\n", "Marfella: MV 6.24 ± 0.83; FV 5.17 + 0.97 (11.7% cs)\n", "Zovko: MV 6.24 ± 0.83; FV 4.26 + 1.19 (0.2% cs)\n", "Piana no data\n", "Bagnolini no data\n", "Luis Maximiano: MV 6.24 ± 0.83; FV 6.75 + 0.74 (97.3% cs)\n", "Svilar no data\n", "Sorrentino A. no data\n", "Ciezkowski no data\n", "Saro no data\n", "Vasquez D. no data\n", "Turk: MV 6.24 ± 0.83; FV 5.24 + 0.78 (5.8% cs)\n", "Dimarco: MV 6.24 ± 1.00; FV 6.79 + 1.87\n", "Smalling: MV 6.25 ± 1.01; FV 6.66 + 1.74\n", "Doig: MV 5.99 ± 1.03; FV 6.26 + 1.61\n", "Carlos Augusto: MV 6.08 ± 1.06; FV 6.49 + 1.85\n", "Kim: MV 6.23 ± 1.02; FV 6.49 + 1.50\n", "Posch: MV 6.16 ± 1.11; FV 6.62 + 2.00\n", "Di Lorenzo: MV 6.21 ± 0.98; FV 6.51 + 1.50\n", "Danilo: MV 6.30 ± 0.99; FV 6.73 + 1.74\n", "Hernandez T.: MV 6.32 ± 1.13; FV 6.96 + 2.22\n", "Udogie: MV 5.90 ± 1.07; FV 6.07 + 1.53\n", "Parisi: MV 6.17 ± 0.98; FV 6.54 + 1.59\n", "Mario Rui: MV 6.14 ± 1.00; FV 6.33 + 1.32\n", "Romagnoli: MV 6.20 ± 0.87; FV 6.37 + 1.23\n", "Bastoni S.: MV 5.98 ± 0.94; FV 6.16 + 1.37\n", "Mazzocchi: MV 6.13 ± 0.86; FV 6.52 + 1.49\n", "Valeri: MV 6.08 ± 0.84; FV 6.36 + 1.28\n", "Tomori: MV 6.17 ± 0.80; FV 6.37 + 1.17\n", "Scalvini: MV 6.27 ± 1.07; FV 6.79 + 1.96\n", "Toloi: MV 6.25 ± 1.02; FV 6.72 + 1.81\n", "Demiral: MV 6.13 ± 0.80; FV 6.37 + 1.21\n", "Maehle: MV 6.19 ± 0.99; FV 6.69 + 1.81\n", "Dumfries: MV 6.00 ± 1.03; FV 6.30 + 1.64\n", "Baschirotto: MV 6.06 ± 0.99; FV 6.32 + 1.46\n", "Bijol: MV 5.76 ± 1.19; FV 5.85 + 1.55\n", "Schuurs: MV 6.20 ± 0.82; FV 6.30 + 1.09\n", "Juan Jesus: MV 6.15 ± 0.74; FV 6.33 + 1.06\n", "Depaoli: MV 5.92 ± 0.87; FV 6.05 + 1.20\n", "Mancini: MV 6.14 ± 0.86; FV 6.37 + 1.25\n", "Ibanez: MV 5.79 ± 1.19; FV 5.92 + 1.57\n", "Rodrigo Becao: MV 5.80 ± 1.09; FV 5.80 + 1.31\n", "Ebuehi: MV 6.08 ± 0.82; FV 6.35 + 1.20\n", "Gosens: MV 6.04 ± 0.75; FV 6.31 + 1.12\n", "Darmian: MV 6.10 ± 0.76; FV 6.37 + 1.17\n", "Reca: MV 5.88 ± 1.02; FV 6.01 + 1.44\n", "Bremer: MV 6.20 ± 1.02; FV 6.60 + 1.66\n", "Sernicola: MV 5.87 ± 1.07; FV 6.03 + 1.55\n", "Rrahmani: MV 6.20 ± 1.02; FV 6.47 + 1.50\n", "Vojvoda: MV 6.02 ± 0.79; FV 6.09 + 0.86\n", "Holm: MV 5.89 ± 0.85; FV 6.01 + 1.17\n", "Bastoni: MV 6.14 ± 0.84; FV 6.27 + 1.07\n", "Milenkovic: MV 6.04 ± 0.93; FV 6.22 + 1.32\n", "Kalulu: MV 6.06 ± 0.92; FV 6.24 + 1.17\n", "Martinez Quarta: MV 6.06 ± 0.94; FV 6.24 + 1.29\n", "Casale: MV 6.09 ± 0.82; FV 6.18 + 0.98\n", "Perez N.: MV 5.79 ± 1.17; FV 5.90 + 1.55\n", "Olivera: MV 6.10 ± 0.71; FV 6.30 + 1.00\n", "Izzo: MV 6.04 ± 0.92; FV 6.18 + 1.19\n", "Luperto: MV 5.90 ± 0.96; FV 5.88 + 0.92\n", "Skriniar: MV 5.99 ± 0.79; FV 6.00 + 0.79\n", "Rodriguez R.: MV 6.12 ± 0.69; FV 6.09 + 0.72\n", "Marusic: MV 6.08 ± 0.79; FV 6.00 + 0.76\n", "Lazzari: MV 6.06 ± 0.75; FV 6.02 + 0.73\n", "Kyriakopoulos: MV 5.99 ± 0.91; FV 6.04 + 1.01\n", "Ampadu: MV 5.77 ± 0.99; FV 5.73 + 1.14\n", "Ismajli: MV 5.95 ± 0.85; FV 5.88 + 0.78\n", "Llorente D.: MV 5.90 ± 1.07; FV 6.08 + 1.55\n", "Cambiaso: MV 5.97 ± 0.77; FV 5.96 + 0.74\n", "Hysaj: MV 6.04 ± 0.65; FV 5.95 + 0.55\n", "Biraghi: MV 6.16 ± 0.87; FV 6.42 + 1.28\n", "Medel: MV 5.97 ± 0.72; FV 5.91 + 0.64\n", "Bonucci: MV 6.24 ± 1.04; FV 6.68 + 1.79\n", "Calabria: MV 6.15 ± 0.96; FV 6.58 + 1.65\n", "Acerbi: MV 6.06 ± 0.72; FV 6.05 + 0.71\n", "Spinazzola: MV 6.16 ± 0.87; FV 6.53 + 1.50\n", "Lykogiannis: MV 6.03 ± 0.81; FV 6.14 + 0.97\n", "Pellegrini Lu.: MV 6.04 ± 0.72; FV 6.01 + 0.69\n", "Djidji: MV 6.00 ± 0.79; FV 6.08 + 0.89\n", "Lazaro: MV 6.16 ± 0.86; FV 6.34 + 1.16\n", "Augello: MV 6.01 ± 0.91; FV 6.33 + 1.49\n", "Gallo: MV 5.77 ± 0.79; FV 5.74 + 0.75\n", "Singo: MV 6.12 ± 0.85; FV 6.39 + 1.25\n", "Mari': MV 5.86 ± 1.07; FV 5.91 + 1.22\n", "Caldirola: MV 5.89 ± 1.02; FV 5.99 + 1.30\n", "Dodo': MV 5.93 ± 0.95; FV 5.98 + 1.13\n", "De Vrij: MV 6.01 ± 0.82; FV 6.07 + 0.88\n", "Patric: MV 6.07 ± 0.80; FV 5.96 + 0.75\n", "Faraoni: MV 5.94 ± 0.88; FV 6.08 + 1.21\n", "Ceccherini: MV 5.85 ± 1.07; FV 5.95 + 1.46\n", "Hateboer: MV 6.06 ± 0.89; FV 6.34 + 1.41\n", "Rogerio: MV 5.66 ± 0.84; FV 5.63 + 0.86\n", "Umtiti: MV 5.80 ± 0.97; FV 5.75 + 1.00\n", "Aina: MV 6.15 ± 0.87; FV 6.46 + 1.36\n", "Birindelli: MV 5.80 ± 0.71; FV 5.78 + 0.74\n", "Lucumi': MV 5.94 ± 0.81; FV 5.90 + 0.79\n", "Ehizibue: MV 5.73 ± 1.01; FV 5.83 + 1.32\n", "Bianchetti: MV 5.69 ± 1.03; FV 5.72 + 1.28\n", "Ferrari G.: MV 5.65 ± 1.13; FV 5.66 + 1.29\n", "Fazio: MV 5.65 ± 1.26; FV 5.69 + 1.40\n", "Gravillon: MV 6.08 ± 0.79; FV 6.02 + 0.78\n", "Buongiorno: MV 6.11 ± 0.75; FV 6.06 + 0.76\n", "Gunter: MV 5.80 ± 0.95; FV 5.72 + 0.88\n", "Troost-Ekong: MV 5.79 ± 0.93; FV 5.77 + 0.94\n", "Soumaoro: MV 5.91 ± 0.92; FV 5.88 + 0.88\n", "Ceccaroni: MV 5.81 ± 1.05; FV 5.83 + 1.21\n", "Pongracic: MV 5.89 ± 0.79; FV 5.83 + 0.74\n", "Soppy: MV 6.03 ± 0.73; FV 6.10 + 0.81\n", "Gendrey: MV 5.84 ± 0.65; FV 5.83 + 0.55\n", "Hien: MV 5.81 ± 0.94; FV 5.76 + 1.06\n", "Ferrari A.: MV 5.69 ± 1.13; FV 5.70 + 1.35\n", "Masina: MV 5.79 ± 0.92; FV 6.10 + 1.37\n", "Zappacosta: MV 6.16 ± 0.81; FV 6.53 + 1.42\n", "Gyomber: MV 5.90 ± 0.85; FV 5.88 + 0.81\n", "Alex Sandro: MV 5.90 ± 0.87; FV 5.81 + 0.78\n", "Pezzella Giu.: MV 5.85 ± 0.68; FV 5.84 + 0.57\n", "Bereszynski: MV 5.82 ± 0.72; FV 5.79 + 0.74\n", "Venuti: MV 5.90 ± 0.79; FV 5.92 + 0.85\n", "Palomino: MV 6.17 ± 0.85; FV 6.40 + 1.26\n", "Nuytinck: MV 5.90 ± 0.91; FV 5.88 + 0.89\n", "Marlon: MV 5.74 ± 0.79; FV 5.67 + 0.77\n", "Magnani: MV 5.77 ± 0.96; FV 5.71 + 1.04\n", "Colley: MV 5.87 ± 1.02; FV 5.91 + 1.11\n", "Nikolaou: MV 5.66 ± 0.88; FV 5.58 + 0.96\n", "Terzic: MV 6.04 ± 0.61; FV 6.00 + 0.53\n", "Igor: MV 5.87 ± 0.93; FV 5.84 + 1.00\n", "Toljan: MV 5.60 ± 0.90; FV 5.55 + 0.90\n", "Zortea: MV 5.74 ± 0.83; FV 5.77 + 0.96\n", "Dawidowicz: MV 5.76 ± 1.00; FV 5.76 + 1.24\n", "Celik: MV 5.77 ± 0.75; FV 5.72 + 0.74\n", "Bellanova: MV 5.99 ± 0.87; FV 6.17 + 1.15\n", "Erlic: MV 5.68 ± 1.06; FV 5.60 + 1.07\n", "Ballo-Toure': MV 6.17 ± 0.74; FV 6.56 + 1.39\n", "Dest: MV 5.93 ± 0.77; FV 5.95 + 0.74\n", "Stojanovic: MV 5.75 ± 0.78; FV 5.70 + 0.76\n", "Amian: MV 5.68 ± 0.82; FV 5.65 + 0.91\n", "Bradaric: MV 5.74 ± 0.93; FV 5.73 + 1.05\n", "Daniliuc: MV 5.75 ± 1.02; FV 5.74 + 1.15\n", "Zima: MV 6.03 ± 0.75; FV 5.99 + 0.73\n", "De Winter: MV 5.77 ± 0.82; FV 5.71 + 0.74\n", "Quagliata: MV 5.88 ± 0.69; FV 5.90 + 0.70\n", "Ebosse: MV 5.62 ± 0.82; FV 5.52 + 0.83\n", "Aiwu: MV 5.83 ± 1.09; FV 5.93 + 1.48\n", "Lochoshvili: MV 5.79 ± 0.99; FV 5.84 + 1.30\n", "Bronn: MV 5.69 ± 0.80; FV 5.65 + 0.74\n", "Thiaw: MV 6.03 ± 0.89; FV 6.05 + 0.91\n", "Zeefuik: MV 5.86 ± 0.94; FV 5.91 + 1.23\n", "Romagnoli S.: MV 5.84 ± 1.10; FV 5.98 + 1.54\n", "Ghiglione: MV 5.81 ± 0.93; FV 5.92 + 1.28\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Rugani: MV 6.07 ± 0.59; FV 5.97 + 0.48\n", "De Sciglio: MV 5.91 ± 0.62; FV 5.91 + 0.48\n", "Djimsiti: MV 6.08 ± 0.72; FV 6.07 + 0.75\n", "Caldara: MV 5.63 ± 1.02; FV 5.56 + 1.09\n", "Karsdorp: MV 5.88 ± 0.80; FV 5.86 + 0.79\n", "Marchizza: MV 5.79 ± 0.70; FV 5.79 + 0.62\n", "Kjaer: MV 6.06 ± 0.70; FV 5.98 + 0.63\n", "Okoli: MV 5.93 ± 0.79; FV 5.89 + 0.73\n", "Amione: MV 5.85 ± 0.96; FV 5.91 + 1.25\n", "Ruggeri: MV 6.07 ± 0.68; FV 6.07 + 0.70\n", "Zanoli: MV 6.08 ± 0.85; FV 6.34 + 1.26\n", "Wisniewski: MV 5.68 ± 0.77; FV 5.59 + 0.78\n", "Radovanovic: MV 5.63 ± 0.80; FV 5.56 + 0.77\n", "Dermaku: MV 5.95 ± 0.86; FV 5.97 + 0.94\n", "D'ambrosio: MV 6.09 ± 0.75; FV 6.14 + 0.85\n", "De Silvestri: MV 5.98 ± 1.01; FV 6.16 + 1.42\n", "Chiriches: MV 5.68 ± 1.14; FV 5.64 + 1.22\n", "Murru: MV 5.70 ± 0.76; FV 5.63 + 0.71\n", "Bonifazi: MV 5.78 ± 0.83; FV 5.69 + 0.80\n", "Donati: MV 5.78 ± 1.22; FV 5.99 + 1.71\n", "Walukiewicz: MV 6.11 ± 0.70; FV 6.08 + 0.71\n", "Ranieri L.: MV 5.92 ± 0.82; FV 5.97 + 1.04\n", "Gabbia: MV 5.84 ± 0.77; FV 5.80 + 0.70\n", "Kumbulla: MV 5.74 ± 1.09; FV 5.67 + 1.12\n", "Adopo: MV 6.10 ± 0.66; FV 6.08 + 0.67\n", "Pirola: MV 5.89 ± 1.10; FV 6.15 + 1.72\n", "Lovato: MV 5.65 ± 1.02; FV 5.59 + 0.96\n", "Tuia: MV 5.99 ± 0.60; FV 5.95 + 0.49\n", "Ferrer: MV 5.80 ± 0.95; FV 5.80 + 1.16\n", "Antov: MV 5.68 ± 1.06; FV 5.54 + 1.00\n", "Vasquez: MV 5.74 ± 0.96; FV 5.69 + 1.09\n", "Ruan: MV 5.69 ± 1.07; FV 5.55 + 1.02\n", "Ostigard: MV 6.08 ± 0.75; FV 6.10 + 0.82\n", "Coppola D.: MV 5.74 ± 0.78; FV 5.64 + 0.82\n", "Cacace: MV 5.82 ± 0.64; FV 5.82 + 0.51\n", "Gatti: MV 6.10 ± 0.80; FV 6.06 + 0.82\n", "Gila: MV 6.02 ± 0.89; FV 5.96 + 0.87\n", "Bayeye: MV 6.08 ± 0.82; FV 6.17 + 0.97\n", "Sambia: MV 5.83 ± 0.81; FV 5.84 + 0.78\n", "Moutinho J.: MV 5.79 ± 0.87; FV 5.76 + 1.03\n", "Conti: MV 5.93 ± 0.83; FV 6.07 + 1.13\n", "Marrone: MV 5.64 ± 0.88; FV 5.53 + 0.89\n", "Tonelli: MV 5.77 ± 0.83; FV 5.71 + 0.75\n", "Murillo: MV 5.68 ± 0.79; FV 5.60 + 0.72\n", "Radu: MV 6.06 ± 0.78; FV 5.83 + 0.69\n", "Paletta: MV 5.91 ± 0.96; FV 6.00 + 1.23\n", "Florenzi: MV 6.16 ± 0.73; FV 6.42 + 1.19\n", "Sala: MV 5.85 ± 0.80; FV 5.90 + 0.97\n", "Fares: MV 5.80 ± 0.76; FV 5.76 + 0.72\n", "Romagna: MV 5.73 ± 0.96; FV 5.72 + 1.08\n", "Cassandro: MV 5.93 ± 0.82; FV 5.94 + 0.87\n", "Muldur: MV 5.60 ± 0.86; FV 5.56 + 0.87\n", "Amey: MV 6.03 ± 0.89; FV 6.09 + 1.00\n", "Zanotti: MV 6.00 ± 0.84; FV 6.11 + 1.01\n", "Ebosele: MV 5.86 ± 0.70; FV 5.87 + 0.72\n", "Buta: MV 5.75 ± 1.05; FV 5.72 + 1.21\n", "Abankwah: MV 5.74 ± 1.01; FV 5.70 + 1.16\n", "Guessand A.: MV 5.75 ± 1.05; FV 5.72 + 1.21\n", "Cabal: MV 5.84 ± 0.67; FV 5.76 + 0.62\n", "Sosa: MV 5.67 ± 0.86; FV 5.59 + 0.89\n", "Guarino: MV 5.97 ± 0.88; FV 6.01 + 0.94\n", "Carboni F.: MV 5.92 ± 0.90; FV 5.97 + 1.05\n", "Zaccagni: MV 6.47 ± 1.27; FV 7.42 + 3.02\n", "Kvaratskhelia: MV 6.49 ± 1.41; FV 7.50 + 3.35\n", "Milinkovic-Savic: MV 6.23 ± 1.22; FV 7.11 + 2.90\n", "Barella: MV 6.29 ± 1.16; FV 7.02 + 2.42\n", "Zielinski: MV 6.24 ± 1.04; FV 6.74 + 1.84\n", "Luis Alberto: MV 6.31 ± 1.01; FV 7.00 + 2.17\n", "Strefezza: MV 6.20 ± 1.01; FV 6.77 + 1.92\n", "Felipe Anderson: MV 6.29 ± 1.15; FV 7.10 + 2.62\n", "Koopmeiners: MV 6.38 ± 1.16; FV 7.19 + 2.55\n", "Calhanoglu: MV 6.31 ± 0.98; FV 6.91 + 1.98\n", "Frattesi: MV 6.04 ± 1.06; FV 6.48 + 1.89\n", "Diaz B.: MV 6.31 ± 1.31; FV 7.21 + 2.92\n", "Vlasic: MV 6.30 ± 1.07; FV 6.93 + 2.11\n", "Zambo Anguissa: MV 6.21 ± 0.98; FV 6.61 + 1.63\n", "Elmas: MV 6.16 ± 1.00; FV 6.73 + 1.91\n", "Miranchuk: MV 6.39 ± 1.16; FV 7.14 + 2.40\n", "Samardzic: MV 6.04 ± 1.08; FV 6.46 + 1.90\n", "Pereyra: MV 5.95 ± 1.20; FV 6.35 + 2.07\n", "Politano: MV 6.18 ± 0.82; FV 6.61 + 1.51\n", "Rabiot: MV 6.32 ± 1.23; FV 7.16 + 2.71\n", "Ciurria: MV 6.06 ± 1.08; FV 6.51 + 1.94\n", "Lazovic: MV 6.14 ± 1.01; FV 6.63 + 1.83\n", "Lobotka: MV 6.18 ± 0.81; FV 6.45 + 1.26\n", "Radonjic: MV 6.26 ± 0.96; FV 6.80 + 1.81\n", "Ferguson: MV 6.13 ± 0.94; FV 6.46 + 1.47\n", "Bonaventura: MV 6.20 ± 1.00; FV 6.71 + 1.83\n", "Pessina: MV 6.09 ± 1.05; FV 6.44 + 1.70\n", "Tonali: MV 6.29 ± 1.14; FV 6.93 + 2.19\n", "Kostic: MV 6.24 ± 1.06; FV 6.84 + 2.01\n", "Baldanzi: MV 6.20 ± 1.03; FV 6.80 + 2.01\n", "Lovric: MV 6.06 ± 0.94; FV 6.40 + 1.53\n", "Pellegrini Lo.: MV 6.09 ± 1.17; FV 6.68 + 2.34\n", "El Shaarawy: MV 6.20 ± 0.88; FV 6.78 + 1.84\n", "Orsolini: MV 6.15 ± 1.32; FV 7.02 + 3.02\n", "Ikone': MV 6.02 ± 1.07; FV 6.48 + 1.91\n", "Candreva: MV 6.15 ± 1.07; FV 6.67 + 2.01\n", "Bennacer: MV 6.16 ± 0.75; FV 6.48 + 1.28\n", "Pasalic: MV 6.25 ± 1.22; FV 7.05 + 2.69\n", "Mkhitaryan: MV 6.12 ± 1.00; FV 6.63 + 1.80\n", "Colpani: MV 6.01 ± 0.81; FV 6.37 + 1.41\n", "Pogba: MV 6.14 ± 0.82; FV 6.23 + 1.00\n", "Chiesa: MV 6.10 ± 0.82; FV 6.34 + 1.15\n", "Bandinelli: MV 5.99 ± 0.80; FV 6.15 + 1.05\n", "Matic: MV 6.16 ± 0.82; FV 6.48 + 1.35\n", "Fagioli: MV 6.21 ± 1.05; FV 6.71 + 1.83\n", "Messias: MV 6.13 ± 1.14; FV 6.84 + 2.39\n", "Arslan: MV 5.86 ± 0.77; FV 5.94 + 0.94\n", "Ricci S.: MV 6.19 ± 0.82; FV 6.46 + 1.29\n", "Ranocchia F.: MV 6.07 ± 0.83; FV 6.39 + 1.34\n", "Verdi: MV 6.18 ± 1.21; FV 6.73 + 2.29\n", "Sensi: MV 6.03 ± 1.08; FV 6.39 + 1.83\n", "Barak: MV 5.94 ± 0.96; FV 6.26 + 1.59\n", "Soriano: MV 6.06 ± 0.88; FV 6.36 + 1.40\n", "Dominguez: MV 6.14 ± 0.98; FV 6.50 + 1.59\n", "Vilhena: MV 5.93 ± 0.97; FV 6.22 + 1.58\n", "Brozovic: MV 6.13 ± 0.86; FV 6.44 + 1.34\n", "Cristante: MV 5.99 ± 0.87; FV 6.11 + 1.16\n", "Thorstvedt: MV 5.85 ± 0.84; FV 6.01 + 1.13\n", "De Ketelaere: MV 5.84 ± 0.73; FV 5.96 + 0.79\n", "Saponara: MV 6.12 ± 1.13; FV 6.67 + 2.09\n", "Vecino: MV 6.06 ± 0.87; FV 6.26 + 1.21\n", "Locatelli: MV 6.11 ± 0.81; FV 6.21 + 0.97\n", "Zaniolo: MV 5.94 ± 1.02; FV 6.20 + 1.64\n", "Duda: MV 5.81 ± 0.69; FV 5.80 + 0.78\n", "Maldini: MV 5.95 ± 0.91; FV 6.22 + 1.46\n", "Marin: MV 6.01 ± 0.94; FV 6.21 + 1.30\n", "Zalewski: MV 6.04 ± 0.77; FV 6.21 + 1.04\n", "Bajrami: MV 5.95 ± 1.00; FV 6.30 + 1.63\n", "Coulibaly L.: MV 5.97 ± 1.04; FV 6.29 + 1.73\n", "Gonzalez J.: MV 5.93 ± 0.80; FV 6.04 + 1.00\n", "De Roon: MV 6.19 ± 0.87; FV 6.59 + 1.55\n", "Mandragora: MV 6.04 ± 0.98; FV 6.35 + 1.59\n", "Wijnaldum: MV 6.19 ± 1.10; FV 6.85 + 2.29\n", "Bourabia: MV 5.85 ± 0.79; FV 5.87 + 0.95\n", "Sottil: MV 6.11 ± 1.10; FV 6.57 + 1.92\n", "Aebischer: MV 5.95 ± 0.83; FV 6.13 + 1.19\n", "Ederson D.s.: MV 6.02 ± 0.79; FV 6.23 + 1.10\n", "Miretti: MV 6.02 ± 0.71; FV 6.15 + 0.80\n", "Blin: MV 5.90 ± 0.74; FV 5.96 + 0.83\n", "Hjulmand: MV 5.87 ± 0.90; FV 5.83 + 0.90\n", "Cataldi: MV 6.03 ± 0.74; FV 5.97 + 0.68\n", "Djuricic: MV 5.85 ± 0.94; FV 6.01 + 1.35\n", "Linetty: MV 6.06 ± 0.82; FV 6.25 + 1.10\n", "Haas: MV 5.96 ± 0.77; FV 6.11 + 0.99\n", "Walace: MV 5.85 ± 0.92; FV 5.85 + 1.07\n", "Agudelo: MV 5.84 ± 0.75; FV 5.92 + 0.96\n", "Pobega: MV 6.11 ± 0.88; FV 6.54 + 1.51\n", "Camara Ma.: MV 6.09 ± 0.87; FV 6.18 + 1.02\n", "Paredes: MV 5.92 ± 0.69; FV 5.85 + 0.58\n", "Ndombele': MV 5.98 ± 0.76; FV 6.14 + 0.97\n", "Nicolussi Caviglia: MV 5.90 ± 1.05; FV 6.16 + 1.68\n", "Rovella: MV 5.97 ± 1.08; FV 6.14 + 1.36\n", "Amrabat: MV 5.98 ± 0.90; FV 6.06 + 1.11\n", "Tameze: MV 5.86 ± 0.78; FV 5.83 + 0.87\n", "Gyasi: MV 5.78 ± 0.91; FV 5.89 + 1.24\n", "Ilic: MV 6.18 ± 0.86; FV 6.51 + 1.39\n", "Matheus Henrique: MV 5.81 ± 0.91; FV 5.94 + 1.22\n", "Harroui: MV 5.89 ± 0.87; FV 6.10 + 1.24\n", "Volpato: MV 6.02 ± 1.14; FV 6.66 + 2.34\n", "Pickel: MV 5.77 ± 0.82; FV 5.78 + 0.99\n", "Moro N.: MV 6.11 ± 0.93; FV 6.43 + 1.43\n", "Duncan: MV 5.94 ± 0.82; FV 6.12 + 1.23\n", "Machin: MV 5.94 ± 0.95; FV 6.05 + 1.27\n", "Cuadrado: MV 6.06 ± 0.92; FV 6.19 + 1.09\n", "Ekdal: MV 5.82 ± 0.78; FV 5.82 + 0.91\n", "Meite': MV 5.85 ± 0.94; FV 5.87 + 1.21\n", "Schouten: MV 5.98 ± 0.85; FV 5.99 + 0.89\n", "Obiang: MV 5.78 ± 0.64; FV 5.75 + 0.57\n", "Kovalenko: MV 5.85 ± 0.79; FV 5.92 + 1.01\n", "Crnigoj: MV 5.95 ± 0.79; FV 6.11 + 1.04\n", "Basic: MV 6.04 ± 0.63; FV 6.03 + 0.61\n", "Asllani: MV 6.04 ± 0.66; FV 6.08 + 0.68\n", "Sabiri: MV 5.91 ± 0.92; FV 6.08 + 1.35\n", "Terracciano F.: MV 5.97 ± 0.70; FV 6.04 + 0.79\n", "Castagnetti: MV 5.88 ± 0.79; FV 5.89 + 0.90\n", "Oudin: MV 5.87 ± 0.62; FV 5.90 + 0.52\n", "Grassi: MV 5.93 ± 0.66; FV 5.91 + 0.55\n", "Krunic: MV 6.00 ± 0.70; FV 6.06 + 0.75\n", "Rincon: MV 5.79 ± 0.76; FV 5.73 + 0.77\n", "Miguel Veloso: MV 5.87 ± 0.73; FV 5.85 + 0.78\n", "Leris: MV 5.87 ± 0.88; FV 5.98 + 1.24\n", "Esposito Sa.: MV 5.74 ± 0.87; FV 5.63 + 0.91\n", "Henderson L.: MV 5.94 ± 0.76; FV 6.04 + 0.89\n", "Lopez M.: MV 5.80 ± 0.84; FV 5.75 + 0.89\n", "Cuisance: MV 5.82 ± 0.63; FV 5.82 + 0.57\n", "Saelemaekers: MV 5.98 ± 0.76; FV 6.24 + 1.05\n", "Maggiore: MV 5.95 ± 0.77; FV 6.08 + 0.96\n", "Akpa Akpro: MV 5.97 ± 0.88; FV 6.03 + 0.98\n", "Maleh: MV 5.87 ± 0.67; FV 5.94 + 0.74\n", "Romero L.: MV 6.12 ± 0.98; FV 6.80 + 2.21\n", "Ceide: MV 5.78 ± 0.66; FV 5.81 + 0.61\n", "D'alessandro: MV 6.05 ± 0.65; FV 6.12 + 0.73\n", "Benassi: MV 5.83 ± 0.80; FV 5.94 + 1.05\n", "Gagliardini: MV 5.86 ± 0.64; FV 5.89 + 0.62\n", "Vieira: MV 5.92 ± 0.66; FV 5.96 + 0.70\n", "Bianco: MV 6.10 ± 0.80; FV 6.26 + 1.07\n", "Vranckx: MV 5.85 ± 0.67; FV 5.90 + 0.63\n", "Galdames: MV 5.87 ± 1.06; FV 5.98 + 1.47\n", "Marcos Antonio: MV 6.11 ± 0.84; FV 6.73 + 1.90\n", "Fazzini: MV 5.82 ± 0.64; FV 5.80 + 0.55\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Sulemana I.: MV 5.83 ± 0.65; FV 5.79 + 0.65\n", "Tahirovic: MV 6.03 ± 0.65; FV 6.02 + 0.65\n", "Abildgaard: MV 5.89 ± 0.61; FV 5.88 + 0.55\n", "Barberis: MV 5.76 ± 0.84; FV 5.74 + 0.90\n", "Kastanos: MV 5.97 ± 0.86; FV 6.16 + 1.18\n", "Vignato: MV 6.01 ± 0.76; FV 6.13 + 0.86\n", "Valoti: MV 5.84 ± 0.68; FV 5.83 + 0.70\n", "Winks: MV 5.92 ± 0.78; FV 5.91 + 0.71\n", "Askildsen: MV 5.73 ± 0.62; FV 5.71 + 0.53\n", "Bove: MV 5.98 ± 0.86; FV 6.25 + 1.38\n", "Bohinen: MV 5.82 ± 0.61; FV 5.84 + 0.48\n", "D'andrea: MV 5.89 ± 0.72; FV 5.98 + 0.82\n", "Iling-Junior: MV 6.01 ± 0.64; FV 6.04 + 0.63\n", "Cipot: MV 5.91 ± 0.80; FV 5.98 + 1.00\n", "Bakayoko: MV 5.86 ± 0.72; FV 5.83 + 0.63\n", "Gaetano: MV 6.15 ± 0.74; FV 6.35 + 1.10\n", "Zurkowski: MV 5.97 ± 0.96; FV 6.25 + 1.55\n", "Castrovilli: MV 6.00 ± 0.80; FV 6.25 + 1.25\n", "Demme: MV 5.90 ± 0.80; FV 5.99 + 1.00\n", "Darboe: MV 5.91 ± 0.93; FV 5.93 + 1.02\n", "Urbanski: MV 5.99 ± 0.90; FV 6.05 + 1.04\n", "Bertini: MV 5.99 ± 0.86; FV 6.02 + 0.91\n", "Yepes: MV 5.75 ± 0.89; FV 5.67 + 0.92\n", "Pyyhtia: MV 5.87 ± 0.82; FV 5.83 + 0.84\n", "Trimboli: MV 5.97 ± 0.88; FV 6.05 + 1.04\n", "Pafundi: MV 5.96 ± 0.74; FV 5.88 + 0.65\n", "Helgason: MV 5.85 ± 0.62; FV 5.85 + 0.51\n", "Adli: MV 5.94 ± 0.69; FV 6.03 + 0.75\n", "Vignato S.: MV 5.96 ± 0.91; FV 6.03 + 1.10\n", "Hrustic: MV 5.67 ± 0.69; FV 5.64 + 0.71\n", "Samek: MV 5.92 ± 0.85; FV 5.96 + 0.95\n", "Zerbin: MV 5.82 ± 0.67; FV 5.84 + 0.74\n", "Ilkhan: MV 5.93 ± 0.78; FV 5.95 + 0.78\n", "Degli Innocenti: MV 5.97 ± 0.88; FV 6.03 + 0.98\n", "Acella: MV 5.90 ± 1.05; FV 6.03 + 1.47\n", "Carboni V.: MV 6.05 ± 0.77; FV 6.14 + 0.87\n", "Paoletti: MV 5.92 ± 0.60; FV 5.91 + 0.45\n", "Malagrida: MV 6.04 ± 0.96; FV 6.26 + 1.32\n", "Faticanti: MV 5.94 ± 0.92; FV 6.02 + 1.15\n", "Osimhen: MV 6.38 ± 1.48; FV 7.89 + 4.50\n", "Martinez L.: MV 6.23 ± 1.44; FV 7.69 + 4.20\n", "Dybala: MV 6.47 ± 1.37; FV 7.51 + 3.43\n", "Rafael Leao: MV 6.44 ± 1.45; FV 7.65 + 3.92\n", "Lookman: MV 6.46 ± 1.42; FV 7.65 + 3.83\n", "Immobile: MV 6.30 ± 1.40; FV 7.57 + 3.90\n", "Vlahovic: MV 6.16 ± 1.34; FV 7.32 + 3.51\n", "Arnautovic: MV 6.16 ± 1.33; FV 7.23 + 3.36\n", "Dia: MV 6.19 ± 1.33; FV 7.29 + 3.44\n", "Dzeko: MV 6.16 ± 1.34; FV 7.25 + 3.36\n", "Milik: MV 6.25 ± 1.20; FV 7.07 + 2.67\n", "Nzola: MV 6.04 ± 1.35; FV 7.05 + 3.21\n", "Beto: MV 5.86 ± 1.09; FV 6.38 + 2.02\n", "Giroud: MV 6.27 ± 1.35; FV 7.38 + 3.54\n", "Abraham: MV 6.14 ± 1.22; FV 7.03 + 2.90\n", "Deulofeu: MV 6.15 ± 1.21; FV 6.84 + 2.47\n", "Lauriente': MV 6.17 ± 1.34; FV 7.04 + 3.07\n", "Simeone: MV 6.12 ± 1.32; FV 7.11 + 3.12\n", "Lozano: MV 6.03 ± 1.04; FV 6.59 + 2.04\n", "Correa: MV 6.05 ± 1.08; FV 6.68 + 2.23\n", "Berardi: MV 6.17 ± 1.38; FV 7.30 + 3.51\n", "Pedro: MV 6.20 ± 1.05; FV 6.86 + 2.24\n", "Lukaku: MV 6.12 ± 1.34; FV 7.15 + 3.26\n", "Sanabria: MV 6.24 ± 1.28; FV 7.22 + 3.15\n", "Thauvin: MV 6.07 ± 1.10; FV 6.54 + 1.95\n", "Cabral: MV 6.15 ± 1.26; FV 7.08 + 2.93\n", "Hojlund: MV 6.25 ± 1.36; FV 7.39 + 3.56\n", "Caprari: MV 5.97 ± 0.95; FV 6.29 + 1.53\n", "Di Maria: MV 6.30 ± 1.26; FV 7.09 + 2.67\n", "Piatek: MV 5.85 ± 0.99; FV 6.21 + 1.66\n", "Rebic: MV 6.22 ± 1.36; FV 7.14 + 3.19\n", "Bonazzoli: MV 6.01 ± 0.96; FV 6.41 + 1.71\n", "Zapata D.: MV 6.09 ± 0.95; FV 6.49 + 1.60\n", "Kouame': MV 6.09 ± 1.24; FV 6.84 + 2.60\n", "Gonzalez N.: MV 6.21 ± 1.27; FV 7.05 + 2.81\n", "Brekalo: MV 6.14 ± 1.18; FV 6.82 + 2.39\n", "Mota: MV 5.99 ± 1.16; FV 6.55 + 2.23\n", "Kean: MV 6.13 ± 1.30; FV 7.05 + 3.06\n", "Okereke: MV 5.85 ± 1.05; FV 6.24 + 1.79\n", "Ceesay: MV 5.89 ± 0.95; FV 6.21 + 1.52\n", "Colombo: MV 5.87 ± 0.99; FV 6.24 + 1.68\n", "Dessers: MV 5.95 ± 1.16; FV 6.53 + 2.25\n", "Muriel: MV 6.13 ± 1.05; FV 6.51 + 1.72\n", "Pinamonti: MV 5.77 ± 0.96; FV 6.11 + 1.58\n", "Di Francesco F.: MV 5.92 ± 0.91; FV 6.13 + 1.31\n", "Jovic: MV 5.97 ± 1.14; FV 6.47 + 2.12\n", "Origi: MV 5.97 ± 1.06; FV 6.40 + 1.91\n", "Caputo: MV 5.99 ± 1.13; FV 6.62 + 2.31\n", "Boga: MV 6.42 ± 1.23; FV 7.25 + 2.65\n", "Cambiaghi: MV 6.14 ± 1.04; FV 6.73 + 2.07\n", "Alvarez A.: MV 5.88 ± 1.01; FV 6.22 + 1.64\n", "Banda: MV 5.91 ± 0.74; FV 6.02 + 0.86\n", "Ciofani D.: MV 6.01 ± 1.12; FV 6.48 + 2.05\n", "Petagna: MV 5.96 ± 1.01; FV 6.32 + 1.68\n", "Barrow: MV 5.98 ± 1.17; FV 6.42 + 2.08\n", "Djuric: MV 5.93 ± 0.71; FV 6.06 + 0.87\n", "Henry: MV 5.89 ± 1.06; FV 6.30 + 1.84\n", "Success: MV 5.89 ± 0.92; FV 6.11 + 1.36\n", "Gabbiadini: MV 5.94 ± 1.09; FV 6.43 + 2.01\n", "Zirkzee: MV 6.01 ± 1.08; FV 6.37 + 1.77\n", "Lammers: MV 5.82 ± 0.87; FV 6.01 + 1.26\n", "Satriano: MV 5.89 ± 0.92; FV 6.09 + 1.33\n", "Kallon: MV 5.87 ± 0.82; FV 6.05 + 1.18\n", "Nestorovski: MV 6.06 ± 0.72; FV 6.52 + 1.43\n", "Raspadori: MV 6.01 ± 1.05; FV 6.54 + 2.00\n", "Botheim: MV 5.93 ± 0.95; FV 6.32 + 1.68\n", "Gytkjaer: MV 5.84 ± 0.84; FV 6.05 + 1.27\n", "Solbakken: MV 5.92 ± 1.00; FV 6.36 + 1.83\n", "Lasagna: MV 5.78 ± 0.78; FV 5.84 + 0.98\n", "Belotti: MV 5.78 ± 0.78; FV 5.90 + 1.01\n", "Pellegri: MV 5.99 ± 0.89; FV 6.30 + 1.44\n", "Buonaiuto: MV 5.91 ± 0.84; FV 6.10 + 1.17\n", "Verde: MV 5.92 ± 1.09; FV 6.29 + 1.85\n", "Destro: MV 6.00 ± 1.20; FV 6.61 + 2.37\n", "Seck: MV 6.13 ± 0.66; FV 6.25 + 0.89\n", "Sansone: MV 6.11 ± 1.16; FV 6.63 + 2.15\n", "Quagliarella: MV 5.86 ± 0.78; FV 6.01 + 1.08\n", "Defrel: MV 5.77 ± 0.87; FV 5.93 + 1.21\n", "Pjaca: MV 5.92 ± 0.81; FV 6.04 + 0.93\n", "Gaich: MV 5.87 ± 0.91; FV 6.08 + 1.39\n", "Soule': MV 6.18 ± 0.80; FV 6.64 + 1.52\n", "Tsadjout: MV 5.88 ± 0.98; FV 6.26 + 1.67\n", "Piccoli: MV 5.81 ± 0.74; FV 5.86 + 0.75\n", "Shomurodov: MV 5.88 ± 0.97; FV 6.23 + 1.63\n", "Afena-Gyan: MV 5.69 ± 0.74; FV 5.72 + 0.87\n", "Ngonge: MV 6.10 ± 1.16; FV 6.59 + 2.12\n", "Karamoh: MV 6.22 ± 1.06; FV 6.92 + 2.30\n", "Ibrahimovic: MV 6.37 ± 1.33; FV 7.38 + 3.18\n", "Pussetto: MV 5.94 ± 1.06; FV 6.42 + 1.99\n", "Cancellieri: MV 5.83 ± 0.62; FV 5.83 + 0.50\n", "Valencia D.: MV 5.70 ± 0.60; FV 5.65 + 0.63\n", "Oddei: MV 5.94 ± 0.86; FV 6.08 + 1.11\n", "Braaf: MV 5.80 ± 0.88; FV 5.87 + 1.17\n", "Raimondo: MV 6.00 ± 0.93; FV 6.08 + 1.07\n", "Kaio Jorge: MV 5.95 ± 0.59; FV 5.98 + 0.50\n", "De Luca: MV 5.97 ± 0.82; FV 6.06 + 0.93\n", "Voelkerling Persson: MV 5.89 ± 0.65; FV 5.96 + 0.64\n", "Montevago: MV 5.65 ± 0.64; FV 5.63 + 0.62\n", "Krollis: MV 5.86 ± 0.95; FV 5.94 + 1.30\n", "Vivaldo: MV 5.82 ± 1.04; FV 5.90 + 1.39\n" ] }, { "data": { "text/html": [ "
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roleteamoppteamhomestartervote%MVMV stdFVFV stdMV locMV scaleMV skewnessMV tailweightFV locFV scaleFV skewnessFV tailweightClean Sheet %
player
SportielloPAtalantaSpezia11.00756.1784020.4280255.7161080.5468025.9284000.2635850.6092681.5888116.2837800.623820-0.6253401.07269240.422094
MussoPAtalantaSpezia10.0056.2468360.4124635.4185290.4188306.0055360.2527780.6122031.5899515.8132680.512082-0.5411221.03104124.998909
Rossi F.PAtalantaSpezia10.0016.2419590.4139164.5331270.5269115.9980540.2497280.6230621.5898425.0662490.630503-0.5907080.9720400.773813
ScalviniDAtalantaSpezia11.00906.2733230.5370456.7866010.9808436.1571860.5984840.1425540.9371845.9679681.2064330.4635731.5998480.000000
ToloiDAtalantaSpezia11.00906.2540100.5106116.7197040.9057506.1626700.5754460.1167880.9588885.9783641.1343930.4489291.5998580.000000
............................................................
GaichAVeronaInter10.55555.8674530.4574206.0846430.6927825.7927380.5192260.1061631.0451695.5228090.8747290.4423011.5998540.000000
DjuricAVeronaInter10.45605.9272610.3529756.0585050.4371225.8834220.4082260.0795311.1544085.8030530.6628720.2787511.5999180.000000
KallonAVeronaInter10.00405.8745990.4109656.0492300.5923005.8060450.4666890.1085031.0951995.5937080.7801420.4070941.5998840.000000
BraafAVeronaInter10.00355.7962740.4400125.8698300.5854895.7579440.5119310.0553471.0603285.5059670.8671160.3018241.5998890.000000
LasagnaAVeronaInter11.00805.7778340.3893635.8360370.4891945.7277980.4488260.0824881.1236435.5066600.6985200.3360681.5999010.000000
\n", "

525 rows × 19 columns

\n", "
" ], "text/plain": [ " role team oppteam home starter vote% MV MV std \\\n", "player \n", "Sportiello P Atalanta Spezia 1 1.00 75 6.178402 0.428025 \n", "Musso P Atalanta Spezia 1 0.00 5 6.246836 0.412463 \n", "Rossi F. P Atalanta Spezia 1 0.00 1 6.241959 0.413916 \n", "Scalvini D Atalanta Spezia 1 1.00 90 6.273323 0.537045 \n", "Toloi D Atalanta Spezia 1 1.00 90 6.254010 0.510611 \n", "... ... ... ... ... ... ... ... ... \n", "Gaich A Verona Inter 1 0.55 55 5.867453 0.457420 \n", "Djuric A Verona Inter 1 0.45 60 5.927261 0.352975 \n", "Kallon A Verona Inter 1 0.00 40 5.874599 0.410965 \n", "Braaf A Verona Inter 1 0.00 35 5.796274 0.440012 \n", "Lasagna A Verona Inter 1 1.00 80 5.777834 0.389363 \n", "\n", " FV FV std MV loc MV scale MV skewness \\\n", "player \n", "Sportiello 5.716108 0.546802 5.928400 0.263585 0.609268 \n", "Musso 5.418529 0.418830 6.005536 0.252778 0.612203 \n", "Rossi F. 4.533127 0.526911 5.998054 0.249728 0.623062 \n", "Scalvini 6.786601 0.980843 6.157186 0.598484 0.142554 \n", "Toloi 6.719704 0.905750 6.162670 0.575446 0.116788 \n", "... ... ... ... ... ... \n", "Gaich 6.084643 0.692782 5.792738 0.519226 0.106163 \n", "Djuric 6.058505 0.437122 5.883422 0.408226 0.079531 \n", "Kallon 6.049230 0.592300 5.806045 0.466689 0.108503 \n", "Braaf 5.869830 0.585489 5.757944 0.511931 0.055347 \n", "Lasagna 5.836037 0.489194 5.727798 0.448826 0.082488 \n", "\n", " MV tailweight FV loc FV scale FV skewness FV tailweight \\\n", "player \n", "Sportiello 1.588811 6.283780 0.623820 -0.625340 1.072692 \n", "Musso 1.589951 5.813268 0.512082 -0.541122 1.031041 \n", "Rossi F. 1.589842 5.066249 0.630503 -0.590708 0.972040 \n", "Scalvini 0.937184 5.967968 1.206433 0.463573 1.599848 \n", "Toloi 0.958888 5.978364 1.134393 0.448929 1.599858 \n", "... ... ... ... ... ... \n", "Gaich 1.045169 5.522809 0.874729 0.442301 1.599854 \n", "Djuric 1.154408 5.803053 0.662872 0.278751 1.599918 \n", "Kallon 1.095199 5.593708 0.780142 0.407094 1.599884 \n", "Braaf 1.060328 5.505967 0.867116 0.301824 1.599889 \n", "Lasagna 1.123643 5.506660 0.698520 0.336068 1.599901 \n", "\n", " Clean Sheet % \n", "player \n", "Sportiello 40.422094 \n", "Musso 24.998909 \n", "Rossi F. 0.773813 \n", "Scalvini 0.000000 \n", "Toloi 0.000000 \n", "... ... \n", "Gaich 0.000000 \n", "Djuric 0.000000 \n", "Kallon 0.000000 \n", "Braaf 0.000000 \n", "Lasagna 0.000000 \n", "\n", "[525 rows x 19 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matchday_out = 33\n", "\n", "output = pd.DataFrame(columns = ['player', 'role', 'team', 'oppteam', 'home', 'starter', 'vote%', 'MV', 'MV std', 'FV', 'FV std', 'MV loc', 'MV scale', 'MV skewness', 'MV tailweight', 'FV loc', 'FV scale', 'FV skewness', 'FV tailweight', 'Clean Sheet %'])\n", "\n", "for i in range(players.shape[0]):\n", " try:\n", " [player, team, oppteam, home] = PlayerMatch(players.index[i], matchday_out)\n", " \n", " [mean, std, dist] = vote_predict_NNb(player, team, oppteam, home = home, log = 1)\n", " \n", " role = players['r'][player] \n", " \n", " starter = 0\n", " voteperc = 0\n", " \n", " cs = 0\n", " if(role == 'P'):\n", " cs = dist[2].probs.numpy()[0] * 100\n", " \n", " if(player in probables.index):\n", " starter = probables['starter'][player]\n", " voteperc = probables['percentage'][player]\n", " \n", " row = [player, role, team, oppteam, home, \n", " starter, voteperc, \n", " mean[0], std[0], \n", " mean[1], std[1], \n", " dist[0].loc.numpy()[0], dist[0].scale.numpy()[0], \n", " dist[0].skewness.numpy()[0], dist[0].tailweight.numpy()[0], \n", " dist[1].loc.numpy()[0], dist[1].scale.numpy()[0], \n", " dist[1].skewness.numpy()[0], dist[1].tailweight.numpy()[0],\n", " cs]\n", " \n", " row_df = pd.DataFrame(data = [row], columns = output.columns)\n", " \n", " output = pd.concat([output, row_df])\n", " \n", " except:\n", " print(players.index[i] + ' no data')\n", "\n", "output = output.set_index('player')\n", "\n", "output = output.sort_values(['team', 'role', 'FV'], ascending = [True, False, False])\n", "#output.to_excel('outputs/pred_matchday_' + str(matchday_out) + '.xlsx')\n", "\n", "output" ] }, { "cell_type": "code", "execution_count": 29, "id": "6befd611", "metadata": {}, "outputs": [], "source": [ "import shutil\n", "\n", "template_file = 'outputs/pred_matchday_base.xlsx'\n", "dest_file = 'outputs/pred_matchday_' + str(matchday_out) + '.xlsx'\n", "\n", "shutil.copyfile(template_file, dest_file)\n", "\n", "with pd.ExcelWriter(dest_file, mode = 'a', engine=\"openpyxl\", if_sheet_exists = 'replace') as writer: \n", " output.to_excel(writer, sheet_name='data')" ] }, { "cell_type": "markdown", "id": "eed7a7ac", "metadata": {}, "source": [ "Predict average Serie A performance for each player" ] }, { "cell_type": "code", "execution_count": null, "id": "2b637a15", "metadata": {}, "outputs": [], "source": [ "gk_starters = ['Maignan', 'Ochoa', 'Silvestri', 'Consigli', 'Provedel', 'Di Gregorio', 'Meret', 'Milinkovic-Savic V.',\n", " 'Terracciano', 'Onana', 'Szczesny', 'Skorupski', 'Vicario', 'Musso', 'Carnesecchi', 'Rui Patricio',\n", " 'Montipo\\'', 'Falcone', 'Dragowski', 'Audero']" ] }, { "cell_type": "code", "execution_count": null, "id": "60d73507", "metadata": {}, "outputs": [], "source": [ "output = pd.DataFrame(columns = ['player', 'role', 'team', 'oppteam', 'home', 'starter', 'vote%', 'MV', 'MV std', 'FV', 'FV std', 'MV loc', 'MV scale', 'MV skewness', 'MV tailweight', 'FV loc', 'FV scale', 'FV skewness', 'FV tailweight', 'Clean Sheet %'])\n", "\n", "tot_matches = 2 # home and not home\n", "\n", "current_season_games = max(players_orig['games'])\n", "\n", "for i in range(players.shape[0]):\n", " try:\n", " home = 0\n", "\n", " for k in range(tot_matches):\n", " #matchday_out = k + 1\n", " #[player, team, oppteam, home] = PlayerMatch(players.index[i], matchday_out)\n", "\n", " player = players.index[i]\n", " team = players['team'][i]\n", " oppteam = 'Avg'\n", " home = not home\n", "\n", " [mean, std, dist] = vote_predict_NNb(player, team, oppteam, home = home)\n", "\n", " role = players['r'][player] \n", "\n", " starter = 0\n", " voteperc = 0\n", "\n", " games = max( players_orig['games'][i], players_orig['gk_games'][i] )\n", " mins = max( players_orig['minutes'][i], players_orig['gk_minutes'][i] )\n", "\n", " cs = 0\n", " if(role == 'P'):\n", " cs = dist[2].probs.numpy()[0] * 100\n", "\n", " starter = int( player in gk_starters )\n", " if(starter):\n", " voteperc = 100\n", " else:\n", " voteperc = 0\n", " else:\n", " starter = int ( 1 * (games >= current_season_games * 2/3 and mins / games >= 45 ) )\n", " voteperc = int( min( 1, games / current_season_games ) * 100) \n", "\n", " if(k == 0):\n", " row = [player, role, team, 'Avg', 1, starter, voteperc]\n", "\n", " numrow_ = [mean[0], std[0], \n", " mean[1], std[1], \n", " dist[0].loc.numpy()[0], dist[0].scale.numpy()[0], \n", " dist[0].skewness.numpy()[0], dist[0].tailweight.numpy()[0], \n", " dist[1].loc.numpy()[0], dist[1].scale.numpy()[0], \n", " dist[1].skewness.numpy()[0], dist[1].tailweight.numpy()[0],\n", " cs] \n", "\n", " if(k == 0):\n", " numrow = numrow_\n", " else:\n", " for j in range(len(numrow)):\n", " numrow[j] += numrow_[j]\n", "\n", " for j in range(len(numrow)):\n", " numrow[j] /= tot_matches\n", "\n", " print(players.index[i] + ' (' + \"{:.2f}\".format(numrow[0]) + ', ' + \"{:.2f}\".format(numrow[1]) + \n", " '); (' + \"{:.2f}\".format(numrow[2]) + ', ' + \"{:.2f}\".format(numrow[3]) + ')' )\n", "\n", " row += numrow # list concat\n", "\n", " row_df = pd.DataFrame(data = [row], columns = output.columns)\n", "\n", " output = pd.concat([output, row_df])\n", " except:\n", " print(players.index[i] + ' no data')\n", " \n", " \n", "\n", "output = output.set_index('player')\n", "\n", "output = output.sort_values(['team', 'role', 'FV'], ascending = [True, False, False])\n", "#output.to_excel('outputs/pred_matchday_' + str(matchday_out) + '.xlsx')\n", "\n", "output" ] }, { "cell_type": "code", "execution_count": null, "id": "b47cbd63", "metadata": {}, "outputs": [], "source": [ "import shutil\n", "\n", "output = output.sort_values(['role', 'FV'], ascending = [False, False])\n", "\n", "template_file = 'outputs/pred_matchday_base.xlsx'\n", "dest_file = 'outputs/pred_avg_seriea.xlsx'\n", "\n", "shutil.copyfile(template_file, dest_file)\n", "\n", "with pd.ExcelWriter(dest_file, mode = 'a', engine=\"openpyxl\", if_sheet_exists = 'replace') as writer: \n", " output.to_excel(writer, sheet_name='data')" ] }, { "cell_type": "markdown", "id": "cf9df3bb", "metadata": {}, "source": [ "Various predictions." ] }, { "cell_type": "code", "execution_count": 36, "id": "7300f3c2", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Meret: MV 6.08 ± 0.61; FV 5.48 + 1.00 (64.3% cs)\n", "Szczesny: MV 6.12 ± 0.63; FV 5.79 + 0.88 (87.3% cs)\n", "Provedel: MV 6.18 ± 0.65; FV 5.49 + 1.00 (55.3% cs)\n", "Maignan: MV 6.50 ± 0.78; FV 5.78 + 0.88 (47.3% cs)\n", "Rui Patricio: MV 6.36 ± 0.72; FV 6.14 + 0.74 (82.2% cs)\n", "Onana: MV 6.11 ± 0.62; FV 5.88 + 0.85 (82.9% cs)\n", "Milinkovic-Savic V.: MV 6.09 ± 0.62; FV 4.87 + 1.25 (6.9% cs)\n", "Musso: MV 6.56 ± 0.81; FV 6.04 + 0.79 (72.9% cs)\n", "Vicario: MV 6.68 ± 0.87; FV 5.75 + 0.88 (37.2% cs)\n", "Silvestri: MV 6.11 ± 1.17; FV 4.85 + 0.64 (13.9% cs)\n", "Terracciano: MV 6.12 ± 0.63; FV 5.38 + 1.01 (29.3% cs)\n", "Skorupski: MV 6.27 ± 0.68; FV 5.46 + 1.00 (47.3% cs)\n", "Falcone: MV 6.42 ± 0.75; FV 5.29 + 1.02 (2.8% cs)\n", "Di Gregorio: MV 6.39 ± 0.75; FV 5.73 + 0.89 (61.6% cs)\n", "Consigli: MV 6.16 ± 0.64; FV 5.22 + 1.11 (45.3% cs)\n", "Carnesecchi: MV 6.27 ± 0.77; FV 5.12 + 0.93 (7.8% cs)\n", "Montipo': MV 6.47 ± 0.77; FV 5.20 + 1.05 (6.4% cs)\n", "Audero: MV 6.48 ± 0.77; FV 5.26 + 0.95 (7.2% cs)\n", "Dragowski: MV 6.52 ± 0.80; FV 6.02 + 0.79 (76.4% cs)\n", "Ochoa: MV 6.46 ± 0.78; FV 5.69 + 0.79 (23.2% cs)\n", "Tatarusanu: MV 6.04 ± 0.69; FV 4.11 + 1.16 (3.0% cs)\n", "Handanovic: MV 6.41 ± 0.74; FV 6.19 + 0.74 (92.1% cs)\n", "Sportiello: MV 6.39 ± 0.75; FV 5.18 + 0.93 (15.2% cs)\n", "Sepe: MV 6.21 ± 0.71; FV 4.87 + 1.05 (4.9% cs)\n" ] }, { "data": { "text/plain": [ "[array([6.21465971, 4.86660706]),\n", " array([0.35387683, 0.5225544 ], dtype=float32),\n", " [,\n", " ,\n", " ]]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predict_player('Meret', log = 1, plot = 0)\n", "predict_player('Szczesny', log = 1, plot = 0)\n", "predict_player('Provedel', log = 1, plot = 0)\n", "predict_player('Maignan', log = 1, plot = 0)\n", "predict_player('Rui Patricio', log = 1, plot = 0)\n", "predict_player('Onana', log = 1, plot = 0)\n", "predict_player('Milinkovic-Savic V.', log = 1, plot = 0)\n", "predict_player('Musso', log = 1, plot = 0)\n", "predict_player('Vicario', log = 1, plot = 0)\n", "predict_player('Silvestri', log = 1, plot = 0)\n", "predict_player('Terracciano', log = 1, plot = 0)\n", "predict_player('Skorupski', log = 1, plot = 0)\n", "predict_player('Falcone', log = 1, plot = 0)\n", "predict_player('Di Gregorio', log = 1, plot = 0)\n", "predict_player('Consigli', log = 1, plot = 0)\n", "predict_player('Carnesecchi', log = 1, plot = 0)\n", "predict_player('Montipo\\'', log = 1, plot = 0)\n", "predict_player('Audero', log = 1, plot = 0)\n", "predict_player('Dragowski', log = 1, plot = 0)\n", "predict_player('Ochoa', log = 1, plot = 0)\n", "predict_player('Tatarusanu', log = 1, plot = 0)\n", "predict_player('Handanovic', log = 1, plot = 0)\n", "predict_player('Sportiello', log = 1, plot = 0)\n", "predict_player('Sepe', log = 1, plot = 0)" ] }, { "cell_type": "code", "execution_count": 36, "id": "bd126870", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Muriel: MV 6.20 ± 1.32; FV 7.29 + 3.53\n", "Tonali: MV 6.23 ± 0.95; FV 6.69 + 1.86\n", "Lobotka: MV 6.17 ± 0.72; FV 6.42 + 1.16\n", "Politano: MV 6.19 ± 0.77; FV 6.62 + 1.48\n", "Zapata D.: MV 6.09 ± 1.19; FV 7.18 + 3.36\n", "Frattesi: MV 6.23 ± 1.19; FV 7.37 + 3.58\n", "Gonzalez N.: MV 6.05 ± 1.07; FV 6.65 + 2.25\n", "Abraham: MV 6.20 ± 1.30; FV 7.66 + 4.26\n", "Pobega: MV 6.02 ± 0.68; FV 6.13 + 0.92\n", "Mario Rui: MV 6.10 ± 0.77; FV 6.25 + 0.98\n", "Cuadrado: MV 5.82 ± 0.95; FV 5.80 + 0.92\n", "Skriniar: MV 5.75 ± 0.68; FV 5.70 + 0.58\n", "Lukaku: MV 6.41 ± 1.42; FV 8.29 + 5.46\n" ] }, { "data": { "text/plain": [ "[array([6.41176047, 8.29456946]),\n", " array([0.70926785, 2.7289915 ], dtype=float32),\n", " [,\n", " ]]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predict_player('Muriel', plot = 0, log = 1)\n", "predict_player('Tonali', plot = 0, log = 1)\n", "predict_player('Lobotka', plot = 0, log = 1)\n", "predict_player('Politano', plot = 0, log = 1)\n", "predict_player('Zapata D.', plot = 0, log = 1)\n", "predict_player('Frattesi', plot = 0, log = 1)\n", "predict_player('Gonzalez N.', plot = 0, log = 1)\n", "predict_player('Abraham', plot = 0, log = 1)\n", "predict_player('Pobega', plot = 0, log = 1)\n", "predict_player('Mario Rui', plot = 0, log = 1)\n", "predict_player('Cuadrado', plot = 0, log = 1)\n", "predict_player('Skriniar', plot = 0, log = 1)\n", "predict_player('Lukaku', plot = 0, log = 1)" ] }, { "cell_type": "code", "execution_count": 37, "id": "4b9f5a7d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Skriniar: MV 6.14 ± 0.82; FV 6.38 + 1.13\n", "Cuadrado: MV 6.29 ± 0.96; FV 6.83 + 1.94\n", "Bastoni: MV 6.13 ± 0.73; FV 6.32 + 0.98\n", "Barak: MV 6.31 ± 1.41; FV 7.50 + 3.97\n", "Politano: MV 6.18 ± 0.82; FV 6.57 + 1.55\n", "Smalling: MV 6.17 ± 0.86; FV 6.56 + 1.43\n", "Gosens: MV 6.05 ± 0.54; FV 6.11 + 0.66\n" ] }, { "data": { "text/plain": [ "[array([6.04807256, 6.10812885]),\n", " array([0.27137518, 0.32888246], dtype=float32),\n", " [,\n", " ]]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predict_player('Skriniar', log = 1, oldseason= True)\n", "predict_player('Cuadrado', log = 1, oldseason= True)\n", "predict_player('Bastoni', log = 1, oldseason= True)\n", "predict_player('Barak', log = 1, oldseason= True)\n", "predict_player('Politano', log = 1, oldseason= True)\n", "predict_player('Smalling', log = 1, oldseason= True)\n", "predict_player('Gosens', log = 1, oldseason= True)\n", "\n", "predict_player('Skriniar', log = 1, oldseason= False)\n", "predict_player('Cuadrado', log = 1, oldseason= False)\n", "predict_player('Bastoni', log = 1, oldseason= False)\n", "predict_player('Barak', log = 1, oldseason= False)\n", "predict_player('Politano', log = 1, oldseason= False)\n", "predict_player('Smalling', log = 1, oldseason= False)\n", "predict_player('Gosens', log = 1, oldseason= False)" ] }, { "cell_type": "code", "execution_count": 34, "id": "10c7ad3e", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Rafael Leao: MV 6.34 ± 1.43; FV 7.95 + 4.79\n" ] }, { "data": { "text/plain": [ "[array([6.3442238 , 7.94607029]),\n", " array([0.71331024, 2.395806 ], dtype=float32),\n", " [,\n", " ]]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predict_player('Rafael Leao', plot = 1, log = 1)" ] }, { "cell_type": "markdown", "id": "30744d7a", "metadata": {}, "source": [ "Tensorflow seems to have a custom definition for SinhArcsinh distribution. \n", "\n", "Here the code to generate the probability density function is reproduced." ] }, { "cell_type": "code", "execution_count": 110, "id": "3ec6c3dc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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36NZbbz3ttStWrJDT6QzcCgoKhlImEPFCuftqX0xiBRANhjWB1WLpP/vfMIwB951sw4YN2rx5s5588kmtXLlSL7zwwmmvve+++9Tc3By4HT58eDhlAhEr1Luv+rHXCIBoMKT/lmVlZclmsw3ogtTV1Q3olpysuLhYknT++efr6NGjevDBB/XVr371lNc6HA45HI6hlAZElVDvvupHZwRANBhSZyQ+Pl4lJSUqKyvrd39ZWZnmzZs36NcxDENut3sobw3ElJYwLO2VCCMAosOQ/1u2fPly3XTTTZozZ47mzp2rNWvWqLKyUkuWLJHkG2KpqqrSr3/9a0nSE088ocLCQk2ZMkWSb9+Rn/zkJ7rrrruC+DGA6OJf2puWGJ7OSF2LW53dHiXE2UL6fgAwHEP+Jly0aJEaGhr08MMPq6amRjNmzNC6detUVFQkSaqpqVFlZWXgeq/Xq/vuu08HDhyQ3W7XhAkT9IMf/EC33XZb8D4FEGVODNOEtjPiTIxTaoJdLZ09OtzYrom5qSF9PwAYDothGBG/NaPL5ZLT6VRzc7PS0tLMLgc4Z59Z+ZZ21bboN7d8QgsmZof0vT73iw3aVuXSLxfP0dXTzjy3CwCCabA/vzmbBjBBuOaMSNK4zGRJ0sH6tpC/FwAMB2EEMEE4toP3G5/lCyP7CSMAIhRhBAgzr9dQa1d49hmRpHG9YeRAfWvI3wsAhoMwAoRZi7tH/pla4eiMFGf5h2lY3gsgMhFGgDDzbwUfb7fKYQ/9Ult/GKl1daq9tyMDAJGEMAKEWTgnr0pSelK8RiX53ovuCIBIRBgBwiyw4VkYhmj8igPzRpjECiDyEEaAMPN3RsIxX8TPP4n1YANhBEDkIYwAYdbi9m8FH55hGqnP8t5jhBEAkYcwAoSZq8O8zgjLewFEIsIIEGb+1TThmsAq9Vne28AEVgCRhzAChJnLjDkjvVvCN7Z1qbm9O2zvCwCDQRgBwqwlTCf29pXssCs3zSFJOsAkVgARhjAChJkrsM9I+DojUt/lvcwbARBZCCNAmJ04JC98nRGpTxhhRQ2ACEMYAcLMjH1GJGlCdookad8xOiMAIgthBAizwGqaMO4zIkkTc1MlSXuPEkYARBbCCBBmZqymkaSJOb7OyIH6NnV7vGF9bwA4E8IIEGZm7DMiSaOdCUqOt6nHa+gQK2oARBDCCBBGXT1edXb7uhLhDiMWi0Xn9XZH9tUxVAMgchBGgDDyd0UkKSXMwzSSdF4O80YARB7CCBBG/pU0KQ67bFZL2N9/Yq6vM7KXzgiACEIYAcLIFdh9NfxdEenEJFbCCIBIQhgBwsisPUb8/HNG9h9rlcdrmFIDAJyMMAKEkVkrafzGjkqSw26Vu8erI8c5wRdAZCCMAGHk6jC3M2KzWgI7sTKJFUCkIIwAYeQyaffVvpjECiDSEEaAMDJr99W+JrLXCIAIQxgBwqil05wTe/vy7zWy+6jLtBoAoC/CCBBG/tU0Zk1glaSpo31hZM/RVvVwRg2ACEAYAcLI1WHuPiOSVDAqScnxNnX1eHWQM2oARADCCBBGZu8zIklWq0WT83zdkR01LabVAQB+hBEgjFrc5q+mkaQpo9MkSbtqmDcCwHyEESCM/PuMpJnYGZGkqb2dkV21dEYAmI8wAoSR2Tuw+k3t7YzspDMCIAIQRoAwMQyjzz4j5oaRSb2dkZrmTjW1d5laCwAQRoAw6ej2BA6nM3MCq+TrzIwdlShJ2skkVgAmI4wAYeJfSWOzWpQUbzO5mhNDNbtqGaoBYC7CCBAmffcYsVgsJlfTZxIrnREAJiOMAGESCefS9OVf3ruTzggAkxFGgDCJlJU0fieGaVrUzbbwAExEGAHCJNI6I0UZSUp12NXV49Xeo5zgC8A8hBEgTCKtM2K1WjRjjFOStLWqydxiAIxohBEgTPy7r5q9x0hfF4z1h5FmkysBMJIRRoAw8XdGImWYRtKJzsgRwggA8xBGgDBxdUbGIXl9+TsjO2tb1NXDJFYA5iCMAGHiH6ZxRlAYKcxIUlqCbxLrnqPsNwLAHIQRIEyaO/wTWCNnmMZiseh85o0AMBlhBAgTfxiJpM6IJJ0/Jl0SYQSAeQgjQJi4IjaMMIkVgLkII0CYBDojSZEVRvyTWHfVuuTu8ZhcDYCRiDAChIFhGCdW00TQPiOSNHZUotKT4tTtMbSTQ/MAmIAwAoRBR7dH3R5DUuQN01gsFs0qSJckfXDouLnFABiRCCNAGPiHaOxWi5LibSZXM1BJ0ShJ0geVhBEA4UcYAcKg70oai8VicjUDzS70hZEtlU3mFgJgRCKMAGHg3/AsknZf7evCgnRZLVJVU4dqmzvNLgfACEMYAcIgsOFZhIaRZIddU/LSJDFUAyD8CCNAGETqhmd9zS5Kl8QkVgDhRxgBwiAqwkghk1gBmIMwAoSBKwLPpTmZf0XNtio2PwMQXoQRIAyioTNSmJGkzOR4dXm82lblMrscACMIYQQIg0g9l6Yvi8Wi2b3dkfcPNppcDYCRhDAChEE0dEYk6eLiDEnSu/sbTK4EwEhCGAHCINKX9vpdMj5TkrT54HH1eLwmVwNgpCCMAGHgPyQv0jsjU0enKTXBrhZ3j3bUMG8EQHgMK4ysWrVKxcXFSkhIUElJiTZs2HDaa1988UVdffXVys7OVlpamubOnatXXnll2AUD0ShahmlsVkufoRrmjQAIjyGHkbVr12rp0qW6//77tWXLFi1YsEALFy5UZWXlKa9/6623dPXVV2vdunUqLy/Xpz71KV177bXasmXLORcPRItoCSOSdHGxb6jmHeaNAAgTi2EYxlCecPHFF2v27NlavXp14L6pU6fquuuu04oVKwb1GtOnT9eiRYv0ve99b1DXu1wuOZ1ONTc3Ky0tbSjlAqZz93g0+bt/lyR9+L1SOZMiO5BsPdKsax/fqFSHXRUPlMpmjbyD/QBEh8H+/B5SZ6Srq0vl5eUqLS3td39paak2bdo0qNfwer1qaWlRRkbGaa9xu91yuVz9bkC08h+SZ7FIqRG86ZnftPw0pTp880Z2Mm8EQBgMKYzU19fL4/EoNze33/25ubmqra0d1Gs8+uijamtr0/XXX3/aa1asWCGn0xm4FRQUDKVMIKL4h2hSHXZZo6DLYLNadFHvvBGGagCEw7AmsFos/b9QDcMYcN+pvPDCC3rwwQe1du1a5eTknPa6++67T83NzYHb4cOHh1MmEBEC80UifHimr0vG+8LIpo8JIwBCb0g946ysLNlstgFdkLq6ugHdkpOtXbtWt9xyi/7whz/oqquuOuO1DodDDodjKKUBEevEuTTRE0bmn5clydcZ6erxKt7OLgAAQmdI3zDx8fEqKSlRWVlZv/vLyso0b9680z7vhRde0Ne+9jU9//zzuuaaa4ZXKRClomWPkb6m5qUpK8Wh9i6Pyg9xii+A0Bryf3eWL1+up59+Ws8++6x27typZcuWqbKyUkuWLJHkG2JZvHhx4PoXXnhBixcv1qOPPqpLLrlEtbW1qq2tVXNzc/A+BRDBomlZr5/VatGCib7uyFt7j5lcDYBYN+QwsmjRIq1cuVIPP/ywZs6cqbfeekvr1q1TUVGRJKmmpqbfniNPPfWUenp6dMcdd2j06NGB29133x28TwFEsOb26AsjknTZpN4wsocwAiC0hrXO8Pbbb9ftt99+yseee+65fn9+8803h/MWQMyIlnNpTnbpedmSpO3VLtW3upWVwjwuAKHBrDQgxJp6w0h6FK2mkaTsVIemjfZtUrRxb73J1QCIZYQRIMSa2rskSaOS4k2uZOgum+TrjjBvBEAoEUaAEDveO2ckPcqGaSTpMv8k1j318nqHdHIEAAwaYQQIseO9nZH0KOyMzBmXoRSHXfWtbm2tYgUcgNAgjAAh5l9NMyo5+joj8XarLu8dqnlt51GTqwEQqwgjQAgZhhGYwBqNc0Yk6appvqMbynYQRgCEBmEECCFXZ488vXMtom2fEb9PTc6RzWrRrtoWHW5sN7scADGIMAKEkH+IJjHOpoQ4m8nVDE96UrzmFI2SxFANgNAgjAAhdDywrDc6uyJ+V0/zHYRJGAEQCoQRIIT8YcQZpfNF/K6c6gsj7+5vDOwoCwDBQhgBQqjJv5ImyjsjxVnJOi8nRT1eQ2/urjO7HAAxhjAChFA07756ss9Mz5Mk/e9HNSZXAiDWEEaAEArsvhrlnRFJuuaC0ZKkN/ccU0snQzUAgocwAoRQU2D31egPI1PyUjUhO1ldPV4msgIIKsIIEELH26N7w7O+LBaLrrkgXxJDNQCCizAChJB/99VoPJfmVD7XO1Tz1p56VtUACBrCCBBCgWGaKN199WSTclM1KTdFXR6vXmN7eABBQhgBQiiw6VkUHpJ3Otec7xuq+ctH1SZXAiBWEEaAEGpqi61hGkm69kLfUM2GvfU61uI2uRoAsYAwAoRIt8erFnePpNiYwOo3PjtFswrT5fEa+nNFldnlAIgBhBEgRPpO8ExLsJtYSfD9y+yxkqQ/lh+RYRgmVwMg2hFGgBDxT15NS7DLboutf2rXXpCveLtVu2pbtL3aZXY5AKJcbH1DAhEkcC5NcuwM0fg5k+J0de/hef/zwRGTqwEQ7QgjQIg0tsXWst6TfanEN1Tz54pqdfV4Ta4GQDQjjAAh4g8jmSkOkysJjQUTs5Sd6lBjW5de38WeIwCGjzAChEhDbxjJiMFhGkmy26yB7sh/v1tpcjUAohlhBAiRQGckRsOIJN3wiUJZLL49Rw7Ut5ldDoAoRRgBQqSh1bchWKx2RiSpICNJl0/KliQ9/+4hk6sBEK0II0CINMT4nBG/Gy8ukiT9ofyIOrs9JlcDIBoRRoAQGQnDNJL0qSk5GpOeqKb2bq3bWmN2OQCiEGEECJHGGJ/A6mezWvTVTxRIkn79T4ZqAAwdYQQIAcMwYn41TV+LLipUvM2qisNNKj/UaHY5AKIMYQQIgVZ3T2AjsMyU2A8j2akOXTcrX5L0y7cOmFwNgGhDGAFCwD9EkxhnU1J8bB2Sdzq3LhgvSXplR60ONbDMF8DgEUaAEBhJQzR+k3JT9cnJ2TIM6dmNdEcADB5hBAiBxlb/st6RE0Yk6Ru93ZHfbz4SOLUYAM6GMAKEQEObb8OzWF/We7J5EzI1dXSaOro9+g0rawAMEmEECIETwzSxveHZySwWi5Zc7uuOPPP2AbW6e0yuCEA0IIwAITBSh2kk6XMX5Gt8drKa2rv1638eNLscAFGAMAKEwEjZ8OxUbFaL7rriPEnS0xsOqI3uCICzIIwAIdAwQraCP51rL8jXuMwkNbZ16bfvMHcEwJkRRoAQCExgHYHDNJJkt1l15xUTJUlr3tqv9i66IwBOjzAChIB/zshIm8Da13Uz81WUmaSGti796u2DZpcDIIIRRoAgMwxD9a0je5hG8nVHll89SZL05JsfB+bRAMDJCCNAkLk6etTl8Z1Lk506cjsjkm/uyPT8NLW4e/T46/vMLgdAhCKMAEF2rLVTkuRMjFNCnM3kasxltVp078IpkqTfvHNQhxvbTa4IQCQijABBVufyTV4d6V0RvwUTs7VgYpa6PYZ+8upus8sBEIEII0CQHWvtDSMphBG/ez7j6478uaJaH1QeN7kaAJGGMAIE2bEWXxjJSSOM+M0Y49SXSsZKkh7483Z5vIbJFQGIJIQRIMj8YYTOSH/3fGaKUh12ba1q1u83Hza7HAARhDACBFldC3NGTiU71aGlvUt9f/T3XWpqZ6kvAB/CCBBkxwgjp7V4bpEm5aboeHu3Hn11j9nlAIgQhBEgyAJzRlITTK4k8sTZrHrw89MlSb999xCTWQFIIowAQVfX4ttnhM7Iqc2bkKUvzh4jw5Du+eNHcvd4zC4JgMkII0AQdfV4dby9WxJh5Ez+85ppykyO1966Vq1642OzywFgMsIIEET+03rjbBalJ8aZXE3kGpUcr4e+4BuuWfXmPu2ubTG5IgBmIowAQeSfL5KV4pDVajG5msh2zfmjdfW0XHV7DP2/P36ont7zfACMPIQRIIjYCn7wLBaL/uu6GUpNsOvDI836BQfpASMWYQQIIv9W8DmEkUHJTUvQf103Q5L0i9f3qvwQq2uAkYgwAgTRURcraYbqCzPH6LqZ+fIa0rK1FWp195hdEoAwI4wAQVTb7Asjo52JJlcSXR76wgyNSU9UZWO7Hnx5u9nlAAgzwggQRDW9YSTPyYZnQ+FMjNNj118oi0X6Y/kRvfjBEbNLAhBGhBEgiE50RggjQ3Xx+EzddcVESdJ//GmrdtW6TK4IQLgQRoAgqmnukEQYGa67r5yoBROz1Nnt1f/97Qdq6ew2uyQAYUAYAYKkzd0jV6dv8mVuGmFkOGxWi372lVnKdyboQH2b/t8fP5JhGGaXBSDEhhVGVq1apeLiYiUkJKikpEQbNmw47bU1NTW64YYbNHnyZFmtVi1dunS4tQIRrbZ3JU2Kw67UBHZfHa6M5Hg98a+zFWez6G/barV6PdvFA7FuyGFk7dq1Wrp0qe6//35t2bJFCxYs0MKFC1VZWXnK691ut7Kzs3X//ffrwgsvPOeCgUhVy+TVoJlVOErfu9a3XfyPX9mtV7bXmlwRgFAachh57LHHdMstt+jWW2/V1KlTtXLlShUUFGj16tWnvH7cuHH62c9+psWLF8vpdJ5zwUCkqmHyalDddEmRbrqkSIYhLf1dhbZVNZtdEoAQGVIY6erqUnl5uUpLS/vdX1paqk2bNgWtKLfbLZfL1e8GRLra3smrecwXCZoHrp2mBROz1NHt0Td+vVl1vUNhAGLLkMJIfX29PB6PcnNz+92fm5ur2trgtVFXrFghp9MZuBUUFATttYFQoTMSfHabVY/fMFsTspNV09ypW/6/zezQCsSgYU1gtVj6n0ZqGMaA+87Ffffdp+bm5sDt8OHDQXttIFT8W8HnsftqUDkT4/TMzRcpIzleW6uaddtvNsvd4zG7LABBNKQwkpWVJZvNNqALUldXN6Bbci4cDofS0tL63YBIR2ckdMZlJeu5f7tIyfE2vb2vQcvWVsjjZckvECuGFEbi4+NVUlKisrKyfveXlZVp3rx5QS0MiDb+MMIeI6Fxwdh0PXXTHMXZLFq3tVbf+/M29iABYsSQh2mWL1+up59+Ws8++6x27typZcuWqbKyUkuWLJHkG2JZvHhxv+dUVFSooqJCra2tOnbsmCoqKrRjx47gfAIgArS5e9TY1iVJGjOKYZpQuXRiln66aKYsFum/363UD/62i0ACxAD7UJ+waNEiNTQ06OGHH1ZNTY1mzJihdevWqaioSJJvk7OT9xyZNWtW4Pfl5eV6/vnnVVRUpIMHD55b9UCEqGryraRJS7DLmciGZ6H0uQvy1dTere++tE1PvbVfFotF93xmclDnrQEIryGHEUm6/fbbdfvtt5/yseeee27AffzPBbHuyPF2SdLYUUkmVzIy3HhJkTxeQw+8vF1Prv9YVov0nU8TSIBoxdk0QBAcbvR1RgoyGKIJl5vnjdOD106TJK1682P95NXd/McHiFKEESAI6IyY42vzi/VAbyB54o2P9dBfdsjLKhsg6hBGgCA4ctzXGRnL5NWw+7f5xXr4C75zbJ7bdFDLf1+hbo/X5KoADAVhBAiCw3RGTLV47jj97CszZbda9FJFtW77Tbk6utgYDYgWhBEgCPydEeaMmOcLM8dozeISOexWvb6rTjc9825guTWAyEYYAc5RS2e3mtq7JUlj0gkjZrpiSq5+e+vFSk2wa/Oh4/o/q97WvrpWs8sCcBaEEeAc+bsi6UlxSk1gjxGzXTQuQy/+33kaOypRhxra9cVVb2vTvnqzywJwBoQR4BwFhmiYLxIxJuam6qU75mt2YbpcnT1a/Ox7+t17lWd/IgBTEEaAc3SooU0S80UiTVaKQ89/4xJde2G+eryG7n1xq+7/01ZO/AUiEGEEOEcH6n1hpDgr2eRKcLKEOJt+/pWZWnbVpMB5Ntc/9Y6qe7fvBxAZCCPAOTrY2xkZl0kYiUQWi0V3XzVRz958kdIS7PrwcJM+94uNzCMBIghhBDhHB475wsj4bMJIJPvUlBz99a4FmjY6TY1tXbrxmXe18rU96mGDNMB0hBHgHHR2e1Td3CmJzkg0KMxM0ou3z9OXSsbKa0grX9urr/7yncB2/gDMQRgBzoF/iCYtwa6M5HiTq8FgJMTZ9JMvX6iVi2YqxWHX+wePa+HPNuivH1WbXRowYhFGgHNwsM/kVY6vjy7XzRqjdd9coFmF6Wrp7NGdz2/Rt37/oZp7N7ADED6EEeAc7GclTVQrzEzS72+bq29ecZ6sFul/Pjiiq3+6Xq/tOGp2acCIQhgBzsGJzkiKyZVguOJsVi0vnaw/LJmr8VnJqmtx69Zfb9aytRVqaudsGyAcCCPAOfDvMTIui91Xo11JUYbW3b1At102XlaL9KctVbrqsbf014+qZRiG2eUBMY0wAgyTYRjac9R3CNt5OXRGYkFCnE33fXaq/uf/ztPEnBTVt7p15/NbtPjZ97T/GAfuAaFCGAGGqa7FreaOblkt0oRswkgsmVU4Sn/95qVaetVExdut2rC3Xp9ZuUGPvrpbHV1sJw8EG2EEGKY9R1skSeOykpUQZzO5GgSbw27T0qsmqWzZZbp8Ura6PF794vV9uvqn6/X3bTUM3QBBRBgBhml3rS+MTMpJNbkShFJRZrKe+7eL9OSNs5XvTNCR4x1a8tsPtOipd/Th4SazywNiAmEEGCZ/Z2RSHmEk1lksFn1mxmi99q3LddcV5ykhzqr3DjbqC0+8rbt/t4UdXIFzRBgBhsk/eXVSLvNFRoqkeLu+VTpZb3z7k/ri7DGSpD9XVOuKR9drxbqdOt7GUmBgOAgjwDB4vYb29nZGJufSGRlpRjsT9dj1M/XXuy7VJeMz1NXj1VNv7delP3xdj726W80d7OIKDAVhBBiGqqYOtXV5FGezaBy7r45YM8Y49cI3LtEzN8/RtNFpauvy6Oev79OCH76uX/xjr1rdPWaXCEQFwggwDNurmyVJE3NSFWfjn9FIZrFYdOXUXP31rku1+l9na1JuilydPXq0bE8glHDeDXBmfIsCw7C1yhdGzh/jNLkSRAqr1aKF54/W3+6+TD/7ykyNz0rW8fZuPVq2R/N+8A99/3936Kir0+wygYhEGAGGYWuVS5I0YyxhBP3ZrBZ9YeYYvbrMF0qm5KWqrcujX244oAU/fEP3/s9H7OYKnMRudgFAtDEMQ9vojOAs7DarvjBzjD5/Yb7e3H1Mq9/8WO8dbNTv3j+stZsP68opOfravGLNPy9TFovF7HIBUxFGgCGqbu5UY1uX7FaLprDHCM7CYrHoU1Ny9KkpOdp8sFGr3/xY/9hVp9d2+m7n5aTo5nnj9MVZY5Ts4CsZIxPDNMAQ+bsiE3NT2QYeQzJnXIae+dpF+se3LtfX5o1TcrxN++pa9Z8vbdMlK/6h//rrjsBJ0MBIQhgBhmjrEf8QTZrJlSBaTchO0YOfn653/uNKPXDtNI3LTFJLZ4+e3nhAn/rJm1r01D/10pYqdXZzKB9GBnqCwBCVHzouyXeyK3AuUhPi9G/zi3Xz3HFav+eYfv3Pg3pzzzG9e6BR7x5olPPlOP2fWWO06KICTR1N+EXsIowAQ9Dt8WrLYV8YmVNEGEFwWK0n5pVUN3Xo95sP6w+bj6iqqUPPbTqo5zYd1IVjnfri7LH63AWjlZniMLtkIKgsRhScg+1yueR0OtXc3Ky0NP53APNUHG7SdU+8rfSkOH3w3atltbIKAqHh8RrasPeY1r5/WGU7jqrH6/uqtlktumxilq6bNUal0/KUGM+8JUSuwf78pjMCDMHmg42SpJLCUQQRhJTNatEnJ+fok5NzdKzFrb98WK2XKqr00ZFmvbH7mN7YfUzJ8TZ9enqevjBrjOZNyGQ3YEQtwggwBJsP9g7RjMswuRKMJNmpDn390mJ9/dJi7atr1Z8rqvRSRZUON3boxS1VenFLlZyJcSqdlquF5+dp/nlZctjpmCB6MEwDDJLXa+ii77+mhrYu/WHJXF1EIIGJDMPQB5XH9actVfrb1lo1tHUFHkt12HXl1BwtPH+0Lp+UzRJ0mGawP78JI8Agbatq1ud+sVHJ8TZt+V6p4u20xBEZPF5D7x1o1N+31ehv22pV1+IOPJYUb9NlE7N1xdQcfWpyjrJTmfyK8GHOCBBkG/bWS5LmTsgkiCCi2KwWzZ2QqbkTMvXAtdO15fBxrdtaq79vq1VVU4f+vr1Wf99eK0m6sCBdV0zO0ZVTczQ9P42t6BERCCPAIG3Ye0yStGBitsmVAKdntVpUUpShkqIMffeaqdpW5dI/dh3V67vq9NGRZn14uEkfHm7ST1/bo9w0h66YkqPLJmZr3oQsOZPizC4fIxTDNMAgtHf1aOZDZeryePX6ty7X+OwUs0sChqzO1ak3dtfpHzvrtHFfvdq7TuzwarX4Dn68dGKWLj0vW7OL0pkEi3PGMA0QRJv2NajL49WY9EQVZyWbXQ4wLDlpCVp0UaEWXVSozm6P3j3QqDd21WnD3mP6+FibPjzSrA+PNOuJNz5WYpxNF4/P0KXnZWnehCxNyUtlOTtChjACDMLftvnG26+elssYO2JCQpxNl0/K1uWTfMOONc0d2ri3Xhv31evtffWqb+3Sm7uP6c3dvuHJtAS7PlGc0XvL1PT8NPY1QdAQRoCz6PZ49drOo5Kkz8zIM7kaIDRGOxP15TkF+vKcAnm9hnYfbdHGvfXasK9e5Qcb5ers0Ws76/TazjpJvlU6JUWj9IlxvoByYUE6S4gxbIQR4Cze2d+g5o5uZaXEs7cIRgSr1aKpo9M0dXSavnHZePV4vNpe7dJ7vQf4vX+wUc0d3dqwtz6wyizOZtG00WmaVThKswrTNatglAoyEukkYlAII8BZrNtaI0m6elqebIyZYwSy26y6sCBdFxak6xuXjZfXa2hPXUsgnLx3oFHHWtyBOSfPbfI9LzM5XrMK0zWzIF2zCkfpgrFOpSawYgcDEUaAM+jo8ugvH/rCyOcvzDe5GiAyWK0WTclL05S8NC2eO06GYejI8Q5tOdykLZXHtaWySdurm9XQ1tVvaMdikYqzkjUj36kZY9I0I9+p6WOcciYSUEY6wghwBn/fXqNWd48KMhJ1cTFDNMCpWCwWFWQkqSAjKRDaO7s92lHj0pbKJlX0hpQjxzu0/1ib9h9r08sfVgeeX5iRpBlj0jQ936kZY5yakZ+mzBR2ih1JCCPAGfz+/SOSpC+XFLCsERiChDibZheO0uzCUYH76lvd2l7t0raqZt+tulmHGztU2diuysZ2rdtaG7g2O9Whybmpmpznu03JS9XEnFQlxjNJNhYRRoDT2F3bon/ub5DVIv1LyVizywGiXlaKo99yYklqbu/W9mpfMNlW5dK26mYdqG/TsRa3jrW4tXFffeBai0UqykjyBZTcVE3OS9PkvBQVZSazzDjKEUaA03h6w35JvuW8Y9ITTa4GiE3OpDjNOy9L887LCtzX5u7R3rpW7a51aVdti3bXtmjP0RbVt3bpYEO7Dja065XtRwPX260WFWYkaXx2iibkJGtClu/X8VkpGpUcb8bHwhARRoBTqHN16qWKKknSrQvGm1wNMLIkO+yaWeBbhdNXfatbu3vDye7aFu062qK9R1vU3uXR/vo27a9v02s7+79WRnK8JmT7gsmEnGQVZ6WoKDNJhRlJ7IsSQQgjwCk8/sY+dXsMzSnqP+YNwDxZKQ5lnefQ/D5dFMMwVOvq1P5jbfr4WKs+rmvV/vo2fVzXqurmTjW2damxrUvvHzw+4PVy0xwqykhWYWaSijKSfL9mJqsoI0npSXHskRJGhBHgJAfr2/T8u5WSpG+VTja5GgBnYrFYNNqZqNHOxH4hRfIdcBkIKb2/Hmpo06GGdrV09uioy62jLrfeO9g44HVTE+wqykxSUUayxoxK1Jj0ROWn+34dMyqR5chBRhgBTrLibzvV4zX0ycnZmjsh0+xyAAxTUrzdt1R4jLPf/YZhqKm9W4ca23WooU2VDe061Nje+2ubjrrcauns8U2orXKd8rVTHXZfOOkbVHp/PyY9UdmpDjZJHALCCNDH37fV6JXtR2W3WnTvwilmlwMgBCwWi0Ylx2tUcvyAeSmSb7PDw8fbdajBt+S46niHqpraVd3UqaqmDjW2danF3aPdR1u0+2jLKd/DZrUoJ9WhnLQE5aU5lJeW0Pv7BOU5E5Sb5lBuWgI70vYijAC9Glrd+t6ft0uSbrt8vKbkpZlcEQAzJMbbNCk3VZNyU0/5eHtXTyCYVB3vUHVTR+D3VU0dqnV1yuM1VNPcqZrmTn14hvdKjrcpNy2h9+ZQrjNB2SkOZabE++bI9N4ykuNjutNCGAEkebyG7v5dhepa3JqQnay7rphodkkAIlRSvF3n5aTovJyUUz7e4/GqvrVLR12dqnV1qq7319pmt+paOlXb3Kmjrk65OnvU1mcl0JlYLb6VQf5w0j+sxCsr1REIMaOS4qNupRBhBCOeYRj6r//doY376pUYZ9PqG0ui7h8ygMhht1mV5/QNx1x4huvau/yTaDv73Nyqb+29tXSpvtWtxvYueQ2pvrVL9a1dkk49NNRXYpxNGcnxGpUcp1FJ8b7fJ8X3/j5Oo5LjlZHkG6oaleS7zmE373uPMIIRzTAMrXxtr3719kFJ0g+/dMFpW7MAEExJ8XYVZ9lVnJV8xut6PF41tncFwsmJW5fqW9w65v99q1vH27rU4zXU0e3xDR01dQy6nh9/6QJ9eU7BuX6sYSGMYMTq8Xj10F926DfvHJIkPXDtNE7mBRBx7DarclITlJOacNZrDcNQi7tHx3v3Vzne3qXjbd063n7iz75fu3Xc/3h7tzxew9TlyoQRjEhHjrfrW7//UO8eaJTFIn33mmn6t/nFZpcFAOfEYrEoLSFOaQlxKso8c8fFz+s11NLZI0eceef7EEYwonR2e/Srtw/qiTf2qdXdo6R4mx798oVaeP5os0sDAFNYrRY5k8xdYjysGLRq1SoVFxcrISFBJSUl2rBhwxmvX79+vUpKSpSQkKDx48frySefHFaxwHDVtXTq5//Yq8t//IZ++PddanX3qKRolP529wKCCACYbMidkbVr12rp0qVatWqV5s+fr6eeekoLFy7Ujh07VFhYOOD6AwcO6LOf/ay+8Y1v6Le//a3efvtt3X777crOzta//Mu/BOVDACfr9ni1q6ZF7x5o0Ks7jmrzwUZ5Dd9j+c4EffvTk3XdzDGyxvC6fQCIFhbDMIyhPOHiiy/W7NmztXr16sB9U6dO1XXXXacVK1YMuP6ee+7Ryy+/rJ07TxyluGTJEn344Yf65z//Oaj3dLlccjqdam5uVloaG1HBFzaa2rt7J2d1qdbVqUMNvh0TD9S3anu1S+4eb7/nzCpM1+K5Rfrs+aNNXcIGACPFYH9+D6kz0tXVpfLyct1777397i8tLdWmTZtO+Zx//vOfKi0t7Xffpz/9aT3zzDPq7u5WXNzAcSq32y23293vw4TC/5Qf0daq5lM+1jejnZzW+sY346RHT452xhke6/vogOcN8j2GUpvO+LzBft7BP2/g2w/+83b1eOUO3Dxyd5/4fbvboxZ3z8nvNkBagl0zC0fpU5OzddXUXBVkJJ31OQCA8BtSGKmvr5fH41Fubm6/+3Nzc1VbW3vK59TW1p7y+p6eHtXX12v06IHj9StWrNBDDz00lNKGZf2eY3r5w+qQvw9Cw2KRnIm+DX2yUx0qykhSUWaSCjOTNSM/TcVZyRwBDgBRYFiraU7+gjcM44xf+qe6/lT3+913331avnx54M8ul0sFBcHfiKV0eq4KT/rfct+SBlTX58GTHzv5o1j6XDHwsdM/92w/PPvXZzntYye/z5nqO9Xj/R876dpBvufJzz3btX0vcNiscsRZ5bBb5bDben9vk8NuVULvzoLOxLiYPqsBAEaKIYWRrKws2Wy2AV2Qurq6Ad0Pv7y8vFNeb7fblZl56uPZHQ6HHA7HUEobls9dkK/PXRDytwEAAGcwpKW98fHxKikpUVlZWb/7y8rKNG/evFM+Z+7cuQOuf/XVVzVnzpxTzhcBAAAjy5D3GVm+fLmefvppPfvss9q5c6eWLVumyspKLVmyRJJviGXx4sWB65csWaJDhw5p+fLl2rlzp5599lk988wz+va3vx28TwEAAKLWkOeMLFq0SA0NDXr44YdVU1OjGTNmaN26dSoqKpIk1dTUqLKyMnB9cXGx1q1bp2XLlumJJ55Qfn6+fv7zn7PHCAAAkDSMfUbMwD4jAABEn8H+/DbvVBwAAAARRgAAgMkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAUw15O3gz+DeJdblcJlcCAAAGy/9z+2ybvUdFGGlpaZEkFRQUmFwJAAAYqpaWFjmdztM+HhVn03i9XlVXVys1NVUWiyVor+tyuVRQUKDDhw+PmDNvRtpn5vPGNj5vbOPzRj/DMNTS0qL8/HxZraefGRIVnRGr1aqxY8eG7PXT0tJi5i9+sEbaZ+bzxjY+b2zj80a3M3VE/JjACgAATEUYAQAAphrRYcThcOiBBx6Qw+Ewu5SwGWmfmc8b2/i8sY3PO3JExQRWAAAQu0Z0ZwQAAJiPMAIAAExFGAEAAKYijAAAAFON6DCyatUqFRcXKyEhQSUlJdqwYYPZJYXEihUrdNFFFyk1NVU5OTm67rrrtHv3brPLCpsVK1bIYrFo6dKlZpcSMlVVVbrxxhuVmZmppKQkzZw5U+Xl5WaXFRI9PT367ne/q+LiYiUmJmr8+PF6+OGH5fV6zS4taN566y1de+21ys/Pl8Vi0UsvvdTvccMw9OCDDyo/P1+JiYn65Cc/qe3bt5tTbBCc6fN2d3frnnvu0fnnn6/k5GTl5+dr8eLFqq6uNq/gc3S2v9++brvtNlksFq1cuTJs9ZlhxIaRtWvXaunSpbr//vu1ZcsWLViwQAsXLlRlZaXZpQXd+vXrdccdd+idd95RWVmZenp6VFpaqra2NrNLC7n3339fa9as0QUXXGB2KSFz/PhxzZ8/X3Fxcfrb3/6mHTt26NFHH1V6errZpYXED3/4Qz355JN6/PHHtXPnTv3oRz/Sj3/8Y/3iF78wu7SgaWtr04UXXqjHH3/8lI//6Ec/0mOPPabHH39c77//vvLy8nT11VcHzvGKNmf6vO3t7frggw/0n//5n/rggw/04osvas+ePfr85z9vQqXBcba/X7+XXnpJ7777rvLz88NUmYmMEeoTn/iEsWTJkn73TZkyxbj33ntNqih86urqDEnG+vXrzS4lpFpaWoyJEycaZWVlxuWXX27cfffdZpcUEvfcc49x6aWXml1G2FxzzTXG17/+9X73ffGLXzRuvPFGkyoKLUnGn/70p8CfvV6vkZeXZ/zgBz8I3NfZ2Wk4nU7jySefNKHC4Dr5857Ke++9Z0gyDh06FJ6iQuh0n/fIkSPGmDFjjG3bthlFRUXGT3/607DXFk4jsjPS1dWl8vJylZaW9ru/tLRUmzZtMqmq8GlubpYkZWRkmFxJaN1xxx265pprdNVVV5ldSki9/PLLmjNnjr785S8rJydHs2bN0i9/+UuzywqZSy+9VP/4xz+0Z88eSdKHH36ojRs36rOf/azJlYXHgQMHVFtb2+/7y+Fw6PLLLx8R31+S7zvMYrHEbPfP6/Xqpptu0ne+8x1Nnz7d7HLCIioOygu2+vp6eTwe5ebm9rs/NzdXtbW1JlUVHoZhaPny5br00ks1Y8YMs8sJmd/97ncqLy/X5s2bzS4l5Pbv36/Vq1dr+fLl+o//+A+99957+uY3vymHw6HFixebXV7Q3XPPPWpubtaUKVNks9nk8Xj0/e9/X1/96lfNLi0s/N9Rp/r+OnTokBklhVVnZ6fuvfde3XDDDTF1mFxfP/zhD2W32/XNb37T7FLCZkSGET+LxdLvz4ZhDLgv1tx555366KOPtHHjRrNLCZnDhw/r7rvv1quvvqqEhASzywk5r9erOXPm6JFHHpEkzZo1S9u3b9fq1atjMoysXbtWv/3tb/X8889r+vTpqqio0NKlS5Wfn6+bb77Z7PLCZiR+f3V3d+srX/mKvF6vVq1aZXY5IVFeXq6f/exn+uCDD2L+77OvETlMk5WVJZvNNqALUldXN+B/G7Hkrrvu0ssvv6w33nhDY8eONbuckCkvL1ddXZ1KSkpkt9tlt9u1fv16/fznP5fdbpfH4zG7xKAaPXq0pk2b1u++qVOnxuRkbEn6zne+o3vvvVdf+cpXdP755+umm27SsmXLtGLFCrNLC4u8vDxJGnHfX93d3br++ut14MABlZWVxWxXZMOGDaqrq1NhYWHg++vQoUP61re+pXHjxpldXsiMyDASHx+vkpISlZWV9bu/rKxM8+bNM6mq0DEMQ3feeadefPFFvf766youLja7pJC68sortXXrVlVUVARuc+bM0b/+67+qoqJCNpvN7BKDav78+QOWau/Zs0dFRUUmVRRa7e3tslr7f3XZbLaYWtp7JsXFxcrLy+v3/dXV1aX169fH5PeXdCKI7N27V6+99poyMzPNLilkbrrpJn300Uf9vr/y8/P1ne98R6+88orZ5YXMiB2mWb58uW666SbNmTNHc+fO1Zo1a1RZWaklS5aYXVrQ3XHHHXr++ef15z//WampqYH/UTmdTiUmJppcXfClpqYOmA+TnJyszMzMmJwns2zZMs2bN0+PPPKIrr/+er333ntas2aN1qxZY3ZpIXHttdfq+9//vgoLCzV9+nRt2bJFjz32mL7+9a+bXVrQtLa2at++fYE/HzhwQBUVFcrIyFBhYaGWLl2qRx55RBMnTtTEiRP1yCOPKCkpSTfccIOJVQ/fmT5vfn6+vvSlL+mDDz7QX//6V3k8nsB3WEZGhuLj480qe9jO9vd7ctiKi4tTXl6eJk+eHO5Sw8fcxTzmeuKJJ4yioiIjPj7emD17dswudZV0ytuvfvUrs0sLm1he2msYhvGXv/zFmDFjhuFwOIwpU6YYa9asMbukkHG5XMbdd99tFBYWGgkJCcb48eON+++/33C73WaXFjRvvPHGKf/N3nzzzYZh+Jb3PvDAA0ZeXp7hcDiMyy67zNi6dau5RZ+DM33eAwcOnPY77I033jC79GE529/vyUbC0l6LYRhGmHIPAADAACNyzggAAIgchBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmOr/B4PQxIYKOZ8oAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# custom franction to calculate the probability density function\n", "\n", "def sinh_archsinh_pdf(x, mu, sigma, eps, delta):\n", "\n", " mul = np.sinh( np.arcsinh(2) * delta)\n", " \n", " mul = 2 / mul\n", " \n", " sigma_corr = sigma * mul\n", " \n", " z = (x - mu) / sigma_corr\n", " \n", " \n", " \n", " S = np.sinh( -eps + (1/delta) * np.arcsinh(z))\n", " \n", " f = np.exp(-0.5 * S * S)\n", "\n", " f /= np.sqrt(2 * np.pi)\n", " \n", " f *= 1 / ( sigma_corr * delta )\n", " \n", " f *= np.sqrt(1 + S * S)\n", " \n", " f /= np.sqrt(1 + z * z)\n", " \n", " return f\n", " \n", "\n", "x = np.arange(start = 0, stop = 15, step = 0.001)\n", "\n", "\n", "plt.plot(x, sinh_archsinh_pdf(x, 5.54, 1.4, 0.8, 1.68))\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "605dc968", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" } }, "nbformat": 4, "nbformat_minor": 5 }