{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0d54533f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\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",
    "tfd = tfp.distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d3654c88",
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'outputs/pred_matchday_4.xlsx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f89d6b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "db = pd.read_excel(file, index_col = 0)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6f61a1fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>role</th>\n",
       "      <th>team</th>\n",
       "      <th>oppteam</th>\n",
       "      <th>home</th>\n",
       "      <th>starter</th>\n",
       "      <th>vote%</th>\n",
       "      <th>MV</th>\n",
       "      <th>MV std</th>\n",
       "      <th>FV</th>\n",
       "      <th>FV std</th>\n",
       "      <th>MV loc</th>\n",
       "      <th>MV scale</th>\n",
       "      <th>MV skewness</th>\n",
       "      <th>MV tailweight</th>\n",
       "      <th>FV loc</th>\n",
       "      <th>FV scale</th>\n",
       "      <th>FV skewness</th>\n",
       "      <th>FV tailweight</th>\n",
       "      <th>Clean Sheet %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>player</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rossi F.</th>\n",
       "      <td>P</td>\n",
       "      <td>Atalanta</td>\n",
       "      <td>Fiorentina</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.109952</td>\n",
       "      <td>0.455623</td>\n",
       "      <td>5.139983</td>\n",
       "      <td>0.751770</td>\n",
       "      <td>6.129853</td>\n",
       "      <td>0.545903</td>\n",
       "      <td>-0.026930</td>\n",
       "      <td>1.024186</td>\n",
       "      <td>5.328891</td>\n",
       "      <td>1.142012</td>\n",
       "      <td>-0.122005</td>\n",
       "      <td>1.029078</td>\n",
       "      <td>3.029286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Musso</th>\n",
       "      <td>P</td>\n",
       "      <td>Atalanta</td>\n",
       "      <td>Fiorentina</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80</td>\n",
       "      <td>6.026820</td>\n",
       "      <td>0.479838</td>\n",
       "      <td>4.632784</td>\n",
       "      <td>0.883422</td>\n",
       "      <td>6.040918</td>\n",
       "      <td>0.574122</td>\n",
       "      <td>-0.018150</td>\n",
       "      <td>1.038445</td>\n",
       "      <td>5.058631</td>\n",
       "      <td>1.267127</td>\n",
       "      <td>-0.248430</td>\n",
       "      <td>0.992984</td>\n",
       "      <td>1.178538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carnesecchi</th>\n",
       "      <td>P</td>\n",
       "      <td>Atalanta</td>\n",
       "      <td>Fiorentina</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5.988562</td>\n",
       "      <td>0.492755</td>\n",
       "      <td>4.077794</td>\n",
       "      <td>1.066340</td>\n",
       "      <td>6.002525</td>\n",
       "      <td>0.589996</td>\n",
       "      <td>-0.017496</td>\n",
       "      <td>1.046207</td>\n",
       "      <td>4.783760</td>\n",
       "      <td>1.400133</td>\n",
       "      <td>-0.389061</td>\n",
       "      <td>0.954299</td>\n",
       "      <td>0.944095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zappacosta</th>\n",
       "      <td>D</td>\n",
       "      <td>Atalanta</td>\n",
       "      <td>Fiorentina</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90</td>\n",
       "      <td>6.083625</td>\n",
       "      <td>0.627924</td>\n",
       "      <td>6.479160</td>\n",
       "      <td>1.053932</td>\n",
       "      <td>5.897724</td>\n",
       "      <td>0.687691</td>\n",
       "      <td>0.197360</td>\n",
       "      <td>0.773252</td>\n",
       "      <td>5.540676</td>\n",
       "      <td>1.267551</td>\n",
       "      <td>0.512369</td>\n",
       "      <td>1.299510</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zortea</th>\n",
       "      <td>D</td>\n",
       "      <td>Atalanta</td>\n",
       "      <td>Fiorentina</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.150186</td>\n",
       "      <td>0.516681</td>\n",
       "      <td>6.476537</td>\n",
       "      <td>0.757362</td>\n",
       "      <td>6.067196</td>\n",
       "      <td>0.583386</td>\n",
       "      <td>0.104385</td>\n",
       "      <td>0.850812</td>\n",
       "      <td>5.875261</td>\n",
       "      <td>0.978617</td>\n",
       "      <td>0.433943</td>\n",
       "      <td>1.299440</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bonazzoli</th>\n",
       "      <td>A</td>\n",
       "      <td>Verona</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>60</td>\n",
       "      <td>6.194463</td>\n",
       "      <td>0.573160</td>\n",
       "      <td>6.719338</td>\n",
       "      <td>1.030227</td>\n",
       "      <td>5.917695</td>\n",
       "      <td>0.593300</td>\n",
       "      <td>0.338062</td>\n",
       "      <td>0.807901</td>\n",
       "      <td>5.607124</td>\n",
       "      <td>1.010478</td>\n",
       "      <td>0.712825</td>\n",
       "      <td>1.299556</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Henry</th>\n",
       "      <td>A</td>\n",
       "      <td>Verona</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.948657</td>\n",
       "      <td>0.576159</td>\n",
       "      <td>6.411596</td>\n",
       "      <td>0.883478</td>\n",
       "      <td>5.582782</td>\n",
       "      <td>0.562697</td>\n",
       "      <td>0.465999</td>\n",
       "      <td>0.808078</td>\n",
       "      <td>5.444796</td>\n",
       "      <td>0.848190</td>\n",
       "      <td>0.732595</td>\n",
       "      <td>1.299526</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kallon</th>\n",
       "      <td>A</td>\n",
       "      <td>Verona</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.984714</td>\n",
       "      <td>0.458801</td>\n",
       "      <td>6.300039</td>\n",
       "      <td>0.708241</td>\n",
       "      <td>5.767212</td>\n",
       "      <td>0.467892</td>\n",
       "      <td>0.336912</td>\n",
       "      <td>0.899963</td>\n",
       "      <td>5.570446</td>\n",
       "      <td>0.741041</td>\n",
       "      <td>0.651962</td>\n",
       "      <td>1.299474</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Djuric</th>\n",
       "      <td>A</td>\n",
       "      <td>Verona</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60</td>\n",
       "      <td>6.158875</td>\n",
       "      <td>0.357994</td>\n",
       "      <td>6.288688</td>\n",
       "      <td>0.468988</td>\n",
       "      <td>6.140615</td>\n",
       "      <td>0.418206</td>\n",
       "      <td>0.032221</td>\n",
       "      <td>0.990635</td>\n",
       "      <td>5.922352</td>\n",
       "      <td>0.611119</td>\n",
       "      <td>0.424342</td>\n",
       "      <td>1.299342</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Braaf</th>\n",
       "      <td>A</td>\n",
       "      <td>Verona</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.610724</td>\n",
       "      <td>0.386826</td>\n",
       "      <td>5.801877</td>\n",
       "      <td>0.469637</td>\n",
       "      <td>5.429425</td>\n",
       "      <td>0.389900</td>\n",
       "      <td>0.336936</td>\n",
       "      <td>0.966595</td>\n",
       "      <td>5.391262</td>\n",
       "      <td>0.572295</td>\n",
       "      <td>0.498442</td>\n",
       "      <td>1.299333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>535 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            role      team     oppteam  home  starter  vote%        MV  \\\n",
       "player                                                                   \n",
       "Rossi F.       P  Atalanta  Fiorentina     0      0.0      1  6.109952   \n",
       "Musso          P  Atalanta  Fiorentina     0      1.0     80  6.026820   \n",
       "Carnesecchi    P  Atalanta  Fiorentina     0      0.0      5  5.988562   \n",
       "Zappacosta     D  Atalanta  Fiorentina     0      1.0     90  6.083625   \n",
       "Zortea         D  Atalanta  Fiorentina     0      0.0      0  6.150186   \n",
       "...          ...       ...         ...   ...      ...    ...       ...   \n",
       "Bonazzoli      A    Verona     Bologna     1      0.4     60  6.194463   \n",
       "Henry          A    Verona     Bologna     1      0.0      0  5.948657   \n",
       "Kallon         A    Verona     Bologna     1      0.0      0  5.984714   \n",
       "Djuric         A    Verona     Bologna     1      0.0     60  6.158875   \n",
       "Braaf          A    Verona     Bologna     1      0.0      0  5.610724   \n",
       "\n",
       "               MV std        FV    FV std    MV loc  MV scale  MV skewness  \\\n",
       "player                                                                       \n",
       "Rossi F.     0.455623  5.139983  0.751770  6.129853  0.545903    -0.026930   \n",
       "Musso        0.479838  4.632784  0.883422  6.040918  0.574122    -0.018150   \n",
       "Carnesecchi  0.492755  4.077794  1.066340  6.002525  0.589996    -0.017496   \n",
       "Zappacosta   0.627924  6.479160  1.053932  5.897724  0.687691     0.197360   \n",
       "Zortea       0.516681  6.476537  0.757362  6.067196  0.583386     0.104385   \n",
       "...               ...       ...       ...       ...       ...          ...   \n",
       "Bonazzoli    0.573160  6.719338  1.030227  5.917695  0.593300     0.338062   \n",
       "Henry        0.576159  6.411596  0.883478  5.582782  0.562697     0.465999   \n",
       "Kallon       0.458801  6.300039  0.708241  5.767212  0.467892     0.336912   \n",
       "Djuric       0.357994  6.288688  0.468988  6.140615  0.418206     0.032221   \n",
       "Braaf        0.386826  5.801877  0.469637  5.429425  0.389900     0.336936   \n",
       "\n",
       "             MV tailweight    FV loc  FV scale  FV skewness  FV tailweight  \\\n",
       "player                                                                       \n",
       "Rossi F.          1.024186  5.328891  1.142012    -0.122005       1.029078   \n",
       "Musso             1.038445  5.058631  1.267127    -0.248430       0.992984   \n",
       "Carnesecchi       1.046207  4.783760  1.400133    -0.389061       0.954299   \n",
       "Zappacosta        0.773252  5.540676  1.267551     0.512369       1.299510   \n",
       "Zortea            0.850812  5.875261  0.978617     0.433943       1.299440   \n",
       "...                    ...       ...       ...          ...            ...   \n",
       "Bonazzoli         0.807901  5.607124  1.010478     0.712825       1.299556   \n",
       "Henry             0.808078  5.444796  0.848190     0.732595       1.299526   \n",
       "Kallon            0.899963  5.570446  0.741041     0.651962       1.299474   \n",
       "Djuric            0.990635  5.922352  0.611119     0.424342       1.299342   \n",
       "Braaf             0.966595  5.391262  0.572295     0.498442       1.299333   \n",
       "\n",
       "             Clean Sheet %  \n",
       "player                      \n",
       "Rossi F.          3.029286  \n",
       "Musso             1.178538  \n",
       "Carnesecchi       0.944095  \n",
       "Zappacosta        0.000000  \n",
       "Zortea            0.000000  \n",
       "...                    ...  \n",
       "Bonazzoli         0.000000  \n",
       "Henry             0.000000  \n",
       "Kallon            0.000000  \n",
       "Djuric            0.000000  \n",
       "Braaf             0.000000  \n",
       "\n",
       "[535 rows x 19 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aa1c6af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_player_distributions(player): \n",
    "    dist_mv = tfp.distributions.SinhArcsinh(\n",
    "    db.loc[player, 'MV loc'],\n",
    "    db.loc[player, 'MV scale'],\n",
    "    db.loc[player, 'MV skewness'],\n",
    "    db.loc[player, 'MV tailweight']\n",
    "    )\n",
    "    \n",
    "    dist_fv = tfp.distributions.SinhArcsinh(\n",
    "    db.loc[player, 'FV loc'],\n",
    "    db.loc[player, 'FV scale'],\n",
    "    db.loc[player, 'FV skewness'],\n",
    "    db.loc[player, 'FV tailweight']\n",
    "    )\n",
    "    \n",
    "    dist_cs = tfp.distributions.Bernoulli(probs = db.loc[player, 'Clean Sheet %']/100)\n",
    "    \n",
    "    return [dist_mv, dist_fv, dist_cs];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d8456c70",
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulate_lineup(squad, ITERS = 1000, MOD = False, CS = False, plot = True):\n",
    "    dist = [None] * len(squad)\n",
    "\n",
    "    defenders = list([0]) * len(squad)\n",
    "\n",
    "\n",
    "    for i in range(len(squad)):\n",
    "        dist[i] = get_player_distributions(squad[i])  \n",
    "\n",
    "        if(db['role'][squad[i]] == 'D'):\n",
    "            defenders[i] = 1\n",
    "            \n",
    "    total_points = np.zeros(ITERS)\n",
    "    clean_sheets = np.zeros(ITERS)\n",
    "    mod_points = np.zeros(ITERS)\n",
    "\n",
    "    mv_samples = [None] * len(squad)\n",
    "    fv_samples = [None] * len(squad)\n",
    "\n",
    "    for i in range(len(squad)):\n",
    "        mv_samples[i] = dist[i][0].sample(ITERS)\n",
    "        fv_samples[i] = dist[i][1].sample(ITERS)\n",
    "\n",
    "    cs_samples = dist[0][2].sample(ITERS)\n",
    "\n",
    "    for k in range(ITERS):\n",
    "        d_points = list([0]) * len(squad)\n",
    "\n",
    "        cleansheet = float(cs_samples[k])\n",
    "        if(not CS):\n",
    "            cleansheet = 0;\n",
    "            \n",
    "        total_points[k] += cleansheet\n",
    "        clean_sheets[k] += cleansheet\n",
    "\n",
    "        for i in range(len(squad)):      \n",
    "            if(defenders[i] == 1):\n",
    "                d_points[i] = float(mv_samples[i][k])\n",
    "\n",
    "            total_points[k] += float(fv_samples[i][k])\n",
    "\n",
    "        d_points.sort(reverse = True)\n",
    "\n",
    "        if(MOD and d_points[3] > 0): # minimum 3 defenders to get MOD\n",
    "            mod_avg = 0\n",
    "            for j in range(3):\n",
    "                mod_avg += round(d_points[j] * 2) / 2\n",
    "            mod_avg /= 3\n",
    "\n",
    "            if(mod_avg >= 7):\n",
    "                mod_points[k] = 6\n",
    "            elif(mod_avg >= 6.5):\n",
    "                mod_points[k] = 3\n",
    "            elif(mod_avg >= 6):\n",
    "                mod_points[k] = 1\n",
    "\n",
    "        total_points[k] += mod_points[k]\n",
    "    \n",
    "    loc_0 = total_points.mean()\n",
    "\n",
    "    squad_model = tf.keras.Sequential(\n",
    "        [\n",
    "            tf.keras.layers.Dense(3),\n",
    "            tfp.layers.DistributionLambda(\n",
    "                lambda t: tfp.distributions.SinhArcsinh(loc= loc_0 + t[..., 0], scale = 1e-3 + tf.math.softplus(t[..., 1]), \n",
    "                                                        skewness = t[..., 2], tailweight = 0.8) # fixed tailweight seems ok\n",
    "            )\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    def negloglik(y, distr):\n",
    "        return -distr.log_prob(y)\n",
    "\n",
    "    squad_model.compile(optimizer=tf.optimizers.Adam(learning_rate=1), loss=negloglik)\n",
    "\n",
    "    dummy_input = np.zeros(total_points.shape)[:, np.newaxis]\n",
    "    squad_model.fit(dummy_input, total_points, epochs=100, verbose=False)\n",
    "    \n",
    "    squad_points_dist = squad_model(np.zeros(1)[:, np.newaxis])\n",
    "\n",
    "    if(plot):\n",
    "\n",
    "        x = np.arange(start = 0, stop = 200, step = 0.001)\n",
    "        prb = squad_points_dist.prob(x)\n",
    "\n",
    "        mn = total_points.mean()\n",
    "        pot = mn + 2 * total_points.std()\n",
    "\n",
    "        f, ax = plt.subplots(1, 2)\n",
    "\n",
    "        ax[0].plot(x, prb)\n",
    "        ax[0].fill_between(x, prb, color = 'lightblue')\n",
    "        ax[0].vlines(x = mn, color = 'black', ymin = 0, ymax = 3, linestyle = 'dashed', label = 'mean = ' + \"{:.2f}\".format(mn))\n",
    "        ax[0].vlines(x = pot, color = 'grey', ymin = 0, ymax = 3, linestyle = 'dashed', label = 'potential = ' + \"{:.2f}\".format(pot))\n",
    "\n",
    "        \n",
    "        ax[0].set_xlim([40, 140])\n",
    "        ax[0].set_ylim([0, 0.12])\n",
    "\n",
    "        ax[0].legend()\n",
    "\n",
    "        #ax[0].hist(total_points, bins = 20, density = True)\n",
    "\n",
    "\n",
    "        ax[1].text(0.1, 0.8, \"\\n\".join(squad), fontsize=10, transform=ax[1].transAxes, verticalalignment = 'top')\n",
    "\n",
    "        text = \"\\n\".join(['Avg Total Points = ' + \"{:.2f}\".format(total_points.mean()), \n",
    "                          'Avg Mod Points = ' + \"{:.2f}\".format(mod_points.mean()), \n",
    "                          'Avg Clean Sheets = ' + \"{:.2f}\".format(clean_sheets.mean())])\n",
    "\n",
    "        ax[1].text(0.5, 0.8, text, fontsize=10, transform=ax[1].transAxes, verticalalignment = 'top')\n",
    "\n",
    "        ax[1].axis('off')\n",
    "\n",
    "\n",
    "\n",
    "        plt.subplots_adjust(right=1.5)\n",
    "\n",
    "        plt.show()\n",
    "    return [squad_model, squad_points_dist, total_points.mean(), mod_points.mean(), clean_sheets.mean()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "852ef39e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sinh_archsinh_pdf(x, mu, sigma, eps, delta):\n",
    "    mul = 2 / np.sinh( np.arcsinh(2) * delta)    \n",
    "    z = (x - mu) / (sigma*mul)            \n",
    "    S = np.sinh( -eps + (1/delta) * np.arcsinh(z))\n",
    "    return np.exp(-0.5 * S * S) * np.sqrt(1 + S * S) / ( sigma * mul * delta ) / np.sqrt(1 + z * z) / np.sqrt(2 * np.pi)\n",
    "\n",
    "def plot_lineup(squad, config):\n",
    "    fig, axs = plt.subplots(6, 5, figsize=(14, 10))\n",
    "\n",
    "    i = 0;\n",
    "    j = 1;\n",
    "\n",
    "    for ax in axs.flat:\n",
    "        ax.axis('off')\n",
    "\n",
    "        if(i < len(config) and config[i] == j):\n",
    "            player = squad[i];\n",
    "\n",
    "            mu = db.loc[player, 'FV loc'];\n",
    "            sigma = db.loc[player, 'FV scale'];\n",
    "            eps = db.loc[player, 'FV skewness'];\n",
    "            delta = db.loc[player, 'FV tailweight'];\n",
    "\n",
    "            x = np.arange(start = 0, stop = 30, step = 0.001)\n",
    "\n",
    "            pxf = sinh_archsinh_pdf(x, mu, sigma, eps, delta)\n",
    "\n",
    "            mf = np.average(x, weights = pxf);\n",
    "\n",
    "\n",
    "            ax.plot(x, pxf, color = 'g', label = player)\n",
    "            ax.fill_between(x, pxf, color = 'lightgreen')\n",
    "\n",
    "            ax.vlines(x = mf, color = 'g', ymin = 0, ymax = 3, linestyle = 'dashed', label = 'mfv = ' + '{:.2f}'.format(mf))    \n",
    "\n",
    "            ax.legend(fontsize=\"9\", loc =\"upper right\", handletextpad=0, handlelength=0)\n",
    "\n",
    "            ax.axis(xmin = 0, xmax = 15, ymin = 0, ymax = 1)\n",
    "\n",
    "            ax.axis('on')\n",
    "            ax.get_yaxis().set_visible(False)\n",
    "\n",
    "            ax.tick_params(axis='both', labelsize=7)\n",
    "\n",
    "            i = i + 1;\n",
    "\n",
    "        j = j + 1;\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dda9acb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "config_442 = [3, 6, 7, 9, 10, 15, 17, 19, 21, 27, 29];\n",
    "config_433 = [3, 6, 7, 9, 10, 13, 17, 19, 26, 28, 30];\n",
    "config_343 = [3, 7, 8, 9, 11, 15, 17, 19, 26, 28, 30];\n",
    "config_4231 = [3, 6, 7, 9, 10, 12, 19, 23, 25, 26, 28];\n",
    "config_433_classic = [3, 6, 7, 9, 10, 17, 18, 19, 26, 28, 30];\n",
    "config_343_classic = [3, 7, 8, 9, 16, 17, 19, 20, 26, 28, 30];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "beea77cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x1000 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "squad = ['Szczesny',\n",
    "        'Di Lorenzo',\n",
    "        'Rrahmani',\n",
    "        'Bastoni',\n",
    "        'Kristiansen',\n",
    "        'Kostic',\n",
    "        'Pellegrini Lo.',\n",
    "        'Barella',\n",
    "        'Strefezza',\n",
    "        'Rafael Leao',\n",
    "        'Osimhen']\n",
    "\n",
    "s = simulate_lineup(squad)\n",
    "\n",
    "plot_lineup(squad, config_442)"
   ]
  }
 ],
 "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
}