Giuseppe Musicco
79 lines
3.8 KiB
Markdown
79 lines
3.8 KiB
Markdown
<font size = "+3"><b>fantabeto</b></size>
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Fantacalcio Bayesian Estimated Team's Outcome
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<i>Machine learning model for predicting Serie A players performance in a match, in terms of Fantacalcio (italian fantasy football) scores.</i>
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https://pub.towardsai.net/how-i-won-at-italian-fantasy-football-fantacalcio-using-machine-learning-ce8fc3fdcaef
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The aim of this project is to predict Fantacalcio (Serie A fantasy football) player performances (vote and fantavote, respectively their match rating and that summed to the bonus/malus given by goals, assists and cards), using players and teams data from http://fantacalcio.it and http://fbref.com.
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"outputs" folder contains predictions for the next Serie A matchdays, and an excel file for analysis. A list of players can be inserted to help selecting an optimal line-up.
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Two neural network models are trained for predicting vote and fantavote for outfield players and goalkeepers, for which the clean sheet probability is also an output.
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The outputs of these models are not raw predictions, but probability distributions, in the form of SinhArcsinh, which is a skewed distribution, meaning that the probability density is asymmetric.
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For predicting clean sheet probability, a Bernoulli distribution is instead used (a sample of which would be clean sheet = 1, with a given probability p, or clean sheet = 0 with probability 1-p)
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See the following code and plot to show an example of vote and fantavote probability distributions.
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In this case, the player would be an attacking one, whose fantavote distribution is very skewed to the right (it is very more probable to score a goal and receive a 10 = 7+3 fantavote, than having an awful performance with a 4 fantavote!).
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```python
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import numpy as np
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import matplotlib.pyplot as plt
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def sinh_archsinh_pdf(x, mu, sigma, eps, delta):
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mul = 2 / np.sinh( np.arcsinh(2) * delta)
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z = (x - mu) / (sigma*mul)
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S = np.sinh( -eps + (1/delta) * np.arcsinh(z))
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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)
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x = np.arange(start = 0, stop = 30, step = 0.001)
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pxv = sinh_archsinh_pdf(x, 6.06, 0.62, 0.33, 1.06)
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pxf = sinh_archsinh_pdf(x, 5.6657, 1.224146, 0.795868, 1.9983)
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plt.plot(x, pxv, label = 'vote', color = 'b')
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plt.plot(x, pxf, label = 'fantavote', color = 'g')
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plt.fill_between(x, pxv, color = 'lightblue')
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plt.fill_between(x, pxf, color = 'lightgreen')
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mv = np.average(x, weights = pxv)
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mf = np.average(x, weights = pxf)
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plt.vlines(x = mv, color = 'b', ymin = 0, ymax = 3, linestyle = 'dashed', label = 'mean vote = ' + '{:.2f}'.format(mv))
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plt.vlines(x = mf, color = 'g', ymin = 0, ymax = 3, linestyle = 'dashed', label = 'mean fantavote = ' + '{:.2f}'.format(mf))
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plt.legend()
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plt.xlim([0, 20])
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plt.ylim([0, 1])
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plt.ylabel('Probability Density')
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plt.xlabel('Vote')
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plt.title('Beto (A) estimated performance')
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plt.show()
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```
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There is code also for computing the expected points outcome of a particular line-up, by sampling multiple times from the players' points distribution and considering bonus for Defense Modifier ("Modificatore") and Clean Sheet. This leads to estimating the team's points probability distribution.
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Credits:
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http://Fantacalcio.it - The game! And of course, a lot of data, including votes, players list and probable line-ups.
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http://FBRef.com - Plenty of stats for football players and teams.
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https://github.com/amiles2233/ff_prob - Inspiration, for using Tensorflow Probability and Bayesian Neural Networks for this task.
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https://github.com/parth1902/Scrape-FBref-data - FBref data scraping code.
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#fantacalcio #fantasy-football #serie-a
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#machine-learning #ai #neural-networks
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#python #tensorflow #tensorflow-probability
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