Témata prací (Výběr práce)(verze: 368)
Detail práce
Přihlásit přes CAS
Srovnání modelů pravděpodobností ve fotbalovém sázení
Název práce v češtině: Srovnání modelů pravděpodobností ve fotbalovém sázení Comparison of Models for Probabilities in Football Betting sázkové kurzy, regresní analýza, strojové učení betting odds, regression analysis, machine learning 2018/2019 bakalářská práce čeština Katedra pravděpodobnosti a matematické statistiky (32-KPMS) doc. RNDr. Jan Večeř, Ph.D. skrytý - zadáno a potvrzeno stud. odd. 08.10.2018 03.01.2019 24.02.2020 13.07.2020 08:00 03.06.2020 04.06.2020 13.07.2020 doc. RNDr. Zdeněk Hlávka, Ph.D.
 Zásady pro vypracování The aim of the thesis is to compare different statistical models for football betting odds and determine the best performing once based on the historical performance of sport teams. There are at least three possible approaches for computing the odds, namely logistic regression, Poisson regression and methods based on statistical machine learning. The idea is that the historical performance of teams is a good predictor of the future performance. Thus we can take the past performances, say all matches in the full season of the English Premier League (380 matches), and use these data for predicting the odds for the following season. The resulting odds should be compared with the actual results using the scoring rules, which will identify the best performing model. The computer implementation should be preferably done using the machine learning library Scikit-Learn in Python, which already includes the above mentioned statistical techniques for determination of the odds. The student is expected to get familiar with both the statistical techniques, basic machine learning approaches and with the programming language Python.
 Seznam odborné literatury Lee, A. J. (1997). Modeling scores in the Premier League: is Manchester United really the best?. Chance, 10(1), 15-19. Efron, Hastie: Computer Age Statistical Inference, Cambridge University Press, 2016 Muller, Guido: Introduction to Machine Learning with Python, O'Reilly, 2016