A Bayesian In-Play Prediction Model for Association Football Outcomes
Zou, Qingrong1; Song, Kai2,3; Shi, Jian2,3
AbstractPoint process models have made a significant contribution to the prediction of football association outcomes. It is conventionally the case that defence and attack capabilities have been assumed to be constant during a match and estimated against the average performance of all other teams in history. Drawing upon a Bayesian method, this paper proposes a dynamic strength model which relaxes assumption of the constant teams' strengths and permits applying in-match performance information to calibrate them. An empirical study demonstrates that although the Bayesian model fails to achieve improvement in goal difference prediction, it registers clear achievements with regard to the prediction of the total number of goals and Win/Draw/Loss outcome prediction. When the Bayesian model bets against the SBOBet bookmaker, one of the most popular gaming companies among Asian handicaps fans, whose odds data were obtained from both the Win/Draw/Loss market and over-under market, it may obtain positive returns; this clearly contrasts with the process model with constant strengths, which fails to win money from the bookmaker.
Keywordbayesian inference point process model in-play prediction betting market adjusting forecast association football
Indexed BySCI
Funding ProjectNational Major Project[2017ZX06002006]
WOS Research AreaChemistry ; Engineering ; Materials Science ; Physics
WOS SubjectChemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS IDWOS:000533352100268
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Document Type期刊论文
Corresponding AuthorShi, Jian
Affiliation1.Beijing Informat Sci & Technol Univ, Sch Appl Sci, Beijing 100192, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Zou, Qingrong,Song, Kai,Shi, Jian. A Bayesian In-Play Prediction Model for Association Football Outcomes[J]. APPLIED SCIENCES-BASEL,2020,10(8):18.
APA Zou, Qingrong,Song, Kai,&Shi, Jian.(2020).A Bayesian In-Play Prediction Model for Association Football Outcomes.APPLIED SCIENCES-BASEL,10(8),18.
MLA Zou, Qingrong,et al."A Bayesian In-Play Prediction Model for Association Football Outcomes".APPLIED SCIENCES-BASEL 10.8(2020):18.
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