A Bayesian In-Play Prediction Model for Association Football Outcomes

Abstract

Point 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.

In-play forecasting in football using event and positional data

“…However, the crucial question of genuine in-play forecasting is whether information from the previous course of the match is valuable to improve upon forecasts based on pre-game information. Zou et al 36 claim promising results in this regard, while other statistical investigations of football matches suggest that in-play effects on goal scoring (i.e. deviations from constant scoring rates), if existent, can be considered very small 38 , 39 .…”

“…However, the forecasting literature does not seem to have kept pace with this development, as the question of effective in-play forecasting in football has not been sufficiently addressed. To the best of our knowledge, the work of Zou et al 36 as well as the work of Robberechts et al 37 are the only articles focussing precisely on this topic so far. While both studies adopted a Bayesian approach to predict the further course of a match based on in-play information, they do not report, how valuable in-play information is for forecasting the outcome of the match, compared to a baseline of pre-game expectation.…”

Goal or Miss? A Bernoulli Distribution for In-Game Outcome Prediction in Soccer

“…As a result, only a few works have been published on in-game soccer prediction. Zou et al [ 26 ] are among the first to use in-game information in predicting the outcome of soccer games. They estimated a large number of likelihood parameters based on Bayesian methods to model the outcome of a soccer game but failed to elaborate on features used.…”

A big data analysis of Twitter data during premier league matches: do tweets contain information valuable for in-play forecasting of goals in football?

“…None of these articles investigates in-play forecasts by calculating the effect of scoring deviations on the accuracy of in-play forecasts. To the best of our knowledge, the only paper investigating the role of in-play information in forecasting football is the recent work of Zou et al ( 2020 ), which, however, is limited to the number of goals as only in-play information. While our paper is limited to football, contributions focused on in-play models and in relation to in-play betting odds have been investigated in other sports such as tennis (Easton and Uylangco 2010 ; Kovalchik and Reid 2019 ) and cricket (Akhtar and Scarf 2012 ; Asif and McHale 2016 ).…”