Betting the system: Using lineups to predict football scores
This work addresses the problem of reducing randomness in football for clubs and bettors, but it is incremental as it applies existing methods to new data with specific gains.
The paper tackled predicting football scores using machine learning models on historical player data from the English Premier League (2020-2022), finding that Support Vector Regression outperformed other techniques and achieved a 42% return in a betting simulation, though lineups did not improve predictions.
This paper aims to reduce randomness in football by analysing the role of lineups in final scores using machine learning prediction models we have developed. Football clubs invest millions of dollars on lineups and knowing how individual statistics translate to better outcomes can optimise investments. Moreover, sports betting is growing exponentially and being able to predict the future is profitable and desirable. We use machine learning models and historical player data from English Premier League (2020-2022) to predict scores and to understand how individual performance can improve the outcome of a match. We compared different prediction techniques to maximise the possibility of finding useful models. We created heuristic and machine learning models predicting football scores to compare different techniques. We used different sets of features and shown goalkeepers stats are more important than attackers stats to predict goals scored. We applied a broad evaluation process to assess the efficacy of the models in real world applications. We managed to predict correctly all relegated teams after forecast 100 consecutive matches. We show that Support Vector Regression outperformed other techniques predicting final scores and that lineups do not improve predictions. Finally, our model was profitable (42% return) when emulating a betting system using real world odds data.