Betting the system: Using lineups to predict football scores
George Peters, Diogo Pacheco

TL;DR
This study develops machine learning models to predict football scores based on player lineups and statistics, demonstrating potential for betting profitability and insights into factors influencing match outcomes.
Contribution
The paper introduces a comprehensive comparison of prediction techniques for football scores, highlighting the limited impact of lineups and identifying effective models like Support Vector Regression.
Findings
Support Vector Regression outperformed other models in score prediction
Lineups did not significantly improve prediction accuracy
The betting system achieved a 42% return on investment
Abstract
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…
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Taxonomy
TopicsSports Analytics and Performance · Sports Performance and Training
Methods((Reservation@Faqs))How do I cancel a reservation on Expedia? · Six Ways To Communicate To Someone At Expedia Via Phone And Email's. · *Communicated@Fast*How Do I Communicate to Expedia? · Dense Connections · 1x1 Convolution · Feedforward Network · Two Time-scale Update Rule · Projection Discriminator · Non-Local Operation · Adam
