Oct. 13, 2022, 1:12 a.m. | George Peters, Diogo Pacheco

cs.LG updates on arXiv.org arxiv.org

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 …

arxiv football

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Senior AI & Data Engineer

@ Bertelsmann | Kuala Lumpur, 14, MY, 50400

Analytics Engineer

@ Reverse Tech | Philippines - Remote