  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.
