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This study investigates what it takes to win a soccer game in three major soccer leagues, (i) English Premier League (EPL), (ii) Spain’s La Liga and (iii) Major League Soccer (MLS) at the USA. To do so, a set of supervised machine learning models were trained and used to predict the outcome of soccer games and investigate the importance of predictors (features) contributing to the outcome. A total of 91 game and team related features (for both home and away teams, and the differences between them) that cover match statistics, attempt types, card situations, pass types and squad information were taken into consideration. The best prediction performance was achieved by a random forest (RF) model with a classification accuracy of 86.31% of the combined dataset. Relative importance percentages of the game and/or team features for each league are investigated to identify the most crucial factors of the winning/losing teams. Furthermore, each league’s characteristics are discussed and compared through the lenses formed by the intuitive experiment results. For instance, the home field advantage in the MLS games has been shown to be two times more important than this advantage in the La Liga and EPL games.

What does it take to win or lose a soccer game? A machine learning approach to understand the impact of game and team statistics