Machine Learning Driven Football Predictions

In this study, we analyzed player performance in 864 Qatar Stars League (QSL) matches (2012-2019) to determine key factors influencing match outcomes. Using a machine learning framework, we classified match results and identified performance metrics that distinguish winning teams from losing ones. Logistic regression emerged as the top model, achieving over 80% accuracy. Key features included opponent analysis, player market value prediction, player profiling, tactical pattern analysis, injury prevention, and team performance metrics. Notably, defenders' roles and fair play significantly impacted match outcomes, and player performance from the last five seasons provided strong predictive power for future matches. Feature Selection: Multiple feature selection methods were used to identify critical performance metrics that contribute to match outcomes, improving the accuracy of the prediction model. Defensive Importance: The analysis highlighted the significant role of defenders, indicating their crucial influence on match results, challenging the common focus on attacking players. Fair Play Impact: Teams that played fair, committing fewer fouls and receiving fewer cards, were more likely to win, showcasing the impact of discipline on success. Historical Data Utility: The model demonstrated that performance data from the last five seasons provides enough predictive power to forecast the winner in upcoming matches. Model Generalization: The machine learning framework showed strong potential to be applied to other leagues and competitions, given its robust predictive accuracy.