Goal and shot prediction in ball possessions in FIFA Women's World Cup 2023: a machine learning approach
A summary of the article:
The article "Goal and Shot Prediction in Ball Possessions in FIFA Women’s World Cup 2023: A Machine Learning Approach" explores the use of machine learning to predict goal-scoring opportunities in elite women’s football. Researchers analyzed 2,346 ball possessions and developed predictive models using Random Forest and XGBoost techniques. The study found that possession characteristics, such as the starting zone, number of passes, and attack duration, played significant roles in determining shot and goal likelihood. While the models performed well in controlled conditions, their ability to predict goals in real-match scenarios was limited due to the unpredictable nature of football. This highlights the challenge of applying artificial intelligence to dynamic sports events.
The study emphasizes the need for refining predictive models by incorporating more variables and enhancing data collection methods. The findings suggest that teams can improve goal-scoring chances by optimizing possession strategies, focusing on attack buildup, and improving decision-making in key areas of the field. While machine learning shows promise in sports analytics, its practical application in predicting rare events like goals requires further development. The research underscores the growing role of data-driven strategies in modern football and the potential for AI to enhance tactical planning.
Key Takeaways
Optimize Possession Strategies: Coaches should focus on structured ball movement, increasing pass accuracy, and extending possession duration in attacking areas to improve goal-scoring opportunities.
Enhance Data-Driven Decision-Making: Teams should integrate real-time analytics to adjust their tactical approaches based on possession characteristics that lead to higher shot and goal probabilities.
Refine Predictive Models: Future research should include more contextual variables, such as defensive pressure and player movement, to improve the accuracy of AI-driven football predictions.
Authors: Iyán Iván-Baragaño, Antonio Ardá, José L. Losada and Rubén Maneiro
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