Predictive Analytics in Sport Management: Applying Machine Learning Models for Talent Identification and Team Performance Forecasting

Abstract

The integration of machine learning in the sphere of sports management is a paradigm shift because there is no longer a need to rely on intuition and make decisions based on data. This study examines the application of predictive analytics to find athletic talent and predict team performance in professional basketball based on a large set of data on ten seasons of player statistics, physiological measurements, and team performance. A number of machine learning models were used to predict player development and team success including random forests, gradient boosting models, and neural networks. The ensemble method achieved an accuracy rate of 87.3 per cent of anticipating future elite players among draft candidates, and was the first such method to do so much better than the traditional method of scouting, which averaged 68.5 per cent. The XGBoost algorithm performed better in making predictions about the outcomes of teams with an RMSE of 4.12 wins per season and an explanation of 82.4 percent of the variance in team outcomes. Importance of feature analysis revealed that the player efficiency, advanced defense measures and the injury history were the most significant to individual and team performance forecasting. The authors establish that human judgment in talent evaluation by experts can be improved but not substituted by algorithmic evaluation. The insights have significant implications on player development investment, recruitment and competitiveness in an industry that is dominated by data. The research, methodologically, presents an amalgamation framework fusing the statistical accuracy with sport-related understandings, providing organizations with a systematized method of implementing machine learning into their current management frameworks.