Performance Evaluation of Player Performance Prediction Models Using Data Mining Techniques

Abstract

This paper explores the performance evaluation of player performance prediction models using various data mining techniques, focusing on the application of machine learning algorithms to predict individual and team performance in sports. The study compares the effectiveness of multiple data mining methods, including Random Forest, Support Vector Machines (SVM), and Neural Networks, in predicting key performance metrics such as player scores, match outcomes, and injury risks. A comprehensive dataset comprising player statistics, team metrics, and environmental factors is used to train and evaluate the models. The results show that Random Forest outperforms the other techniques in terms of accuracy, interpretability, and robustness, effectively handling missing data and noisy inputs. Additionally, feature importance analysis reveals key factors influencing player performance, such as stamina, passing accuracy, and team dynamics. This research demonstrates the potential of data mining techniques in sports analytics, offering valuable insights for coaches and analysts in optimizing team strategies and individual player performance.