Performance Analysis and Optimization of Player Selection Using Machine Learning Algorithms

Games like Cricket and Football are very much popular in the world. In India IPL is very much popular, in the similar way games like Pro-Kabaddi, Hockey are also become very popular. Player selection is one the difficult and crucial task in such sports, where the performance of the team is heavily depends on the selection of right players. This research aims to develop an application that employs various algorithms which involves RF, KNN and LR, in order to predict the optimal player line-up based on historical performance data. The study also emphasizes the application of optimization techniques like hyper parameter tuning and cross validation to enhance the accuracy for the prediction of the models. The performance of these models is evaluated using metrices such as Accuracy, Precision, Recall and F1-Score, with results compared to assess the most effective approach for player selection in each sport depending on the selected dataset under one roof. The results of the study show that all three Machine Learning models – RF, KNN, and LR – can predict player selection with varying degrees of accuracy. After optimization with Grid Search method, Random Forest emerged as the most accurate model for football and cricket, while KNN performed well in Pro-Kabaddi. Expanding beyond traditional Machine learning models to deep learning techniques like Neural Networks could capture more patterns in player performance data.