Game Statistics Forecast Based on Sports Using Machine Learning

Cricket is a popular sport worldwide, with millions of fans and players. One of the critical aspects of cricket is analyzing player statistics to forecast future performances. In order to examine player performance data and forecast future results, machine learning (ML) techniques have been used in a variety of fields, including sports. In this study, we present a machine-learning model that predicts cricket player performance using data from actual cricket matches. We use a dataset collected from international cricket matches and analyze various statistical features, including batting and bowling averages, strike rates, and fielding performance. We use this data to train a machine-learning model that predicts a player's future performance. We evaluate our model's performance by comparing its predictions to actual gameplay data and show that our model achieves high accuracy in predicting player performance. Our proposed model can be used to provide insights into a player's performance and help players improve their skills. It can also be used by cricket coaches and selectors to evaluate player skills and select teams. Overall, our work demonstrates the potential of machine learning techniques in predicting player performance in cricket and can have significant implications for the game's future.