IPL Score Forecaster: Using Machine Learning to Predict First Innings Scores

Abstract

Abstract: This paper introduces a novel application of linear regression to predict first-innings scores in the Indian Premier League (IPL), aiming to enhance analytical capabilities and strategic planning in IPL cricket. Leveraging a comprehensive dataset of historical match data, including vital factors like venue, team order, overs played, and wickets have fallen, the study utilizes meticulous preprocessing and feature selection techniques. The model undergoes training and evaluation using a split dataset, with performance assessed through metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). Successful model training enables predictions for upcoming IPL matches, providing valuable insights for teams to make informed strategic decisions. The findings highlight the efficacy of linear regression in forecasting first innings scores, offering teams a potential competitive advantage in the IPL. Furthermore, the study underscores the critical role of cricket analytics in modern strategic planning, emphasizing the significance of data-driven approaches in cricket management.