| Summary: | Cricket is a popular sport that involves a high degree of variability in terms of game conditions and player performance. The ability to accurately predict cricket scores could provide valuable insights for coaches, analysts, and fans, as well as offer opportunities for sports betting and fantasy games. This paper explores the use of machine learning techniques to predict cricket scores based on a variety of contextual and historical factors. The publicly available cricket dataset is used to build and evaluate several regression models that predict the total runs scored by a team in a limited-overs cricket match. This analysis includes feature engineering to extract and transform relevant input variables, model selection to compare and choose among different regression algorithms, and performance evaluation to assess the accuracy and robustness of the models. This paper also conducts sensitivity analysis to identify the most influential predictors and explore the potential biases and limitations of the models. The results indicate that machine learning techniques can effectively predict cricket scores and provide valuable insights into the factors that contribute to team performance. Automated cricket prediction is useful for cricket teams, coaches, and analysts who seek to improve their game strategies and player selection, as well as for sports betting and fantasy game platforms that seek to provide more accurate experiences for users.
|
|---|