Cricket Player’s Performance Prediction Using Machine Learning

This paper presents a predictive model for cricket player performance utilizing machine learning techniques to optimize team selection for competitive matches. By analyzing historical performance data, opponent dynamics, and stadium conditions, the model aims to identify the most suitable combination of players—comprising 6 batsmen, 3 bowlers, and 2 all-rounders. Key inputs, including pitch type, batting and bowling preferences, and winning probabilities, are integrated into a rule-based algorithm. Data is collected through web scraping from reputable sources such as ESPN Cricinfo, facilitating accurate predictions based on batting and bowling averages. The findings highlight the effectiveness of combining traditional cricketing knowledge with advanced data analytics to enhance decision-making in team management.