Abstract :

Cricket Is A Sport That Heavily Relies On Player Performance And Precision, Making Accurate Performance Prediction A Valuable Tool For Teams, Coaches, And Analysts. This Project Focuses On Predicting The Accuracy Of Cricket Players Using Machine Learning Techniques. By Analyzing Historical Match Data, Player Statistics, And Contextual Factors Such As Pitch Conditions, Opposition Strength, And Match Formats, The System Aims To Forecast Players’ Performance Metrics With High Precision. Various Machine Learning Algorithms, Including Random Forest, Support Vector Machines (SVM), And Neural Networks, Are Implemented To Model Player Performance. The Model Is Trained On Datasets Containing Player Batting, Bowling, And Fielding Records, And Evaluated Using Metrics Such As Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), And Accuracy Scores. The Outcome Provides Predictive Insights Into Which Players Are Likely To Perform Well In Upcoming Matches, Assisting Coaches In Strategic Planning, Player Selection, And Optimizing Game Tactics. This Project Demonstrates The Practical Application Of Data Science In Sports Analytics, Bridging The Gap Between Raw Statistics And Actionable Intelligence. Keywords: This Project Focuses On Cricket Analytics And Player Performance Prediction Using Machine Learning Techniques. It Involves Sports Data Analysis To Predict The Accuracy Of Players, Employing Algorithms Like Random Forest, Support Vector Machine (SVM), And Neural Networks. The Study Emphasizes Data-driven Decision Making And Match Performance Forecasting To Assist In Strategic Planning And Player Selection.

Published:

28-10-2025

Issue:

Vol. 25 No. 10 (2025)

Page Nos:

201-205

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite