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Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning.

Hafeez Ur Rehman Siddiqui¹, Faizan Younas¹, Furqan Rustam²

¹Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Abu Dhabi Road, Rahim Yar Khan 64200, Punjab, Pakistan.

Sensors (Basel, Switzerland)

|August 12, 2023

Summary

This summary is machine-generated.

This study uses computer vision and machine learning to predict cricket batting strokes with 99.77% accuracy. The random forest algorithm shows significant potential for improving cricket coaching and player performance.

Keywords:

batsman stroke prediction computer vision machine learning random forest

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Area of Science:

Sports Analytics
Computer Vision
Machine Learning

Background:

Cricket is the second most popular sport globally, with 2.5 billion fans.
Batting in cricket demands rapid decisions based on dynamic game factors.
Computer vision and machine learning are emerging as powerful tools for sports performance analysis.

Purpose of the Study:

To develop and evaluate a computer vision and machine learning approach for predicting eight specific cricket batting strokes.
To identify the most effective machine learning algorithm for accurate stroke prediction.

Main Methods:

Utilized the MediaPipe library for extracting player features from video data.
Trained and compared multiple machine learning and deep learning algorithms: random forest (RF), support vector machine, k-nearest neighbors, decision tree, linear regression, and long short-term memory.
Evaluated model performance using accuracy and k-fold cross-validation.

Main Results:

The random forest (RF) algorithm achieved a peak prediction accuracy of 99.77%.
RF significantly outperformed all other tested algorithms in stroke prediction.
K-fold cross-validation for the RF model yielded a 95.0% accuracy with a standard deviation of 0.07.

Conclusions:

Computer vision and machine learning techniques demonstrate high efficacy in predicting cricket batting strokes.
The random forest algorithm presents a robust solution for accurate cricket stroke prediction.
These findings offer a pathway to enhance cricket coaching methodologies and elevate player performance.