Modelling Tennis Using Data Analytics & Machine Learning

Modelling tennis using data analytics & machine learning

Objective

• This project aims to develop a robust analytical framework that utilizes combinatorics, Monte Carlo simulations, and Maximum Likelihood Estimation coupled into classical deep learning to understand and predict match dynamics in tennis

Description

• Drawing on professional match statistics from ATP, WTA, and ITF databases and combining structured tournament data with real-time performance metrics, this IIT Madras initiative develops a robust analytical framework using combinatorics, Monte Carlo simulations, Maximum Likelihood Estimation, and deep learning to understand and predict tennis match dynamics. By bringing advanced analytics to grassroots players and coaches and democratizing access to sports insights, the research extends its principles to badminton, squash, and table tennis ? broadening its societal and sporting impact.

Impact

• One of the primary objectives of this research is to bring advanced tennis analytics to grassroots players and coaches, The impact will not be confined to tennis alone as the principles and methodologies can be extended to other racquet sports such as badminton, squash, and table tennis, The social impact of this project lies in its ability to democratize access to sports analytics