Analysing Long Short Term Memory Models for Cricket Match Outcome Prediction

TL;DR

This paper introduces a novel LSTM-based model that predicts cricket match outcomes at any point during the game using ball-by-ball data, providing real-time strategic insights.

Contribution

It presents a new recurrent neural network approach that leverages detailed ball-by-ball statistics for dynamic match outcome prediction in cricket.

Findings

The LSTM model can predict match outcomes at any point during the game.

Ball-by-ball data improves the accuracy of outcome predictions.

The model offers real-time insights for strategic decision-making.

Abstract

As the technology advances, an ample amount of data is collected in sports with the help of advanced sensors. Sports Analytics is the study of this data to provide a constructive advantage to the team and its players. The game of international cricket is popular all across the globe. Recently, various machine learning techniques have been used to analyse the cricket match data and predict the match outcome as win or lose. Generally these models make use of the overall match level statistics such as teams, venue, average run rate, win margin, etc to predict the match results before the beginning of the match. However, very few works provide insights based on the ball-by-ball level statistics. Here we propose a novel Recurrent Neural Network model which can predict the win probability of a match at regular intervals given the ball-by-ball statistics. The Long Short Term Memory (LSTM) Model takes as input the ball wise features as well as the match level details available from the training dataset. It gives a prediction of winning the match at any time stamp during the match. This level of insight will help the team to predict the probability of them winning the match after every ball and help them determine the critical in-game changes they should make in their game strategies.