Prediction of the Outcome of a Twenty-20 Cricket Match - A Machine Learning Approach

Aug 3, 2026

Upload by Patrick

Twenty20 cricket (T20) is a fast, limited-overs format that is highly unpredictable, motivating data-driven methods to forecast match outcomes. This project evaluates four predictive strategies using player prior performance statistics, ratings from established cricket statistics websites, clustering based on similar performance profiles, and an ELO-based player rating method. Model performance is compared using logistic regression, support vector machines, Bayesian networks, decision trees, and random forests with features engineered from match and player data.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Introduction

Twenty20 match structure and motivation

Cricket analytics goals and player impact

Cricket terminology

Dataset Preparation

Source selection and data scope

Web crawling and scorecard extraction

Feature extraction for model training

Prediction of the outcome of a Twenty-20 Cricket Match Arjun Singhvi, Ashish V Shenoy, Shruthi Racha, Srinivas Tunuguntla Abstract—Twenty20 cricket, sometimes written Twenty-20, and often abbreviated to T20, is a short form of cricket. In a Twenty20 game the two teams of 11 players have a single innings each, which is restricted to a maximum of 20 overs. This version of cricket is especially unpredictable and is one of the reasons it has gained popularity over recent times. However, in this project we try four different approaches for predicting the results of T20 Cricket Matches. Specifically we take in to account: previous performance statistics of the players involved in the competing teams, ratings of players obtained from reputed cricket statistics websites, clustering the players' with similar performance statistics and using an ELO based approach to rate players. We compare the performances of each of these approaches by using logistic regression, support vector machines, bayes network, decision tree, random forest and their Keywords—clustering; ELO[8]; I. INTRODUCTION A typical Twenty20 game is completed in about three hours, with each innings lasting around 75–90 minutes and a 10–20-minute interval. Each innings is played over 20 overs and each team has 11 players. This is much shorter than previously existing forms of the game, and is closer to the timespan of other popular team sports. It was introduced to create a fast-paced form of the game, which would be attractive to spectators at the ground and viewers on television. Since its inception the game has been very successful resulting in its spread around the cricket world and spawned many premier cricket league competitions such as the Indian Premier League. On most international tours there is at least one Twenty20 match and all Test-playing nations have a domestic cup competition. One of the International Cricket Council's (ICC) main objectives in recent times is to deliver real-time, interesting, storytelling stats to fans through the Cricket World Cup app or website. Players are what fans obsess most about so churning out information on each player's performance is a big priority for ICC and also for the channels broadcasting the matches. Hence to solving an exciting problem such as determining the features of players and teams that determine the outcome of a T20 match would have considerable impact in the way cricket analytics is done today. The following are some of the terminologies used in cricket: This template was designed for two affiliations. 1) Over: In the sport of cricket, an over is a set of six balls bowled from one end of a cricket pitch. 2) Average Runs: In cricket, a player’s batting average is the total number of runs they have scored, divided by the number of times they have been out. Since the number of runs a player scoresand how often they get out are primarily measures of their own playing ability, and largely independent of their team mates, batting average is a good metric for an individual player's skill as a batsman. 3) Strike Rate: The average number of runs scored per 100 balls faced. (SR = [100 * Runs]/BF) . 4) Not Outs: The number of times the batsman was not out at the conclusion of an innings they batted in. 5) Economy: The average number of runs conceded per over.(Economy=Runs/Overs bowled) . 6) Maiden Over: A maiden over is one in which no runs are scored. 7) No Balls: In domestic 40-over cricket, a noball concedes two runs. In Twenty20 cricket, a noball is followed by a 'free hit', a delivery from which the batsman can not be bowled or caught out, but can still be run out. 8) Wides: In the sport of cricket, a wide is oneof two things: a) The event of a ball being delivered by a bowler too wide or (in international cricket) high to be hit by the batsman, and ruled so by the umpire. b) The run scored by the batting team as a penalty to the bowling team when this. II. DATASET PREPARATION

FAQs

What makes Twenty20 (T20) cricket especially challenging for prediction?

A T20 match is short, typically finished within about three hours with each innings limited to 20 overs, making outcomes highly variable and less predictable than longer formats.

Which data sources and tools are used to build the dataset?

Player and match data are collected from ESPNcricinfo, including coverage such as ball-by-ball commentary. A Python web crawler using BeautifulSoup is built to extract and parse scorecards and related statistics.

How are player features used to train predictive models?

Features are derived from cricket expert parameters, supplemented with engineered batting position features. Statistics are computed using only matches played on or before the relevant date to form training inputs for multiple classifiers.

Aug 3, 2026

Upload by Patrick