A Comprehensive View of IPL Score Prediction Using Machine Learning

Abstract

The rapid advancement of data analytics and machine learning has significantly transformed
various domains, including sports. Cricket, particularly the Indian Premier League (IPL),
generates vast amounts of data in the form of match statistics, player performance, and
environmental conditions. Analyzing this data to predict match outcomes and scores has become
an important area of research in sports analytics. This project presents a comprehensive study on
IPL score prediction using machine learning techniques.
The primary objective of this study is to develop a predictive model capable of estimating the
final score of a team in an IPL match based on various input parameters. These parameters
include batting team, bowling team, venue, overs completed, runs scored, wickets fallen, and
recent performance indicators such as runs in the last five overs. By leveraging historical match
data, the system identifies patterns and relationships that influence scoring behavior in T20
cricket.
The proposed system employs supervised machine learning algorithms, particularly regression
models, to predict continuous score values. Data preprocessing plays a crucial role in improving
model performance, involving steps such as handling missing values, encoding categorical
variables, and feature selection. Multiple algorithms such as Linear Regression, Decision Tree
Regression, and Random Forest Regression are evaluated to determine the most effective
approach