# Enhanced cricket match prediction using kernel methods for feature extraction and back-propagation neural networks

## TL;DR

This paper introduces a machine learning model that predicts ODI cricket match outcomes by analyzing game states in real-time, achieving higher accuracy than existing methods.

## Contribution

The novel combination of the League Championship Algorithm for feature extraction and Back-Propagation Neural Networks for classification in cricket match prediction.

## Key findings

- The model achieves 83% accuracy in predicting ODI match outcomes.
- It outperforms conventional methods by 5–10% in key metrics like true positive rate and F1-score.

## Abstract

This study presents a dynamic machine learning framework for predicting the outcome of One Day International (ODI) cricket matches by analysing match progression at multiple game states. Each over is treated as a distinct match state, enabling real-time outcome prediction throughout the innings. Six key criteria are employed for classification, namely balls remaining, lead of Team A, wickets remaining, relative team strength, home advantage, and toss outcome. Feature extraction is performed using the League Championship Algorithm (LCA), which selects the most informative features from historical cricket data, followed by classification using a Back-Propagation Neural Network (BPNN). Experimental results demonstrate that the proposed model achieves an accuracy of 83%, a true positive rate of 0.81, a positive predictive value of 0.79, and an F1-score of 0.80 on the validation dataset, outperforming conventional prediction approaches by 5–10% across key performance metrics. The findings confirm the effectiveness of combining optimized feature extraction with neural network-based classification for accurate and interpretable cricket match outcome prediction.