Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)

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Cricket Score Prediction using Player-Specific Performance and Dynamic Metrics

Authors
U. Vishal Raj1, S. Sudarsan1, S. Aditya Srivatsan1, M. Indumathy1, *
1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani, Chennai, 600026, Tamil Nadu, India
*Corresponding author. Email: indumathym@gmail.com
Corresponding Author
M. Indumathy
Available Online 31 October 2025.
DOI
10.2991/978-94-6463-866-0_51How to use a DOI?
Keywords
Cricket analytics; T20 score prediction; player-vs-player matchups; machine learning; probability modeling; regression analysis; IPL data; sports data science
Abstract

Traditionally, predictions for cricket matches have concentrated on team performance metrics, frequently neglecting the effects of individual player interactions. This study introduces a new player-vs-player method for predicting T20 innings scores, utilizing historical ball-by-ball data from IPL games. Our framework includes four essential components: (1) Probability Model Training, which calculates the probabilities of individual players derived from previous matches; (2) Match-Specific Player Probability Generation, which adapts these probabilities according to the chosen playing XI; (3) Refined Match Probability Aggregation, which integrates individual player probabilities into a team-oriented format; and (4) Innings Score Prediction, which utilizes regression methods to estimate the ultimate score. Our approach improves the precision of cricket score forecasts by incorporating historical match data, probability distributions, and machine learning, focusing on dynamic player matchups over fixed team metrics. The suggested method represents a major progression in sports analytics and can be applied to multiple match types beyond the IPL.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
31 October 2025
ISBN
978-94-6463-866-0
ISSN
2589-4919
DOI
10.2991/978-94-6463-866-0_51How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - U. Vishal Raj
AU  - S. Sudarsan
AU  - S. Aditya Srivatsan
AU  - M. Indumathy
PY  - 2025
DA  - 2025/10/31
TI  - Cricket Score Prediction using Player-Specific Performance and Dynamic Metrics
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 613
EP  - 628
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_51
DO  - 10.2991/978-94-6463-866-0_51
ID  - Raj2025
ER  -