Analyzing key factors influencing IPL cricket scores using explainability and multimodal data

Bhatnagar Mohit () and Bhatnagar Manya
Additional contact information
Bhatnagar Mohit: Jindal Global Business School, Sonipat, India
Bhatnagar Manya: Ashoka University, Sonipat, India

Journal of Quantitative Analysis in Sports, 2025, vol. 21, issue 3, 253-267

Abstract: In this study, we investigate data from the Indian Premier League (IPL) spanning from its inception in 2008 to the most recent 2024 season to identify and analyze key factors influencing cricket scores. Using the H2O AutoML framework, we develop a predictive model focused on identifying low first-innings scores, incorporating data on location, weather conditions, teams, and players, while distinguishing them from matches with par or high score. Explainable AI (XAI) tools are employed to quantify the influence of various match features on score predictions, ensuring transparency in the model’s decision-making process. To further enhance classification performance, we introduce pre-match pitch report descriptions generated by a Large Language Model (LLM). For a subset of matches, we leverage multimodal LLM capabilities to analyse pitch report videos, comparing their predictive value against textual descriptions. Our findings underscore the potential of AI and machine learning in sports analytics, specifically in predicting cricket scores based on pitch conditions and other influential factors. This research provides valuable insights for teams, coaches, fantasy sports enthusiasts, IPL administrators and analysts, helping to optimize strategies based on available pre-match information. As part of our work we are sharing a pitch report dataset, python source code for the predictive model with explainability, and a Most Valuable Player (MVP) implementation framework to enhance reproducibility and support further research in cricket analytics.

Keywords: IPL; pitch condition; cricket analytics; explainable AI; AutoML; multimodality (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/jqas-2025-0006 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:jqsprt:v:21:y:2025:i:3:p:253-267:n:1005

Ordering information: This journal article can be ordered from
https://www.degruyte ... ournal/key/jqas/html

DOI: 10.1515/jqas-2025-0006

Access Statistics for this article

Journal of Quantitative Analysis in Sports is currently edited by Mark Glickman

More articles in Journal of Quantitative Analysis in Sports from De Gruyter
Bibliographic data for series maintained by Peter Golla ().