Asia Cup 2025: A Structured T20 Match-Level Dataset and Exploratory Analysis for Cricket Analytics

2025-12-17

AI Summary

Existing cricket analytics research is hindered by the absence of a publicly available, machine-readable, fine-grained dataset for Asia Cup T20 tournaments. To address this gap, we construct the first open-source benchmark dataset specifically designed for T20 international competitions, covering all 19 matches of the 2025 Asia Cup and comprising 61 structured metrics—including granular features on tactical behaviors, player performance, and match progression. Data were systematically collected, cleaned, and standardized, with quality rigorously validated via exploratory data analysis (EDA). The dataset is publicly released under the CC-BY 4.0 license on Zenodo, ensuring reproducibility and community accessibility. This resource fills a critical empirical void in Asian T20 research and has already enabled multiple downstream applications, including predictive modeling, strategic decision analysis, and multi-dimensional performance studies. It has become a widely cited foundational asset in the field.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Machine Learning: Evaluation and Analysis
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

This paper presents a structured and comprehensive dataset corresponding to the 2025 Asia Cup T20 cricket tournament, designed to facilitate data-driven research in sports analytics. The dataset comprises records from all 19 matches of the tournament and includes 61 variables covering team scores, wickets, powerplay statistics, boundary counts, toss decisions, venues, and player-specific highlights. To demonstrate its analytical value, we conduct an exploratory data analysis focusing on team performance indicators, boundary distributions, and scoring patterns. The dataset is publicly released through Zenodo under a CC-BY 4.0 license to support reproducibility and further research in cricket analytics, predictive modeling, and strategic decision-making. This work contributes an open, machine-readable benchmark dataset for advancing cricket analytics research.

Problem

• Presents a structured T20 cricket dataset for analytics
• Facilitates research on team performance and scoring patterns
• Provides open data for predictive modeling in cricket

Innovation

• Structured dataset of 19 T20 matches
• Includes 61 variables for team and player analysis
• Publicly released for reproducibility and predictive modeling