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

AI summary

Overview

• Research area: Cricket analytics / sports data science — specifically the release of a structured, machine-readable T20 match-level dataset and an accompanying exploratory analysis.
• Technical level: Beginner-Friendly. The paper introduces a dataset and describes basic exploratory analysis; no modeling architecture, no statistical derivations, and no benchmark results are presented in the text.
• Scope: The paper documents and publicly releases a 19-match, 61-variable CSV dataset for the 2025 Asia Cup T20 tournament, together with exploratory figures on toss outcomes, team batting, and boundary counts.

What This Paper Is About

Cricket analytics needs well-documented, openly accessible datasets, but most publicly available data for specific international tournaments is either ball-by-ball only or not clearly documented. The authors address this gap for the 2025 Asia Cup T20 by compiling a match-level dataset from public scorecards and cross-verifying it against official tournament information. Their goal is to provide an open, reproducible benchmark that other researchers can use for statistical analysis, predictive modeling, and strategy research.

Key Contributions

1. A new structured match-level dataset. A single CSV file, asia_cup_2025_complete_dataset.csv, containing records for all 19 matches of the 2025 Asia Cup T20 tournament with 61 attributes per match.
2. A documented variable taxonomy. The 61 variables are organized into labeled categories (Table 1), including match identification, teams, toss information, match outcome, team performance, powerplay statistics, boundary data, and tournament context.
3. An exploratory analysis demonstrating reuse potential. The paper presents three exploratory views — toss impact on results, average team scores, and fours/sixes distribution — to show what the dataset supports.
4. Open release for reproducibility. The dataset is released on Zenodo under a CC-BY 4.0 license (DOI: 10.5281/zenodo.17228056), with the exploratory analysis code on GitHub at github.com/kousarraza/AsiaCup2025.

Main Findings

• Dataset size and structure: The released dataset covers all 19 matches of the tournament and contains 61 variables per match, stored as one CSV file.
• Variable coverage: Recorded categories include Match_Number, Date, Venue, Series, Team1, Team2, Playing_XI, Toss_Winner, Toss_Decision, Result, Win_Margin, Player_of_the_Match, Total_Runs, Wickets, Overs, Extras, Powerplay_Runs, Powerplay_Overs, Fours, Sixes, Stage, Group, and Match_Format.
• Data provenance: Data were collected from publicly available ESPNcricinfo scorecards and cross-verified with official tournament information from the Asian Cricket Council (ACC), with manual verification, entity-name standardization, and consistency checks across sources.
• Exploratory analysis scope: The analysis targets three themes — toss outcome versus match result (Figure 1), average team scores (Figure 2), and the distribution of fours and sixes (Figure 3).
• Numeric EDA results: The specific values behind the toss-impact, average-score, and boundary analyses are reported only in Figures 1–3. No numeric outcomes from these analyses are given in the paper text, so no figures for those results can be stated here.
• No predictive modeling results: The paper reports no machine learning model, no accuracy or performance metric, and no benchmark comparison against other datasets.

Methodology in Plain English

The authors did not run a model or an experiment in the conventional sense. Their approach was data engineering plus descriptive analysis:

1. Collect. Gather scorecard data for every match of the 2025 Asia Cup T20 from publicly available ESPNcricinfo scorecards.
2. Cross-check. Verify that data against official information published by the Asian Cricket Council.
3. Clean and standardize. Manually verify entries, normalize how teams, players, and venues are named, and run consistency checks across the two sources.
4. Structure. Encode each match as a row with 61 attributes covering match identity, teams, toss, result, batting performance, powerplay, boundaries, and tournament context, and publish it as one CSV.
5. Describe. Produce three exploratory visualizations (toss impact, average team scores, boundary distribution) to show the dataset's analytical potential.

Why This Matters

Impact on research. Most cricket analytics work depends on data that is either expensive, proprietary, or undocumented. This paper lowers that barrier for a specific, high-profile tournament by providing an openly licensed (CC-BY 4.0), machine-readable match-level file with an explicit variable taxonomy and a citable DOI — which makes results reproducible and lets other researchers build on the same records rather than re-scraping scorecards.

Real-world applications:

• Team strategy and selection: Analysts can examine how toss decisions relate to match outcomes and how powerplay scoring and boundary counts differ between teams.
• Fantasy cricket and sports betting research: Match-level toss, outcome, and player-of-the-match fields provide structured inputs for studying scoring and outcome patterns.
• Broadcast and editorial analytics: Media outlets can use the dataset for tournament retrospectives and comparative team statistics.
• Teaching data science: A small, clean, single-file dataset (19 rows, 61 columns) is well suited to classroom exercises in data cleaning, visualization, and introductory modeling.

Industry relevance. Professional cricket teams, analytics firms, and media organizations all work with match-level and ball-by-ball data. An open, documented, citable tournament dataset gives smaller organizations and independent researchers access to the same starting point, and its CSV format makes it easy to load into standard analysis and machine-learning tooling without specialized sports-data infrastructure.

Future Directions

• Extend to ball-by-ball granularity. The authors explicitly note that ball-by-ball data exists for major tournaments while match-level datasets for specific competitions are limited; adding delivery-level records would enable richer modeling.
• Broaden to other tournaments and seasons. The same collection-and-verification workflow could be applied to other Asia Cup editions and other T20 competitions to enable cross-tournament comparison.
• Move from description to prediction. The stated aim includes supporting "predictive modeling," but no model is built here — a natural next step is to use the dataset for outcome or score prediction tasks.
• Publish the numeric EDA results in full. The exploratory figures are presented but the underlying values are not reported in the text; making those numbers explicitly available would strengthen reproducibility and enable direct comparison.

Target Audience

This paper is most useful to:

• Sports analytics researchers who need an open, citable match-level cricket dataset.
• Machine learning practitioners looking for a small, well-labeled tabular dataset for prediction or benchmarking experiments.
• Cricket analysts and statisticians interested in toss effects, powerplay scoring, and boundary patterns in T20 cricket.
• Students and instructors in data science courses who want a real, compact dataset (19 matches, 61 variables) for exercises in cleaning, exploration, and visualization.
• Open-science advocates interested in domain-specific, CC-BY-licensed benchmark datasets.

Authors’ 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.