IPL Data Sources & Open Datasets:
Kaggle, Cricsheet & Pipeline Architecture
Reproducibility, open science, and community data stewardship. Explore the underlying datasets, schema definitions, and cleaning pipelines behind our 1,243 match records.
IPL Complete Dataset (2008–2024+)
The primary backbone for match outcomes, playing XIs, toss decisions, venues, and city locations. Regularly benchmarked and cross-checked against official match referee scorecards for zero data divergence.
View on KaggleCricsheet Ball-by-Ball Cricket Data
The gold standard in ball-by-ball cricket data. Utilized for deep verification of super over outcomes, over-by-over run rates, bowling figures, and dot-ball distributions across all playoff encounters.
Explore Cricsheet.orgBCCI / IPLT20.com Official Archives
Source for regulatory rulings, official Orange Cap and Purple Cap tie-breakers, team salary caps, purse regulations, Player of the Tournament awards, and environmental tree-planting metrics.
Visit IPLT20.com| Field Name | Data Type | Description & Constraints |
|---|---|---|
match_number | Integer | Unique chronological identifier for the match |
team1 | String | First team in the official match schedule |
team2 | String | Second team in the official match schedule |
match_date | Date (YYYY-MM-DD) | Calendar date on which the match commenced |
toss_winner | String | Franchise that won the pre-match coin toss |
toss_decision | Enum ('bat' | 'field') | Decision taken by the toss-winning captain |
result | Enum ('Win' | 'Tie' | 'No Result') | Official ICC/IPL match classification |
eliminator | String / NA | Winner of the Super Over in tied encounters |
winner | String | Franchise that won the match (effective winner) |
player_of_match | String | Official Player of the Match (POTM/MVP) awardee |
venue | String | Stadium where the match was contested |
city | String | Host city (e.g., Mumbai, Kolkata, Dubai, Johannesburg) |
team1_players | List<String> | Comma-separated list of 11 playing XI players |
team2_players | List<String> | Comma-separated list of 11 playing XI players |
Franchise Rebrand Normalization
Franchises often undergo official renames or corporate rebranding. Our data pipeline maps legacy entities into unified modern franchises while preserving historical toggles:
Delhi Daredevils→ Delhi Capitals (Unified since 2019 rebrand)Kings XI Punjab→ Punjab Kings (Unified since 2021 rebrand)Royal Challengers Bangalore→ Royal Challengers Bengaluru (Unified since 2024 update)Rising Pune Supergiants→ Rising Pune Supergiant (Spelling normalization)Deccan Chargers→ Deccan Chargers / SRH Heritage (Tracked individually and cumulatively)
Stadium Fortress Normalization
Stadium names evolve with corporate sponsorships and official renaming. Our pipeline unifies aliases to calculate accurate multi-decade venue win rates:
Feroz Shah Kotla→ Arun Jaitley Stadium, DelhiSardar Patel Stadium, Motera→ Narendra Modi Stadium, AhmedabadSubrata Roy Sahara Stadium→ MCA Stadium, PunePunjab Cricket Association IS Bindra→ PCA Stadium, MohaliMA Chidambaram Stadium, Chepauk→ MA Chidambaram Stadium (Chepauk), Chennai
# Automated processing flow:
1. Load raw matches.csv (1,243 rows)
2. Execute Canonical Entity Resolution (franchises & venues)
3. Calculate Toss Decision Biases (Bat First vs. Chase Win %)
4. Generate Head-to-Head Pairings matrix (210 unique team matchups)
5. Synthesize All-Time Player of the Match (POTM) MVP leaderboard
6. Inject verified tournament databases (Finals, Caps, Records, FAQs)
7. Emit static build payload → src/data/ipl_data.json
8. Compile Astro SSG → 288 layout-stable, zero-CLS HTML pagesAccess the Raw Data & Source Code
The entire dataset, normalization scripts, and web application source code are 100% open-source on GitHub under the MIT / CC-BY license.