--- license: cc-by-4.0 language: - en tags: - knowledge-graph - graph-database - cypher - cricket - sports - ball-by-ball pretty_name: Cricket Knowledge Graph (Cricsheet) size_categories: - 1M Source data from **[Cricsheet.org](https://cricsheet.org/)**, licensed > **[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)**. That requirement travels with the data — anything you redistribute, publish or build on top of must carry it too. | | | |--|--| | **Data** | **CC-BY-4.0** — attribution required | | Source | [Cricsheet.org](https://cricsheet.org/) | | Loader code | Apache-2.0, in the GitHub repo. **This does not govern the data.** | ## What this is Every match Cricsheet has published, as a property graph rather than a pile of JSON. Because it is a graph, questions needing several hops are direct: which bowler has dismissed a given batter most often across formats, which venues a partnership performs best at, how a player's strike rate moves across seasons and competitions. **Every node is connected** — there are no isolated records in this graph. | Format | Matches | | Gender | Matches | |--------|--------:|--|--------|--------:| | T20 | 13,069 | | men's | 17,166 | | ODI | 3,098 | | women's | 4,158 | | MDM (multi-day) | 2,085 | | | | | ODM (one-day) | 1,852 | | | | | Test | 900 | | | | | IT20 | 320 | | | | ## Files Nodes carry an `id`; edges reference those ids as `src` and `tgt`. Join on `id`. ### `nodes/` | File | Rows | Columns | |------|-----:|---------| | `match.csv` | 21,324 | `id, file_id, date, gender, match_type, season, win_by_runs, win_by_wickets, winner` | | `player.csv` | 12,933 | `id, cricsheet_id, name` | | `tournament.csv` | 1,053 | `id, name` | | `venue.csv` | 877 | `id, city, name` | | `team.csv` | 383 | `id, name` | | `season.csv` | 49 | `id, year` | ### `edges/` Several edges carry performance data as properties — that is where the detail lives. | File | Rows | Connects | Properties | |------|-----:|----------|------------| | `batted_in.csv` | 401,165 | Player → Match | `runs, balls, fours, sixes, strike_rate, innings_num, super_over` | | `dismissed.csv` | 308,216 | Player → Player | `kind, over, match_file_id` | | `bowled_in.csv` | 279,426 | Player → Match | `wickets, overs, maidens, runs_conceded, economy, innings_num` | | `fielded_dismissal.csv` | 215,869 | Player → Player | `kind, over, match_file_id` | | `competed_in.csv` | 42,648 | Team → Match | — | | `played_for.csv` | 24,011 | Player → Team | — | | `hosted_at.csv` | 21,324 | Match → Venue | — | | `in_season.csv` | 21,324 | Match → Season | — | | `won_toss.csv` | 21,324 | Team → Match | `decision` | | `part_of.csv` | 21,235 | Match → Tournament | `group, match_number` | | `won.csv` | 19,679 | Team → Match | `by_runs, by_wickets` | | `player_of_match.csv` | 15,796 | Player → Match | — | `DISMISSED` is bowler → batter; `FIELDED_DISMISSAL` is fielder → batter. Both carry the `kind` of dismissal and the `over` it happened in. ### Also included `cricket.sgsnap` — the same graph as a Samyama snapshot. Loads into the engine in one step, without running the ETL. ## Usage ### As tables ```python from datasets import load_dataset batting = load_dataset("VaidhyaMegha/cricket-kg", "edge_batted_in", revision="v1.0") print(batting["train"].num_rows) # 401165 ``` ### As a graph ```bash docker run --rm -p 8080:8080 -p 6379:6379 public.ecr.aws/f9f6l5u4/samyama-graph:1.1.0 curl -X POST localhost:8080/api/tenants -H 'Content-Type: application/json' \ -d '{"id":"cricket","name":"Cricket KG"}' curl -X POST localhost:8080/api/tenants/cricket/snapshot/import -F "file=@cricket.sgsnap" ``` ```cypher // Leading run scorers across all formats MATCH (p:Player)-[b:BATTED_IN]->(:Match) RETURN p.name AS player, sum(b.runs) AS runs ORDER BY runs DESC LIMIT 5 // V Kohli 36,545 · KC Sangakkara 30,651 · DA Warner 28,317 · RG Sharma 26,748 // Which bowler has dismissed a given batter most often — a question about the // relationship itself, which is awkward in SQL and direct in a graph MATCH (bowler:Player)-[d:DISMISSED]->(batter:Player) RETURN bowler.name, batter.name, count(d) AS times ORDER BY times DESC LIMIT 10 ``` ## Provenance **1. Original source — [Cricsheet.org](https://cricsheet.org/)**, ball-by-ball match data as JSON, **CC-BY-4.0**. 21,325 files at the time of extraction. **2. ETL — [samyama-ai/cricket-kg](https://github.com/samyama-ai/cricket-kg)**, which maps those files to a property graph. **3. This dataset** — exported from a Samyama snapshot taken **2026-03-14** (`sgsnap` format v1, engine **v0.6.0**), then flattened to CSV. Totals were asserted against the snapshot header — **36,619** nodes and **1,392,017** edges — and re-verified after upload. ## Versioning | Version | Date | Contents | |---------|------|----------| | **`v1.0`** | 2026-08-17 | Initial release — 36,619 nodes, 1,392,017 edges from the 2026-03-14 snapshot | **Pin the tag, not `main`.** `main` moves; a tag does not. ```python load_dataset("VaidhyaMegha/cricket-kg", "player", revision="v1.0") ``` Data changes get a new tag (`v1.1`, `v2.0`); corrections to this card alone land on `main`. ## Limitations - **Not literally ball-by-ball.** It is *built from* Cricsheet's ball-by-ball files, but the graph stores **per-innings aggregates** (`BATTED_IN`, `BOWLED_IN`) plus individual dismissals with their over. Delivery-level rows are not preserved. For per-ball analysis, go to the Cricsheet source files. - **Coverage starts 2001-12-19** and ends **2026-03-11** — Cricsheet's own range, not the whole history of the sport. Older Tests are absent. - **Player identity is per-Cricsheet.** `cricsheet_id` is the key; players are not reconciled against any external registry, and name spellings follow Cricsheet's. - **Teams are strings** — national sides and franchises share the `Team` label with no distinction between them. - No ball-by-ball commentary, no partnerships as first-class objects, no fielding positions. Verified: totals match the snapshot header exactly, **every node has at least one edge** (0 orphans of 36,619), and aggregates reproduce known records — V Kohli leading run-scorer, JM Anderson leading wicket-taker at 1,140. ## Citation CC-BY-4.0 **requires** attribution. Cite both the source and this build. **Original source:** ``` Cricsheet. Ball-by-ball cricket data. https://cricsheet.org/ Licensed CC-BY-4.0. https://creativecommons.org/licenses/by/4.0/ ``` **This graph build:** ``` Cricket Knowledge Graph, v1.0 (36,619 nodes, 1,392,017 edges). Built with Samyama Graph. https://huggingface.co/datasets/VaidhyaMegha/cricket-kg ETL: https://github.com/samyama-ai/cricket-kg Source data: Cricsheet.org, CC-BY-4.0, https://cricsheet.org/ ``` ```bibtex @misc{cricket_kg_samyama, title = {Cricket Knowledge Graph}, howpublished = {\url{https://huggingface.co/datasets/VaidhyaMegha/cricket-kg}}, version = {v1.0}, note = {Derived from Cricsheet.org ball-by-ball data, CC-BY-4.0, \url{https://cricsheet.org/}. ETL: \url{https://github.com/samyama-ai/cricket-kg}}, year = {2026} } ``` ### The three pieces | Piece | Where | |-------|-------| | **Code** — ETL, schema, loaders | [github.com/samyama-ai/cricket-kg](https://github.com/samyama-ai/cricket-kg) | | **Data** — this dataset, and the raw source it derives from | here, and [cricsheet.org](https://cricsheet.org/) | | **Graph** — importable snapshot | `cricket.sgsnap` in this repository |