datamatters24/ringside-analytics

Ringside Analytics — Pro Wrestling Match Archive

A relational snapshot of professional wrestling history from 1980 to the present: 292K matches, 611K wrestler-match participations, 35K events, and 12.8K wrestlers across WWE, AEW, WCW, ECW, NXT, TNA, and others. Sourced from public Cagematch.net scrapes and the alexdiresta profightdb dump, normalized into a Postgres schema, and exported as parquet files that preserve the relational structure (one file per table, joinable by id).

This is the source-of-truth companion to the trained model at theodorerubin/ringside-wrestling-archive-match-winner. If you want to train your own model, reshape the features, or just explore 40+ years of booking patterns — start here.

Files

Schema (join keys)

promotions.id ─┬─< wrestlers.primary_promotion_id
               ├─< events.promotion_id
               ├─< titles.promotion_id
               └─< wrestler_aliases.promotion_id

wrestlers.id ──┬─< match_participants.wrestler_id
               ├─< wrestler_aliases.wrestler_id
               ├─< title_reigns.wrestler_id
               └─< alignment_turns.wrestler_id

events.id ─────┬─< matches.event_id
               └─< alignment_turns.event_id  (nullable)

matches.id ────── match_participants.match_id

titles.id ─────── title_reigns.title_id

Starter queries

import pandas as pd

matches = pd.read_parquet("matches.parquet")
participants = pd.read_parquet("match_participants.parquet")
wrestlers = pd.read_parquet("wrestlers.parquet")

# Every match The Rock has wrestled, with opponents
rock_id = wrestlers.query("ring_name == 'The Rock'")["id"].iloc[0]
rock_matches = participants[participants["wrestler_id"] == rock_id]

-- If you load these into DuckDB:
SELECT w.ring_name, COUNT(*) AS wins
FROM match_participants mp
JOIN wrestlers w ON w.id = mp.wrestler_id
WHERE mp.result = 'win'
GROUP BY 1
ORDER BY 2 DESC
LIMIT 20;

Provenance

• —Cagematch.net (public HTML scrape, non-commercial use): the bulk of match-level data for 1990-present.
• —alexdiresta/all-wwe-and-wwf-matches Kaggle dataset (profightdb dump): cross-validation + pre-1990 coverage.
• —Normalization + dedup: entity resolution on wrestler names, match-type classification into a fixed ENUM, and natural-key deduplication to collapse records across sources.

The ETL code and scraper are open source at tedrubin80/wrastlingfirst.

Caveats

• —Kayfabe, not athletics. Pro wrestling is scripted. A result field records who was booked to win, not who would win an athletic contest.
• —Temporal coverage is uneven. 2000-present is well-covered; 1980s are thinner, especially for regional/territory promotions.
• —Gender imbalance. Women's division sample size is smaller — expect wider confidence intervals for any women's-division model.
• —Ratings are crowd-sourced (Cagematch user ratings). They're a proxy for match quality as perceived by Internet wrestling fans — biased toward work-rate and away from entertainment/story.

License

Released under CC0 1.0 (public domain dedication). Attribution is appreciated but not required. Note that the underlying sources (Cagematch.net, profightdb) have their own terms; this archive is a derivative work made available for research and entertainment.

Citation

@dataset{ringside_analytics_2026,
  author = {Rubin, Theodore},
  title  = {Ringside Analytics: Pro Wrestling Match Archive (1980--present)},
  year   = {2026},
  url    = {https://www.kaggle.com/datasets/theodorerubin/ringside-wrestling-archive}
}