bytes int64 | sha256 string | rows dict | coverage dict |
|---|---|---|---|
2,456,731,648 | cf7eb0a621a6937111300a0daeb4d2ff2ae28fce471904295a4763ce32d10be5 | {
"edge_goalie_season": 525,
"edge_shot_location": 110418,
"edge_skater_season": 4733,
"event": 6478514,
"fixture": 20485,
"game": 20547,
"goalie_game": 82189,
"goalie_game_adv": 44036,
"note": 126967,
"player": 3260,
"season_roster": 16396,
"shift": 15556630,
"skater_game": 739588,
"skater_... | {
"first_game": "2010-10-07T00:00:00",
"last_game": "2026-06-14T00:00:00"
} |
interactive-sports: NHL research database
One SQLite file, 2.46 GB, covering 2010-10-07 to 2026-06-14: 16 tables and ~23M rows of NHL box scores, play-by-play, shifts, and 126,967 dated news notes. It is the database the agents in interactive_sports query.
Agents never read it directly. The harness builds cutoff-scoped views over it,
filtered to game_date <= as_of_date, with every player and team replaced by an
opaque P#### / T#### token minted fresh per run.
Use
python -m data.download
Downloads and checks the file against nhl_manifest.json, which also carries the
per-table row counts. Verify rather than trust the transfer: a truncated SQLite
file still opens, and answers some queries but not others.
sha256 cf7eb0a621a6937111300a0daeb4d2ff2ae28fce471904295a4763ce32d10be5
bytes 2,456,731,648
Built by data_generation/build_nhl_db.py from the public NHL API. Distributed
for reproducibility: re-scraping does not reproduce the same bytes, and different
bytes mean different player tokens.
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