KashRock isn't just an aggregator. We are a high-throughput normalization engine that transforms fragmented sportsbook and provider data into a single, deterministic stream of truth.
The three layers that make KashRock reliable at scale.
Our kr_ ID system uses cryptographic hashing so a FaZe vs NaVi match is identified identically across every book and provider. No duplicate fixtures.
Round-by-round ingestion from multiple primary sources. Player stats update faster than most broadcast streams can keep up with.
1,000+ stat types normalized into one schema. Filter by player, book, or stat across PrizePicks, Underdog, Sleeper, and more.
KashRock automates the full lifecycle of a prop: scheduled → live → completed → verified.
Full lifecycle player prop data with hit/miss tracking built in.
{
"source": "kashrock",
"sport": "cs2",
"props": [
{
"prop_id": "kr_prop_5992b0df9d7afc33",
"player_name": "fear",
"stat_type": "ESPORTS_KILLS_MAPS_1_2",
"prop_value": 25.5,
"direction": "over",
"actual_value": 28,
"status": "hit",
"team": "Fnatic",
"opponent": "G2",
"event_time": "2026-04-21T13:30:00Z",
"verification_id": "v_94401a"
}
]
}Raw provider APIs only show active markets. KashRock tracks a prop through its entire lifecycle, so you can build bet-trackers and leaderboards without writing your own scoring engine.
Every prop is indexed against our player game logs. Query the last 2 years of props for any player to build hit-rate benchmarks or predictive models.
All major Tier-1 titles including CS2, League of Legends, Dota 2, and Valorant, along with emerging titles like Rainbow Six and Apex Legends.
Verification finalizes within 60–120 seconds of match completion, as soon as official scores are confirmed by our primary providers.
Yes. Every projection includes provider metadata, so you can filter by PrizePicks, Underdog, Sleeper, and 10+ other sources.
Yes. Every player and team in our database has media assets served from our CDN — no scraping or third-party image hosting required.
Get your production API key and stop worrying about data normalization.