Poker Statistics & Research
Original studies of how cash-game players play, each published with its sample, method and limits.
C-Bet Frequency by Stake Level
Population-level c-bet data across 5.2 million opportunities and 7 stake levels. Double barrel rates, triple barrel follow-through, and heads-up vs multiway splits.
Read study → 25,001 hands · Study 02Nash Equilibrium in Poker vs Best Response
What a Nash equilibrium guarantees, where CFR solvers stop, and a 25,001-hand test of the Best Response Engine against the GTO baseline.
Read study →More studies are in progress. Each one publishes with its method, sample, and limitations attached.
Opponent methodology
How we build the opponents
Each opponent type is written by hand. We start from published research on how that kind of player plays, including tracker studies and population reports, and from our own analysis of real hands. From that we write the type's preflop ranges and its rules after the flop, by position, bet size and board. We may also use hand histories players submit to refine the types.
Then we measure what we built. Each type plays 20,000 simulated hands at 100 big blinds against GTO opponents, and the stats on its file are the result. They are the opponent's numbers, not population averages. The recommended play depends on the opponent you choose. Change your read, and the recommendation can change.
- 01
Start from research
Collect published studies and tracker reports on how each kind of player plays, and check them against our own hand analysis.
- 02
Write the type
Set its preflop ranges and its rules after the flop, by position, street, bet size and board.
- 03
Measure it
Play 20,000 hands with each type and record VPIP, PFR, aggression, 3-bets and how it folds under pressure.
- 04
Check and correct
Compare the measured stats with what the type is meant to do, then fix the spots where it drifts.
Scope and Limitations
Each study states its own sample and definitions.
Population statistics are starting priors, not predictions about one person. Online and live environments differ, and an opponent can change during a session. The trainer therefore exposes confidence and repeated evidence so players learn when to trust a read and when to update it.
Player data handling is described in the data policy.
See these opponents in action
Practice against the opponent types described on this page.
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