Next, AIVAT also determines whether Martin was fortunate from each of DeepStack’s randomized actions. When compared to folding or raising pre-flop, which DeepStack will indeed do some of the time in this spot, AIVAT suggests that Martin gained an average of 22 chips from a call instead. Again, this is not just factoring being up against ace-eight, but being up against any possible two cards that DeepStack will hold. Each of DeepStack’s actions is analysed in this way and we again see a very large value at the river; the dramatic effect of Martin’s all-in bet being called. Against all possible holdings that DeepStack would call 300 pre-flop, check and then re-raise to 900 on the flop, lead out 1200 on the turn, and then check the river, the average difference in results from Martin going all-in and getting called, as opposed to Martin going all-in and DeepStack folding, is a gain of 6997 chips.
And so in summary for Hand 71, we would expect Martin to gain 11134 chips from the way he played his ace-deuce, given the community cards and DeepStack’s actions. But AIVAT additionally estimates that he got lucky by a total of 11434 chips. Martin’s net result is therefore an overall rating of -300.
So how good are these AIVAT estimates? The short answer is ‘pretty damn good’. But for those seeking further comfort, DeepStack’s intuition (or ‘heuristic evaluation of states’) comes from hundreds, perhaps thousands, of CPU years of computation. Advances in the understanding of deep neural networks have allowed DeepStack to teach itself the value of specific poker scenarios. By additionally being able to simulate a vast number of games against itself in real-time, this new value assessment tool, of cards and of actions, surpasses what human beings could possibly hope to achieve in several lifetimes of gameplay experience.
While the actual chips won and lost give a somewhat distorted view of the actual skill involved in a single hand of poker, over time this measure will tend to the same conclusion that the AIVAT ratings will. The DeepStack research team has proven that AIVAT significantly reduces the variance in poker results in a fair and unbiased fashion. What AIVAT therefore gives us is the ability to assess a player’s true win-rate after vastly fewer hands than if we were just using chips, with additional in-depth insights into the merits and drawbacks of individual plays. By analyzing trends in one’s own AIVAT data one can appreciate the game of poker and improve one’s own ability like never before. The International Federation of Poker is working with the University of Alberta’s Computer Poker Research Group to power an official skill rating system adopting these revolutionary techniques that AI can provide. It is hoped that this data will be used to determine the finalists at the next IFP World Poker Championships, and ultimately who is the best poker player on the planet.