Data science really has somewhat limited application to poker. Or at least, it's a very big hammer for a pretty small screw. The two big stats he talks about

Data science really has somewhat limited application to poker. Or at least, it's a very big hammer for a pretty small screw. The two big stats he talks about (VPIP and PFR) were well known more or less as soon as the first tracking software came out around 2003 or so, and they're very simple counting stats. Those two stats alone probably cover 90% of what you ever need to know to profile a player. I'd guess that the next most important stat is WTSD, "went to showdown", which is basically a measure of how often someone folds postflop. Everything after that is slicing a small amount of data into increasingly smaller slices, at least as far as opponent modeling goes. Even for your own hands, where you see every result, there's so much noise on the individual hand level that it's hard to do good direct comparisons. You can easily have a million hand sample where, for example, you make more money with 22 than 88. Some people will (most likely erroneously) conclude that they there's something wrong with how they play 88. The more likely explanation is that even with a million hands, once you break out how often you get dealt 22, choose to play it preflop, flop some hand where you'll continue (most likely a set), have the other person in the pot have enough hand where they'll continue far enough to generate a big pot, and then play some sequence where you actually do generate a big pot, you're down to maybe a dozen instances, so a single outlier influences your results a ton. tl;dr - don't worry about data science. If you play online, use one of the standard HUDs, look at VPIP, PFR, WTSD, and ignore everything else except overall win rate and standard deviation until you're damn sure you know what you're talking about.