Former professional poker player here, no limit holdem cash games, mid stakes. NLHE cash games are considered the hardest to solve given the depth of the decision trees. Other formats like sit and gos, spin and gos, and tournaments can be modeled and solved in a more simplistic way. A group of us (cash game pros) discovered a group of players on the Ongame network around 2010. We combined our databases and found a fe…
What made you think that these groups of players were associated with each other? What made you think that they were bots, beyond just winning often?
For example, there are two related stats: went to showdown, and won money at showdown. These calculate how often you get to the last card and reveal your hand against another player/group of players to win the pot, as well as the frequency you actually win these showdowns. Generally, the more you are showing down hands against opponents, the lower your chances of winning money, simply because it's not always possible on average to have better hands than your opponents above a certain frequency of times you're getting to showdown. If a bot/player had access to other players' hole cards (like actually happened on the Ultimate Bet network) a telling example might be that they go to showdown way more than average while also winning money at showdown far above an average rate.
There are tens (if not hundreds by now) of common poker stats, this is just a simplified look at one related pair.