I play a fair amount of online poker on the Ignition platform, almost entirely cash games. So far this year I’ve been a winner, but not at a rate I’m particularly proud of. Unless I compare it to last year when I won just a very tiny bit, or the year before which was worse.
One of the benefits of Ignition’s poker site is that after a few days have passed, the hand history details show all of the hole cards for all of the other players. We can study hands that did not go to showdown and see exactly what each player was doing. I can only see histories of the hands I played and on this site all players are anonymous – no avatars, no screen names, no other identifiers. Still, this hole card visibility can be used to build a profile of the “typical” player (absent any specific observations) and to spot leaks in my own game (of which there are plenty).
So I’ve embarked on a study project, not using Poker Tracker or similar software for meta-data analysis, but scrolling through hand-by-hand, picking out hands with certain attributes or very large pots, and entering some of the data into a spreadsheet for further review when the sample size is larger.
It’s tedious, laborious work. There are additional insights to be gained from the meta-data and maybe I’ll go there eventually. For now, this is good enough.
At the top of my list of situations to analyze is pre-flop 3-bets. When one player raises, then another re-raises (this is the 3rd bet, after the posting of the big blind and the initial raise), can we rely on any general conclusions about the strength of the re-raiser’s hand? Do those ranges change – wider or narrower – as we move up or down in stakes?
The sample is still very small, but so far 3-bets have included:
AA – 11x KK – 7x QQ – 3x
88 – 1x AK – 6x AQ – 1x
AJ – 3x Other/junk – 5x
I’ve seen some hands as strong as AK or QQ/JJ calling instead of 3-betting. And the 3-bets made from the blinds after a cutoff or button opening raise, that look like a blind-steal vs. re-steal situations, are still dominated by the strongest hands. The basic range here is QQ+/AK, which accounts for 27 instances of 3-bets in this sample (73%), with only 10 instances of a 3-bet outside of that range (27%).
Tentative conclusion: respect the 3-bets. It’s OK to call the smaller sized 3-bets with low-to-medium pocket pairs when the math is right for set-mining (especially in position). Otherwise, as Idina Menzel sang in the movie Frozen, “Let It Go!”
I can even fold hands as strong as JJ or QQ to the larger sized 3-bets, without bothering to set-mine. Does this seem too nitty? Let’s look at the math. Using Poker Cruncher, I’ll set Player 1’s (my) hand as QQ, and give Player 2 (villain) a strong range of which 72% is QQ+/AK, to approximate the sample above. Against this range, it’s a coin flip. That’s gambling, and I have better things to do, unless I have a very player-specific read to go on.
Change my hand to JJ vs. a similar range that is 72% QQ+/AK, and my equity drops below 41%. That’s worse than gambling at a casino, and I have much better things to do.
As the opening raiser from the cutoff or button against 3-bet by the small or big blind, I can let these go as well. My initial investment will be small, and the data so far doesn’t suggest a high enough frequency of re-steal attempts to warrant fighting back.
It would be a mistake, however, to assume live players’ 3-bet follow the same pattern of distribution as online players. This might be the case… or not. Gathering enough data on live players would be vastly more difficult, as most of these hands don’t go to showdown nor get voluntarily shown on hands that end prior to a showdown.
In later posts, we’ll look at the ranges of hands involved in other common situations…
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I really liked this post. I like them all but this was one was a stand out !
Thanks for the kind words, “Michael.”
Out of curiosity, what stakes are you playing mostly? Lower stakes players tend to really cap their 3bet ranges too narrowly to premium hands, so this could be certainly influencing your data. Although since you are just trying to identify the typical player persona then you could do this at any stakes as long as all data was compiled from the same level of games.
If be very curious to see how the data changes as you move up or down in stakes.
So far all data is at blinds of $0.50/1.00 and $1.00/2.00. When I accumulate some more data, I’ll check to see if there is any difference at one level vs. the other.