It’s a very useful tool for analyzing hands and situations away from the tables, and allows you to specify a 🧲 number of variables in order to recreate or simulate specific situations. Your cards, your opponents’ cards, their range of potential 🧲 holdings, board cards and dead cards can all be individually tweaked to set up the exact scenario you wish to 🧲 explore.

A: very tight (raises 3% of hands UTG)

B: average (raises 10% of hands UTG)

Selecting a Range for an Opponent It’s 🧲 rare that we can put an opponent on a specific two-card combination, but narrowing down their range, or ‘Hand Distribution’, 🧲 is something you should be doing constantly. And with PokerStove, a little knowledge of your opponent’s range can go a 🧲 long way. There are a few different ways of setting your opponent’s range, the easiest one is to just type 🧲 in a percentage of hands they would play. For example, if you know from PokerTracker that your opponent raises pre-flop 🧲 with 10% of hands in this position, you can input that 10% as their range. PokerStove can convert that 10% 🧲 to a range, generally taking in 77+, A9s+, KTs+, QTs+, AJo+ and KQo (“77+” means any pocket pair 77 and 🧲 higher, “KTs+” means any suited king, with a ten or better kicker, and so on). It’s also possible to enter 🧲 a range of hands manually, as not all players think the same way. You can even adjust the range that 🧲 PokerStove suggests after you give it a percentage, adding or removing hands that you believe an opponent would or wouldn’t 🧲 play.

“Enumerate All” vs. “Monte Carlo” PokerStove doesn’t calculate, it simulates. So when you run the software, it will pit the 🧲 hands and ranges you entered, on the board that you put in (if any), randomize all the unknown variables many 🧲 times, and tell you how often on average the different players win. There are two ways it can do this, 🧲 which are selectable in the PokerStove interface: “Enumerate all” goes through every possible combination. For some scenarios this is very 🧲 fast since there are only a few possible combinations. Most cases involving only two players take mere fractions of a 🧲 second to calculate. When you have three or more players involved in a pot, the number of possible cases grows 🧲 exponentially, and it may take a long time for the program to run every single combination of possibilities. That’s when 🧲 using the “Monte Carlo” option comes in handy, as it randomizes the simulations. This means that instead of following a 🧲 pattern and grinding its way through every possible holding, it will randomly run simulation after simulation. As computers are so 🧲 fast, a huge number of samples (millions) can be simulated in around a second. This method is substituting precision for 🧲 speed, but if left to run for a while it will quickly stabilize towards the true value.