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While it's possible to argue that a novice poker player may make a mistake at the table, that argument doesn't hold up if it involves an experienced poker pro. That was the excuse Brazilian poker player Rafael Caiaffa made during a recent tournament, but his antics have cost him an indefinite ban from the game.

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How To Use PokerStove

PokerStove is a program for calculating hot-and-cold equity,

providing your exact chance of winning a certain hand 🗝 at showdown. It’s a program that

you download and run directly on your computer, as opposed to online odds calculators,

🗝 which means it will generally work a lot faster.

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.

For instance,

let’s say the player UTG raises in a 6-max limit 🗝 ring game, and you call from the BB

with JsTs. How are you doing on a flop of Jh-7s-7d?

The answer 🗝 will depend on your

opponent. Let’s break them down into four different styles of player:

A: very tight

(raises 3% of 🗝 hands UTG)

B: average (raises 10% of hands UTG)

C: loose/aggressive

(raises 20% of hands UTG), or

D: maniac (raises at least 50% 🗝 of hands UTG)

Using

PokerStove you can enter these ranges, plus your exact hand and this exact flop, to

find your 🗝 chances of winning vs. each respective type of opponent:

A: 41%

B: 61%

C:

67%

D: 73%

This is known as your hot-and-cold equity, and 🗝 understanding this value is a

great first step in being able to figure out the best course of action. Whether 🗝 you

should call or raise the flop in this example can be debated, but at the very least you

can 🗝 establish that you shouldn’t fold, at least not on the flop.

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.

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