## Archives

*(excerpt, click heading for full article)*

This Flop Analysis section is being posted in 7 parts and the final post will contain a detailed statistical analysis of a large number of NLHE flops (hundreds of millions). If you want to jump around click the topic at the top of the page for a list.

—————

One of the most common complaints about online poker [...]

*(excerpt, click heading for full article)*

Of the removal effects I’ve identified, this one alone is fairly well-known. Players tend to see more flops when holding high cards, and so the board will tend to contain more low cards than high cards. The graphs below show that this effect becomes more pronounced for the turn, and even more pronounced for the river, as player requirements [...]

*(excerpt, click heading for full article)*

Here we examine the card-removal effect that results from players tending to see flops when they hold paired hole cards. This part is a little tedious but it’s necessary to predict what we should see when analyzing an enormous sample of community cards. If you aren’t interested in the math then just skip to the bottom for [...]

*(excerpt, click heading for full article)*

Before we get to the real data, we first need to check our code and our calculations. The input for the run shown below is the set of all possible flops enumerated, rather than real hand data. There are 132,600 possible flops (permutations, not combinations, of which there are 132,600/3! or 22,100), and so we [...]

*(excerpt, click heading for full article)*

Here we look at the effect on the suit distribution of the board according to the suitedness of the hole cards held by players seeing the flop.

Given random hole cards, the flops would break down like this (from full enumeration):

Flops seen: 132600 (100.0%) Known hole cards: [...]

*(excerpt, click heading for full article)*

Let’s review our predictions from the card removal effects before finally looking at the actual flop data. In the next post on this topic I’ll be showing the results from several hundred million actual flops.

In part 2 we examined the Rank Bias on the board caused by players seeing more flops when they hold higher cards and folding more often when [...]

*(excerpt, click heading for full article)*

If you haven’t yet read the first six parts of this series, it will be helpful to understand some of the data shown here. Please click the Flop Analysis link at the top of the page to read the whole series.

This post will show a series of scans of real NLHE flops and how they [...]

## Recent Comments