Poker Analytics For Beginners and theories — Statistical Arbitrage

Expected Value

All decision making based on the expected value of the decision. EV is the probability of winning the hand multiply by the size of the pot. If the money/chips we put into the pot is less than the expected value of winning money or chips, we are making the right bet and it’s not if it’s negative and it’s a losing decision.


Given the need to compute a somewhat accurate guess of our equity before every decision, the position becomes one of the most important aspects of the games. AFAIK, it’s more important than the hands held by the player.

Hand History Example

No Limit Hold’em Rs5.00/Rs10.00
6 players
Formatted by Poker HUD for Mac and Windows

Playing Styles

  • Rock/Nit: This is very rare in my experience but this is someone who plays very few hands and very aggressively. Think VPIP of ~5–10%, which means, they only play 5–10% of hands dealt.
  • TAG (Tight and Aggressive): Has a broader range than a Nit, opening range could be 2–3 times that of a nit with VPIP around 10–15%.
  • LAG (Loose and Aggressive): Further evolution over the TAG, the game needs to be aggressive and quite a difficult style to play. VPIP around 30%.
  • Calling Station (Fish): VPIP over 40%, passive play. Very difficult to be profitable with such a broad range for a consistent period of time.

HUD (Heads Up Display )

Besides your strategy and play, HUD is a key tool everyone requires to have a positive win rate over online poker. In addition to this, the fact that other players use it makes it impossible to be able to act/ make decisions without one.

  • PFR(Pre-Flop Raise): Gives a sense of aggressiveness, which is given for any player but too much can be a sign of too many bluffs. This combines well with VPIP to categorise the players. This is the sum of both initiated by the player and when he calls.
  • Called Pre-Flop Raise: How many times we called our way into the pot and not initiated the raises.
  • Unopened Pre-Flop Raise: The number of times, we were the initiators of the raise
  • Flops Seen: For a session, this is an indication of how many flops were seen. Because, every time you put money into the pot, either you or others might have folded pre-flop itself.
  • 3-Bet Pre-Flop: This means, this is the 3rd bet in the Pre-Flop round. First being the Blinds, second being a raise on it and then a further raise by you or others. Very strong move, when you believe you hold the strongest hands. Similarly, the other way around is when you folded to 3-bet from your opponent.


  • Squeeze Bet: Quite a rare occurrence but is defined as a Pre-Flop bet wherein a player raises, another one or more call in between before being raised again.
  • Aggression Factor: Similar to the first metric but we take the ratio of raise or bet over calls. This could be used alternatively with aggression.
  • Check Raised: One of the interesting plays which can happen over the flop, turn or river. Here, the Hero can check the flop, allow the opponent to raise and then re-raise them. Perfect play against an overtly aggressive player against whom, we are sure of an advantage whereas folded to Check-Raise is the opposite, where we folded against such a move.
  • Continuation Bet (C-Bet): This is when a Post-Flop bet is continued with our initial raise pre-flop. Thus the name continuation bet, aggressive continuation bet can be used to barrel your opponents out of a weak or moderate hand. There are two more metrics related to it, folded to C-Bet and raised a C-Bet. The second signals a strong flop or maybe even nuts.
  • Donk Bet: This is when an out of position player raises before the Pre-Flop Raise. This supposed to have a negative image and is a bit of a complicated move. You can read more here.

Post Flop

This is where the big bucks are either made or lost. The most important round IMO given it sets up the stage for both river and turn.


The above are the broad categories of the different metrics which can be further broken down by turn, river. Also by whether you raised, folded or called your moves. As can be seen from above, the game has gotten fairly advanced in terms of strategies employed by players.

Machine Learning For Poker

What makes this an interesting use case for the application of machine learning is the nature of the problem. While a self-playing AI-based bot or player is out of scope. Imperfect information games are yet to be completely solved. A few attempts have been ongoing though, such as Pluribus by Facebook here.


When tried applying for my hand history analysis, this resulted in giving some very interesting outputs. By running it for very specific use cases, players, tables or positions, a decision tree, in this case, can give some insightful and actionable output.



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