1. Title: Poker Hand Dataset

2. Source Information

   a) Creators:

   Robert Cattral (cattral@gmail.com)
   
   Franz Oppacher (oppacher@scs.carleton.ca)
   Carleton University, Department of Computer Science
   Intelligent Systems Research Unit
   1125 Colonel By Drive, Ottawa, Ontario, Canada, K1S5B6
   

   c) Date of release: Jan 2007

3. Past Usage:

   1. R. Cattral, F. Oppacher, D. Deugo. Evolutionary Data Mining with Automatic Rule Generalization. Recent Advances in Computers, Computing and Communications, pp.296-300, WSEAS Press, 2002.
      • Note: This was a slightly different dataset that had more classes, and was considerably more difficult.
   • Predictive attribute: Poker Hand (labeled ‘class’)
   • Found to be a challenging dataset for classification algorithms
   • Relational learners have an advantage for some classes
   • The ability to learn high level constructs has an advantage

4. Relevant Information: Each record is an example of a hand consisting of five playing cards drawn from a standard deck of 52. Each card is described using two attributes (suit and rank), for a total of 10 predictive attributes. There is one Class attribute that describes the “Poker Hand”. The order of cards is important, which is why there are 480 possible Royal Flush hands as compared to 4 (one for each suit – explained in more detail below).

5. Number of Instances: 25010 training, 1,000,000 testing

6. Number of Attributes: 10 predictive attributes, 1 goal attribute

7. Attribute Information: 1) S1 “Suit of card #1” Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs}

   2) C1 “Rank of card #1” Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King)

   3) S2 “Suit of card #2” Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs}

   4) C2 “Rank of card #2” Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King)

   5) S3 “Suit of card #3” Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs}

   6) C3 “Rank of card #3” Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King)

   7) S4 “Suit of card #4” Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs}

   8) C4 “Rank of card #4” Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King)

   9) S5 “Suit of card #5” Ordinal (1-4) representing {Hearts, Spades, Diamonds, Clubs}

   10) C5 “Rank of card 5” Numerical (1-13) representing (Ace, 2, 3, ... , Queen, King)

   11) CLASS “Poker Hand” Ordinal (0-9)

   0: Nothing in hand; not a recognized poker hand 1: One pair; one pair of equal ranks within five cards 2: Two pairs; two pairs of equal ranks within five cards 3: Three of a kind; three equal ranks within five cards 4: Straight; five cards, sequentially ranked with no gaps 5: Flush; five cards with the same suit 6: Full house; pair + different rank three of a kind 7: Four of a kind; four equal ranks within five cards 8: Straight flush; straight + flush 9: Royal flush; {Ace, King, Queen, Jack, Ten} + flush

8. Missing Attribute Values: None

A collection of poker hand histories, covering 11 poker variants, in the poker hand history (PHH) format.

To contribute, please create a pull request or an issue at the accompanying GitHub repository.

Contents:

• 21,605,687 uncorrupted no-limit hold'em hands from anonymized hand history logs scraped from July 1st to July 23, 2009, uploaded by HandHQ, of varying stakes (from 25NL to 1000NL).
  • Absolute Poker (1,270,658)
  • Full Tilt Poker (1,299,503)
  • iPoker Network (5,996,345)
  • Ongame Network (1,647,765)
  • PokerStars (3,092,698)
  • PartyPoker (8,298,718)
• All 83 televised hands played in the final table of the 2023 World Series of Poker Event #43: $50,000 Poker Players Championship | Day 5
• All 10,000 hands played by Pluribus, published in the supplementary of Brown and Sandholm (2019).
• 4 selections of historical poker hands.
• 1 badugi hand from the Wikipedia page on badugi.

Texas Holdem Monte Carlo

This dataset contains various tables that represent statistical data related to to simulated poker games and estimate equity in a number of situations, which ultimately could be useful to gain insights into player behavior, game dynamics, and strategies that can improve performance in poker games. Equity estimates are based on hand features for winning hands as they appear over Monte-Carlo simulation data.

The entire project which generated this dataset is linked here.

Andrew's Preflop Calls

This dataset contains (preflop call amount, hand) tuples for my No Limits Texas Hold'em Poker games. The data was collected over a period of one year, and includes over 10,000 hands.

The dataset can be used to study the preflop calling behavior of poker players. It can also be used to develop strategies for preflop calling.

Data

The dataset consists of two files:

• preflop_calls.csv: This file contains the (preflop call amount, hand) tuples.
• hands.csv: This file contains the full hand histories for the hands in the preflop_calls.csv file.

The preflop_calls.csv file has the following columns:

• hand: The hand that was played.
• preflop_call_amount: The amount of money that was called preflop.

The hands.csv file has the following columns:

• hand: The hand that was played.
• board: The board cards.
• players: The players in the hand.
• actions: The actions taken by the players in the hand.

Usage

The dataset can be used to study the preflop calling behavior of poker players. It can also be used to develop strategies for preflop calling.

To study the preflop calling behavior of poker players, you can use the preflop_calls.csv file to create a histogram of the preflop call amounts. You can also use the preflop_calls.csv file to create a scatterplot of the preflop call amounts against the hand strength.

To develop strategies for preflop calling, you can use the preflop_calls.csv file to identify the factors that are most predictive of preflop calling. You can then use these factors to develop a model that predicts preflop calling.

References

• No Limit Texas Hold'em Poker
• Preflop Calling

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