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

PokerBench Overview

This dataset contains natural language game scenarios and optimal decisions computed by solvers in No Limit Texas Hold’em. It is divided into pre-flop and post-flop datasets, each with training and test splits. The data is stored in both .json and .csv formats:

JSON files: Contain the natural language prompts (instruction) and optimal decisions (output) derived from the game scenarios.

CSV files: Contain structured game information from which the JSON files… See the full description on the dataset page: https://huggingface.co/datasets/RZ412/PokerBench.

Dataset

This dataset was created by Gaurav Dutta

Contents

Context

I am working in the area of Privacy Preserving Big Data Publishing. The state-of-art approaches were tested on Poker had dataset and its synthetic versions. I found that poker dataset is available at UCI repository but synthetic version wasn't available anywhere. As I am working with big data, I need large size of data to justify my contribution. Therefore, I created my own version of synthetic datasets with 10 millions and 100 millions numbers of records. Here I am sharing the original Poker dataset with 1 million records and both the synthesis versions Poker10m and Poker100m

Content

Poker-hand dataset contains 11 columns all numerical with 5 pair of suit and card number which represent the suit and card number of respective card. last column shows the class of the 5 pair of cards.

Acknowledgements

I would like to thank UCI repository for providing the base dataset without which I may not be able to synthesis the large data.

Inspiration

The dataset might be helpful to all those who wants to work on large numerical dataset.

Set of poker table data for the purpose of training a model for statistical study of the game.

Poker Flat, AK, Ground-based Vector Magnetic Field Level 2 Data, 0.5 s Time Resolution, Station Code: (POKR), Station Location: (GEO Latitude 65.1, Longitude 212.6), University of Alaska Network

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.

61 Global export shipment records of Poker with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This dataset provides information about the number of properties, residents, and average property values for Poker Flat cross streets in Penn Valley, CA.

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

123

.POKER Whois Database, discover comprehensive ownership details, registration dates, and more for .POKER TLD with Whois Data Center.

Comprehensive dataset containing 32 verified Poker locations in Colombia with complete contact information, ratings, reviews, and location data.

The online poker market, while facing regulatory hurdles in certain regions, demonstrates significant growth potential. Driven by increasing smartphone penetration, readily available high-speed internet, and the inherent appeal of poker as a game of skill and chance, the market is projected to experience substantial expansion over the next decade. The diverse range of poker variations, from classic Texas Hold'em to more specialized games like Omaha and Pineapple, caters to a broad player base, fueling market segmentation and innovation. Furthermore, the rise of mobile poker apps and the integration of social features within online poker platforms are contributing to increased accessibility and player engagement. Competition among established players like PokerStars and 888 Poker, alongside the emergence of new entrants and innovative platforms, intensifies market dynamics, prompting continuous improvement in game features, user experience, and security measures. The global reach of online poker is evident in the diverse regional data, with North America and Europe consistently holding significant market share, while emerging markets in Asia and South America offer untapped growth opportunities. However, challenges remain, including stringent regulations and licensing requirements in various jurisdictions, concerns about responsible gambling practices, and the need to counter illicit online poker operations. Despite these challenges, the long-term outlook for the online poker market is positive. Technological advancements, strategic partnerships, and an evolving regulatory landscape will likely shape market growth in the coming years. The market's continued evolution will depend on the industry's ability to adapt to changing player preferences, maintain a balance between profitability and responsible gaming practices, and navigate the complexities of international regulations. Successful operators will be those who leverage data analytics to personalize user experiences, invest in robust security measures to build trust, and effectively market their platforms to attract and retain players. Assuming a conservative CAGR of 10% based on industry trends and considering the market size (let's assume a base year 2025 market size of $5 Billion USD) for the sake of example, the market will likely be substantially larger by 2033. Further, individual game segment data and market share by company would refine this analysis but can be inferred through market research.

Subscribers can access export and import data for 80 countries using HS codes or product names-ideal for informed market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

54 Global import shipment records of Poker Game Set with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This dataset is about books. It has 1 row and is filtered where the book is Starting out in poker. It features 7 columns including author, publication date, language, and book publisher.

This dataset provides information about the number of properties, residents, and average property values for Poker Flats cross streets in Nora, VA.