Detecting Bots: Uncovering Insights from GGPoker Data Analysis

This report exists solely thanks to datamaning and mass data analysys (MDA)

I have examined cash games on GGPoker over the past six months and conducted an analysis of bots based on the y2da report. I have identified 15 bots (11 inactive and 4 active).
 

Results:
 

What Identifies Them as Bots:

1) The combination of WWSF and WaSD is remarkable. These statistics are unusually high, which is challenging for humans to achieve. Currently, it's rare to find a regular player with this combination.



2) Their 58% WaSD suggests that their checking ranges out of position (OOP) on the river are strong enough to win at showdowns when opponents check behind, with an average fold rate of 47% against bets afterward. Most human players have higher fold rates.

Check OOP River Total (1x1 Pot)
 

3) Their construction of a C-betting range on the flop implies weaker ranges in larger bet sizings, leading to higher fold rates (around 50-55%) versus check-raises after C-bets between 45-90% of the pot.

 

4) In single raised pot (SRP) scenarios, their C-bet sizing in three-way pots does not fall below 40% when facing at least one opponent in position against them. However, this detail was omitted from their solution but may be included in future updates.

SRP Raiser Cbet 3-Way Pot vs OOP/OOP (SB+BB) - Small Betsizing exist
 

SRP Raiser Cbet 3-Way Pot vs IP/OOP (IP+SB/BB) - Small Betsizing does not exist
 

SRP Raiser Cbet 3-Way Pot vs IP/IP - Small Betsizing does not exist
 

**** happens that they forgot to write it into the solution, probably add it in the future.

5) Their winnings graph shows a minor downturn occurring between January 9-16.
 

During this period, they experienced significant losses and played many random hands, which is improbable for a skilled regular player with such a graph.
 

For a detailed mathematical proof, refer to the link provided.
https://mega.nz/file/Mj5gmJwB#KJigZv...EWZ1KDe0tXscyE

You can also access hands played by the bots to verify and develop methods for detecting them in the future.
https://mega.nz/file/YqAW2aDI#3dPlzT...UAxYz7r5VeLijY

I am sharing this work to illustrate the importance of openly disseminating data about the game and the advantages of employing specialists in poker big data analysis. While some consider poker player education schools detrimental to the gaming ecosystem, they also produce data scientists. Following my reports on bots in the Winning Poker Network (WPN), independent teams have emerged to detect and report them, thereby fostering fairer gameplay. I hope this trend continues, with more teams working to identify cheaters at the tables. I will continue conducting research and posting findings on suspicious cases on GGPoker to contribute to a more transparent and fair gaming environment.

There can be no fair game when crucial information is concealed.