I am the manager of Leagues C and D of the PGETC project. One of the teams in one of my leagues has been accused of cheating by player substitution. What I mean is that, according to the accusation, when player A was supposed to play, player B (who has a higher GoR) took player A's place and used player A's account to play.

Do you have IP addresses? Or some data from computer on which it was played? (software version, OS, processor, serial, etc...)
On forums those things are main tool in such analysis.

PS just to note that games are transmitted live, and that player B can simply phone player A when necessary...

Because it seems to be difficult to separate the message from the (jocular) medium, I'll spell it out:

I believe that in light of pro-level-AI-free-for-everyone it will be impossible to prevent this kind of cheating. Not even webcams and screen mirroring will help because the player can just look at a different screen behind the webcam. All these "security measures" just react to existing possibilities - much like airport "security" - and just encourage other forms of cheating while making things more awkward for the honest majority. And cheating is much simpler from one's home than it is at a real-life tournament.

And because players study new moves and variations played by AI, their play will reflect that. So it's an exercise in futility.

Unless you trust players to have some "code of honor" (and you can't) and as long as one in a hundred players (e.g., in the case of the Pandanet tournament) is enough to skew the results, online tournaments simply cannot be trusted.

You only said that you had several game records for player B. Can you get the same or similar number of game records for player A?

You only said that you had several game records for player B. Can you get the same or similar number of game records for player A? If so, you should be able to train a neural network to distinguish between the two. That is in general an easier task to learn than asking it to distinguish between, say, player B and a player who could be anybody. You give it a game record and ask whether Black or White, depending, is player A or player B? How well it can learn to do that, I don't know, but you will get an error rate saying how well it can distinguish between the two. Which is what you want.

With only a few hundred games upon which to learn, the error rate may be high, but I doubt if humans can do better.

Edit: Even with a high error rate for a single game, you may be able to get a low error rate for the whole set of games. For instance, if the network guesses that player B played 4 out of 5 games instead of player A, there is a good chance that player B played at least one of the games. Which is all you need for cheating.

EDIT: Unless someone can help request and obtain data from an ISP, please ignore the IP part of the equation.

This looks like a nice experiment!

Could be possible to use bots to generates plenty of games (gnugo, Leela, pachi...) and make a proof of concept to see if it can work first.

What would you consider for the network input?

Would something like this work? Choose Black or White to identify, and then after each move by that player, ask whether it is by Player One on Player Two. By the end of each game you should be able to get both a prediction and a confidence factor. Correct guesses are rewarded.

Early plays are probably more revealing than late plays. It might work to stop after move 150 (or resignation), or even earlier in the game.

OK, so basically, one training entry per move (of that player) and per game, with maybe a limit at move 150.

Then, after the network is trained, one feed it with each move of the suspicious game. Something like this.

I will see if I can download games from the computer go server and make a trial this week.

OK, so basically, one training entry per move (of that player) and per game, with maybe a limit at move 150.

Then, after the network is trained, one feed it with each move of the suspicious game. Something like this.

I will see if I can download games from the computer go server and make a trial this week.

I am the manager of Leagues C and D of the PGETC project. One of the teams in one of my leagues has been accused of cheating by player substitution. What I mean is that, according to the accusation, when player A was supposed to play, player B (who has a higher GoR) took player A's place and used player A's account to play.

I'm investigating the matter, trying to pursue different avenues. Here I will focus only on one, for which I need assistance.

I have gathered a few hundreds of 19x19 games played by B in the last few years. It's a long shot, but is there any way to use them to extrapolate B's style and check whether it matches the style of A's PGETC games? How should I proceed if I wanted to establish a "fingerprint" of a player's style?

At the time being I won't reveal any other information in order not to introduce bias.

If you want to obtain many games without downloading them from an active Go server (they may limit access if there are too many requests), there are a few databases that should be freely available: see section "Other Game Collections" of https://senseis.xmp.net/?GoDatabases. The NNGS and Online-Go collections could be good for this, since they have a huge number of games for many different ranks.

Also, if you need processing power, I have an extra (ordinary) Windows machine that is often off.

For information, how many games from player A and player B do you have? (excluding games between player A and B). I might try with those numbers.

You are wasting time with such analysis.
In League A topic, if direct similarities between Leela and suspicious games were not good enough for some - and they were measured by program itself, and program plays very consistent - how do you think that you can compare two human games against each other? With human play varying wildly. And probably lot of contaminated sources.

And even more surprising, some of the forum members who are not convinced about League A case, are optimistic about this one.