zlacker

Throwing in ML jargon and going straight to modelling before understanding the problem reduces your credibility as a data scientist in front of engineers and stakeholders.

As always, one of the most difficult parts is getting good features and data. In this case one difficulty is measuring and defining the reaction time to begin with.

In Counter Strike you rely on footsteps to guess if someone is around the corner and start shooting when they come close. For far away targets, lots of people camp at specifc spots and often shoot without directly sighting someone if they anticipate someone crossing - the hit rate may be low but it's a low cost thing to do. Then you have people not hiding too well and showing a toe. Or someone pinpointing the position of an enemy based on information from another player. So the question is, what is the starting point for you to measure the reaction?

Now let's say you successfully measured the reaction time and applied a threshold of 80ms. Bot runners will adapt and sandbag their reaction time, or introduce motions to make it harder to measure mouse movements, and the value of your model now is less than the electricity needed to run it.

So with your proposal to solve the reaction time problem with KL divergence. Congratulations, you just solved a trivial statistics problem to create very little business value.

>>dchftc+(OP)
More like congrats, you just made every cheater far less effective by forcing them to play nearer to human limits.

You arent eliminating cheaters, that's impossible, you are limiting their impact.

>>Xss3+Xc
If cheaters play indistingushable from normal people, the seems like mission accomplished.

>>Akrony+fx
Yep

>>Akrony+fx
Cheaters don't have to play like normal people to avoid detection. They just have to make it expensive to police them. For example, the game developer may be afraid of a even a 10% false positive ban rate, and as a result won't ban anyone except perhaps a small number of clean-cut cases.

>>dchftc+4T
Most cheaters are playing well outside of human limits and doing huge amounts of damage to the legitimate player experience. A 10% safety margin beyond human play sounds reasonable. A world where cheaters can only play 10% better than humans is a far better world than the one we are in at the moment.

>>dchftc+(OP)
Appreciate the feedback, you're right - armchair speculation is different than actual data science. Without actual data to examine, we're left with the latter and that can still be a fun exercise even if it doesn't solve any business problem. We're here to chitchat and converse after all.

>>Xss3+pZ
"A world where cheaters can only play 10% better than humans is a far better world than the one we are in at the moment."

My world is pretty fine, as I don't play games on servers, without active admin/mods that kick and ban people who obviously cheat.

ML solutions can maybe help here, but I believe they can reliable detect cheats, without banning also lucky or skilled players, once I see it.

>>lukan+7J2
Human administration is not scalable.

>>kelsey+OT1
Yeah, apologies if it was too harsh. I was more irked by someone else who kept trying to asset it's an easy problem, and confused it with your display of raw curiosity, which is something I don't wish to discourage.

>>dchftc+4T
Yes, the current status is that cheaters can play distingushable from humans. But my point was more that, if we create a system that allows cheating that still is equivalent to a good player, then it just feels like playing against good players. Which, to me, feels like it'd be mission accomplished.

This is one of the cases where ML methods seem appropriate.

>>Xss3+RW2
Why not? As long as there are players, some of them also want to be admins. You maybe mean commercial administration is not scalable for games with a fixed price? Sure, but give the option to the community to manage (rent) servers on their own and they will solve it themself.