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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 st
by dchftcs 10mo ago
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.
- Xss3 10mo agoMore 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.
- Akronymus 10mo agoIf cheaters play indistingushable from normal people, the seems like mission accomplished.
- Xss3 10mo agoYep
- dchftcs 10mo agoCheaters 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.
- Xss3 10mo agoMost 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.
- lukan 10mo ago"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.
- Xss3 10mo agoHuman administration is not scalable.
- lukan 10mo agoWhy 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.
- Xss3 10mo agoIts not even an option in most titles and the industry as a whole has moved away from such hosting models, partly to ensure players receive a consistent and fair experience. Community servers were rife with admin abuse. Its okay if you havent played an online game in 20 years mate
- kelseyfrog 10mo agoAppreciate 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.
- dchftcs 10mo agoYeah, 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.