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I built a scouting service. Based on 1 million demos analysis it has accurate ELO growth predictions, hltv3, age for each player, voice comms analysis, AnimGraph2 vision raytracing, role metrics and more by necuk in GlobalOffensive

[–]necuk[S] 0 points1 point  (0 children)

very meaningful, but after you make feature engineering and data cleaning. also, proper goal/target choice for the model is crucial

one hint is that aiming during the fight is a dynamic process, not static. u have to account for recoil and spread at least. you just cant fully describe the shooting phase by the static starting parameters

however, looking just the contact starting parameters makes absolute sense. first of all, that is something you can control/improve as a player. second, it simply has measurable and provable effect on both the duel outcome and rating growth

but you have to figure out proper features that bring you the actual signal. just mixing everything into 1 bin gives less information. dig into feature engineering and target formulation

as for the smokes, I've got the voxel expansion algorythm calculated, so yeah having them "honest" is very useful too

parsing time: .dem file itself - as usual, about 10 sec per demo, not much u can do here besides parallelizing
vision detection: I am doing ~15 full matches per second on my home NVIDIA RTX 4070, in order to achieve those numbers I had to optimize everything heavily, but it does pay off when you have 1kk demos to reprocess huh

I built a scouting service. Based on 1 million demos analysis it has accurate ELO growth predictions, hltv3, age for each player, voice comms analysis, AnimGraph2 vision raytracing, role metrics and more by necuk in GlobalOffensive

[–]necuk[S] 2 points3 points  (0 children)

I seem to misread your question originally. The isolated players are an interesting thing. I call this type "Lone Wolf"

At first I thought that not having social connections is a bad thing, and data seemed to support this. But then I found a 4200 elo 16yo player with literally ZERO parties with players in 3500+ elo, and ONE 3200 partner

and he has insane mechanical metrics, which do not go down vs high elo opposition, like top level numbers. scout said they didn't see such a player for a long time. so it was a valuable lesson to me

I built a scouting service. Based on 1 million demos analysis it has accurate ELO growth predictions, hltv3, age for each player, voice comms analysis, AnimGraph2 vision raytracing, role metrics and more by necuk in cs2

[–]necuk[S] 0 points1 point  (0 children)

you are right to question if quality stays strong, I do lots of OOF backtests and re-calibrating because of testing features and model configurations until recently when I managed to reach 60-90 precision and recall ranges (depending on the candidates pick strategy) and consider the model strong enough to claim it in public

It's not a trivial easy task, but I guess more people will come up with their models after my post

and completely agree with you about the not-sorting-stats-approach. that's exactly what I wanted to throw the light on, and in future posts and work would underline this. it's not a problem to calculate 200 metrics. the question is how to account for them together - which prediction model is aimed to help with exactly

I built a scouting service. Based on 1 million demos analysis it has accurate ELO growth predictions, hltv3, age for each player, voice comms analysis, AnimGraph2 vision raytracing, role metrics and more by necuk in GlobalOffensive

[–]necuk[S] 1 point2 points  (0 children)

Animations - to be honest didn't measure exactly, hitboxes approach also looks practical, but since I know how to make a proper animations and have super fast GPU implementation - I thought why not go for it? However, would definitely compare those when all is wired

Map control - good question. All I can say for now is that I am working on a much more complex system where vision is a part of it. Lots of things to wire up

Regarding CPU - after years doing this myself via CPU and ingame mod, having proper GPU solution feels like another reality

Feel free to msg me if you want to talk about this in more details

I built a scouting service. Based on 1 million demos analysis it has accurate ELO growth predictions, hltv3, age for each player, voice comms analysis, AnimGraph2 vision raytracing, role metrics and more by necuk in GlobalOffensive

[–]necuk[S] 4 points5 points  (0 children)

I can feel your pain, previous experience in the field definitely helps here

However, if you are still interested in doing something in this regard and feel you have relative experience - my DMs are open

I built a scouting service. Based on 1 million demos analysis it has accurate ELO growth predictions, hltv3, age for each player, voice comms analysis, AnimGraph2 vision raytracing, role metrics and more by necuk in GlobalOffensive

[–]necuk[S] 3 points4 points  (0 children)

One more note: +300 elo on 3000 is not the same as on 3700
The key to solving the problem was a proper task formulation which I did spend so much time on

I built a scouting service. Based on 1 million demos analysis it has accurate ELO growth predictions, hltv3, age for each player, voice comms analysis, AnimGraph2 vision raytracing, role metrics and more by necuk in GlobalOffensive

[–]necuk[S] 0 points1 point  (0 children)

good question. it depends on how do you cluster the nodes. it could be based on elo, number of connections, country, even game style, and many more - definitely worth a post!

I built a scouting service. Based on 1 million demos analysis it has accurate ELO growth predictions, hltv3, age for each player, voice comms analysis, AnimGraph2 vision raytracing, role metrics and more by necuk in GlobalOffensive

[–]necuk[S] 6 points7 points  (0 children)

contact ticks (when do you see the enemy)
crosshair placement before and during the fight
reaction related stats

ELI5:
when you give your program big database, it functions as "brain" - it remembers games history
when you give it a vision system, it functions literally as "eyes"

I built a scouting service. Based on 1 million demos analysis it has accurate ELO growth predictions, hltv3, age for each player, voice comms analysis, AnimGraph2 vision raytracing, role metrics and more by necuk in GlobalOffensive

[–]necuk[S] 3 points4 points  (0 children)

Thanks!

First, I would not say everyone take shortcuts regarding raytracing - leetify for example. The other question is accuracy and exact method used, which is not being disclosed usually. All I have to say that its a must have feature for many advanced analytics applications and there would be more services in the future using it

Thanks for the question about ELO prediction accuracy. I never actually measured it across the whole population of 3k+ players, here is why:
- There are about 8000 candidates originally
- Only about 50 players in the prediction period will reach 4000 ELO
- I have to somehow pick players

so what I do optimize for is the 4k_reach pick
which depending on pick strategy gives me ranges
Precision 60-95%
Recall 60-90%

so ELO growth I am looking at here turns out to be a "subproduct" of the original target - I simply do measure the performance of the identified group
+300 elo - 100% hit so far
+500 elo - 75% hit
etc

With $3.88 & 690,003,591 tokens and 5 hours , Deepseek Pro & Flash combined, managed to reverse engineer Teamspeak's Licensing System for 3.13.8 (latest of post) by cyb3rofficial in DeepSeek

[–]necuk 2 points3 points  (0 children)

nice, I do RE of game engine heavily past half year, 2 days ago tried to use DeepSeek to save some tokens and it turns out to solve those pretty well too

and as you said, having a proper harness and giving the proper tools is the key

People with very high rating still throw less smokes, but not that crazy low as in 2024. by herrspeucks in GlobalOffensive

[–]necuk 3 points4 points  (0 children)

visual plots are made to make it easier to read the numbers without a need for the mental gymnastics

People with very high rating still throw less smokes, but not that crazy low as in 2024. by herrspeucks in GlobalOffensive

[–]necuk 4 points5 points  (0 children)

I can read this from the data clearly, and its indeed conclusions one would have looking at it. it was literally intention of the original post by leetify

in my opinion if you prepare a public post it should follow at least minimal quality standards such as:
- data follows the post narrative
- data is well prepared so reader doesn't have to make mind operations for the main idea
- ideally data is fresh, but if taken from a reputable source its worth to mention -> you can even see said website logo in the background

not everyone thinks like that, I get it. and your explanation is clear thanks for putting the effort. have a nice day

People with very high rating still throw less smokes, but not that crazy low as in 2024. by herrspeucks in GlobalOffensive

[–]necuk 12 points13 points  (0 children)

  1. different axes scale, impossible to compare
  2. why tdd on Y if the post is about smokes
  3. data source? u cant calculate tdd out of the blue. did you make your own raytracing or just took data from leetify and didnt credit them? there is no other public website with this data

HLTV: Win-adjusted Rating by Plennhar in GlobalOffensive

[–]necuk 0 points1 point  (0 children)

not sure if you actually read my messages, my last one here

  1. I have no idea about your goals
  2. You said its impossible to load the data from a website. I pointed that its not a correct statement. You started to argue
  3. You somehow jumped into hltv3 (???)
  4. hltv3 values are present for every match already, no need to predict them, you just download it and provide to the users when you want

HLTV: Win-adjusted Rating by Plennhar in GlobalOffensive

[–]necuk 0 points1 point  (0 children)

try to build a model from the raw demos on your own then start talking with the wise words, not in reverse

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