Good luck man
thats all cool and shit but can ai predict when kennys will 30bomb or go 5-1-20 again? i arent think that
krasava anyway huli but sounds fake
You're webscarping data and analysing statistics or download demos?
Easy money goes just as easily, if there's no emotional attachment to it you cannot hold it and it will perish rather quickly. A moment's joy at best.
Good luck, but as a pessimist and someone experienced in both gambling and ML, I think it is very tough. As I understand, all the bookmakers purchase their CS odds from a very small number of sources whose whole business model is employing multiple engineers that use ML, fed with a lot more comprehensive stats that aren't available to us on HLTV (it's also worth being wary of data provided for free by a gambling-driven site like this). So you are up against tough competition, especially when you add the bookies' juice advantage.
That said, there are hypothetical reasons you could come up with a more accurate algorithm, e.g. you've adapted some recent paper, vs them maybe having organizational/bureaucratic bottlenecks slowing them down from updating their stuff constantly to the new state of the art
There is always a human factor in competition so even machine cant 100 % predict outcome.
I thought this was going to be some bait about machine (Caster) predicting matches
Good m8, best of luck with it.
Did you use Python for machine learning?
Could you say something regarding the variables that you are using? I havent gotten to svms yet, but I would think that you are using some sort of black box model like that?
how did you learn it so quickly? im totally stuck at the ML part lol :D
I minored in Applied AI and also worked a lot with ML. I'm willing to bet you're predicting the matches based on a statistic like "won_rounds", "total_rounds" or something to that extent (everyone can predict which team wins when you know the rounds). I know because I've done the same research a while back with all top 30 pro games played from 2015-2020. I was able to accurately predict a matches outcome with just the average player rating and KDR and got upto a ~73% success rate with a RandomForestClassifier (DecisionTree ensemble) and a LogisticRegression model, both yielded similar results with I think the RFC just edging the LR out. Can you let me know which stats and models you are using?
Sounds interesting please update us about your projet
if it rly works u would be millionaire from betting
can it predic my mm games?
woah gj men maybe you can make money with this later
damn that sounds interesting gl
Sounds very cool!
I would say if it works go bet haha
But keep in mind that if you download all matches from the past 7 years you might face a problem.
The % will be useless if the teams changed their lineup. For example Virtus Pro, they were amazing years ago and they won a lot against other teams. The scores from the past wont match up with the current state of the team though.
Maybe you should just focus on scores/game which were in the past year so you can avoid that.
Very awesome! I'm in software myself, so I'm crazy about this stuff.
What are you basing the victory odds on? Teams? Players? I feel like both of these metrics would be skewed. For example, there are loads of games that FaZe was dominating in 2018, so the AI might predict FaZe winning a game in 2020 which is more unlikely. Also players, in 2015 you'd be pretty safe assuming olofmeister would win any given game, but he had a role shift in FaZe from star player to support-player, and his stats dropped remarkably.
I would understand you wouldn't wanna release the entire thing but I'm just wondering what input-metrics you're using.
A dedicated gambling addict
Model was trained on matches ranked with 1+ star on hltv.
I just tried to see how much winrate is on 0stars matches. It was same as on tests before: 90%
Model saw all these 0star matches first time, i did not train it on them, and it gave 90% winrate...
what if it really does predictions with this winrate? i still cant believe myself even if i rechecked everything like 4 times alraedy..
Good Job!! I am starting to do something similar to that but with soccer. Can you tell more how do you collect data to do that kind of stuff?
I'd imagine most ML algorithms would do somewhat equally well with this type of data as long as you set the right hyperparameters. Make sure you're not overfitting to the test set and use something like k-folds cross validation for better test set accuracy if you aren't already.
how good was it in predicting upsets? i think it might be good to look into detail there maybe.
also does it differ between the stage of play i.e lan, online, qualifier etc. the way humans approach the game might reflect on the stats.
Goos3 gunna be millionaire programmer
Hi _g00s3_. It is good that you can forecast it well. But your challenge is to forecast it better than forecasters since you want good performance in a market for predictions. You should try to calculate the implicit probabilities within multiples paid by betting sites before the start of the match. Then sample a decision-maker that bets drawing from these probabilities and compare its forecasting ability with yours (do this enough times to be confident you are better than the average market bettor...). Otherwise, you can let your strategy bet on these multiples and backtest it.
The last thing you should do is to post sources and/or tutorials how to achieve your results (if you're not lying).
Use it for your own profit. Bet by yourself or try to sell the data to the bettors (e-sport prognoses), or betting companies (analytics).
If your claims about winrate are true, you can make a shitload of money. You shouldn't share this secret with anyone.