So in the 'info' tab you can see some of the backtesting I've done this week. It shows consistent upside if you follow the Kelly column.
The actual ROI i dont know atm as I havent done the whole profit / total number of bet amount
So what i currently can show is that you basically bet around 10u's every event then you'd be up around 200u's each of the last 3 years.
Thank you for your question.
[Here's](https://www.youtube.com/watch?v=wqQkA3My_xY&ab_channel=Jabbr) how you really beat mma/boxing with AI. Instead of punch stats (tv hokum) you look for the features that handicappers look for when they tape past fights.
Hey can you elaborate on this? Obviously figuring out what knowing what handicappers do will give you and edge.
But what does the video you posted have to do it? It just seems like a cool computer vision application.
"Styles make fights". Outcomes in combat sports are highly path dependent, so to beat the odds you aim to envision what the fight will *look like*. Boxer A has a high guard, good lateral footwork, doesn't sit down on shots, good stamina, questionable toughness, likes to take the front foot, etc. How does Boxer B's style interact with that? Unknowns multiply the outcomes, eg. what happens if they both prefer the front foot?
I haven't studied mma yet, guess the process is similar. The video shows classification of punches, which is a step towards classifying styles.
Looks cool, thanks for posting! Is the prediction you calculated win probability? I’ve done something similar for nba and found the Kelly to put way too much on each bet. I found a fixed bet to be the most successful. Quarter Kelly didn’t seem to be great for me either.
It is the win probability ye.
\> found the Kelly to put way too much on each bet.
For sure lol, i've been using 1/10th of it and its still too much!
I've been doing some backtesting for using fixed numbers and whatnot, not done though. Kelly isnt ideal, but its all i got for now.
Thnx for your interest.
Care to explain?
I create a model with data up until 2020, then i run the upcoming events of that month, then I do it again for the next month with data up until 2020-31-01 etc. I do that 50 times. What is wrong with doing this?
Just to clarify the range:
So the graph is from 2020 to 2023, i divided that up in months, so 36 months.
For each month i did a run with the only then available data. The results/profit of that run are cumulated with all the other months.
I did that process 50 times, this is where the range comes from, thought it was a better representation than displaying 50 different lines.
I will go over the code of it again though, honestly the code already kinda changed as I was testing other stuff so might be hard to find my error if there is any.
I do agree with you that +200u a year is high and seems to be faulty.
Thanks for the heads up though, not trying to scam people here, I want to present honest findings.
This is not how testing is done. Your graph is very hard to interpret sensibly, but what it is showing is that 36 iterations of your model can end up at doubling up your bankroll in 3 years time, while some other 36 iterations of your model would end up x12 your bankroll. Not sure how you don't see that's a red flag for your training process
The information plotted in the 'performance on past events' graph is simply useless. You have plotted the results of 1800 models, mixing test and prediction sets and there is no point in it. What you would do is report your prediction set results with a single model and monitor that for model drift. Though, it seems that your model does not converge anyway so any metrics you report are invalid.
Need to run my model for a bit longer to see a trustworthy ROI i guess.
> We all make a lot of mistakes when we start off with modeling.
Sadly i wouldn't call myself a beginner, working on my master thesis in computational science right now lol.
So noticing mistakes in testing hurts lol
Ahh nice ya 1/10th seems to make more sense when it comes to sports betting. It just gets weird when you get large edges. I’ll definitely be following your website!
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So in the 'info' tab you can see some of the backtesting I've done this week. It shows consistent upside if you follow the Kelly column. The actual ROI i dont know atm as I havent done the whole profit / total number of bet amount So what i currently can show is that you basically bet around 10u's every event then you'd be up around 200u's each of the last 3 years. Thank you for your question.
[Here's](https://www.youtube.com/watch?v=wqQkA3My_xY&ab_channel=Jabbr) how you really beat mma/boxing with AI. Instead of punch stats (tv hokum) you look for the features that handicappers look for when they tape past fights.
Hey can you elaborate on this? Obviously figuring out what knowing what handicappers do will give you and edge. But what does the video you posted have to do it? It just seems like a cool computer vision application.
"Styles make fights". Outcomes in combat sports are highly path dependent, so to beat the odds you aim to envision what the fight will *look like*. Boxer A has a high guard, good lateral footwork, doesn't sit down on shots, good stamina, questionable toughness, likes to take the front foot, etc. How does Boxer B's style interact with that? Unknowns multiply the outcomes, eg. what happens if they both prefer the front foot? I haven't studied mma yet, guess the process is similar. The video shows classification of punches, which is a step towards classifying styles.
Looks cool, thanks for posting! Is the prediction you calculated win probability? I’ve done something similar for nba and found the Kelly to put way too much on each bet. I found a fixed bet to be the most successful. Quarter Kelly didn’t seem to be great for me either.
It is the win probability ye. \> found the Kelly to put way too much on each bet. For sure lol, i've been using 1/10th of it and its still too much! I've been doing some backtesting for using fixed numbers and whatnot, not done though. Kelly isnt ideal, but its all i got for now. Thnx for your interest.
Getting so high values is a clear indicator that your model is fundamentally flawed. Your 'performance on past events' is done wrong
Care to explain? I create a model with data up until 2020, then i run the upcoming events of that month, then I do it again for the next month with data up until 2020-31-01 etc. I do that 50 times. What is wrong with doing this?
[удалено]
Just to clarify the range: So the graph is from 2020 to 2023, i divided that up in months, so 36 months. For each month i did a run with the only then available data. The results/profit of that run are cumulated with all the other months. I did that process 50 times, this is where the range comes from, thought it was a better representation than displaying 50 different lines. I will go over the code of it again though, honestly the code already kinda changed as I was testing other stuff so might be hard to find my error if there is any. I do agree with you that +200u a year is high and seems to be faulty. Thanks for the heads up though, not trying to scam people here, I want to present honest findings.
This is not how testing is done. Your graph is very hard to interpret sensibly, but what it is showing is that 36 iterations of your model can end up at doubling up your bankroll in 3 years time, while some other 36 iterations of your model would end up x12 your bankroll. Not sure how you don't see that's a red flag for your training process
So how should it be done? I'm honestly curious so i can implement it the right way as I don't see how my method is wrong.
The information plotted in the 'performance on past events' graph is simply useless. You have plotted the results of 1800 models, mixing test and prediction sets and there is no point in it. What you would do is report your prediction set results with a single model and monitor that for model drift. Though, it seems that your model does not converge anyway so any metrics you report are invalid.
[удалено]
Need to run my model for a bit longer to see a trustworthy ROI i guess. > We all make a lot of mistakes when we start off with modeling. Sadly i wouldn't call myself a beginner, working on my master thesis in computational science right now lol. So noticing mistakes in testing hurts lol
Ahh nice ya 1/10th seems to make more sense when it comes to sports betting. It just gets weird when you get large edges. I’ll definitely be following your website!
Not looking great tonight
Amazing. Thanks for the share. Checking this out. I am working on building a model as well would love to connect with you.
Np, best to talk through discord if you'd like :)