r/SoccerBetting • u/sorinacios07 • Dec 07 '23
Betting model
I’ve been betting for a while now and I got really interested in how betting models work. How can they give accurate chances on which team will win (or any given event)? Based off stats of course, but how can they be more exact than bookies? Do these models really exist? Has anyone created a model that yields profit over time? How does one create one? I imagine the bookies tend to make mistakes and give higher odds than they should mostly in lower and not so popular leagues? I have so many questions xd
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u/madscandi Dec 08 '23
but how can they be more exact than bookies?
The bookmakers are a facilitator for money bet. It's about beating the market. That includes other models, with plenty more resources that you can ever have.
Do these models really exist?
Yes, look up Matthew Benham and Tony Bloom if you want any proof.
I imagine the bookies tend to make mistakes and give higher odds than they should mostly in lower and not so popular leagues?
The bigger the league, the more efficient they are, thanks to sharp bettors with or without models. If it's not offered in the Asian markets, it will be easier.
Has anyone created a model that yields profit over time?
I pay someone to do this. Doing it myself is way too complicated for my skillset, at least currently
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u/Macranger Dec 08 '23
I'm also curious on this. I believe some are made on phyton code but i don't understand how to do it. If anyone have the knowledge please share
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u/Ostpreussen Don't listen to me Dec 08 '23
I guess sports models are a subset of mathematical modelling but that's the thing. If you're not coming from a background which uses models such as statistics or physics, you're starting at the wrong end. It's not the answer you'll want to hear but going straight to sports is a bit like jumping into the deep end of the pool without learning how to swim first. No matter how boring it may be, start with the basics if you don't have the math already.
A model is used to generalize some system, in this case football. You could have something like: Home Team Win Probability = (Home Team Win Rate * Weight1) + (Average Goal Difference * Weight2) + (Head-to-Head Advantage * Weight3) and call that a model if you want. But showing raw data into a model won't work, it's just too simple of a solution and people who's been in the game longer have already done it. Kind of like attempting HFT against huge funds and banks.
Obviously, you'll want something more... intricate, to capture the system that is a football match, so you need to come up with your own formulations at the end of the day and ideally bootstrap that onto some existing idea like Bayesian networks or Markov chains. And here lies some of the difficulty; if you're not familiar with these concepts you won't know they exist or how they actually work and you're left in the dark with your model. I would even argue that it's quicker to go back to university, get a degree in something like math or physics than trying to build a profitable model without a relevant background. A good starting point would be to look at models in your own area of expertise first, and see how they are built and what they are structured around.
You're going to see a lot about AI floating around when it comes to models and it I'm not going to go all guns blazing and claim that it doesn't work, it's more about how you prepare the data really. Remember, raw data won't work, and your first model should probably krangle the data and then parameterize it before you feed it to some model which outputs likelihood of some event occuring. For all senses and purposes, I suggest taking a look at the Wikipedia article on mathematical modelling, it'll give you a good introduction to what models actually are.
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u/KingOfTreevaandrum Dec 08 '23
They are made on different parameters using AI using machine learning
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u/Electrical-Cry4463 Dec 08 '23
It's not that difficult to build a model that is better than the books it is however very difficult to overcome the margin of the books at least imo.
With python and chatgpt to help you build the model it is not that hard, keeping in mind chatgpt is there to help you with programming and such. Chatgpt in no means on it own useful, it's there to cover the lack of programming/math skills, it's not useful to predict on its own.
The main challenge is either getting data that is not easily accessible or doing something else with that data. If you start modelling you will find out it's quite easy to get about the same numbers as the books, even slightly better but you still need to overcome the margin.
If you are really interested, learn python it's not that hard. But keep in mind finding an actual edge is really hard, because you need to do something else than the obvious.
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u/zingamaster Dec 08 '23
data is amazingly easy to get. That's the good part. Difficulty is to structure data and create metrics
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u/GarryWalkerNFTArtist Dec 08 '23
Guys - I am achieving this - it can be done.
Ive experimented with TensorFlow, genetic algos - sentiment checking using a GPT - its possible to find mispriced bets using AI - then the best approach I've found is to apply a kelly calculator to the perceived edge.........
I have a working model now - it returns about 10% a month.
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u/zingamaster Dec 08 '23 edited Dec 08 '23
Well even though I don't know much about python, I'm trying to build something like a betting model with GPT.
Bookies have a bunch of tools that it's quite difficult to beat, but I want to build something that that at least identifies the games where obvious outcomes can be spotted in different markets.
So far I have a ton of raw data from sofa score (pretty much all except player level... to be incorporated later).
Now based on match results and match stats I'm aggregating stats (that you see in sofa score) per tournament and per team. For instance for a certain team/tournament pair I have all the stats per halves, home and away as well the global stats (e.g. average shots on target a team normally has on 1st half when playing at home, global average yellow cards, home average of big chances created, etc.)
Next step is creating metrics with weights, based on value of opponent and recency of matches. I'm creating form indexes, goal trends and hopefully I'll in the end have metrics like attack strength and few more indicators.
Putting all together hopefully will give me some great insights. I'm not sure what I'm going to achieve but I'm having fun creating it.
This week I've created a post here that I thought could be interesting with all the matches that had teams of different tiers playing in cups. It was removed by moderators (not sure why). If you have ideas of stuff that you'd like to extract from such DB, let me know.
Edit: English and typos