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Comment by ghgr | Hacker News Reader

Thanks for sharing (I recommend taking a look at his GitHub repo [2], where he nicely described the game and his approach).

Some time ago I had a similar project with BetFair soccer matches. I assumed that the number of goals from each team is given by a Poisson process. Then, I used the largest market (Win/Lose/Draw) to estimate this parameter. With this information I could estimate the "fair" odds of more exotics bets (like e.g. team A will score 3 goals more than team B), which were often mispriced, and then use the Kelly criterion to estimate how much to bet.

All in all a nice project, but... at the very end -just before deploying the bot- I realised that the BetFair Exchange is not open to Germans, so I wrote a blog post and open sourced the code. Still, a nice learning opportunity :-)

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Thanks. I would like to point out again that the code is out of date, and the margins displayed there are miscalculated due to not accounting for commission. The general spirit remains.

Did you backtest and show potential returns? Can you link to your model? Did you come across "Scoring dynamics across professional team sports: tempo, balance and predictability" [1]? They apply a Poisson model to team sports. I mentioned this paper to a company involved in betting who I interviewed with once, and they told me that they were doing something similar, but way more involved. It demotivated me somewhat regarding implementing it, making me wonder whether there really was any juice left to be squeezed from it.

[1] https://link.springer.com/article/10.1140/epjds29

I wasn't aware of that paper, but it seems we came to a similar conclusion. From the paper:

> Across all sports, scoring tempo - when scoring events occur - is remarkably well-described by a Poisson process, in which scoring events occur independently with a sport-specific rate at each second on the game clock.

So, yes, basically that was my concept. Estimate its parameters and then you'll have the "real" odds. Any difference with the market ones is a potential of profit (considering the house fees).

I couldn't backtest it since I couldn't find free historical data for all types of markets (then again, maybe I didn't look hard enough). I forward tested it, though, and it seemed to work.

My code is here [1], which also links to a blog post describing the method. Feel free to take a look and contact me to discuss these ideas.

Also, if you're interested in mathematically modelling probabilities, we are looking for team members to develop a product to estimate the risk of default in loans. After all, a loan is another kind of bet, that takes place in a slightly harder to model environment.

[1] https://github.com/ghgr/BetFair_Arbitrer

Thanks for the tips. The loans idea sounds interesting. I will send you an email.

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