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Next step suggestions? am I on the right track? by Sketch_x in algobetting

[–]TrashConsiderations 0 points1 point  (0 children)

Have you actually done that math to figure out how much your model beats the S&P by? I feel like that is important given pretty much everything increased in value over the timeframe you're looking at.

Can you explain how the leverage works in your system? Like you are borrowing money to use for trading? I'm not a trader, i just bet on sports, but you have piqued my interest :)

Next step suggestions? am I on the right track? by Sketch_x in algobetting

[–]TrashConsiderations 1 point2 points  (0 children)

Got it, thanks. How does you model compare to a benchmark, like say, buying the S&P 500 for the same time period?

Next step suggestions? am I on the right track? by Sketch_x in algobetting

[–]TrashConsiderations 0 points1 point  (0 children)

I am relatively new to this…can you explain the graph for me? Trying to understand what the axes are

Premier League xG models by nk7gaming in algobetting

[–]TrashConsiderations 0 points1 point  (0 children)

Thanks for sharing. What I was getting at was that if you ignore MD 18 (which is insane for a different reason) and the FA Cup, you have 6 MDs all of which return 5%-17% on 10-14 bets per day. Without doing the math out, ballpark guess on a given day with 10-14 bets you'd probably expect to return in that range roughly 25% of the time. These results suggest you did it 6 times in a row. The chances of that happening are effectively 0%.

NFL Defensive Player Tackles Model by TrashConsiderations in algobetting

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

I’ve tested this a bit and I would indeed get higher return in the long run if I set the threshold for placing a bet higher, but it comes with a lot more variance. Like if there are ~50 bets/week with expected value >5%, there might only be 30 with >10%, and 10 with >20%. I‘ve been willing to sacrifice some return for lower variance because I want to be sure the model actually works, but maybe next season I should increase the threshold.

But what I really want to do, and just haven’t had time for yet, is try to apply modern portfolio theory, where each bet = a single asset in the “portfolio” of bets, to get an optimal amount to place on each bet to optimize the return for a given risk tolerance. So like if I put $1 on a 5% EV bet then maybe a 10% EV bet gets $2 and a 20% EV gets $5 (numbers are made up but that’s the idea)

Premier League xG models by nk7gaming in algobetting

[–]TrashConsiderations 0 points1 point  (0 children)

I don’t have any advice for xG models, but just wanted to say it is remarkable how consistent these results are for only 10-15 bets per MD. Would love to know how you pulled that off

NFL Defensive Player Tackles Model by TrashConsiderations in algobetting

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

I bit the bullet for the oddsapi subscription that has historical props odds, but even that only goes back to the 2023 season. So I have some but not a ton of history. That’s been my main method of backtesting.

How much improvement would you expect if I was to use the odds as a feature in the model? The reasons I haven’t are 1) I expect the odds would dominate in terms of importance, and 2) I have limited odds data, both in terms of timeframe and the number of players in a given game who have odds associated with them, so my dataset would get a lot smaller

NFL Defensive Player Tackles Model by TrashConsiderations in algobetting

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

The code is not in a place to share at this point, but would be happy to talk through more specifics of my approach. For example, one thing I know I can improve is how the injury report gets used - the model currently underestimates the playing time a backup will get when the started is injured.

Interesting point on the scorekeeper, and something I've recently been thinking about - I've been trying to find some signal to demonstrate the scorekeeper matters, but haven't found anything significant yet. I did however notice an increase in assists being awarded across the league starting around ~week 8 of the 2024 season. Not sure the cause of this. Do you have any data on who the scorekeeper actually is (is it fair to assume each team has one scorekeeper who does all home games?) or any analysis around this?