Which machine learning model for my use case?

20d

I've said this a ton of times and I'll say it again, the best way to figure this kind of question out would be to just throw some ideas at the wall and see what works. If you have a working method of how you find these locks, use what you've learned from developing that method as a basis for a feature space and start trying a ton of different techniques. I am personally very partial to utilizing logistic regression in my models because it's quick and the probability projections are meaningful, but that doesn't necessarily mean it's a one-size-fits-all. Play around with stuff and see what works! That's the fun part of this hobby, learning new things and discovering techniques

20d

That makes sense, im pretty busy these couple of weeks so I guess i wanted a short cut when there really isnt one 😅

20d

To echo what has already been said: you need to experiment with different models and feature generation.

That being said, LightGBM is a very good place to start.

20d

Thank you! I am pretty new to this so was kind of lost

20d

No problem, takes a lot of time and effort to really dig down into it all if you have the time to be exhaustive.

If you are trying to predict quantities of counting statistics, which it sounds like you might be, then you'll also want to play around with ridge regression as a quick and robust estimator.

20d

Thanks again, for this type of stuff is it just best to try out a bunch of stuff or is there any papers/resources that you found useful in the past? I am currently having a tough time determining a target value to test if its a lock, and was trying to find a machine learning model that would try out different target values. Sorry if the question is confusing, since i am new i feel like I may be asking things wrong. If it doesnt make sense feel free to just ignore it 😅

20d

No papers or anything I'd suggest, but fundamentally what you want to end up with is the probability you win your bet, that's what your target needs to be and what the model needs to output. So if your bet is on event A happening, then your model needs to be able to produce a probability for event A happening.

20d

if your bet is on event A happening, then your model needs to be able to produce a probability for event A happening

/u/playful_match_9556 This is the key. Took me a bit to figure out but projecting accurate probabilities is the best thing you can do, then find lines where you're +EV

Strong-Ad-4490

19d

Yes this is key, you aren’t finding locks you are finding bets that are miss-priced.

You will quickly realize that instead of betting multiple legs in a parlay you will have a better edge betting a single leg at plus odds when the bet shows an edge.

It is also important to note that alternate markets almost always have a relatively small edge because the spread gets wider.

20d

I appreciate you good sir 🫡, excited to see where this journey may end

20d

Oh, one more thing: do yourself a favour and use Python for all of this.

20d

Feel free to drop me a message if you have any confusion, I can't guarantee I'll respond quickly but I'll do my best.

20d

Great suggestion, I have personally had horrid success with any gradient boosting methods but would love to see others doing better

20d

For what I'm doing, NBA outcomes mostly, it's been consistently the best option. As you say it isn't one size fits all, though.

I find it considerably better for classification than regression across most of my data.

20d

Yeah I dig that. I might shoot you a DM to discuss methods if that's cool

20d

Of course. I may not always respond immediately but I'm happy to give some advice and have a chat.