Comment by cubano | Hacker News Reader

Comment by cubano | Hacker News Reader

So being semi-retired (unable to find work due to 10yr+ past background issues) and living in Vegas, I've really given serious thought to building some sort of NN to help with gambling in general and specifically the picking of horses (or perhaps even baseball outcomes).

I can't help but think that buried in all the race data their are correlations that are impossible to figure with standard statistical methods but can be sussed out using NNs. Of course I am vaguely aware that many people are already using NNs to predict race outcomes, but I am purposely trying to keep that info at arms length as I don't really want to be negatively influenced existing attitudes.

However, if someone does happen to know of a good NN/horseracing blog that has a positive outlook and is making money, I would be interested in such a site.

Anyway, this type of easy-to-use NN could be exactly what I'm looking for with getting started on this project, and once again, I would be interested in hearing about the pros and cons of using such a lightweight easy-to-use NN for my explorations.

There might not be enough data to train a NN, and it's probably prone to overfitting the model. It's a similar problem to using NN to predict the stock market. You might have better luck with shallow learning statistical techniques that require less data to train a model.

Tangentially, I'm just curious, is it something that appears in your criminal background check that causes you to not receive offers? Or is it a lack of interviews due to gaps on a resume?

I am a firm believer that if someone demonstrates they have motivation and skill in a writing sample, they deserve an interview. If that person then passes a technical interview, they deserve a job. And if that person is friendly and does not commit any crimes while working, they deserve to keep that job.

what do you think the bookmakers are using to set the odds?

If you don't have a PHD level and a large amount of money to start with, don't even try to think about it.

I've spent a few months tinkering with this kind of stuff, with a huuuge dataset, my simulations never went past the annual return of an average ETF, and by eliminating 99/99.5% of bets possible, so you'd only have a few bets that are worth it per month, a bad streak would ruin your return for the year, and you'd have to have access to a lot of bookmakers to get the best odds.

It's just not worth it IMO.

If you can consistently make money the bookies / betting platforms will just ban you.

There is a good book that I recommend

Precision Statistical and Mathematical Methods in Horse Racing

the book will set you in the right path at least and I learnt some good mental models from it

I just want to clarify I never gambled in my life. But I did have similar thoughts to yours and the book scratched that itch for me.

    unable to find work due to 10yr+ past background issues

The broken url in your profile might also be a factor.

I had that url active for over 5 years here and never once was contacted by anyone interested in talking to me about a project, so forgive my cynicism with not being very concerned about keeping it alive.

Fpr the sake of not being a total jerk about it, however, I will take it down soon....thanks for reminding me btw!

Those standard statical methods probably don't have the gift of thousands of parameters.

Well no, but what’s your point?

It's a pretty strange comparison if you think about it computationally.

I'm very much on the deep learning train but comparisons with traditional statistics are rather somewhat tortured.

I’m still learning about a lot of this stuff so apologies if this is wrong.

Isn’t part of the motivation for using deep learning that it can actually generalize over that many parameters? My understanding is that classical statistical models are prone to overfitting on data as you add more parameters to the model. Deep learning tries to learn the underlying distribution of data, enabling predictions not found in the dataset that still match the underlying positional statistics of the dataset.

In that context I’d say the comparison is apt. It’s not just throwing more compute at the problem - it’s doing so in a way that actually achieves scalable accuracy gains. With pre training and self-supervised learning, you can (feasibly) train such a model without even needing to understand statistics.

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