What I’ve Learnt Predicting Soccer Matches with Machine Learning

Neo Yi Peng
Towards Data Science
5 min readJan 15, 2019

I’ve always been fascinated by the markets. Maybe it started in my teens when my mate told me this “sure-win” betting strategy that involved betting on football matches being a draw and doubling my bet until I won — technically, he wasn’t wrong, but technically, I also did not have an infinite bankroll, so thanks a lot ZJ.

When I first heard of machine learning, I thought it was so much better than modelling football using traditional statistics, partly because of the restricting assumptions of those models and partly because I was pretty lazy to learn R at the time.

That was 1.5 years ago, and since then I’ve picked up Python (so much easier than R!) and read/applied everything about machine learning that I could find and finally created a model that’s slightly better than a martingale strategy. There is still a lot more to do, but this essay aims to consolidate what I’ve learnt so far and to share with anyone who’s starting out.

1. The bookmakers are really good at their job, i.e. markets are quite efficient

“I’d compare stock pickers to astrologers but I don’t want to bad mouth astrologers” — Eugene Fama

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Published in Towards Data Science

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Written by Neo Yi Peng

i think, write and code. focused on applying LLMs to finance right now.

Responses (5)

What are your thoughts?

Entry level to Machine Learning can be quite high as you alluded to in your article. I have been working on a GUI based approach to creating sports betting models which you can view here

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I remember I dreamed to find the magic prediction formula when I was a child. It was a funny mental adventure...

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When I first started the project, I just threw everything on the wall and see what stuck

What sort of features did you throw at the wall and which ones stuck?

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