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christopherjenness / NBA-prediction

Predict scores of NBA games using regularized matrix completion
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Historical Performance #1

Closed vladiki closed 7 years ago

vladiki commented 7 years ago

Hello

This looks like a really cool project and a novel approach to this problem. Do you have any results to report on how accurate this method is for historical data?

christopherjenness commented 7 years ago

Not yet. This weekend I'm going to sit down and do some back-testing. One can easily leave values out of the known matrix, and see how well they are predicted in the rank-reduced SVD matrix. I can say, however, that I have tested it against the last ~100 or so games, and I've beat the line about 73% of the time so far.

Thanks for the feedback!

vladiki commented 7 years ago

That's awesome. I think the consensus is that the naive baseline (predicting home team to win every time) is 60% and state-of-the-art (using vegas line) is ~70%. Would also be interesting to see how this performs not only picking winners, but versus the o/u line.

christopherjenness commented 7 years ago

Oh, sorry. I meant, I've been putting it against the spread (not just picking the winners). Although, I have been accurately predicted a few upsets, which has been nice!

To be honest, the 73% will likely drop. I can't image it preforms that well asymptotically.

vladiki commented 7 years ago

Oh in that case 73% is even more impressive. Does this approach extend to more dimensions as opposed to just OR and Pace?

christopherjenness commented 7 years ago

The method (SVD Factorization with rank reduction) is completely blind to what metric you are putting in. Here, I used pace and OR, but there is no reason why whatever incomplete matrix you wanted to look at couldn't be inputed.

christopherjenness commented 7 years ago

Added cross_val.R