Comments (6)
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!
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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.
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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.
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Oh in that case 73% is even more impressive. Does this approach extend to more dimensions as opposed to just OR and Pace?
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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.
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Added cross_val.R
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Related Issues (12)
- KeyError when Update=True HOT 5
- TypeError: a bytes-like object is required, not 'str' HOT 1
- FileNotFoundError: [WinError 2]
- predictions.csv is missing HOT 1
- Can't run model without predictions.csv HOT 5
- Add unit testing
- great!! so interesting..
- pytest - 404 error
- Somes error
- Calculation used to update pace and OR dataframe values
- Updates to basketball-reference layout
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