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Building a machine learning model to predict the NBA MVP

12 点作者 robmoore大约 4 年前

3 条评论

mlex大约 4 年前
This is a great article and a great read, even as someone who knows very little about machine learning models but is a fan of the NBA.<p>One question I have is whether it’s possible to model voter fatigue. There was a point brought up that LeBron had a solid candidacy in the Rose MVP season but voters didn’t want to give it to him again, and I feel like this equally applies to Giannis this year. Though Giannis’ season isn’t as good as Jokic’s for sure, if they were closer I feel like Giannis would have to do WAY better than the competition to have a shot at the award, given general sentiment about him and his playoff results.
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ccmonnett大约 4 年前
Really enjoyed the article, and followed on twitter. Good luck with the launch.<p>Follow-up question: Do you see a correlation between your predictions and actual finishing order increasing over time? I ask because of some very weak seasons (Nique &#x27;86 and MJ &#x27;87) for 2nd-place finishers in the 80s. It made me wonder if the MVP is becoming more &#x27;objective&#x27; now that the media is able to follow teams around the league more easily than before.
robmoore大约 4 年前
XGBoost with LambdaMART ranking performed the best across all models(neural nets, log reg also tested).<p>Adding pairwise ranking with LambdaMART helped to solve overfitting with only some 360 data points. Instead of looking at binary win&#x2F;loss MVP, I looked at the ranking of all players receiving votes in each of the past 38 years.
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