Building a machine learning model to predict the NBA MVP(perthirtysix.com)
perthirtysix.com
Building a machine learning model to predict the NBA MVP
https://perthirtysix.com/essay/2021-nba-mvp-race
10 comments
I have not watched NBA basketball in years, but the NBA had the most vague definition of MVP going back decades.
What it certainly is not - since the 1980s or longer - is “best player”. Magic over Jordan in the late 90s set that precedent, followed by Karl Malone winning a few as a good but not great player.
This continued in the 2000s, with Shaquille O’Neal not winning as many as one might predict. I suspect that is still the case.
What it certainly is not - since the 1980s or longer - is “best player”. Magic over Jordan in the late 90s set that precedent, followed by Karl Malone winning a few as a good but not great player.
This continued in the 2000s, with Shaquille O’Neal not winning as many as one might predict. I suspect that is still the case.
Right, it’s cliché but it’s clear that MVP isn’t the “best player” award given how infrequently LeBron has won it. It’s more some nebulous combination of “how good you are”, “how much you contribute to your team’s success”, and “are you doing so without the support of another star”.
I stopped reading at Karl Malone supposedly being a good but not great player.
He was a good numbers guy, but there were always five or more players better than him throughout his tenure. Certainly not 2 MVP's good.
Sure, whether he deserved to win a couple of MVPs is a perfectly valid argument. The fact that there were probably single-digits worth of better players than him during the greatest era the NBA has ever seen is the definition of a great player, no?
I think Karl is one of the players that history looks back on more kindly than the present did.
He was seen as not a winner/unable to win at the time, and a lot of fans valued that more so than at present. If you asked 10 people to choose between Malone and Pippen, it would have been 5 and 5.
Really enjoyed the article, and followed on twitter. Good luck with the launch.
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 '86 and MJ '87) for 2nd-place finishers in the 80s. It made me wonder if the MVP is becoming more 'objective' now that the media is able to follow teams around the league more easily than before.
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 '86 and MJ '87) for 2nd-place finishers in the 80s. It made me wonder if the MVP is becoming more 'objective' now that the media is able to follow teams around the league more easily than before.
XGBoost with LambdaMART ranking performed the best across all models(neural nets, log reg also tested).
Adding pairwise ranking with LambdaMART helped to solve overfitting with only some 360 data points. Instead of looking at binary win/loss MVP, I looked at the ranking of all players receiving votes in each of the past 38 years.
Adding pairwise ranking with LambdaMART helped to solve overfitting with only some 360 data points. Instead of looking at binary win/loss MVP, I looked at the ranking of all players receiving votes in each of the past 38 years.
Would you be open to publishing the code for your model?
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.