APBRmetrics

I posted this on /r/nba yesterday and got a suggestion to post it here. This is my first attempt at making a statistical model for basketball so I know I have lots of room for improvement and it is not perfect. I'm looking for all the feedback I can get so I can improve on this project. I've already begun work on adding PER, TS%, BPM, VORP, DBPM, TOV%, TRB%, and FTr as variables. Please let me know if there are any other variables I should consider adding. Here is a link to my reddit post... https://www.reddit.com/r/nba/comments/6 ... t_the_mvp/

Any feedback on the statistics or my analysis is appreciated. Thanks everyone.

Seems like a worthwhile analysis. If you are adding other stats / metrics you might add RPM as well. Advantage Westbrook.

When done and done with the super detail write-up, you might consider writing a streamlined version too. Both levels have their place. Most will prefer the one where you stick to the main points / big picture.

Predicting MVP is too easy.

Using anlaytics for such a task is overkill.

permaximum wrote:Predicting MVP is too easy.

Using anlaytics for such a task is overkill.

While that might be a fair point, this is my first attempt at an analytics project. This was a current discussion so I thought it'd be an interesting project that I could learn a lot from. I've already learned a ton of things I can apply to future projects. Have to start somewhere

Crow wrote:Seems like a worthwhile analysis. If you are adding other stats / metrics you might add RPM as well. Advantage Westbrook.

When done and done with the super detail write-up, you might consider writing a streamlined version too. Both levels have their place. Most will prefer the one where you stick to the main points / big picture.

Okay, thanks for the idea. I'll add RPM into the mix. I got a lot more work to do and I'm learning a ton as I go so I appreciate all the help I can get. Also an excellent idea on the write up. I'll certainly do that.

White-Iverson wrote:

permaximum wrote:Predicting MVP is too easy.

Using anlaytics for such a task is overkill.

While that might be a fair point, this is my first attempt at an analytics project. This was a current discussion so I thought it'd be an interesting project that I could learn a lot from. I've already learned a ton of things I can apply to future projects. Have to start somewhere

Alright then. Consider these 3 variables (sorted by weight).

1. Team Wins

2. Total (PPG+RPG+APG+SPG+BPG-TPG)

3. Difference between the 1st and 2nd player in Total (for each team).

These 3 are all you need. But if you want more you can consider adding TS% and USG%. And don't forget: MVP should play at least 75% of his team's games. It's a requirement.

Before 1979-80 the MVP was voted on by the players instead of the media. So if your data set spans the entire history of the NBA your model will have a difficult time discerning what criteria is important by the standards of the media, since the players and media likely had different opinions on what stats were important when voting for MVP.