So far, I have created two draft models, a linear model and logit model, an Update Model for rookies, combining the draft model and their rookie stats, and a Scouting Informed Draft model. Now in a sure sign that I have been looking at this data set for too long, I have a statistics based Pick Prediction model.
The idea for this one is to simply use the same playing statistic data used in the draft models to predict where a player will land in the draft. The model does fairly well, explaining 39% of the variance in selection place using a curved draft pick scale (reflecting the more significant differences between the top picks compared to the lower picks).
While the end result is probably this year's worst NBA mock draft, the more interesting result is the differences between what the GM's appear to value and what P-AWS model indicates enhances performance in the NBA.
Things GM's have historically 'Over Valued:'
- Scoring, this the least surprising finding ever, is about 15% more valuable in Pick Prediction Model.
- Youth, in the Pick Prediction model age is an even larger factor than in the P-AWS model. There is some dual causation here since players that are highly thought of by GM's are more likely to enter the draft, and less sought after players are more likey to stay in college or European leagues.
- Blocks, slight surprise here, but the P-AWS model is less of a fan of blocks than many other draft models. Also, when the Pick Predict model is applied to this year's top one hundred prospects, it is clear that big men have tended to valued more highly than their overall statistics may have warranted otherwise, particularly the 'rim protector' type bigs.
Things GM's have historically 'Under Valued:'
- Offensive Rebounds, the P-AWS model treats offensive rebounds as approximately twice as valuable as defensive rebounds, in line with the Alt Win Score model, as well as a good deal of other research. The Pick Prediction model shows offensive rebounds as only about as valuable as defensive rebounds. That's driven, in part, by examples of players like Paul Milsap, DeJuan Blair and Kenneth Faried falling further than their ultimate production indicates they should have.
- Turn Overs, basically the Pick Prediction model indicates that GM's have not cared a lick about turn overs. Part of this is likely to be the preference for offensive stars that handle the ball all the time and part is the belief that turnovers are one of the more correctable elements of a young player's game.
- Assists, assists were consistently indicated as a positive sign for future success, particularly in front court players in all of the development of the P-AWS model. In the Pick Prediction model, however, assists were seen as much less valuable.
In essence the Pick Prediction model is the reverse exercise of the Scouting Informed Model. In the SI model I was trying use selection rank as a way to glean what independent information non stat sources could give me, though with a bit of a blunt instrument. Here I am using that same blunt instrument to gain some inferences about how the stats I have been using have been weighed by NBA teams.
Finally, the part where I put up a pretty conditionally formatted table for your viewing pleasure. The prediction model is likely no closer to what this year's draft order will look like than either the P-AWS model or Player Odds model, so this is probably the least accurate 'Mock Daft' you'll see this summer.
This one is just for fun, so please, no wagering.
I guarantee that this is not how draft night will go. But, don't be surprised to see some of the young Centers move up on draft night.