This should be my final update of my base draft model, the Projected Alternate Win Score (AWS) model (P-AWS) before this year's draft. This one represents a modest change to model, which I will detail, as well as a completion of nearly all of the player's data.
In terms of possible draft prospects, I think Alessandro Gentile is the only one still playing as Olimpia Milano plays for the Italian championship. (Gentile is on the bubble of being draftable, according to my model moving up from 77th to 63rd with a good run at the end of the season in Euro League and the Italian Playoffs. So these are big minutes for him!)
To review, breifly, my draft model is built off of a statistical analysis using AWS as the measure of efficiency at the end of their rookie contract, with a replacement level reversion included in order to stabilize the measure and account for the fact that better players tend to get played more. However, the value of playing time is not linear, a player putting in 30 minutes a night shouldn't be assumed to be twice as good as some one playing 15 minutes. Something I looked at in a post called Time is a Flat-ish Curve. It helps avoid what I call the Sebastian Telfair fallacy, over crediting an inefficient player who happens to get a good deal of time on the court.
I used pace adjusted data from Draft Express, in both the training set and applying it the current class. I also use Draft Express's Top 100 as a filter for applying the model to this class, since that more closely mimics the training set built off of actually drafted players.
There were a couple of changes applied to the P-AWS 2.0 model from the last model. The biggest is the inclusion of the prior year's data, which is something that had been dropped from the original model during development. However, I decided to look at it again during some other data cleaning.
The informational value of data tends to depreciate over time, how fast depending on the source and rapidity of change. In basketball, for example, looking at Kevin Garnett's efficiency in 2007 is obviously much less predictive of his prowess now than last year. For developing players there is also a depreciation in the predictive ability of prior years as the player develops.
In making predictions about team records last year, I found that performance one year removed was less predictive of performance than the immediate prior year, even if the player remained on the same team and an age factor was applied. In a sub-study for the P-AWS 2.0 model I found that the difference was even bigger in pre-NBA stats. But the earlier year does have some independent value, and including both years weighted towards the most recent year does improve the model's predictive ability in a robust number of filters tested.
One of the follow on changes resulting from the inclusion of prior year data is that age is slightly less weighted, as players in the development stage typically have worse numbers some of the skill development is captured more directly.
An example from this year shows the scale of this effect and the direction. Kyle Anderson, is a favorite of the P-AWS 1.0 model, putting up fantastic numbers last year at UCLA in his sophmore year. Anderson's numbers, in fact, jumped considerably from his freshman year from an 7.9 AWS to 10.8. The freshman number was still a good one, a little above Andrew Wiggins number this year, but the jump is well above the typical developmental improvement. Consequently, Anderson drops from 2nd in the class projection to 7th.
On the other hand, Adeian Payne moves up from 72nd to 56th, his numbers last year were good also, though not great, but his consistency improves his overall rating.
An interactive variable between rebounding and three point shooting was also added,which had only a limited effect but tested as significant and fit other studies I have done.
Also, previously I had posted the P-AWS results alongside a Logit model version that estimated the odds of a player becoming a 'quality starter' player. Right now, I am working through that model and I think it deserves its own post in any case, which I hope to do later in the week.
Finally, a couple of notes on the top prospects according to the model.
- Jordan Adams is the darling of a number of analytics models, in fact he's the third highest projected prospect in the last three years. He's significantly behinf Nerlens Noel and Anthony Davis, but ahead of everyone else. But he is not a favorite of any of the scouts, due in part to limited athleticism. So he will be an interesting case both in the draft and n his career. If the Spurs snag him in the late first, you have been warned.
- Clint Capela's rating is right behind Adams, and the difference isn't really significant. Capela also topped Kevin Pelton's international rankings at ESPN. In contrast with Adams, Capela has athleticism and length to spare. His strengths are just the kind of thing GM's seem to like, and his weaknesses, physical strength and mid-range touch, are fairly fixable as the 19 year old develops. I really can't see how he falls out of the lottery.
- Jabari Parker is the first of the traditional top three in the model. Definitely looks like an "NBA ready game."
- Nikola Jokic is a bit of a surprise in the four spot, but he was almost made for the P-AWS 2.0 model, a young big that passes and shoots the three (though not as well as one would like). There are concerns about his foot speed and defense, but he could be a sleeper high upside kind a kid.
- Noah Vonleh and Aaron Gordon are right around the spots they're expected to go in the draft. Both very young players with good numbers. Of the two I prefer Gordon because of his defensive potential.
- Kyle Anderson, as discussed above, drops not because of the 'who does he guard' question, rather because the model doesn't 'believe' that all of his increase in production is real. Anderson's increase in three point range is most obvious case for skepticism, going from 21.1% to 48.3%.
- Skipping down to Joel Embiid, he comes in tenth in part because of his lack of minutes and because he is actually a fairly old freshman. There is no factor on his late basketball start, which may or may not indicate he has more development potential than a typical player of his age.
Here's an image of the model results applied to this year's draft class, and the results in Google Docs: