(Cap the Wins for Capela)
I've been working on a couple of projects to compare the level of competition between different basketball leagues, including the NBA-Development League and an in the works project looking at Europe. Gathering the data for projects gave me the opportunity to take a preliminary look at the stats for the prospects in the up coming draft.
The method I have been using to compare league competition levels utilizes the box score metric Alternative Win Score (AWS), one of a number of metrics that weights positive and negative contributions by a player recorded in the box score. A previous study by Neil Paine indicated that this metric was the best at predicting wins by teams with high turnover in personel, making it potentially useful for looking at draft prospects and comparing the effects of moving between leagues.
In order to analyze the statistical production of draft prospects there are a couple of adjustments needed to make the stats comparable; adjusting for pace, competition level faced, age and random variation.
The first two issues are of particular importance looking at potential draftees playing outside the NBA. As of today, the DraftExpress.com mock draft had two players currently in the D-League and six players from Europe. My analysis of the D-League and the higher levels of European basketball indicated that they represent a higher level of competition, so I developed a simple adjustment factor to account for that by boosting the AWS score based on the central tendencies of score movement by players moving between leagues. Conversely, both pro-European teams and the D-League play at a faster pace than college, so I adjusted their stats down to the NCAA pace.
Age is a tremendous factor in the NBA draft. Players in numerous studies have been shown to have an "age curve," improving play generally until their mid to late twenties then declining in productivity. The increases in the eighteen to early twenties can be quite steep, so the same level of productivity in an eightteen year old is a better predictor of potential value than it would be in a twenty-two year old player. I did some preliminary work developing adjustments based on the central tendencies of prospects improvements, as measured by AWS, with the biggest boost coming from 18 to 19. This adjustent is preliminary, but I think gets it 80% of the way there and certainly improves the estimate of talent.
The last adjustment is a mean reversion adjustment that is weighted as 150 minutes of average play against the player's minutes and actual production. This accounts for some of the random variance basketball stats will inevitably display, and temper over-reaction to small sample sizes. The effect is most pronounced currently for someone like PJ Hairston, who is playing very well in the D-League, but has only logged 160 minutes so far.
Here are some of the highlights:
- Jabari Parker came out on top narrowly
- A Swiss big man that you have probably never hear of, Clint Capela, came out second. He is playing some very high level ball against some pretty difficult competition. Slightly hurt by the reversion due to fewer minutes to date than most NCAA players.
- KJ Daniels is having a hell of a season. He's been a solid player but really upped his score so far this year. His two year average weighted to this year puts him as a late lottery pick.
- Noah Vonleh and Doug McDermott did well. Vonleh I expected, McDermott I did not. I wouldn't take McDermott this hight (see defense), but he's putting up numbers.
- Joel Embiid gets lowered a bit because he has played fewer minutes than many other players and is a smidge older than Parker.
- Andrew Wiggins falls to 26, the statistical production just isn't there, even allowing for age. The 'model' doesn't know the difference between Wiggins' athleticism and McDermott's.
- The first ten prospects scored very close together. In those cases scouting and athletic measures should become magnified in importance.
- Again I am not to the point of going into what 'projects' and what does not. For example, earlier I looked at transition verse non-transition points going from college to pro, here I am just taking the measures as presented.
In any case here are the results: