Counting the Baskets

European and D-League Draft Prospect Update

Rather brief update on my under construction draft model, I refreshed the data with Draft Express's latest.

Using that I recalculated the Alternative Win Score per forty minutes for each prospect.  I also tweaked my age adjustment, which is now based on months while the first version was by year.  To do that I needed refine the adjustment made for age, but I consider this one still in beta. 

Using Draft Express's top 100 as a filter here are the top prospects according to my model using the last two years of data:

Name	Sum of Minutes	Age at Draft	Average of TwoYr AWS
Clint Capela	590	20.1	10
Jusuf Nurkic	571	19.9	8.87
P.J. Hairston	406	20.5	8.78
Dario Saric	998	20.2	7.22
Damien Inglis	319	19.1	6.84
Walter Tavares	373	22.3	6.76
Average	9377	20.6	5.7
Mouhammadou Jaiteh	446	19.6	5.67
Ondrej Balvin	444	21.8	5.45
Mateusz Ponitka	305	20.8	5.01
Marcus Eriksson	456	20.6	4.71
Bogdan Bogdanovic	1186	21.9	4.61
Vasilije Micic	618	20.5	4.12
Rasmus Larsen	366	19.6	3.76
Viktor Gaddefors	482	21.7	3.42
Alessandro Gentile	832	21.6	3.01
Thanasis Antetokounmpo	987	20.5	2.99

For reference this iteration has Clint Capela ranked sixth over all and Jusuf Nurkic at 13th overall.  Both Bogdan Bogdanovic and Dario Saric are hurt by the inclusion of last year's data, even though prior years are age adjusted for the age at time of performance.  That is one of the many things I'll be looking at as I test and build out the model.  Jabari Parker rates number one at this point.

A Draft Model, Of Sorts

(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:

HTML:

Download AWS Model

Google Doc:

AWS Model Results

Data via Draft Epress

 

Europe > D-League > NCAA

I just followed up with Celtics 2013 second round draft pick out of Colorado State Colton Iverson, who has been playing this year in Turkey for Besiktas in the Turkish League and Eurocup league. Iverson's stats are generally somewhat down from his last year in college, with rebounds per minute, scoring and true shooting all down and personal fouls and turnovers up.  Fellow late second round pick and advanced stats darling Mike Muscala is playing in Spain, and the story is much the same.  

My immediate question was whether this statistical deterioration by both players is a typical effect a higher level of competition playing pro ball in Europe.   

Previuosly, I did a comparison of the NBA Development League (D-League) to the NCAA to compare the competition level and in particular to evaluate PJ Hairston, a former North Carolina player who has recently started playing in the D-League in hopes of getting drafter this summer.  

The method there revolved around comparing a player's score on a metric called Alternative Win Score in both leagues that he played, in that case, the NCAA and the D-League.  There was a clear pattern of players showing a degradation in their production against D-League competition, especially when the slower college pace is taken into account.

As a reference for the AWS scale I made this chart, as a rough idea what different  AWS numbers per 40 minutes mean.

AWS Score	11.00	8.50	6.50	4.50	2.50	0.00
Description	Duant-ian	All-Star Conversation	Solid Starter	Role Player	Back Bench Player	Soon to be Out of the League
Example	Kevin Durant	Al Horford	Kyle Korver	Brandon Bass	Ryan Hollins	Marquis Teague

 

Looking at play in Europe is a bit more difficult than the D-League, as there is no single European league.  The league Bucks first round selection Giannis Antedekoumpo played in was a decidedly junior Greek league, the kind of which many young 'draft and stash' European prospects taken in the second round have the majority of their on court experience.  For this comparison, I am using the seven larger European leagues tracked by  DraftExpress.com.  The level of play isn't exactly the same in the different European leagues used here, with the Spanish ACB and Euro-League being the best.  However, it is relatively close, especially compared to the distance  apparent between general NCAA competition and the European leagues.

I looked at 29 NCAA players that went from the college game right over to Europe like Iverson and Musina concentrating on the last three years.  The per 40 minutes stats for both college and European Leagues were pace adjusted to the NBA level, as I did in the D-League comparison.  The median AWS fell by 2.1 points going from college over to the major European leagues.  All of these players are coming out of college and still developing, so the difference in competition levels is probably understated slightly.

The differences in some of the major stats categories are below shown as percentages of possessions: 

AWS Diff	TS% Diff	Reb% Diff	TOV% Diff	PF% Diff
-2.13	-2%	-1.1%	0.3%	1.9%
-2.28	-1.7%	-0.9%	0.2%	2.1%

 All of the categories showed deterioration moving to the European pros.  

A good example of a player that falls fairly close to the central tendencies here is Kyle Singler, whose AWS per 40 fell from 6.6 his senior year at Duke, a solid starter, to role player numbers in the Spanish ACB and the Euroleague with an average AWS of 4.3 (It dipped again his first year in the NBA though is hovering between role and deep bench level this year).

Another is Robbie Hummel, who put up an All-Star level 9.7 his senior year at Purdue and fell to a 6.8 Quality starter level in the ACB. (And is scoring a 4.5 role player level with the T-Wolves).

For fans of the transitive property, I also tested movement between the D-League and European leaguesm finding a difference of -.88 moving from the D-League to Europe using 65 players going from Europe to the D-League or the other way around.  Since the college players going to the D-League scored about -1.5 AWS lower, the total of those added difference are roughly in line with the college to Europe total difference. 

This one of those projects with a lot of data collection and calculations that ends up with a pretty simple answer and not one all that unexpected.  But, still a necessary step since I plan to keep including European draft prospects in my Draft potential model.  (And happily these findings follow my initial estimate closely).