In The Wages of Wins, David Berri and company introduce a basketball valuation system called Win Score. This easily calculated metric merely requires a box score and some basic math. To learn more about Win Score, check out The Wages of Wins
book or numerous posts in this forum.
The Win Score model was built off of NBA statistics, but the same metric can be used to assess players entering the NBA. In past years, I found that Win Score has been particularly adept in identifying late 1st round and second round gems such as Josh Howard
and Carlos Boozer
. For 2006, Win Score successfully foretold slot over-performers like Brandon Roy
, Rajon Rondo
, and Paul Millsap
while identifying underperformers in J.J. Redick
, Adam Morrison
, and Randy Foye
. The 2007 College Prospects, an Overview
For 2007, Win Score offers several opinions. Like everybody else, it shares excitement for Oden and Durant. More interestingly, Win Score offers opinions on who will outperform or under-perform their draft projection. Here is an over-view of these projections.Pick Booms: Nick Fazekas
, Stephane Lasme
, Rashad Jones-JenningsViews favorably: Greg Oden
, Kevin Durant
, Al Horford
, Joakim Noah
, Julian Wright
, Morris Almond
, Aaron GrayViews unfavorably: Jeff Green
, Taurean Green
, Gabe Pruitt
, Nick Young
, Thaddeus Young
, Wilson Chandler
, Javaris CrittentonPick Busts: Corey Brewer
, Acie Law
, Spencer HawesPAWS and the Tier System
Let's go a bit further into the numbers.
Chad Ford of ESPN gave an analysis of the NBA Draft based on the concept of a Tier System. In essence, rather than rank the prospects in order, Ford argues that a better system is to group players into tiers. The top tier in this draft consists of Greg Oden
and Kevin Durant
. Tier two includes players like Al Horford
and Mike Conley Jr
Ford offered six tiers, consisting of both international and college players. Let's focus on the 30 college players Ford considered and rank these players in terms of Position Adjusted Win Score (PAWS) ranked per 40 minutes from the players last year in college.Table One: PAWS40 and the Top Prospects
The top player according to this view is Nick Fazekas
. Is he better than Oden or Durant? Not sure anyone would make that argument. Still, it does suggest he might be better than a 6th tier prospect.
The top players in PAWS40 among the players generally thought of as lottery choices include Oden, Durant, Horford, and Noah.
And looking at the bottom of the list
. perhaps teams should think a bit more about drafting Hawes or Aaron Afflalo.Adjusting for Level of Competition
Since Win Score was derived from the evaluation of the NBA, there wasn't a strong need to adjust for level of competition. The NBA teams sport a much narrower talent gap than the NCAA conferences and International leagues. In looking at college players, though, we have to note that the wider NCAA talent distribution allows for players to pick on the less skilled teams. The following tables offer an assessment of how well various players played against NCAA tournament teams, versus their performance otherwise. These tables suggest, in a very limited sample, that maybe Fazekas is not quite as good as his overall numbers indicate.
[c]Prospect's per-minute Performance Against Tournament Teams[/c]
[c]Prospect's per-40 minute Performance Against Tournament Teams [/c]Looking at International Players
International leagues suffer from this problem and more. The shorter seasons provide for smaller sample sizes. Key statistics for Win Score are often not included in international box scores. To assess the 2007 class, turnover and fouling stats were approximated by taking the median rate of their draft class position peers. In spite of these issues, Win Score suggests the following:Favorites: Yi Jianlian
, Marco Belinelli
, Luka Bogdanovic
, Jonas Maciulis
, Kyrylo Fesenko
, and Mirza BegicBuyer Beware: Tiago Splitter
, Petteri Koponen
, Marc Gasol
, Sidiki Sidibe
, and Dimitri Sokolov
Win Score loves Yi Jianlian
, even if we change our assumption and have him post double the median foul and turnover rate.More information at DraftExpress
Perhaps you wish to see more than is provided in this post. To quickly examine the scores of previous years, access the DraftExpress stats database
, select Stat Type: Usage stats and sort by WS/40 (Win Score per 40 minutes).
One can also go to the following position-by-position analysis offered by Mike Schmidt at DraftExpress, which details all the numbers for many of the top prospects in 2007.Just by the numbers (Part One)
Evaluating this Year's Point Guard Crop Just by the numbers (Part Two)
Evaluating this Year's Shooting Guard Crop Just by the numbers (Part Three)
Evaluating this Year's Small Forward Crop Just by the numbers (Part Four)
Evaluating this Year's Power Forward Crop Just by the numbers (Part Five)
Evaluating this Year's Center Crop A Disclaimer and a Claim
As noted, the Win Score metric was created to analyze the NBA. Berri tells us that further research is ongoing, including studies on the college to NBA transition and player development.
While Win Score should not be used as the only basis to order up a draft board, it serves as an objective tool in assessing player performance. To my knowledge there is no better tool to identify potential difference makers after the lottery picks have come and gone.
A Second Look at the International PlayersAKA: Making An Ass Out of Me
International players present a problem to the average draftnik. For most of us, its hard to assess foreign players due to many factors, one being they are not on TV in March. The following is a sequel to the Win Scores 2007 Draft Preview, taking a look specifically at the international draft class given a few necessary assumptions.
This article is aimed towards the curiosity seekers rather than hardcore statisticians. The same assumptions can look "reasonable" to one person and "completely insane" to others. To that end, I'll happily provide the skeptics with the following potential ammo:
1. Assumption about personal fouling rate
2. Assumption about turnover rate
3. Assumption on playing time
4. Unknown league difficulty
Now that's out of the way, let's have some fun and look at some data. Below, I'll detail the assumptions made and conclude with a few predictions. Feel free to skip to the end if thats what you are seeking.
The international data was collected from the prospect pages of nba.com
. The pages were consistently formatted and offered playing time data in some season commentary. Notably absent was turnover and personal foul information, which are part of the Win Score calculation.
To estimate turnovers and personal fouls, I relied on data from DraftExpress.com's stats database (invaluable!) and a series of stat-loving columns by Mike Schmidt. I took the median turnover and fouling rates for 2007 draft prospects by position and applied them to the international players.
Nearly half of the available data from NBA.com did not have playing time data. For those that did not have playing time data, Win Score per minute was calculated as if they had MPG numbers of 15, 20, 25, and 30 minutes. The attached table shows the resulting PAWS/min (position-adjusted Win Score per minute, .100 is average, higher is better). The table also notes in blue which seasons required a playing time assumption.
Win Scores loves Yi Jianlian
, Rudy Fernandez, and Jonas Maciulis
. Marco Belinelli
, Brad Newley
, and Luka Bogdanovic
also appear to be recent bloomers on the rise. On the flip side, Win Scores is down on Tiago Splitter
and Yannick Bokolo
In my previous article, I shared excitement on Kyrylo Fesenko
and Mirza Begic
, but upon review, the sample sizes are a bit too small to warrant enthusiasm. Likewise, small sample sizes grants temporary reprieves to Koponen, Sidibe and Sokolov. Unfortunately, insufficient data was available to assess prospects Joao Gomes
and Sun Yue