Kevin’s Summer Project

November 5th, 2012 by Kevin Hetrick

Cavs fans, bear with me here.  This summer, my NBA draft-experting led me down a rabbit-hole I could not evade.  As the draft approaches, a plethora of athleticism data arrives in late May and early June, and I struggle with the question: “what does it mean”?  Dion Waiters is only 6’ – 4”; Jeremy Lamb has long arms; in a laboratory, Harrison Barnes jumps really high.  Should I care about any of this?  I embarked on a project to track how pre-draft measurements correlate to actual, eventual NBA production.  In today’s post, I hope to introduce the process.

Who is this guy? I don't know, but he has a 7-foot wingspan.

I started by compiling the pre-draft measurements for every drafted NCAA player from the 2000 through 2010 drafts.  This data was gleaned from the world’s most comprehensive draft website: drafexpress.com.  I focused on eight measurements:

  • Barefoot Height
  • Wingspan
  • Reach
  • No-Step Vertical Leap
  • Maximum Vertical Leap
  • Three-Quarter Court Sprint Speed
  • Lane Agility (Cone) Drill
  • Bench Press reps of 185 pounds.

To my spreadsheet, I assembled every player’s Offensive Win Shares (OWS), from basketball-reference.com, for each of their first four seasons.  For players drafted in 2000 through 2007, their “peak” season of OWS from their first four years is also evaluated.  The analysis ignores the strike-shortened 2011 – 2012 season, hence no four-year-peak season for 2008 draftees.  I purposefully chose separate offensive and defensive metrics.

Also, players were sorted into two groups by age; 21 and under as of February 1st of their rookie year, or Older.  Additionally, players were categorized by the five standard basketball positions.  Utilizing the positional-labels proves important, as comparing OWS across the entire spectrum of possible heights and athleticism would be meaningless; obviously both tall and short players are successful; clearly little guys are faster than big men, but both succeed.

After sorting into those various categories, I correlated each of the eight measurements with the players’ OWS’s.  Each player had a maximum of five OWS values: 1st season, 2nd season, 3rd season, 4th season, and peak.   Near-zero correlation meant no discernible relationship between the measurement and NBA offensive performance.  Highly positive correlations reflect that players strong in that particular measurement were likely to be successful offensive players.  Negative correlations can largely be regarded as near-zero; I won’t advance any theories that a certain group of players is better off being smaller or less athletic.  (As a final note, speed and agility correlations were made negative; i.e. smaller sprint times resulting in larger OWS are reflected as positive correlation.)

With that as the basics of the study; the pre-draft measurements provide a fairly minimal array of predictive uses for offensive production.  For five positions, two age groups per position, five seasonal OWS values, and eight measurements; there were four-hundred correlations.  The graph below reflects their distribution.

As you can see, this is approximately a bell-curve centered near zero.  Of the 400; only 228 (57%) are positive correlations, only sixty-five (16.3%) exceed 0.25, and only two exceeded 0.50.  As a frame-of-reference, here are graphs reflecting 0.25 and 0.50 correlations:

The 0.25 correlation graph is fairly useless.  In the specific case of this graph, Danny Granger was both tallest and overwhelmingly most offensively successful.   This alone drove the positive correlation.  The next four-tallest players were all offensively worthless.

The 0.5 correlation starts to resemble something meaningful.  Four of the five highest-leapers managed successful seasons, and the fifth was Greg Oden.  With one exception, the low-fliers all struggled.

Part of the draw towards the low correlations is second-round picks adding noise to the data, as every NBA flame-out was awarded zero win-shares for each season.  Looking only at first-round picks, with their guaranteed contracts, provides the distribution below.  There are only 320 correlations here, due to sample-size issues.  For both guard positions, there were relatively few upperclassmen first round picks, so I left everyone in one age-group.

194 (60.6%) were positive, with sixty-six (20.6%) exceeding 0.25. Most encouragingly, a tantalizing eighteen rose above 0.50.

Well, hopefully I have capably communicated the basics.  The only conclusion I hope you drew today is that the pre-draft size and athleticism measurements offer little predictive information relating to NBA offensive performance.  Over the course of the season, I plan on providing insights into:

  • What are those high-correlation measurements?  How useful are they?
  • What about defense?  A reasonable hypothesis would say that size & athleticism are more critical there.
  • Which measurements rarely or never provide strong correlation with offensive or defensive performance and hence, are reasonable to ignore?
  • Are there athletic traits that NBA teams are over or under-valuing?  Certainly some negative correlations are due to GM’s overpersuing players based on certain athletic profiles that do not reliably prove successful.
  • Are there combinations of traits that prove highly reliable towards predicting success of a drafted player?  What about failure?
  • Did the hand-check rules initiated in 2004 – 2005 make speed & athleticism more important?

I hope this turns out to be an interesting and provocative series.  Let me know your thoughts in the comments section.