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Andrew Wiggins And The Problem With Scorers

Minnesota Timberwolves swingman Andrew Wiggins was named the NBA’s 2014-15 Rookie of the Year on Thursday. The announcement came as no surprise: It’s an award he’s essentially been a lock to win since at least February.

But there’s a big disconnect between what the eye test (plus basic statistics such as points per game) and the analytics say about Wiggins, both in terms of his current production and his future potential. And because of that discrepancy, Wiggins is emblematic of what’s long been one of the most difficult problems to solve in basketball analysis.

According to conventional analysis, Wiggins had a standout rookie campaign. Despite being a callow 19-year-old, he averaged nearly 17 points per game for the season, including 19.1 PPG from New Year’s Day onward, and provided some of the most sharable Vines of any player in the league. On top of his scoring output, he’s also regarded by scouts as a player with elite defensive potential because of his length and athleticism. Viewed in those terms, Wiggins’s Rookie of the Year nod could be seen as a launchpad for a Hall of Fame career.

The advanced statistics are far less impressive. As others have noted, according to Value Over Replacement Player (VORP) Wiggins had one of the worst seasons by a Rookie of the Year winner since 1973-74, which is as far back as the statistic can be calculated. Also judging by VORP, 60 other rookies provided more value to their teams this season. And even after a high-scoring spike in performance at midseason, Wiggins’s final 2014-15 Statistical Plus-Minus (SPM) of -2.4 was barely better than the -2.9 mark that could have been expected by simply regressing his stats to the mean back in December.

ESPN’s single-season Real Plus-Minus (RPM) for Wiggins’s offense was higher than his SPM, suggesting he makes an impact at that end that can’t be fully detected by the box score. But in his supposed strong suit — defense — RPM ranked Wiggins in the 14th percentile of all NBA players in terms of per-possession performance, even after adjusting for the quality of his teammates (or lack thereof) and the opponents he faced. And Synergy Sports judged his capabilities as an individual defender only marginally better, ranking him in the 32nd percentile of defenders according to its video-scouting metrics.

So what gives? Why are the eyes so high on Wiggins, but the numbers so down?

Part of it is age. If we give a bonus to Wiggins’s SPM according to an aging curve, setting every Rookie of the Year winner on equal footing at age 22,1 Wiggins shoots up the list of winners, from No. 40 (out of 41) in rookie wins above replacement (WAR) to No. 27.

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Michael Jordan 1985 21 -7.6 16.9 18.5 +1.6
Chris Paul 2006 20 -8.5 13.2 16.4 +3.2
Shaquille O’Neal 1993 20 -2.9 10.6 14.0 +3.5
Tim Duncan 1998 21 -0.4 12.2 13.8 +1.6
David Robinson 1990 24 -2.5 15.1 13.2 -1.9
LeBron James 2004 19 -5.4 6.3 12.4 +6.1
Chris Webber 1994 20 -2.0 9.3 12.0 +2.8
Blake Griffin 2011 21 -7.5 10.3 11.9 +1.6
Larry Bird 1980 23 +2.6 12.2 11.1 -1.1
Mark Jackson 1988 22 -4.3 9.4 9.4 0.0
Tyreke Evans 2010 20 -7.1 6.1 9.2 +3.0
Terry Cummings 1983 21 -7.8 7.8 9.1 +1.3
Elton Brand 2000 20 -12.5 5.2 8.6 +3.4
Adrian Dantley 1977 20 -6.2 5.3 8.5 +3.2
Kyrie Irving 2012 19 -9.6 4.6 8.3 +3.8
Larry Johnson 1992 22 -6.9 7.7 7.7 0.0
Pau Gasol 2002 21 -10.6 6.1 7.6 +1.5
Steve Francis 2000 22 -3.9 7.3 7.3 0.0
Allen Iverson 1997 21 -9.1 5.7 7.2 +1.5
Buck Williams 1982 21 -1.6 5.7 7.1 +1.4
Jason Kidd 1995 21 -5.1 5.3 6.6 +1.3
Walter Davis 1978 23 +0.4 7.4 6.4 -1.0
Vince Carter 1999 22 -4.4 6.3 6.3 0.0
Grant Hill 1995 22 -10.3 6.1 6.1 0.0
Kevin Durant 2008 19 -9.3 0.6 6.0 +5.4
Derrick Rose 2009 20 -1.2 2.3 5.7 +3.4
Andrew Wiggins 2015 19 -9.0 -0.7 5.1 +5.8
Mike Miller 2001 20 +0.1 2.4 5.1 +2.7
Chuck Person 1987 22 -2.7 5.0 5.0 0.0
Amar’e Stoudemire 2003 20 +0.5 1.9 4.8 +2.9
Brandon Roy 2007 22 -6.6 4.5 4.5 0.0
Ralph Sampson 1984 23 -5.2 5.5 4.5 -1.0
Damian Lillard 2013 22 -5.2 4.5 4.5 0.0
Phil Ford 1979 22 +0.9 4.3 4.3 0.0
Damon Stoudamire 1996 22 -9.3 3.3 3.3 0.0
Derrick Coleman 1991 23 -6.1 4.0 3.1 -1.0
Michael Carter-Williams 2014 22 -11.6 2.8 2.8 0.0
Patrick Ewing 1986 23 -6.8 3.0 2.3 -0.7
Mitch Richmond 1989 23 -1.6 3.2 2.2 -1.0
Emeka Okafor 2005 22 -6.9 1.1 1.1 0.0
Darrell Griffith 1981 22 -5.0 -2.7 -2.7 0.0

There was also the matter of Wiggins’s awful teammates. According to SPM,efficiency differential would be if we removed the player from the roster and gave his minutes to a replacement-level player.

">2 Wiggins was saddled with the ninth-worst teammates of any Rookie of the Year winner since the ABA-NBA merger. Teammate Zach LaVine posted the worst WAR of any player in the league, Anthony Bennett ranked 17th-worst, and Adreian Payne was sixth-worst in the league based on his time in Minnesota alone, despite not joining the team until February. Simply put, Wiggins had to carry more of the Timberwolves’ load because he played with a truly terrible supporting cast.

But that doesn’t explain all of the disparity between Wiggins’s conventional accolades and his feeble advanced metrics. After all, the quality of a player’s teammates is barely correlatedcorrelation of 0.098 between a player’s “teammate rating” and his own SPM; there’s also a correlation of 0.117 between the year-to-year change in a player’s teammate quality and the change in his (age-adjusted) SPM.

">3 with his own performance. A bigger reason might relate to a question APBRmetricians have grappled with for years: How exactly should we deal with high-volume scorers?

Former ESPN Director of Production Analytics (and current Sacramento Kings Director of Analytics and Player Personnel) Dean Oliver devoted an entire chapter (titled The Problem With Scorers) in his seminal book “Basketball on Paper” to the issues involved in statistically evaluating players who perform what seems the most essential of on-court acts: putting the ball in the basket. Although he determined that per-possession efficiency was the best measure of a team’s offensive prowess and developed equivalent efficiency metrics for individual players, Oliver also posited that a player’s offensive efficiency was prone to changes based on how much of a scoring workload he took on.

That theory, which has largely been borne out by subsequent studies, implies that a player’s efficiency numbers aren’t even close to being all his own — and that, crucially, high scorers such as Wiggins represent the group most centrally affected by such interplay between teammates. Furthermore, raw scoring ability may suggest heightened potential even after controlling for a player’s actual rookie production. If you run a regression attempting to predict a rookie’s remaining career WAR from his first-year statistics, the second-most important predictor (though dwarfed by the effect of his age-adjusted rookie WAR itself) is usage rate, a measure of how frequently a player was called on for scoring attempts within his team’s offense, regardless of their success.

The idea that Wiggins’s scoring and athleticism speak volumes about his potential in a way that can’t be captured by his rookie-season value metrics goes a long way toward explaining the gulf between his subjective reputation and the numbers. Only time will tell which is right, but that differential could position Wiggins as his generation’s Allen Iverson or Antoine Walker — players who served as early battlegrounds in the war between analytics and conventional wisdom.


  1. The average age of all NBA rookies since the 1976-77 season.

  2. Specifically, a calculation estimating how poor the team’s efficiency differential would be if we removed the player from the roster and gave his minutes to a replacement-level player.

  3. Since the merger, there’s only a correlation of 0.098 between a player’s “teammate rating” and his own SPM; there’s also a correlation of 0.117 between the year-to-year change in a player’s teammate quality and the change in his (age-adjusted) SPM.

Neil Paine was the acting sports editor at FiveThirtyEight.