Nylon Calculus: Resetting Andrew Wiggins’ ceiling

Dec 6, 2016; Minneapolis, MN, USA; Minnesota Timberwolves guard Andrew Wiggins (22) dribbles in the first quarter against the San Antonio Spurs at Target Center. Mandatory Credit: Brad Rempel-USA TODAY Sports
Dec 6, 2016; Minneapolis, MN, USA; Minnesota Timberwolves guard Andrew Wiggins (22) dribbles in the first quarter against the San Antonio Spurs at Target Center. Mandatory Credit: Brad Rempel-USA TODAY Sports /

I recently wrote about Andrew Wiggins, scouting out the type of player that he is and what he is developing into. He has thus far been a good volume scorer that hasn’t shown much of an all-around game. This season he has shown signs of becoming an elite scorer, but still hasn’t shown much in the way of creating shots for teammates on offense, rebounding consistently, or making much defensive impact. What is the future of a player like that?

When Wiggins was coming up through high school he was projected to be a Michael Jordan/Kobe Bryant type wing. But I’ve pointed out that, in a lot of ways, Wiggins plays more like a 1980s small forward than Jordan or the superstar wings since. Forwards in the 80s were often primarily scorers that, when they weren’t trying to score, often operated off the ball. In the post-Jordan era, though, wings have become more involved in creating offense for their teams off the dribble.

Wiggins has increased his scoring in each of his three seasons to date, with current averages through 21 games (per 100 possessions):

Wiggins: 30.5 pts (53.2% TS), 5.4 reb, 3.3 ast (assist% = 11.6%), 3.7 TO, 0.8 stl, 0.5 blk

Here is a list of many of the small forwards and big wings in the last 40 years that have put points on the board the way that Wiggins has this year, along with their prime box score stats per 100 possessions:

fig-1-wiggins-high-scoring-wings-sorted-by-pts-per-100 /

That’s a bit of a wall-of-text, but it’s a veritable who’s-who of high-scoring wings in the last 40 years that makes a great test-bed for evaluating the type of player that Wiggins is and what he might do to maximize his upside. To make that evaluation, we need two primary things:

1) A group of testable characteristics to place Wiggins within the group
2) A method to estimate the size of each player’s impact

To the first point, in this article we will focus primarily on offense, discussing defense in a subsequent post. There are many ways that a player can make an offensive impact, but in this article we will be examining players that had “volume scorer” as one of their main areas of contribution. From within that population of volume scorers, then, we will further examine how a) their scoring efficiency and b) their ability to create offense for others correlated with their estimated impacts.

Read More: The Boston Celtics are still working it out

Scoring efficiency will be estimated using true shooting percentage, but offense creation is more difficult to measure. There is no one stat in the boxscore that truly captures which player is most responsible for creating a scoring opportunity, but the closest measured output would be assists. To best normalize assists to largely varying pace environments, in this article assist percentage will be used.

To the second point, the family of +/- stats have been the best “impact stats” available. However, those stats are only available universally back through the mid-90s, while in this article we are evaluating players back to the 70s. Recently, a new stat called WOWYr has emerged as a method of estimating impact all the way back through NBA history. WOWY, With-Or-Without-You, examines players by tracking their teams scoring margins in games that they play vs games that they sit.

Previously, this approach was limited to years in which a player either missed enough games to be analysis-worthy or to instances when a player was traded in mid-season. WOWYr is a regressed version of the stat that expands the scope of the stat to include all players, using ridge regression to estimate the impact of players across all seasons, whether they missed games or not. Because the stat is new it has not been vetted as thoroughly as the available +/- stats and ElGee’s Back Picks is currently the only publicly available source, but it does offer an objective impact measure across NBA history for use in this study.

Scoring efficiency: WOWYr vs TS%

fig-2-wiggins_wowyr_vs_ts_labeled_color /

In plotting the WOWYr score from the prime years of this group of volume-scoring wing-forwards against their true shooting percentage, a general trend of higher efficiency correlating with higher WOWYr emerges, but with observably large variance. In the plot above, four arbitrary categories were chosen purely by demarcating breaks in true shooting percentage and those categories were color-coded to make group identification easier.

The highest efficiency group (red) contains the two highest WOWYr marks, but also the second-lowest (mean ± S.E.M. = 6.0 ± 1.9). This trend continues for the purple efficiency tier, with Pierce and Erving with high WOWYr scores and Gervin and Johnson with low (group mean 4.1 ± 1.0). In the light blue tier, Bryant and Drexler had high WOWYr scores while the Worthy/Anthony/English group was in the bottom half of WOWYr scores (group mean 4.4 ± 0.8). The lowest efficiency tier (dark blue) showed McGrady among the top-half of WOWYr scores but Wilkins and DeRozan among the bottom-four (group mean 2.3 ± 1.0).

Shot creation: WOWYr vs Assist %

fig-3-wiggins_wowyr_vs_astpercent_labeled_color /

Here, there is a much clearer correlation between positive measured impact and assist percentage. The groups were made the same way (e.g. arbitrary separation based upon gaps in the assist percentages of the players) and color coded similarly. But here, the red group had distinctly and consistently higher WOWYr scores (group mean = 6.9 ± 0.8) than the purple (5.3 ± 0.7), who was higher than the light blue (2.9 ± 0.5) which was higher than the dark blue (1.1 ± 0.4). There were no large outliers in any of the groups, which resulted in tighter clusters with less variance.

Andrew Wiggins’ true shooting percentage (53.2%) and assist percentage (11.6%) through 21 games this season would put him within the lowest (e.g. dark blue) categories for both measures, in the plots above. This would suggest that, if Wiggins continues to play offense the way that he does today, his ceiling would be at the lower end of this group of players. instead of the Jordan or Bryant who he was expected to be out of high school, he would project more along the lines of Kiki Vandeweghe or what we’ve seen prior to this season from DeMar DeRozan.

The findings here match with what Andrew Johnson reported in his article about the relative importance of shot creation vs shot making:

"“The higher COV on passing efficiency within positions indicates that passing efficiency is a measure with some real separation in talent. Further, the outliers on the upper end tend to line up with the players we think of as stars. With Draymond Green, LeBron James, James Harden and Russell Westbrook all leading the league in their respective positions by this metric. The somewhat more traditional points created by assists per turnover also shows more separation and variance by position than any of the scoring play types do in efficiency as well.In fact, Justin Willard’s research found that there is an interactive quality between scoring and passing proficiency; being a superior passer increases the impact of a player’s scoring and vice versa. The idea being that those high volume scorers and passers are typically the players putting pressure on the defense, drawing double teams and disrupting their opponent’s defensive scheme.”"

So, for Wiggins to reach the impact level of the greatest wings, to increase his ceiling beyond that of the non-versatile inefficient volume scorer he needs to improve his offense beyond just increasing his volume. Perhaps an increase in scoring efficiency might help his impact, but becoming more of an offense-creator for his team is a much more effective method of improving impact.