The Point-Value of a WNBA Live-Ball Turnover

Update: 23 April 2017 10:45pm. I detected an inaccuracy in the table in the subsetting of live ball TOs leading to scoring opportunities. Originally, I accidentally computed the ns and values for the subsets using all live ball TOs. Also, although never explicitly mentioned in the post, the value (or cost) of a live ball turnover is equal to the value of a steal.
 
So, I’ve got this trove of WNBA PBP data and a range of exhausting ideas. However, I’ve been inundated with projects and limited time to pursue said ideas. One of those ideas involves a metric for expressing WNBA player productivity as points. In this post, I’ll discuss the value of a turnover for the purposes of using it in that metric.

My idea of expressing player productivity in points is not a unique notion. It is similar to the concept of Marginal Productivity, described by its author David Sparks here and here and applied to the WNBA, here. And as I contended in a previous post, Sparks notes that because the “…regression coefficients … were fitted for the NBA, it is unclear whether or not their values translate identically to WNBA play…” Sparks does suspect, however, there will be little difference between the leagues. The WNBA MP spreadsheet appears to have been designed to update automatically and since the post is dated 2008, there are no values in the sheet. 

Nonetheless, in the Marginal Productivity model, a steal is valued at ~1.60 points and a non-steal turnover is valued at ~1.45 points; we’ll call these live- and dead-ball turnovers. Here, the live-ball turnover is worth about 10% more points than a dead ball turnover such as a travel or a pass hurled out of bounds. This makes sense intuitively and thus, we expect that live-ball turnovers will be more valuable than dead-ball turnovers.

Probably while in the shower or wading through traffic I proposed to myself borrowing a concept from football analytics, expected points. Scoring in the game of basketball is much more fluid than in football, so I rebutted to myself that instead of average next points we should use average points per next scoring opportunity, which include a field goal attempt (FGA) or freethrow attempt (FTA) but also clear path fouls and flagrant fouls. This contrasts with the caveat in the following paragraph because the live-ball turnover leads directly to a scoring opportunity that is criminally prevented by the foul and thus, the FTAs are the scoring opportunity.The points at the next scoring opportunity for live-ball turnovers that directly resulted in a foul with FTAs equal the points accrued for that trip to the line. 

This approach excludes 2-points scored, say, when a live-ball turnover leads to a missed FG, followed by an offensive rebound put-back layup. Inarguably, the turnover in this example leads to the positioning of the player completing the put-back, the defense being out of position, and the points. However, there are numerous outcomes other than an offensive rebound that could have occurred. Furthermore, we’ll have isolated the value of live-ball turnovers, or keep that value separated when we engage in a similar analysis of rebounds.
 
The data were all plays with FGs, missed FGs, FTs, FTAs, and turnover from every WNBA regular season game 2014-16. Something like 121,834 plays; 35238 FGs, 45864 missed FGs, 23735 FTAs, and16,997 TOs. There were 9012 live-ball TOs and 7985 dead-ball TOs. 7683 of the live-ball TOs directly resulted in a scoring opportunity. Table 1 contains the average points per scoring opportunity. 

Table 1. Average Points Per Next Scoring Opportunity from Turnover WNBA, 2014-16

Turnover	n	pts
Live-Ball TO	9012	1.160
Live-Ball TO to Scoring Opportunity	7683	1.173
bad pass	6014 5142	1.167 1.182
lost ball	2945 2499	1.149 1.161
possession lost	53 42	0.887 0.833
Dead Ball TO	7985	0.979

As we expected and as is consistent with prior research, live-ball rebounds have a higher average point per scoring opportunity than dead-ball rebounds. For the present findings, the point-value of live- and dead-ball TOs are less than that of prior research. This could be due to the different computations in the analytics employed. It could also be due to differences in NBA and WNBA gameplay styles. That is, the value of a TO is less in the WNBA because a greater proportion of WNBA possessions end in TOs by way of stealing. 

Summarily, an expected points approach was used to compute the average points per next scoring opportunity directly resulting from a TO in the WNBA. This author proposed that the WNBA live-ball TO may be worth less than the NBA TO because there is a higher rate of TOs in the WNBA (different analytic approaches from prior research notwithstanding).

Comparison of WNBA and NBA 2-Point Field Goals

Comparisons of WNBA and NBA Association-wide, season-level data were compared in a previous post. For the season of each that was analyzed, disparities in FG% were evident. Initially, the NBA appeared to be more successful shooting, 44.9% to 42.5%. However, when excluding dunks, the WNBA was slightly but significantly more successful, 42.5% to ~40%. Having recently acquired WNBA play-by-play (PBP) data for the 2016 season, we can more granularly analyze FG%.

All WNBA data were extracted from the regular season 2016 PBP which is available upon request. I will dump this data along with the regular season PBP data from the 2014 and 2015 seasons in a future post. The NBA shot type and assisted shot type data for the 2015-16 season was culled from Basketball Reference.

Table 1a. WNBA & NBA 2016 counts

Type	Total	NBA	WNBA
All 2FG	Made	75549	9815
	Attempted	153768	20676
Shots	Made	35680	4769
	Attempted	89487	12318
Layups	Made	30439	5045
	Attempted	53920	8356
Dunks	Made	9430	1
	Attempted	10361	2

Table 1a contains counts and 1b proportions of 2FGAs segmented by shots, dunks, and layups between the Associations for the regular seasons ending in 2016. The class of ‘shots’ includes not only jump shots but also others identified in the PBP as floaters, hooks, and runners. Table 1b also contains Chi-square test-statistics demonstrating that the is NBA is slightly more successful shooting 2-point shots. The WNBA is significantly more successful with layups. The Chi-square was not performed for dunks because only 2 were attempted by WNBA players.

Table 1b. 2pt-FG% by Shot Types

Type	NBA	WNBA	χ²	p
All	0.491	0.475	6.961	0.008
Shots	0.399	0.387	2.623	0.105
Layups	0.565	0.604	12.230	0.000
Dunks	0.910	0.500

So, these findings provide a more nuanced perspective on the findings from the previous post that WNBA is more successful shooting non-dunk shots. The two posts did employ data from different seasons. Nonetheless, given the present data, the two Associations ostensibly shoot 2-point shots (i.e., not dunks and layups) with similar successfulness—the p¬¬-value approaching conventional significance levels is likely a result of large quantities. That is, a 1 percentage-point advantage to the NBA may approach significance statistically, but I suspect it is ecologically meaningless.

Alternatively, the WNBA was significantly more successful than the NBA shooting layups. My initial notional hypothesis was that because males are predisposed to heightened athleticism, there are more contested or blocked layup attempts in the NBA. Ironically (to the impetus for pursuing this line of research), although blocked layup attempts can be extracted from the WNBA PBP, I am unable to locate blocked layup attempt data for the NBA (short of scraping). Likewise, contested shots attempted from with ≤5ft from the basket are available for the NBA but shot contesting is not recorded in the WNBA PBP.

Table 2. Assists on Dunks + Layups

Dunk + Layups	NBA	WNBA	χ²	p
Made & Assisted	15536	2749	6.596	0.010
Made	64281	8358
Proportion Assisted	0.242	0.329

Table 2 contains the proportion of dunks and layups, combined, that were assisted. The WNBA assists on significantly more of their successful layups (and dunks) than does the NBA. So, this might also explain why WNBA players are more successful executing layups. Because the WNBA assists on a greater proportion of layups, there may be more floor spacing or player movement such that defenders are less frequently positioned to defend layup attempts. Indeed, this notion is interrelated to there being greater athleticism in the NBA, as well as greater size, and thus less floor space in the NBA. Lastly, WNBA players may execute successful layups in certain scenarios whereas NBA players would likely execute successful dunks such as on uncontested fast breaks.

If athleticism were the sole determinant in explaining WNBA layup FG% superiority, there would be little recourse for defensive strategists other than playing larger or quicker players. However, if it is the result of floor spacing or player movement, I suspect WNBA coaches transiently employ zone defenses to narrow passing and driving lanes to reduce opponents’ layup success.

Summarily, this report indicates that WNBA and NBA players shoot with similar accuracy on 2-point FGs that are not dunks or layups. Also, the WNBA is more successful on layups than the NBA. This author posited three reasons why this may be: (a) greater athleticism and size on NBA players results in more contested layup attempts and passes; (b) relatedly, the WNBA assists on a higher proportion of its layups which may be the result of more floor space or player movement, but is also related to point (a); and, (c) NBA players may execute dunks in many scenarios where most WNBA players would have to execute layups, also related to point (a).

Comparison of WNBA and NBA League-wide Team Data

Monday 30 MAY 2016. Update: I prepared and published this post in a haste only to realize while in the shower several days later the mistaken inclusion of three variables in the Table of this post: "ORB / FG missed", "DRB / FG missed", and "Dead Ball Rebounds". With the data that was used in computation, these are inaccurate and meaningless statistics because rebounds can be gathered on missed shots as well as missed freethrows and we are unable to distinguish from league and team totals whether rebounds were gathered on FGAs or FTAs. Likewise, Dead Ball Rebounds was inaccurately included in my haste and computation of such a statistic would require additional data. Apologies.

Point guard of the WNBA Seattle Storm Sue Bird contributed to a recent installment of the 538 podcast HotTakedown. Bird’s appearance was precipitated by an op-ed she authored for the Player’s Tribune illuminating the paucity of accessible, informative data for the WNBA and for female sports more broadly. Days later, hysteria followed comments made by a veteran sports writer regarding the unprecedented dominance of the UConn women basketball team. The implications of that writer’s statement have been summarized elsewhere.

After listening to Sue Bird on the 538 podcast and before learning of the galvanizing comments, I watched much of Uconn’s decimation of Mississippi State. I watched because I enjoy that sort of competitive dominance; plus, their team is just good. I’ve yet to write about it here but I also watched because I love basketball. After hearing the galvanizing comments, all I could think was: with increased access to informative data and with an array of perspectives creating narratives using such data, maybe a weakness would be identified.

Admittedly, I watch less female than male basketball but that is partly attributable to the greater viewing options for the latter. In my opinion, the female game demands greater acceptance of and adherence to strategy. Also, it appears that fewer female players exhibit an inclination to rely on and expect officiating. There are absolutely aspects of female basketball that I prefer. That should be unsurprising—consider the blog title—because there is simply less palming of the ball, at least in my observational comparisons of the female and male varieties.
 
Indeed, there is diversity and variety in the female and the male games. This post is devoted to a simple exploration of that variety. The data in Table 1 was computed using league-wide WNBA and NBA data from the 2015 and 2014-15 regular seasons, respectively. Data from Basketball Reference. Possessions were estimated with an equation used by ESPN.com and NBA.com, developed I believe by legend Dean Oliver. 

Table 1. Comparison of WNBA and NBA League-wide Team Data for Regular Season Ending 2015

SHOOTING
STAT	WNBA	NBA
FG%	42.5%	44.9%
3FG%	32.5%	35.0%
2FG%	45.4%	48.5%
FT%	79.5%	75.0%
% of FGA are 2PA	77.4%	73.2%
% of FGA are 3PA	22.6%	26.8%
ORB / FG Missed	23.4%	23.6%
DRB / FG Missed	65.9%	70.4%
Dead Ball RBs	10.7%	6.0%
Assists / FGs	58.7%	58.7%
Blocks / FGA	6.6%	5.7%
FT / FGA	22.6%	20.1%
FTA / FGA	28.4%	27.3%
POSSESSIONS
FGA / POSS	0.877	0.897
Fouls / POSS	0.248	0.217
Turnovers / POSS	0.173	0.154
Steals / POSS	0.096	0.083
Pts / POSS	1.008	1.073
Pts / FGA	0.923	0.992
Pace	88.7	92.5

A few items of note upon review of this table. Offenses in both leagues rebound their own misses at similar rates and assist FGs at essentially identical rates. The pace of the NBA game is somewhat faster. In the WNBA there appears to be a greater proportion of dead-ball rebounds the cause of which is unclear. Also, for what it’s worth, the NBA teams scored .069 points per possession more than did WNBA teams. 

Before concluding, I would like to provide some depth to the discrepancies in FG% between the two leagues. NBA players tallied 8793 successful dunks in 2014-15 season. From what I can ascertain, there were maybe 2 dunks in the 2015 WNBA season. Successful dunking is nearly guaranteed, although NBA did make only 91% of dunk attempts. Contrarily, NBA players sunk 27,080 lay-ups that season at a clip of 58%. As you know, we don’t have that sort of data for the WNBA; well, at least I don’t. 

So how does the discrepancy change if we exclude all dunk attempts from the computation of NBA FG%? We’ll even remove the 2 WNBA dunks. Excluding dunk attempts, NBA FG% drops from 45% to 40%. Considering that FG%, WNBA players appear to shoot a higher percentage that is statistically significant, but it is a small effect.[1] Now, it should be noted that the WNBA players do play with a slightly smaller and lighter-weight ball, about 96.6% the circumference of the NBA ball and 91% the weight of the NBA ball but the rim diameters are equal. Thus, the argument could be made that there is a greater area of the basket plane available for the WNBA ball to enter the basket. 

There is evidence to suggest that basketball of light weightwill facilitate increased FG%. We do know that Illinois males played Oakland in December 2010. The first 7:22 of game time was played with the slightly smaller ball used by females. Illinois did shoot 3 of 13 in that time and Oakland shot 7 of 16. That is a small sample, however, there appears to be a lack of effect of ball size on free throw shooting percentage and on shooting kinematics. Moreover, one study compared collegiate 71 female and 35 male basketballers. Neither an effect of ball size nor weight was found for on “side shots” (elbow jumper) and “lay ups” but smaller, lighter balls were passed fastest by both sexes. 

I provided in this post a comparison of the WNBA and NBA league-wide team data. However, I was not attempting a comparison, per se. More precisely, I attempted to highlight the variety of game-play in each League and how that variety manifests statistically because, when informative statistics are tracked for female basketball, it is not enough to simply adopt existing metrics from the male game—ecological considerations specific to the female game are warranted. Simple calculations were used to demonstrate how the high-percentage of successful dunking inflates FG%s in the NBA. Likewise, the effect of ball-size was considered. In sum, this exploratory analysis indicates several avenues of further study of the statistical manifestations of female basketball including, the high proportion of dead ball rebounds and slight differences in turnovers, fouls, steals, and blocked shot-attempts to their male counterparts.

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[1] χ²(df = 1, N = 27055) = 103.35, p < .001, Ṽ  = .061.