Predicting the WNBA draft: what matters most from college performance?
Harris, Jill ; Berri, David J.
Introduction
The Women's National Basketball Association (WNBA) utilizes a
version of the reverse-order draft lottery similar to the NBA. While
previous research has examined factors impacting the draft picks and
subsequent professional performance in the NBA, (1) we do not believe
this subject has been explored for the WNBA. In fact, little has been
published on this league. This particular study will build upon the work
of Berri and Krautmann (2013), which presented a model of player
performance for the WNBA. This work will be paired with the approach to
the NBA draft introduced by Berri, Brook, and Fenn (2011). In the end,
this research will explore both what factors determine draft position in
the WNBA and how such factors relate to subsequent performance.
Conceived in 1996, the WNBA has experienced the typical growing
pains of many relatively young sports leagues. (2) As we saw in the
early history of the NBA, teams in the WNBA have come and gone while
profits and attendance have expanded and contracted. With respect to the
number of teams, the league began with eight teams in 1997, expanded to
16 teams by 2000, before contracting to its current 12 teams by 2010.
Currently the league remains with just the following teams: Atlanta
Dream, Chicago Sky, Connecticut Sun, Indiana Fever, Los Angeles Sparks,
Minnesota Lynx, New York Liberty, Phoenix Mercury, San Antonio Silver
Stars, Seattle Storm, Tulsa Shock, and Washington Mystics.
Relative to the NBA, these teams appear--when we look at with-in
season variation--to be somewhat competitive. (3) When we look at league
championships, though, we see less competition. Of the 18 franchises
that have existed in the WNBA, only eight have ever won a title. And 15
of the league's 17 titles have been won by just six organizations.
One proposed solution to such imbalance embraced by sports leagues
throughout North America is the reverse-order draft. This institution
rewards the worst teams in a league with a choice of the best available
talent not currently employed in the league. (4) For this to improve
competitive balance, the teams selecting first must be selecting more
productive players than those taken later. There is a problem, though,
with the selection process. At the time of the draft a team has not seen
how a given player will perform against the talent seen in the
professional league. Consequently, it is possible the worst teams do not
actually get better via the draft.
This is essentially the story told by much of the previous research
on drafts in professional sports. Both Massey and Thaler (2006) and
Berri and Simmons (2011) uncovered problems with how talent is selected
in the NFL draft. With respect to the NBA, Kahn and Sherer (1988)
reported no statistical relationship between draft position and a
player's statistical performance in college. More recently, Berri
et al. (2011) report that draft position is related to scoring totals,
(5) but other factors--like shooting efficiency and rebounds--that are
more closely aligned with winning were not found to matter much in a
player's draft position. These results are similar to those of
Coates and Oguntimein (2010). Using data from 1987 to 1989, the authors
found points scored were important for draft position, but not
indicative of professional point scoring. Rebounds, blocks, and assists
were correlated more with pro performance.
The importance of scoring in player evaluations in the NBA is not a
new finding. Berri and Schmidt (2010) offer evidence of the primacy of
scoring in terms of salary allocation. (6) But a study of salaries in
the WNBA is problematic. The collective bargaining agreement in the WNBA
results in salaries that are much more regimented and far lower than the
NBA. (7) Consequently this study into player evaluation in the WNBA
turns to the draft, an arena that allows us to examine which factors
decision-makers consider in evaluating basketball talent.
Research on the WNBA in this area is interesting for at least four
reasons. More broadly, an examination of decision-making in
sports--where labor productivity data is abundant--allows one to assess
whether or not decision-makers act in a fashion consistent with the
neoclassical model. Related to this point, decision-makers in the NBA
have been found to focus on scoring (seemingly) above all else when
evaluating player talent. Prior research establishes that efficiency in
scoring is not significant in player evaluation. Will the WNBA
management suffer the same efficiency blindness?
A third reason for interest is the analysis of player productivity
from the college draft into the pro league. Specifically, can we
determine whether the factors highlighted on draft day predict WNBA
performance? Berri et al. (2011) reviewed how Hollinger (2003) and
Oliver (2004) assert that wins are determined by a team's ability,
relative to its opponent, to elicit points from its possessions. Gaining
possession is a function of rebounding, steals, and turnovers. We are
curious whether these factors have more impact in the WNBA than they did
in the Berri et al. (2011) research.
Finally, this research may contribute to the broad literature on
gender economics and how the work of females and males is evaluated. The
WNBA and NBA are closely linked; the NBA initially owned the WNBA and
many decision-makers in the former have extensive experience with the
latter. Papers published in organizational behavior, higher education,
and other behavioral journals point to differences in performance
evaluation based on gender. For example, in a widely cited paper, Basow
(1995) found significant interactions between teacher gender and student
gender in student course evaluations. Male professors' evaluations
were not impacted by student gender while female professors'
evaluations were impacted by student gender. Female instructors received
their lowest ratings from male students and highest ratings from female
students. This type of effect is reported in a variety of studies across
multiple fields. (8) Our findings suggest female player performance does
not appear to be evaluated differently than male player performance. If
this result holds then it would certainly be of interest to audiences
outside of sports economics.
Modeling the Draft
This study of the draft uses amateurs chosen by WNBA teams from
NCAA programs. International players are excluded since comparable
statistics for their pre-professional experience are not available. (9)
We collected draft data from 2010 to 2013 and college performance data
from the drafted players' last year of college play. Summary
statistics for our data are detailed in Table 1. The sample is
small--only 128 players. However, the model generates results similar to
those found in Berri et al. (2011), suggesting managers in the WNBA
suffer similar impairments to the NBA when it comes to assessing player
performance and productivity.
The average player in our sample is 6-foot tall, the tallest is
6-foot-6, and the smallest is 5-foot-4. Unlike the male athletes drafted
into the NBA, all the female players in the sample completed four years
of college, making the age range tighter. The average age is a little
over 22 while the oldest players were 24 when drafted. Another
interesting feature of the data is that more draft picks come from the
SEC than any other conference (the ACC and PAC 10 have the honors in the
NBA study conducted by Berri et al.). Almost twice the number of draft
picks in the WNBA played in the NCAA Final Four versus the NBA. Since
the dependent variable is draft position, a variable that positively
impacts draft position will have an estimated coefficient with a
negative sign. (10)
As with Berri et al. (2011), the dependent variable is draft pick
selection number. This value ranges from 1 to 36 in each draft year (12
teams with a three-round draft). Each player's performance is
adjusted for position played as in other prior work by Berri and Berri
et al. (11)
What factors impact draft position? We begin with player
performance in college. As noted by Berri et al. (and in contrast to the
work of Kahn and Scherer [1988]) players with better college performance
statistics should be drafted higher. After all--as noted earlier--the
primary stated purpose of the draft is to give poor performing teams
access to better players. Next, height and age are included in the
model. Although it is cliche: you cannot teach height. Particularly in
the female game we might expect the short supply of tall women to be
even more critical to the draft decision. Because the advantage of
height relative to position played could also impact draft position we
model height relative to position. (12)
Given the attention paid to age and experience in the NBA it makes
sense to include age as a control characteristic in our sample. However,
the women's game is not as lucrative at the professional level.
Female athletes have fewer incentives to leave college early to enter
the WNBA. Therefore, we do not expect age to be as important in the
draft selection story here.
In addition to the regressors mentioned above we include dummy
variables for athletic conference to capture the difference in quality
of college team played for and the degree of competition faced. We also
include a dummy for Final Four experience. As with the NBA, experience
in post-season play may be perceived as a plus by decisionmakers and
should improve draft position. Finally, dummy variables for the draft
class years are included in the model for potential variations in the
draft pool year to year over the sample.
To summarize, we expect draft pick to be influenced by college
performance (captured by PROD--a vector of player specific position
adjusted performance statistics including points, rebounds, steals,
blocked shots, assists, turnovers, and measures of shooting efficiency),
RELHT or relative height, AGE, college conference given by dummies for
each, experience in post-season play (DFIN4), and relative quality of
pool of draftable athletes in a given year (captured by a time dummy for
each year, D10, etc.). These influences are all noted in Equation 1,
which we will estimate in an effort to explain where a player will be
chosen in the WNBA draft.
[PICK.sub.n] = [[beta].sub.0] + [[alpha].sub.N]PROD +
[[beta].sub.1]RELHT + [[beta].sub.2]DFIN4 + [[beta].sub.3]DCHAMP +
[[beta].sub.4]AGE +[[beta].sub.5]DACC + [[beta].sub.6]DPAC12 +
[[beta].sub.7] DBIGEAST + [[beta].sub.8]DSEC + [[beta].sub.9]DBIG10 +
[[beta].sub.10]DBIG12 + [[beta].sub.11]DAMER + [[beta].sub.12] DCOLON +
[[beta].sub.13]DWEST + [[beta].sub.14]DCONFU + [[beta].sub.15]DSUN +
[[beta].sub.16] DC + [[beta].sub.17] DF + [[beta].sub.18]DG +
[[alpha].sub.J]DYEAR + [e[[beta].sub.it] (1)
Empirical Findings
Equation 1 is estimated with four years of draft data. Estimation
of the model was conducted with both a Poisson Distribution model and a
Negative Binomial model. (13) The estimations are reported in Table 2.
Since the Poisson and Negative Binomial models return coefficients from
a Maximum Likelihood estimation process the coefficients are not
equivalent to estimated slopes. The coefficients are used to estimate
marginal effects at the sample means. These marginal effects are
reported in Table 2.
Before discussing the results on the performance measures, the
non-performance factors invite comment. As was the case in the NBA,
shorter female players are at a disadvantage. Other things the same, the
taller you are--relative to the average at your position--the better you
are going to do in the draft. In contrast to the NBA, age is not
important in the WNBA draft sample. This is not surprising given the
tight distribution of ages compared to the men when entering the draft.
Appearing in the Final Four, however, is important for drafting. A
player with this experience will see her draft position improve by
almost six slots. In addition, coming out of the ACC, SEC, or the Big
East gives a bigger boost than from the PAC 12 or Big Ten.
Turning attention to the performance factors, which of those
predicted skills that were statistically significant had the most
economic significance? As Table 2 indicates, points scored, assists, and
shooting efficiency from the two-point range are all positive
influencers of draft position while personal fouls work against the
athlete. Steals, blocks, and rebounds are not telling much of the story
of draft selection in our sample. Given these predicted results, how
meaningful are they?
Table 3 reports how an estimated one standard deviation increase in
each statistically significant performance variable impacts draft
position. Again, we suspect--given the aforementioned research into the
NBA--that scoring might matter the most. And the results in Table 3
indicate that scoring matters most! A one standard deviation increase in
points improves draft position by over eight slots; a similar increase
in assists improves drafting by almost five slots and improved field
goal percentages improve position by three slots. (14) Relative to the
NBA, assists are about twice as valuable in improving draft position in
the WNBA while points scored and efficiency are about a third more
valuable to the potential professional player.
Performance from College to the Pros
Are the factors that impact a college player's draft selection
important to subsequent professional performance in the WNBA? Answering
this question requires a measure of performance.
Berri (2008) demonstrates how the statistics gathered by the NBA
(and thus the WNBA) can be used to estimate a player's Wins
Produced. (15) This model of performance proves very reliable season to
season and explains about 94% of the variation in team wins. Using this
metric we now investigate how each drafted player's Wins Produced
per 40 minutes (WP40) might be related to the performance statistics
used in the draft model.
Specifically we estimate the following regression: (16)
[WP40.sub.n] = [[lambda].sub.0]+ [[gamma].sub.N]PROD +
[[lambda].sub.1]RELHT + [[lambda].sub.2]DFIN4 + [[lambda].sub.3]DCHAMP +
[[lambda].sub.4] AGE + [[lambda].sub.5]DACC + [[lambda].sub.6]DPAC12 +
[[lambda].sub.7]DBIGEAST + [[lambda].sub.8]DSEC + [[lambda].sub.9]DBIG10
+ [[lambda].sub.10]DBIG12 + [[lambda].sub.11]DAMER +
[[lambda].sub.12]DCOLON + [[lambda].sub.13]DWEST +
[[lambda].sub.14]DCONFU + [[lambda].sub.15]DSUN + [[lambda].sub.16]DC +
[[lambda].sub.17]DF + [[lambda].sub.18]DG + [e.sub.it] (2)
where PROD is a collection of player statistics including points,
rebounds, steals, blocked shots, assists, and measures of shooting
efficiency.
The estimated results for Equation 2--for the first year of a
player's WNBA career (17)--are reported in Table 4.
The results indicate that only PTS, PF, 2FGPER, and DC are
significant predictors of first-year performance. To provide some sense
of the relative influence on WP40 of each of these significant factors
an estimated elasticity coefficient is reported, with shooting
efficiency found to have the largest impact. This result runs counter to
what we found when examining where a player is chosen in the draft. That
study noted the primacy of points scored. Although points scored does
predict future performance, our results indicate that more attention
should be paid to shooting efficiency.
Clearly, given the results in Table 4, where a player is drafted
does not reveal very much about her subsequent performance in the WNBA.
But "how much" is relative. For an alternative view, consider
Table 5, where we consider how much of future performance is explained
by where a player is selected.
Table 5 considers two different measures of player performance. The
first is WP40. The second is NBA Efficiency. (18) As Berri and Schmidt
(2010) note, the former is correlated with team wins but not as
correlated with player evaluations in basketball. NBA Efficiency has the
opposite properties.
As Table 5 notes, only about 6% of a player's career
performance as captured by WP40 is explained by where a player is
drafted. Explanatory power increases when NBA Efficiency is used to
measure performance. Of course--as noted in Berri and Schmidt
(2010)--NBA Efficiency is a poor measure of player productivity.
We should note that similar results held in the work done by Berri
et al. (2011) with respect to the NBA draft. This confirms the strange
result that--armed with more information (in-person observations of the
player in action and other qualitative data)--decision-makers do not
predict professional performance very well. Our model of college
performance measures seems to predict future performance better than
that used by the teams. Furthermore, management seems to be evaluating
female and male players in a similar fashion. Any bias in the hiring
process is clearly linked to points scored by the player; NBA and WNBA
executives alike tend to overemphasize this statistic. Even though this
research does not directly test for gender neutrality it hints
indirectly at it. The absolute dollar value of poor decisions is
obviously lower in the WNBA than in the NBA. Still, important
consequences exist for decision-makers. Losing coaches tend to be fired.
Given the short supply of tall productive players it is reasonable to
imagine most teams want to make judicious draft decisions. In this
regard, if these choices reflect the sum of collective wisdom of the
team management, the draft does not seem to really accomplish its
intended purpose.
Concluding Observations
This research on the draft in the WNBA confirms prior results in
the NBA with respect to performance predictions. Specifically it shows
that teams appear to consistently rely on faulty indicators when making
draft picks. For example, factors like Final Four experience, conference
affiliation, and relative height influence draft selection but fail to
accurately predict the players' performance in the WNBA. In
contrast to studies of NBA performance, rebounds, steals, and turnovers
are not an important part of the story for draft selection in the WNBA.
This result could be a function of our smaller sample size, but may also
be indicative of a different type of play on the court. With more data
this is certainly another potential line of inquiry to pursue.
Since evidence of gender bias has been found in a variety of
studies, these results could be important in this field as well. If the
performance of female basketball players is evaluated the same way male
performance is then additional studies into other sports might provide a
new laboratory for those interested in measuring productivity and
marginal revenue product.
Given the above, female college basketball players can benefit from
two pieces of advice aside from standing up as tall as possible when
their height is officially measured: (1) score as many points as you can
during your college career and (2) do whatever you can to make sure that
career occurs in the SEC, ACC, or Big East. For the time being, it looks
like that's what matter most to WNBA teams when they make their
draft decisions.
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Endnotes
(1) A sample of this literature would include Coates D, Oguntimein
B (2010) and Berri, DJ, Brook S, Fenn A, (2011).
(2) The league began play in 1997. Recent notable events include
the Shock relocating from Detroit to Tulsa, a league-wide multiyear
marketing partnership with Boost Mobile landing a logo on jerseys of 10
out of the 12 teams,; and in April of 2011, the appointment of Laurel J.
Richie as President of the league.
(3) The Noll-Scully measure of competitive balance ranges between
1.1 to 2.6 for 1997 to 2013 for the WNBA; with an average value of 1.9
In comparison, the NBA--from 1997-98 to 2013-14 had a range of 2.3 to
3.4 with an average value of 2.8. Berri and Krautmann (2013) discuss
potential reasons for this difference.
(4) Much of this talent is found in the college ranks for the NBA
and NFL. For MLB and NHL, high school talent is also considered. The
WNBA also primarily drafts from the college ranks. And like the NBA, the
top picks in the draft are assigned via a lottery. Rookies may only sign
a three year, non-guaranteed contract with an option in favor of the
team for a fourth year.
(5) The Berri, Brook, and Fenn (2011) paper looked at drafts from
1995 to 2009. A similar argument with respect to college scoring was
also made by Coates and Oguntimein (2011) in a study of drafts from 1987
to 1989. For research on other character traits like criminal violations
and draft order see Weir and Wu (2014). Treme and Allen (2009) find
faster more accomplished football players are drafted earlier but 40
yard dash times are not correlated with professional performance in
early career years.
(6) These authors also cite research that scoring dominates the
allocation of minutes and voting for post-season awards (by coaches and
the media).
(7) For the 2013 season minimum salaries ranged from $37,950 to
$55,000 and maximum salaries range from $105,000 to $107,500. Many
players supplement their income by playing in international leagues in
the off -season.
(8) See for example Bertrand and Hallock (2001), Humphreys (2000)
or Marlowe Schneider and Nelson (1996)
(9) This approach follows the lead of Berri, Brook, and Fenn
(2011).
(10) The -1.57 minimum for blocks occurs due to the position
adjustment process. If the "'worst" blocker in our sample
performs well below the average for her position it is possible for the
position adjustment to return a negative value. See footnote 12 for a
full description of how position adjustments are made.
(11) Position bias is overcome by calculating a position adjusted
value for each metric. Each player's per-minute performance with
respect to points, rebounds, steals, blocked shots, assists, and
turnovers is determined. Then, the average per-minute accumulation at
each position in our data set is subtracted. The average value of the
statistic across all positions is added back in. After these steps, the
result is multiplied by 40 minutes (the length of a college game), to
return the player's per 40 minutes production of each statistic.
(12) Relative height is determined by calculating the average
height--in inches--of the drafted players in the sample at each
position. The position average is then subtracted from each
player's height. The average height in the entire sample is then
added back in.
(13) As discussed in Berri, et al (2011) we adopted a two-step
negative binomial quasi-gneralized pseudo-maximum likelihood estimate to
correct for overdispersion and to generate a robust variancecovariance
matrix. More information on this estimator can be found in Gourieroux,
et al. (1984).
(14) Note that for an increase in personal fouls of one standard
deviation draft position will decrease by a little over 1 slot.
(15) This model was updated for Berri and Schmidt (2010). Details
can be found at http://wagesofwins.com/how-to-calculate-wins-produced/
(16) This is equation (1) with WP40 as the dependent variable
(instead of Pick).
(17) Relative to the aforementioned work of Berri, Brook and Fenn
on the NBA draft, our sample for the WNBA study is quite small.
(18) NBA Efficiency is calculated as follows: PTS + TREB + STL +
AST + BLK--All missed shots --Turnovers. As Berri and Schmidt (2010)
note, this model does not explain wins very well. That is because it
de-emphasizes shooting efficiency. But it does a good job of explaining
player evaluation in the NBA (primarily because the NBA decision-makers
tend to de-emphasize shooting efficiency).
Jill Harris (1) David J. Berri (2)
(1) Pomona College
(2) Southern Utah University
Jill S. Harris, PhD, is a visiting assistant professor of economics
and teaches Economics of Sport and Economics of Crime. Her research
interests include the nature and behavior of the National Collegiate
Athletic Association (NCAA), women in sport, acquatic sports, and
non-compliance behavior.
David J. Berri, PhD, is a professor of economics in the Department
of Economics and Finance. His current research focuses on the economics
of sports, specifically the topics of consumer demand, competitive
balance, worker productivity, and women in sport.
Table 1. Descriptive Statistics for Dependent and Independent
Variables (2010-2013) *
Variables Label Mean SD Min. Max.
Draft position PICK 18.37 10.33 1 36
Points scored PTS 18.80 4.70 8.3 31.03
Rebounds REB 6.18 3.43 0.35 13.17
Assists AST 4.35 1.92 1.36 13.60
Steals STL 2.80 0.93 1.18 5.42
Blocked Shots BLK 0.86 1.14 -1.57 3.29
Personal Fouls PF 2.45 0.76 0.81 4.94
Turnover % TOPER 0 0 0 0
3 pt field goal % 3FGPER 0.29 0.14 0 0.75
2 pt field goal % 2FGPER 0.47 0.07 0.31 0.71
Free Throw % FT 0.74 0.09 0.47 0.89
Age AGE 22.39 0.55 22 24
Height, in RELHT 72.10 2.20 64.80 78.20
Final Four player DFIN4 0.16 0.37 0 1.0
Played in ACC DACC 0.19 0.39 0 1.0
Played in PAC 12 DPAC12 0.10 0.31 0 1.0
Played in Big East DBIGEAST 0.05 0.22 0 1.0
Played in SEC DSEC 0.24 0.43 0 1.0
Played in Big Ten DBIG10 0.09 0.29 0 1.0
Played in Big 12 DBIG12 0.09 0.29 0 1.0
Played in Conf USA DCONFU 0.03 0.17 0 1.0
Played in Mt West DWEST 0.09 0.29 0 1.0
Played in Colonial DCOLON 0.01 0.10 0 1.0
Played in American DAMER 0.08 0.27 0 1.0
Played in Sun DSUN 0.01 0.10 0 1.0
Played Center DC 0.14 0.34 0 1.0
Played Forward DF 0.40 0.49 0 1.0
Played Guard DG 0.45 0.50 0 1.0
* There are 128 observations. Notes: PTS, REB, AST, STL, BLK, and
PF are per 40 min and adjusted for position played. TOPER is also
adjusted for position played. Turnover Percentage = [(Turnovers)/
(Turnovers + Field Goal Attempts + 0.44*Free Throw Attempts)]
Sources: college performance data from NCAA.com; height data is
from WNBA.com.
Table 2. Estimation of Equation (1) (2010-2013)
Variable Poisson Z-stat Negative Bin Z-stat
ME ME
PTS -0.95 *** -9.58 -1.08 *** -5.51
REB -0.05 -0.28 -0.24 -0.63
AST -2.18 *** -7.07 -2.58 *** -4.32
STL -0.41 -0.74 -0.21 -0.19
BLK -0.52 -1.08 -0.68 -0.71
PF 1.41 ** 2.67 2.11 ** 2.04
TOPER 0 0 0 0
3FGPER 4.87 ** 2.05 3.47 0.78
2FGPER -50.38 *** -5.90 -42.71 ** -2.69
FT -7.09 -1.29 -1.85 -0.17
RELHT -0.90 *** -5.80 -1.18 ** -2.73
DFIN4 -5.83 *** -4.64 -5.65 *** -3.25
AGE -0.41 -0.62 -0.28 -0.22
DACC -6.92 *** -6.34 -7.32 *** -3.48
DPAC12 -3.34 ** -2.61 -3.51 -1.39
DBIGEAST -4.99 *** -3.92 -5.79 ** -2.51
DSEC -6.75 *** -6.29 -6.60 *** -3.13
DBIG10
DBIG12 -3.95 *** -3.22 -4.59 ** -1.95
-0.37
-3.57 -1.18 -2.47
DCONFU 3.26 1.10 5.01 0.73
DWEST 3.46 * 1.68 2.85 0.68
DCOLON 2.23 0.76 1.89 0.33
DAMER -2.71 -1.59 -4.08 -1.35
DSUN 1.59 0.64 1.57 1.12
DC 0.78 0.40 1.26 0.32
DF 0.16 0 -0.03 -0.01
DG -0.16 -0.13 -0.20 0.62
Observations: 128. *Denotes significance at 10%, ** 5%, *** 1%
Table 3. The Impact of a One Standard Deviation Increase in
Statistically Significant Performance Variables (2010-2013)
Variable # slots a player gains
from + 1 s.d
PTS -8.76
AST -4.95
2FGPER -2.99
PF 1.60
Observations: 128
Table 4. How Much Career Performance (WP40) is Explained by
Factors Influencing Draft Position?
Variable 1st Year t-stat Elasticity
PTS 0.002 ** 1.97 1.02
REB -0.001 -0.31
AST 0.002 0.86
STL -0.007 -1.34
BLK -0.004 -0.80
PF 0.008 * 1.63 0.53
3FGPER -0.000 -0.02
2FGPER 0.224 *** 2.94 2.86
FT -0.020 -0.38
RELHT -0.001 -0.71
DFIN4 0.008 0.78
AGE -0.002 -0.36
DCONF -- --
DC -0.32 * -1.85
DF -0.010 -0.87
DG 0.052 1.03
obs 80
R-squared 0.23
* denotes significance at 10%, ** 5%, *** 1%
Table 5. How Much Career Performance Can Draft Position Explain
(as captured by WP40 and NBAEfficiency)
Year Observations WP40 NBAEfficiency
1st Year out 80 -0.051 *** -7.300 ***
R-squared 0.06 0.14