Are NBA Draft Markets Efficient?

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Are NBA Draft Markets Efficient? Evidence from

Pace-Adjusted Measures*

Joseph Engelberg

Grant Goodstein‡

Abstract: Professional sports teams are often faced with the daunting task of evaluating amateur

talent for the purposes of a draft. How can they best use available data, say of a player in college, to

forecast how that player will perform in the pros? Using data on all NBA draft picks of college athletes

between 2003 and 2012, we find that NBA teams could improve their draft picks by incorporating

pace-adjusted metrics in their selection decisions. Specifically, in linear regressions we find

differential predictability for an NBA player’s performance. When we use measures of his collegiate

productivity without pace-adjustment, points produced per game, we find no predictability for win

shares or player efficiency rating (PER) after controlling for the player’s draft number. However,

when we use pace-adjusted measures of collegiate performance, such as points produced per

possession, we find significant predictability for win shares and player efficiency after controlling for a

player’s pick number. A one standard deviation change in collegiate points produced per possession

(CPPP) predicts a change in win shares commensurate with 14 draft spots. That is, by increasing the

CPPP of its selection by one standard deviation an NBA team would be able to draft a player with the

26th pick that had the average performance of a 12th pick. Taken together, the results suggest NBA

draft markets are inefficient because they do not correctly incorporate all available information when

assigning picks.

* We have benefited from discussions with Christopher Parsons, Ken Pomeroy, Paul Tetlock and Jared Williams. We thank Ken Pomeroy for his assistance with the data. ‡ Contact: Joseph Engelberg, Associate Professor of Finance, University of California San Diego, (Email) [email protected], (Tel) 858-822-7912; Grant Goodstein, University of Michigan, (Email) [email protected], (Tel) 760-585-6882.

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I. Introduction

A critical function of most professional sports organizations is the selection of players in a

draft. This task is notoriously difficult: each team must use their information to select the best players

from a large pool of candidates. Teams cannot, of course, make selections that maximize the realized

performance of players because realized performance is subject to random chance (such as a post-

draft injury). The question of draft market efficiency, the topic of this paper, is whether teams use all

available information at the time of the draft to select players that maximize expected performance.

Draft market efficiency has several implications. First , if draft markets are efficient and teams

have access to the same information then the draft should provide a particular rank-order of players:

the first pick should, on average, be better than the second, the second better than the third, the third

better than the fourth and so on. Moreover, draft market efficiency implies that there exists no

available information at the time of the draft which could, on average, improve selection. For

example, suppose, all-else equal, a 7’0” center in basketball performs better in the National Basketball

Association (NBA) than a 6’10” center. Because player height is available at the time of the draft we

would expect, on average, a 7’0” center to be drafted before a 6’10” center. If this were not the case, we

would say draft markets are inefficient because they did not correctly incorporate all available

information at the time of the draft.

In this paper we examine whether draft markets are efficient in the NBA by asking whether

certain measures of productivity in college are correctly incorporated into the NBA draft order. The

NBA offers a nice laboratory for examining whether draft markets are efficient for a few reasons.

First, because the salaries of draftees are essentially fixed for the first few years -- ever since the 1995

collective bargaining agreement -- teams wishing to maximize productivity per dollar spent have an

incentive to pick players based on expected productivity. Moreover, professional basketball has well-

developed measures of individual performance, such as win-shares (Kubatko (2013)) and player

efficiency rating (Hollinger (2003)).

Our key finding is that NBA draft markets appear efficient when considering per-game

measures of collegiate productivity, such as points produced per game, but inefficient when

considering per-possession measures of college productivity, such as points produced per possession.

In short, NBA teams fail to fully appreciate differences in pace and possessions across

college basketball teams and players when making their draft selections.

To illustrate the point, consider the case of Kevin Love from UCLA and Michael Beasley from

Kansas State in the 2008 NBA draft. Both were highly-touted freshmen power forwards of similar size

(Love is 6-10 and Beasley is 6-9). Based on per-game measures, Beasley held a clear advantage over

Love. He averaged 26.2 points per game (compared to Love’s 17.5) and 12.4 rebounds per game

(compared to Love’s 10.6). However, these per-game statistics reflected a much faster pace at Kansas

State compared to UCLA during the 2007/2008 season. Kansas State ranked #30 in the nation based

on possessions per 40 minutes while UCLA ranked #250. Moreover, Beasley spent more time on the

court than Love (78.3% vs. 73.9%) and participated in more possessions when on the court (33.5% vs.

27.7%). All of these contributed to higher per-game averages for Beasley. However, when considering

per-possession measures of collegiate productivity, Love held the advantage. For every 100

possessions, Love produced 126.6 points compared to Beasley’s 119.8. While Beasley was the second

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overall pick in the 2008 draft and Love was the fifth, Love’s NBA career has been more successful than

Beasley’s by most measures. For example, Love’s career win-shares per 48 minutes are .172 compared

to Beasley’s .044, and Love’s career player efficiency rating is 22.1 compared to Beasley’s 15.

The anecdote generalizes to our sample of 440 collegiate players drafted into the NBA between

2003 and 2012 when we compare the differential predictability of collegiate points produced per

game (CPPG) and collegiate points produced per possession (CPPP). We find that CPPG is a better

predictor of draft number than CPPP (as in the previous example), but it does not predict various

measures of NBA performance after controlling for draft number. CPPP, on the other hand, reliably

predicts various measures of NBA performance after controlling for draft number. A one standard

deviation increase in CPPP is commensurate with an improvement of 14 draft spots. That is, by

increasing the CPPP of its selection by one standard deviation an NBA team, for example, would be

able to draft a player with the 26th pick that had the average performance of a 12th pick.

Our paper belongs to a small, but growing, literature which has examined the efficiency of

professional drafts (e.g., Massey and Thaler (2010), Coates and Oguntimein (2010), Berri, Brook and

Fenn (2011), Spurr (2000) and Burger and Walters (2009)). To the best of our knowledge, this paper

is the first to document a fixation on per game measures of collegiate productivity and find evidence

that draft picks could be improved by incorporating per possession measures of collegiate

productivity.

II. Data

Our data come from two primary sources. We collect from Basketball Reference

(www.basketball-reference.com) a history of all NBA draft picks dating back to 1950. We also

download from Basketball Reference a set of NBA performance measures – win shares per 48 minutes

(WS48) , win shares and player efficiency rating (PER) -- for each player selected in each draft. Win

shares was developed by Kubatko (2013) for basketball following the well-known measure developed

by Bill James for baseball (James and Henzler (2002)) and estimates an individual player’s share of

his team’s wins. PER was developed by Hollinger (2003) and is a rating based on a player’s per-

minute productivity.

We collect data on collegiate activity from Ken Pomeroy’s website (www.kenpom.com)

including team tempo (possessions per 40 minutes), individual offensive rating, individual percent

minutes played and individual percent possessions. From these data we calculate for each player in

his last year prior to the draft: (1) the total number of points he produced, (2) the total number of

possessions he used and (3) the total number of games he played. (1) and (2) are calculated following

Oliver (2004). From these we define collegiate points produced per game (CPPG) as the ratio of (1) to

(3) and collegiate points produced per possession (CPPP) as the ratio of (1) to (2).

The intersection of the two datasets is 440 college players drafted into the NBA between 2003

and 2012. Those 440 players have a mean CPPG of 14.81 with a standard deviation of 3.87, and a

mean CPPP of 1.12 with a standard deviation of 0.077. Of those 440 players, 397 played at least one

NBA game so that we can measure performance. Those 397 players have a mean WS48 of 0.0665

with a standard deviation of 0.0674 and a mean PER of 12.39 with a standard deviation of 4.61.

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III. Analysis

We begin with the following question: can collegiate measures of productivity predict NBA

performance after accounting for a player’s draft number? Or, said another way, is there valuable

information in measures of college productivity that is not fully captured by a player’s draft number?

Figure 1 illustrates our main result. We first sort each collegiate player into one of four draft

groups: players selected with one of the first 15 picks are in the top group and players selected with

one of the last 15 picks are in the bottom group. Then, within each draft group we further sort into

two groups based on measures of collegiate productivity. In Panel A the measure of productivity is

points produced per game and in Panel B the measure of productivity is points produced per

possession.

From Panel A, a few trends emerge. First, it is clear that draft number is strongly associated

with NBA performance: those drafted in the top group have an average WS48 (0.086) which is more

than double those drafted in the bottom group (0.035). The steady rise of the bars in the graph

indicate the relationship is monotonic across draft groups . Moreover, within each draft group,

collegiate points produced per game have little predictability for win shares. For example, those in the

top draft group with above-median CPPG have an average WS48 of 0.084 compared with 0.089 for

those with below-median CPPG (t-stat -0.59). Likewise, those in the bottom draft group with above-

median CPPG have an average WP48 of 0.036 compared with 0.034 for those with below-median

CPPG (t-stat 0.23).

From Panel B, however, we find strong predictability for WS48 within draft group when we

sort on collegiate productivity per possession: in every draft group the above-median CPPP group

outperforms the below-median CPPP group. For example, considering the top 15 draft picks, above

median CPPP players had an average WS48 of 0.095 compared to 0.0761 for below-average CPPP

players (t-stat 2.15); for draft picks 16 – 30 above median CPPP players had an average had an average

WS48 of 0.085 compared to 0.056 for below-average CPPP players (t-stat 3.51); and for draft picks 31

– 45 above median CPPP players had an average had an average WS48 of 0.071 compared to 0.047 for

below-average CPPP players (t-stat 2.51). Only in the bottom group is the difference between above-

median and below-median CPPP players insignificant (0.036 vs. 0.034 with a t-stat of 0.18).

In Table 1, we formalize the sorting exercise in a regression framework. In the first two

columns, we ask how CPPG and CPPP predict a player’s draft number. Formally we estimate the

following two linear regression models:

(1)

(2)

where Draft Number is a player’s pick number in the NBA draft (1 through 60), CPPG is

collegiate points produced per game, CPPP is collegiate points produced per possession and Controls

consist of draft year fixed effects and position fixed effects. CPPG and CPPP are standardized so we

can interpret the units in terms of standard deviations. The coefficient estimates in columns 1 and 2

suggest teams pay more attention to points produced per game when making their draft selections. A

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one standard deviation increase in CPPG corresponds to a player being selected 4.2 picks earlier.

However, a one standard deviation increase in CPPP corresponds to a player being selected 2.4 picks

earlier. Because the dependent variable is a count, we also run count regressions (poisson and

negative binomial) in an appendix table and find no substantive change in the results: NBA teams

appear to be more sensitive to points produced per game rather than per possession when selecting

players in the NBA draft.

The remaining columns of Table 1 suggest that this is a mistake. The columns present the

coefficients from several specifications of the following form:

(3)

(4)

where Performance Measure is win shares per 48 minutes (WS48) in columns 3 and 4, win

shares in columns 5 and 6 and player efficiency rating (PER) in columns 7 and 8.1 Controls include

draft year fixed effects, position fixed effects and draft number fixed effects (i.e., 59 separate dummy

variables for each pick number). This last control is worth emphasizing because if NBA draft markets

were efficient we should not be able to find any variable which predicts NBA performance after

controlling for draft number. And yet that’s precisely what we find: CPPP reliably predicts each of our

measures of NBA performance after controlling for draft number. For example, column 4 suggests

that a one standard deviation increase in CPPP corresponds to an increase in WS48 of .0131 (t-stat

3.52). To appreciate the magnitude of this result, in an appendix table we consider the same

specification with a linear control for draft number (rather than draft number fixed effects). That

table indicates that moving up the draft one pick improves WS48 by 0.0009 so that a one standard

deviation increase in CPPP is akin to an improvement of .0131 / .0009 ≈ 14 draft spots. In other

words, by increasing the CPPP of its selection by one standard deviation an NBA team would be able

to draft a player with the 26th pick that had the average performance of a 12th pick. Columns 3, 5 and

7 indicate the predictability for NBA performance is only found with our pace-adjusted metric, CPPP.

When we examine collegiate points produced per game, we find no predictability after controlling for

draft number. Thus, whatever value-relevant information exists in CPPG is subsumed by the draft

number controls.

IV. Conclusion

Most reports of player productivity come in the form of per game averages (e.g., points per

game or rebounds per game). Such reports can mask crucial differences in pace and possessions

across players and teams. We find evidence that NBA teams fail to fully appreciate these differences

when making their draft choices. The draft picks of NBA teams could be substantially improved by

incorporating per-possession -- rather than per-game -- measures of collegiate performance into their

selection decisions.

1Thenumberofobservationsfallsto397incolumns3–8because43ofour440NBAdraftpicksdidnotplayinanNBAgame.Inanappendixtableweshowourresultsdonotchangeifinsteadweassignavalueofzero(ratherthanmissing)forthese43observations.

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Figure 1: NBA Performance and Measures of Collegiate Productivity Panels A and B plot average win shares per 48 minutes. Averages are calculated by draft group and collegiate points produced per game (per possession) in Panel A (Panel B). Possessions, points produced and games are taken from Ken Pomeroy’s website (www.kenpom.com) and are calculated in a college player’s final year prior to draft. PANEL A

PANEL B

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

Picks 46 ‐ 60 Picks 31 ‐ 45 Picks 16 ‐ 30 Picks 1 ‐ 15

NBA Win Shares (per 48 minutes)

PointsProduced perGame inCollege (Low)

PointsProduced perGame inCollege(High)

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

Picks 46 ‐ 60 Picks 31 ‐ 45 Picks 16 ‐ 30 Picks 1 ‐ 15

NBA Win Shares (per 48 minutes)

PointsProduced perPossesion inCollege (Low)

PointsProduced perPossesion inCollege (High)

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Table 1: Draft Number, NBA Performance and Measures of Collegiate Productivity Each observation corresponds to a college basketball player drafted by an NBA team between 2003 and 2012. The dependent variable in columns 1 and 2 is the player’s draft number (1 through 60). The dependent variables in columns 3 – 8 are various measures of NBA performance: win shares per 48 minutes (columns 3 and 4), win shares (columns 5 and 6) and player efficiency rating (columns 7 and 8). Draft number, win shares per 48 minutes, win shares and player efficiency rating are taken from Basketball Reference (www.basketball-reference.com). The main independent variables are collegiate points produced per game in the odd columns and collegiate points produced per possession in the even columns. Possessions, points produced and games are taken from Ken Pomeroy’s website (www.kenpom.com) and are calculated in a college player’s final year prior to draft. Position and draft year fixed effects are included in each specification. Draft number fixed effects are included in columns 3 – 8. Standard errors robust to heteroskedasticity (White (1980) are given in parentheses. *, ** and *** indicate significance at the 10%, 5% and 1% levels respectively.

Dependent Variable:

NBA Draft Number

NBA Draft Number

NBA Win Shares per 48 minutes


NBA Win Shares

NBA Win Shares

NBA Player Efficiency Rating

NBA Player Efficiency Rating

Collegiate Points ‐4.197*** ‐0.0008 0.9191 0.3620

Produced Per Game (0.8790) (0.0041) (0.7650) (0.2519)

Collegiate Points ‐2.4084*** 0.0131*** 2.0538*** 0.7055***

Produced Per Possession (0.8502) (0.0037) (0.5753) (0.2230)

Position Fixed Effects YES YES YES YES YES YES YES YES

Draft Year Fixed Effects YES YES YES YES YES YES YES YES

Draft Number Fixed Effects NO NO YES YES YES YES YES YES

Observations 440 440 397 397 397 397 397 397

Adjusted R2 0.0422 0.0095 0.1077 0.1431 0.3435 0.3571 0.2486 0.2655

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Appendix Table A.1: Robustness Each observation corresponds to a college basketball player drafted by an NBA team between 2003 and 2012. The dependent variable in columns 1 - 4 is the player’s draft number (1 through 60). Columns 1 and 2 (3 and 4) report the results from a poisson (negative binomial) regression with the same independent variables as columns 1 and 2 of Table 1. Columns 5 and 6 repeat the specifications in columns 3 and 4 of Table 1 with the exception that missing values for the dependent variable (win shares per 48 minutes) are set equal to zero. The final two columns repeat the same specifications in columns 3 and 4 of Table 1 except that they include the draft number itself as a control rather than draft number fixed effects. Standard errors are given in parentheses. *, ** and *** indicate significance at the 10%, 5% and 1% levels respectively.

Dependent Variable:

NBA Draft Number

NBA Draft Number

NBA Draft Number

NBA Draft Number





Collegiate Points ‐0.1427*** ‐0.1596*** 0.0009 ‐0.0010

Produced Per Game (0.0316) (0.0394) (0.0028) (0.0038)

Collegiate Points ‐0.0841*** ‐0.0821** 0.0114*** 0.0125***

Produced Per Possession (0.0308) (0.0345) (0.0026) (0.0033)

Draft Number ‐0.0010*** ‐0.0009***

(0.0002) (0.0002)

Position Fixed Effects YES YES YES YES YES YES YES YES

Draft Year Fixed Effects YES YES YES YES YES YES YES YES Draft Number Fixed Effects

NO NO NO NO YES YES NO NO

Observations 440 440 440 440 440 440 397 397

Adjusted R2 ‐ ‐ ‐ ‐ 0.1725 0.2180 0.1198 0.1519

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References

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Coates D., and Oguntimein, B., 2010, The Length and Success of NBA Careers: Does College

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Hollinger, J., 2003, Pro Basketball Prospectus: 2003–04, Brassey’s Inc, Washington, DC.

James, B., and Henzler, J., 2002, Win Shares. STATS, Morton Grove, IL.

Kubatko, Justin. "Calculating Win Shares” Basketball-Reference.com - Basketball Statistics and

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for Heteroskedasticity, Econometrica, 48, 817-830.

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Are NBA Draft Markets Efficient?