No. 102
On the Value of Individual Athletes in
Team Sports
Falk Scherzer
July 2010
An analysis conducted by the Chair of Macroeconomics
at HHL – Leipzig Graduate School of Management
HHL-Arbeitspapier
HHL Working Paper
No. 102
On the Value of Individual Athletes in
Team Sports
Falk Scherzer
ISSN 1864-4562 (Online version)
 HHL – Leipzig Graduate School of Management
On the Value of Individual Athletes in Team Sports
——
July 2010
Abstract
This paper deals with the valuation of individuals in teams. Historical data for the National Basketball
Association (NBA) was used to analyze the individual athletes’ contribution to team success. The analysis
is conducted with data of all players who have played in the NBA since its foundation in 1946. A panel
analysis is used to measure age effects. After adjusting the data for these effects, a multiple regression
is applied to examine the players’ value assuming constant returns to added value. In a final step, the
marginal returns to added value are examined and individual ’effective talent’ is calculated.
Key words: production function, basketball, talent, team sports, evaluation
1
Introduction
This papers deals with the valuation of individuals in teams. To examine this question, historical data
for the North American National Basketball Association (NBA) was used to analyze the individual athletes’
contribution to team success. The discussion about players’ individual value was especially vital after the
National Basketball Association (NBA) published a list of ”the 50 Greatest Players in NBA History” at the
50th anniversary of the league in 1996. The players were chosen by a panel consisting of ”media, former
players and coaches, current and former general managers and team executives”1 . Panelists were asked
for the 50 greatest players of all time without ranking them. However, it is difficult to define ”greatness”
and criteria obviously considered in the selection include standard basketball statistics like points scored,
rebounds, assists, shots blocked and championships won. But since basketball is a team sport, even the
1 http://www.nba.com/
1
statistics of great players will deteriorate when they team up with other great players and certain decisive
contributions like defense, hustle or clutch plays do not show up in any statistic. Did the 1996-97 Chicago
Bulls win a record of 72 of their 82 regular season games just because of Micheal Jordan scoring 30 points
per game? How big was the impact of the newly acquired defensive specialist Dennis Rodman who scored a
mediocre 5.5 points per game but grabbed a league high of 14.9 rebounds per game. One could argue about
this endlessly...or make use of the fact that NBA players usually change teams several times in their career
and simply run a multiple regression. The economic analysis of sports has become a vital field of research
as documented by the growing number of journals specialized on this topic. Since basketball is a team game
and the team constitution varies each season, the impact of single players can be calculated. A data set
containing all 3478 players who have played at least one regular season game since the NBA was founded
in 1946 until 2008 is used to calculate the individual value of each player and analyze the marginal effect of
adding player value to a team.
2
Theoretical Foundation
The evaluation of athletes in team sports and the analysis of a production function are an uncommon but
interesting field of economic research. Stadelmann and Eichenberger [2008] evaluate the talent of Formula
One drivers using a multiple regression.2 One of the first authors to deal with the topic was Scully [1974]
who estimated a production function for Major League Baseball (MLB). Scully uses team statistics as the
slugging average and the average strikeout-to-walk ratio as well as overall team quality dummies as input
and the winning percentage as output to estimate a linear production function. Other studies like Berri
[1999] evaluate individual talent by estimating a team production function based on individual statistics.
The contribution of each statistic to team success is estimated and the players’ value is calculated based
on individual statistics. These studies are useful to measure individual contribution to team success but
not to asses individual talent of team members.3 Chatterjee et al. [1994] use team statistics instead of
2 Formula One races are not a typical team sport. The fact that there are always two drivers in one team who use the same
material is used to compare teammates.
3 It can regularly be observed that individual player statistics change significantly after players move from a mediocre team
to a top contender or vice versa. This should not be interpreted as a change in talent. Individual statistics depend on individual
talent but also on the teammates’ talent, amongst other things. This method could also create a bias towards front court players
due to unobserved defensive skills. Good defense causes the opponents shooting percentage to drop and will be measured as an
increase in defensive rebounds - but not necessarily by the player who defended the shot but likely by the team’s front court
2
individual statistics as input to estimate the winning percentage of NBA teams for 1991-92 and try to make
mid-season predictions for the play-offs and the 1992-93 season based one these estimates. Gustafson et al.
[1999] estimate a joint production function for MLB using individual statistics as inputs and victories as
well as attendance as output. Kahane [2005] analyzes the efficiency in the National Hockey League (NHL).
A production function which uses player ability as proxied by team payroll as input is estimated using a
stochastic frontier analysis.
In order to determine the effective talent of individual athletes, the effect of aging on performance has
to be considered. In this paper, this will be done by an adjustment of the impact proxies (games played
per season) based on an analysis of individual performance over time. This aspect has been studied by
Sowell et al. [2005], among others. The authors perform a aging frontier analysis to asses the development
of performance over time using data from the Iron Man Triathlon World Championship. Fair [1994] uses a
similar technique to analyze the effect of aging using data of men’s track and field competitions.
As the wages of professional athletes in the major sports are enormous and continue to rise, the valuation
of these athletes is of high economic interest. Players are often evaluated based on individual statistics.
While most individual statistics certainly are good indicators of a player’s value, they leave much room
for interpretation in team sports. Individual statistics will depend on a player’s position, his team mates’
abilities, and the team’s tactic, for example. The evaluation of individual players’ value is based on the
following function of performance:
P = F (talent, ef f ort, training, age)
(1)
Performance (P) is a function of inherent talent, effort, training and age, where talent, effort, and training
are assumed to be constant over time for each individual. It is further assumed that training is equally well
for all professional basketball players. The effect of ‘age’ here contains not only the pure effect of aging
but also the gain of experience. This combined effect will be calculated and controlled for. What is left is
talent and effort. Since it cannot be assumed that effort is identical for all athletes, the analysis will yield
estimators of a combination talent and effort. This combination can be called the ’effective talent’ of an
players.
3
individual athlete. Initially, it is assumed that the strength of a team is a linear function of the values of the
team’s individual players and that team strength translates into victories:
Vij = (
n
X
Pkij · GPkij )α + ǫij .
(2)
k=1
Here, Vij is the percentage of regular season games won by team i in season j, Pkij is the value of the n
players who are on team i in season j, GPkij is the number of games played by player k and α is the elasticity
of team success with regard to the combined player value. After initially assuming α to be equal to 1, the
value which optimizes the fit of the regression will be determined. This function recognizes the fact that
players miss games due to injuries or suspensions and that, unlike other team sports, in basketball, players
on every position have equal influence on the game’s outcome. The assumption of a linear strength function
is a simplification but seems to be appropriate for the case of NBA basketball. Of course, if a team hoarded
star players and barely used them, the marginal effect of adding a strong player would be decreasing. This
possibility is however dampened by the restrictions for the roster size and the NBA salary cap rules which
severely punish teams that spend more than a certain amount of money on salaries. The result is a fairly
balanced league. Different assumptions regarding the marginal returns to adding player value to a team are
analyzed in Section 7. The number of regular season victories is chosen as the measure of success. This could
create a possible bias since not every NBA team plays the same number of games against all opponent teams.
Currently, the league is divided into the Eastern and the Western Conference. During the regular season,
each team plays four games against each team from its respective conference and two games against each
team from the other conference. A bias could stem from possible imbalances between the two conferences.
However, even if such imbalances should temporarily exist, they can expected to be considerably small. The
inclusion of play-off games as in Berri [1999], would create a bias against players in strong teams who reach
the play-offs, since these teams face only opponents with above average strength in the post season.4
A point which is of more importance in certain sports than in others is average playing time. In a
basketball game, players are substituted several times each game and playing time varies. This aspect will
4 The eight teams with the best regular season record in each of the two conferences reach the play-offs. Since the 2002-2003
season, the team that prevails in a best-of-seven series advances to the next round. Before 2003, there have been several changes
in the number of required victories in the different play-off rounds. Today, a team could play up to 28 additional games in the
play-offs.
4
remain disregarded in this analysis. The differences in playing time among players of a certain status are
fairly small, however. The analysis also implicitly assumes that the average strength of opponent teams is
constant over time. Should this assumption not hold true, only a comparison of players that played at about
the same time would be possible and the estimates would be the athletes’ effective talent relative to players of
a similar time period. An increase in average team strength could be attributed mainly to improvements in
training, however. Assuming this factor to be constant over time also allows us to assume the average team
strength to be fairly constant over time without creating a bias in measurement of ’effective talent’. The
result will be an estimation of the constant ‘effective talent’ of individual athletes. Applying the estimated
age function to these estimates results in individual values which are not constant over time.
3
Data
The data set includes accurate data for all 3800 players who have played at least one minute in the NBA
or the American Basketball Association (ABA) - a rival league that existed from 1967 until it merged with
NBA in 1976 - between 1946 and 2008.5 Between 1967 and 1976, several players switched between the two
competing leagues. After the merger in 1976, the Denver Nuggets, Indiana Pacers, San Antonio Spurs and
New York Nets were integrated into the NBA. Because of differences in the rules6 in these two leagues,
statistics of ABA seasons are not included in the analysis. Of the 3800 players, 322 players have not played
in the NBA.
The player statistics used are the player’s age, the number of games played for a certain team in a certain
season and the average points scored with that team. For players who were traded to another team during
the regular season, the games for the respective teams are considered. With the data available it is impossible
however, to determine the exact date of player trades. The measure of team performance used is the total
number of regular season wins. The number of teams in the NBA has continuously increased from eleven in
1946 to 30 in 2004. The total number of seasons played by all teams was 1141 in 2008.
5 All
data was gathered from http://www.basketballreference.com/.
of the most important differences was the new three point line.
6 One
5
4
Age Adjustment
Since the aim of this paper is to shed light on the evaluation of individual athletes’ contribution in team
sports, the final multiple regression should yield a time constant ’effective talent’ factor for each individual
player. The number of games played in a particular season for a particular team is used as a proxy for each
player’s influence on this teams total number of regular season victories. However, the impact of a player
with a certain constant talent and effort will vary over time due to unobservable changes in physical condition
and experience. Leaving these changes over time unaccounted for would result in a meaningless regression
biased towards players who ended their career early or are still active (See Section 8 for results without age
adjustment). It seems reasonable to expect the impact which a 35 year old Jason Kidd has on the number of
regular season victories to be smaller than the impact of a 26 year old Jason Kidd, for example. Since this
difference is not related to the time constant effective talent, it has to be controlled for in the final multiple
regression. To control for the effects of physical deterioration and experience, the players’ impact factors
(i.e. the number of games played in a particular season for a particular team) are weighted according to an
estimated career performance function. To estimate this function, a panel regression with cross-section fixed
effects is conducted. This career performance function expresses the average performance development of
each player over his career, assuming cross-section fixed talent and effort for each player. Here, points per
game are used as a proxy for individual performance. Of course, comparing different players only on the
basis of this criterion would be misleading but accounting for cross-section fixed effects eliminates individual
differences (which are of no interest here). For the career performance function, a polynomial function of
the 3rd degree is estimated. In the panel analysis, only the 2514 cross-sections consisting of more than one
season are considered.7 The results are shown in table 1.
The estimated polynomial of the 3rd degree has a local maximum at 25.54. The values for the variable
AGE are the ages the individual players reach in the year a season started8 , so many players will be older
than the value of AGE at the end of that particular season. This means according to the chosen specification,
an average NBA player’s prime age is about 26. From this point on, physical deterioration outweighs gains
in experience. The 3rd degree polynomial has a value of 10.32 at the local maximum. This value is regarded
as the prime performance value. The ratios of the performance values for the different ages relative to the
7 That
8 The
means 964 of 3478 players got to play only one season.
NBA season usually starts at the beginning of November and ends in the middle of April.
6
Table 1: Age Adjustment
Variable
Coefficient
Std.Error
t-Statistic
C
AGE
AGE2
AGE3
−156.743068
16.344105
−0.511547
0.005001
6.897752
0.734799
0.025811
0.000299
−22.72379
22.24296
−19.81929
16.72694
R2
0.755857
Adjusted R2
0.716945
Prob (F-statistic)
0.000000
Prob.
0.0000
0.0000
0.0000
0.0000
Number of Cross-Sections: 2514
prime performance value are used as weights for the impact proxies (games per season).9
5
Methodology
Since a regression with 3478 explanatory and 1141 observations would be underidentified, not all players
could be included. Of the 3478 players who have played at least one game in the NBA between 1946 and
2008, 964 have played only one season. It can be expected that the impact of those players on the total
number of regular season victories is negligible. In order to decrease the number of explanatories further,
only the 949 players who have played at least 400 regular season games in their career are included in the
regression. The number of regular season games has increased over time with the number of teams. In 1946,
the regular season included 62 games. The number dropped to 48 the next season but from then increased
steadily to reach 80 in 1962 and now stands at 82 since 1968. To play 400 games, players had to be in the
league for at least 4 seasons. This can be justified since the objective is to find the most valuable players and
to determine their impact on team success. It can be expected that the most valuable players played long
enough to fulfill this criterion. Problematic are the early years of the league because of the smaller number
of regular season games and the time between 1967 and 1976 when several star players switch between the
NBA and the ABA. This exclusion of players creates a further problem: Since many players who were active
in the early years of the league, the ABA time, or have started their career after 2003 did not or not yet play
9 The
weight for games played at the age of 26 is 0.997, 0.8715 for the age of 29, 0.6121 for the age of 32.
7
400 career games, there are team seasons with very few players who are actually included in the regression.10
This problem renders these seasons useless as observations of the dependent variable since their inclusion
would result in poor coefficient estimates. In order to receive meaningful results, those observations had
to be excluded. In the preferred specification, the 58 team seasons were excluded in which on average less
than 3.1 players included in the regression were used per game (cut), leaving us with 1083 observations and
949 independent variables plus a constant. A lower barrier would result in low t-values for the parameters
and lower the overall fit. Increasing this barrier would further decrease the degrees of freedom and a lower
overall fit. A higher barrier would also mean that more players who are included in the regression ”lose”
part of their seasons which decreases the significance of the estimated coefficient for those players. These
difficulties also determined the choice of the minimum number of 400 career games. With a less restrictive
mark, more players would be included in the regression but due to the very few degrees of freedom, only
very few team seasons (i.e. observations) could have been excluded resulting in a meaningless regression.
Alternative specifications are discussed in Section 8. The equation to be estimated is of the form
y =X ·β+ǫ
(3)
where y is the vector of regular season victories for the different teams and years (for i team seasons).
X is a i × k matrix of the age adjusted numbers of games played by the k − 1 players in the i team seasons
and a constant. β is the vector of the estimated values of ’effective talent’. The estimation equation takes
the following form:
10 There are six team seasons in which no player was used who ever reached the 400 game mark and in 18 team seasons less
than two included players were used on average per game.
8
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Results assuming Constant Marginal Returns
The results of the preferred specification regarding the cut value are shown in table 2. In this regression,
age adjustment is applied and only players with at least 400 NBA career games are included. Also, only
team seasons, in which at least 3.1 players considered in the regression were used on average are included.
In this specification, the overall fit is maximized.
Of the 949 calculated parameters, 649 are significant at the 1 percent level, 70 are significant at the 5
percent level and 35 are significant at the 10 percent level. The lower significance of the 195 parameters
stems from the necessity to exclude several team seasons. This results in a decrease of the number of games
actually considered in the regression for some players with 400 and more career games and in a deterioration
in the level of significance for those coefficients.
The estimates represent the constant ’effective talent’ of the individual athletes. Applying the age
adjustment function would yield individual values for certain seasons in the individuals’ careers. The average
‘effective talent’ is 0.06. The individual values can be interpreted as individual values relative other players’
values. Players with estimated ‘effective talent’ of more than 0.06 can be regarded as above average (relative
to this group of 949 players). The null hypothesis of no heteroscedasticity can not be rejected with the
Breusch-Pagan-Godfrey-Test. Of the 30 players with the highest estimated effective talent, twelve are already
in the Basketball Hall of Fame11 and twelve are still active.12
11 Players
12 Still
can only be inaugurated into the Hall of Fame after their retirement.
active are Parker, Kirilenko, Marion, Prince, Nowitzki, Miles, Garnett, Haywood, Maggette, Ilgauskas, Iverson and
9
The high and insignificant coefficients for George Senesky and Bob Davies are the results of the necessary
cutting off of several seasons. These two coefficients are, unlike the majority of coefficients, not robust to
changes in the cut level. With a cut level of 3.0 instead of 3.1, none of the two players ranks among the
top 30 (see table 8). George Senesky played eight seasons from 1946 until 1954, all with the Philadelphia
Warriors. Five of these eight seasons are cut off in the preferred specification and only 180 of his 482 games
are considered in the regression. One of the two seasons that are lost by the increase of the cut level from
3.0 to 3.1 is the 1950-51 season of the Philadelphia Warriors in which George Senesky played 69 games.
This could be considered a small sample problem due to the required negligence of several seasons. The lack
of robustness in the Bob Davies coefficient is caused by a high correlation with other coefficients. Davies
played seven seasons of professional basketball (1948-55), all with the Rochester Royals. The 1948-49 season
is the second season which is lost by increasing the cut level from 3.0 to 3.1. In all of the other seasons,
Bob Davies played together with Arnie Risen, Bobby Wanzer and Jack Coleman. The only other players
considered in the regression who played together with these four are Jack McMahon (1952-55) and Alex
Hannum (1951-54). After the 1954-55 season, Davies, Risen and Coleman leave the Royals together. This
unusual correlation leads to the insignificance of the estimated coefficient. Jack Coleman joined the team
only after the cut off 1948-49 season. The inclusion of this season in the specification with the 3.0 cut level
diminishes the very high correlation of these four variables. The estimated effective talent of Bob Davies in
the 3.0 specification is 0.6234 as compared to 1.2210 in the preferred specification. The estimated effective
talent of Jack Coleman increases to -0.0132 in the 3.0 specification as compared to -0.5151 in the preferred
specification. These are unusually high changes representing the lack of robustness in these coefficients.
As a result of this multiple regression analysis, Tony Parker seems to be the NBA player with the greatest
effective talent in NBA history. The insignificant estimates for George Senesky and Bob Davies are results
of the above mentioned data problems. Is this result plausible? Parker, a French, turned 27 in 2009. He
entered the league in 2001 and spent all his first seven NBA seasons with the San Antonio Spurs. The Spurs
have been one of the most successful teams in the NBA since 1989. In the 18 seasons from 1989-90 to 2007-08
they won at least 57 percent of their regular season games except for the dismal 1996-97 season. In the seven
seasons with Parker on their roster they won at least 68 percent of their regular season games each season
and three championships in 2003, 2005 and 2007.
Turkoglu.
10
Table 2: Regression Results (400G; cut 3.1; α = 1)
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
1
Name
β
Standard Error
Senesky,George
Davies,Bob (HOF)1
Parker,Tony
Russell,Bill (HOF)
Olajuwon,Hakeem (HOF)
Kirilenko,Andrei
Schayes,Dolph (HOF)
Marion,Shawn
Abdul-Jabbar,Kareem (HOF)
Thomas,Isiah (HOF)
Bradley,Bill (HOF)
Prince,Tayshaun
Nowitzki,Dirk
Miles,Darius
Garnett,Kevin
Catchings,Harvey
Thompson,Lasalle
Macauley,Ed (HOF)
Barkley,Charles (HOF)
Haywood,Brendan
Maggette,Corey
Ilgauskas,Zydrunas
Mullin,Chris
Unseld,Wes (HOF)
Gale,Mike
Iverson,Allen
Pollard,Jim (HOF)
Turkoglu,Hidayet
Jones,Bobby
Sharman,Bill (HOF)
6.5304
1.2210
1.1527∗∗∗
0.9742
0.7709∗∗∗
0.7651∗∗∗
0.7095∗∗∗
0.7085∗∗∗
0.6609∗∗∗
0.6487∗∗∗
0.5958∗∗∗
0.5909∗∗∗
0.5825∗∗∗
0.5782∗∗∗
0.5662∗∗∗
0.5294∗∗∗
0.5279∗∗∗
0.5275∗∗∗
0.5213∗∗∗
0.5195∗∗∗
0.5184∗∗∗
0.5164∗∗∗
0.5143∗∗∗
0.5142∗∗∗
0.5060∗∗∗
0.5013∗∗∗
0.4957∗∗∗
0.4945∗∗∗
0.4923∗∗∗
0.4881∗∗∗
R2
0.9734
Adjusted R2
0.7833
*** : significant at 1 percent level
** : significant at 5 percent level
* : significant at 10 percent level
’HOF’ indicates that the player is a
110.2833
2.9845
0.1013
0.7337
0.0715
0.0853
0.2567
0.0324
0.1207
0.0623
0.1916
0.0465
0.0501
0.0697
0.0291
0.0950
0.0794
0.1730
0.0327
0.0519
0.0237
0.0309
0.0498
0.0355
0.0482
0.0289
0.0599
0.0136
0.0679
0.1568
(649 of 949)
(70 of 949)
(35 of 949)
member of the Basketball Hall of Fame
11
7
Analysis of non-constant Returns to Value
The results in Section 6 assumed constant returns to value added to a team. In this section, different
values for the elasticity of team success with regard to the combined player value (see equation 2 in Section
2) and the impact on the overall fit of the regression are examined. The elasticity that maximizes the overall
fit for the preferred regression specification (cut level 3.1) is 0.75.13 For values smaller or larger than 0.75,
the overall fit is constantly decreasing. Furthermore, regarding only team seasons where at least 3.1 players
considered in the regression where used (cut level 3.1) is also maximizing the overall fit when an elasticity
of 0.75 is assumed.
Table 3: Regression Characteristics with different Elasticities
cut 3.1
cut 3.0
cut 3.3
0.71
0.75
0.79
1.0
0.75
0.75
α
R2
0.974155 0.974178 0.974150 0.973365 0.973674 0.974143
adj.R2 0.789745 0.789928 0.789793 0.783316 0.788610 0.788243
***
645
650
648
649
645
643
**
61
60
57
70
70
64
44
39
40
35
39
42
*
*** : coefficients significant at 1 percent level
** : coefficients significant at 5 percent level
*
: coefficients significant at 10 percent level
The regression results for a cut level of 3.1 and an elasticity of team success with regard to the combined
player value of 0.75 are reported in table 4. The value of adjusted R2 increases by 0.006612. Even though
this value seems to be small, it indicates that this specification describes the translation of combined player
value into victories better than the model with constant returns to value.
13 The
precision of examination is 0.01.
12
Table 4: Regression Results (400G; cut 3.1; α = 0.75)
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
1
Name
β
Standard Error
Senesky,George
Davies,Bob (HOF)1
Parker,Tony
Russell,Bill (HOF)
Olajuwon,Hakeem (HOF)
Schayes,Dolph (HOF)
Kirilenko,Andrei
Thomas,Isiah (HOF)
Marion,Shawn
Nowitzki,Dirk
Abdul-Jabbar,Kareem (HOF)
Prince,Tayshaun
Garnett,Kevin
Thompson,Lasalle
Mullin,Chris
Bradley,Bill (HOF)
Miles,Darius
Macauley,Ed (HOF)
Pollard,Jim (HOF)
Sharman,Bill (HOF)
Catchings,Harvey
Kersey,Jerome
Iverson,Allen
Unseld,Wes (HOF)
Gale,Mike
Barkley,Charles (HOF)
Dawkins,Darryl
Robertson,Oscar (HOF)
Smith,Phil
Jones,Bobby
6.069222
1.604724
1.171252∗∗∗
1.139786
0.823782∗∗∗
0.792839∗∗∗
0.788791∗∗∗
0.730043∗∗∗
0.654463∗∗∗
0.641779∗∗∗
0.623396∗∗∗
0.620207∗∗∗
0.604069∗∗∗
0.591433∗∗∗
0.582369∗∗∗
0.575740∗∗∗
0.571254∗∗∗
0.560595∗∗∗
0.552907∗∗∗
0.537741∗∗∗
0.535172∗∗∗
0.525826∗∗∗
0.513277∗∗∗
0.508085∗∗∗
0.503575∗∗∗
0.499925∗∗∗
0.493097∗∗∗
0.488487∗∗∗
0.485816∗∗∗
0.484186∗∗∗
R2
0.9742
Adjusted R2
0.7899
*** : significant at 1 percent level
** : significant at 5 percent level
* : significant at 10 percent level
’HOF’ indicates that the player is a
114.5482
3.1000
0.1053
0.7621
0.0742
0.2666
0.0886
0.0647
0.0336
0.0521
0.1253
0.0483
0.0303
0.0825
0.0517
0.1990
0.0724
0.1796
0.0622
0.1629
0.0987
0.0625
0.0300
0.0369
0.0500
0.0340
0.0469
0.0469
0.0368
0.0705
(650 of 949)
(60 of 949)
(39 of 949)
member of the Basketball Hall of Fame
13
8
Robustness
The results of the multiple regression including only players who played at least 400 regular season games
and excluding all team seasons in which less than 3.1 of these players have played per game on average (cut)
is relatively robust to changes in the exact specification. The applied age adjustment increases the overall
fit. As can be seen in table 5, the difference in the adjusted R2 is 0.0358.
In the panel regression which yielded the career performance function, only those players were considered
who played at least two regular seasons in the NBA. Table 6 shows the estimation results when all players
are included in the estimation.14 The estimated function is much flatter, the overall fit is lower and the
estimator for the intercept term is not significant at the 5 percent level.
Table 7 shows the results for the estimation of the career performance function including only players
who have played at least three seasons in the NBA.15 The estimated function differs only slightly from the
one estimated with the preferred specification (table 1) but the overall fit is slightly lower.
Tables 8, 9 and 10 give an overview of the estimation results for different specifications concerning the
cut level. Decreasing the cut value from 3.1 to 3.0 would increase the number of degrees of freedom from 133
to 135 but lower the adjusted R2 by 0.0072. An increase of the cut value from 3.1 to 3.2 does not result in
any changes. An increase to 3.3 would reduce the degrees of freedom from 133 to 132. In this specification,
649 (70, 35) of the 949 parameter estimates are significant at the 1 (5, 10) percent level as compared to 667
(61, 31) in the preferred specification which includes one more team season and adjusted R2 decreases by
0.0017. A further increase of the cut level to 4 (which is equivalent to a decrease of the number of degrees
of freedom to 98) leads to even less significant coefficient estimates and a lower overall fit (see table 10).
The ranking varies since the differences between the estimated coefficients of players with similar ‘effective
talent’ are very small. The actual estimates do not vary much in the different cut-specifications.
The high and insignificant coefficients for George Senesky and Bob Davies, who rank at the top two
positions in the preferred specification are the results of the necessary cutting off of several seasons. These
two coefficients are, unlike the majority of coefficients, not robust to changes in the cut level. With a cut
level of 3.0 instead of 3.1, none of the two players ranks among the top 30 (see table 8. In the case of George
Senesky, it is simply a short sample problem since changing the cut level from 3.0 to 3.1 decreases the number
of Senesky’s games considered in the regression from 249 to only 180. In the case of Bob Davies, the high,
insignificant and non-robust coefficient results from an unusually high correlation between his career and
14 The
15 The
number of cross-sections increases by 964; the number of players who left the league after one season.
number of cross-sections decreases by 432 as compared to the preferred specification.
14
the careers of Arnie Risen, Bobby Wanzer and Jack Coleman. Increasing the cut level to 3.1 would add
one season in which Davies has not played together with Coleman and therefore decease this correlation.
Regardless of these obstacles, the cut level of 3.1 is the preferred specification since it yields the highest
overall fit.
15
Table 5: Regression Results without Age Adjustment (400G; cut 3.1; α = 0.75)
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
1
Name
β
Standard Error
Wanzer,Bobby (HOF)1
Senesky,George
Marion,Shawn
Ilgauskas,Zydrunas
Prince,Tayshaun
Gasol,Pau
Thomas,Isiah (HOF)
Collins,Jason
Miles,Darius
Ellis,Joe
Peterson,Morris
Bradley,Bill (HOF)
Schayes,Dolph (HOF)
Jones,Bobby
Parker,Tony
Blount,Mark
West,Doug
McGrady,Tracy
Anderson,Derek
Nowitzki,Dirk
McMillan,Nate
Smith,Phil
Stockton,John
Catledge,Terry
Lewis,Rashard
Miller,Brad
Unseld,Wes (HOF)
Macauley,Ed (HOF)
Williams,Monty
Maggette,Corey
1.9739
1.1786
0.7663∗∗∗
0.7403∗∗∗
0.7370∗∗∗
0.6501∗∗∗
0.6403∗∗∗
0.6230∗∗∗
0.6104∗∗∗
0.6014∗∗∗
0.6001∗∗∗
0.5876∗∗∗
0.5583∗∗∗
0.5394∗∗∗
0.5317∗∗∗
0.5280∗∗∗
0.5273∗∗∗
0.4982∗∗∗
0.4968∗∗∗
0.4939∗∗∗
0.4929∗∗∗
0.4876∗∗∗
0.4813∗∗∗
0.4614∗∗∗
0.4608∗∗∗
0.4601∗∗∗
0.4599∗∗∗
0.4578∗∗∗
0.4522∗∗∗
0.4518∗∗∗
R2
0.9694
Adjusted R2
0.7541
1.7299
5.9860
0.0499
0.0290
0.0878
0.0696
0.0401
0.0544
0.0436
0.1908
0.0230
0.0963
0.0750
0.0380
0.0881
0.0360
0.0520
0.0325
0.0210
0.0515
0.0340
0.0216
0.0409
0.0239
0.0320
0.0275
0.0216
0.0310
0.0548
0.0299
*** : significant at 1 percent level (730 of 949)
** : significant at 5 percent level (37 of 949)
* : significant at 10 percent level (29 of 949)
’HOF’ indicates that the player is a member of the Basketball Hall
of Fame
16
Table 6: Age Adjustment (Data from all Players)
Variable
Coefficient
Std.Error
t-Statistic
Prob.
C
AGE
AGE2
AGE3
2.09495
−0.55630
0.07739
−0.00174
1.21998
0.13808
0.00563
0.00008
1.71720
−4.02868
13.75519
−22.40331
0.0860
0.0001
0.0000
0.0000
R2
0.759607
Adjusted R2
0.707364
Prob (F-statistic)
0.000000
Number of Cross-Sections: 3478
Table 7: Age Adjustment (Data from Players who playeda min. of 3 Seasons)
Variable
Coefficient
Std.Error
t-Statistic
Prob.
C
AGE
AGE2
AGE3
-160.78296
16.77703
-0.52577
0.00516
7.04867
0.75024
0.02633
0.00031
-22.81039
22.36219
-19.96775
16.91399
0.0000
0.0000
0.0000
0.0000
R2
0.747523
Adjusted R2
0.713070
Prob (F-statistic)
0.000000
Number of Cross-Sections: 2082
17
Table 8: Regression Results (400G; cut 3.0; α = 0.75)
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
1
Name
β
Standard Error
Parker,Tony
Olajuwon,Hakeem (HOF)1
Kirilenko,Andrei
Thomas,Isiah (HOF)
Schayes,Dolph (HOF)
Marion,Shawn
Abdul-Jabbar,Kareem (HOF)
Nowitzki,Dirk
Thompson,Lasalle
Prince,Tayshaun
Davies,Bob (HOF)
Hagan,Cliff (HOF)
Garnett,Kevin
Mullin,Chris
Miles,Darius
Russell,Bill (HOF)
Kersey,Jerome
Pollard,Jim (HOF)
Iverson,Allen
Dawkins,Darryl
Robertson,Oscar (HOF)
Gale,Mike
Catchings,Harvey
Unseld,Wes (HOF)
Barkley,Charles (HOF)
Dunn,T.r.
Issel,Dan (HOF)
Turkoglu,Hidayet
Smith,Phil
1.174145∗∗∗
0.840222∗∗∗
0.788865∗∗∗
0.715460∗∗∗
0.679768∗∗
0.657190∗∗∗
0.650409∗∗∗
0.644412∗∗∗
0.638763∗∗∗
0.628995∗∗∗
0.623297
0.611505∗∗∗
0.599919∗∗∗
0.594626∗∗∗
0.564872∗∗∗
0.560776
0.544696∗∗∗
0.542488∗∗∗
0.523742∗∗∗
0.523537∗∗∗
0.522566∗∗∗
0.522313∗∗∗
0.521079∗∗∗
0.520992∗∗∗
0.508831∗∗∗
0.490538∗∗∗
0.490276∗∗∗
0.489453∗∗∗
0.488432∗∗∗
R2
0.9729
Adjusted R2
0.7827
*** : significant at 1 percent level
** : significant at 5 percent level
* : significant at 10 percent level
’HOF’ indicates that the player is a
0.1061
0.0748
0.0893
0.0650
0.2639
0.0339
0.1262
0.0525
0.0824
0.0487
2.7625
0.1403
0.0305
0.0521
0.0730
0.6616
0.0628
0.0616
0.0302
0.0468
0.0458
0.0503
0.0992
0.0371
0.0342
0.0412
0.1286
0.0143
0.0364
(667 of 949)
(61 of 949)
(31 of 949)
member of the Basketball Hall of Fame
18
Table 9: Regression Results (400G; cut 3.3; α = 0.75)
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
1
Name
β
Standard Error
Senesky,George
Davies,Bob (HOF)1
Parker,Tony
Russell,Bill (HOF)
Olajuwon,Hakeem (HOF)
Kirilenko,Andrei
Schayes,Dolph (HOF)
Thomas,Isiah (HOF)
Marion,Shawn
Abdul-jabbar,Kareem (HOF)
Nowitzki,Dirk
Prince,Tayshaun
Garnett,Kevin
Thompson,Lasalle
Bradley,Bill (HOF)
Mullin,Chris
Macauley,Ed (HOF)
Pollard,Jim (HOF)
Miles,Darius
Sharman,Bill (HOF)
Catchings,Harvey
Iverson,Allen
Kersey,Jerome
Unseld,Wes (HOF)
Barkley,Charles (HOF)
Gale,Mike
Dawkins,Darryl
Jones,Bobby
Smith,Phil
Robertson,Oscar (HOF)
6.158393
1.626560
1.150434∗∗∗
1.135268
0.838693∗∗∗
0.790633∗∗∗
0.778523∗∗∗
0.732367∗∗∗
0.654509∗∗∗
0.639485∗∗∗
0.628137∗∗∗
0.612023∗∗∗
0.596467∗∗∗
0.596396∗∗∗
0.579517∗∗∗
0.570739∗∗∗
0.559580∗∗∗
0.553902∗∗∗
0.533887∗∗∗
0.532732∗∗∗
0.522795∗∗∗
0.513598∗∗∗
0.510182∗∗∗
0.508970∗∗∗
0.497935∗∗∗
0.494841∗∗∗
0.494486∗∗∗
0.492615∗∗∗
0.485849∗∗∗
0.484345∗∗∗
R2
0.9741
Adjusted R2
0.7882
115.1403
3.1167
0.1070
0.7659
0.0752
0.0890
0.2686
0.0650
0.0338
0.1267
0.0529
0.0488
0.0306
0.0829
0.2000
0.0523
0.1805
0.0625
0.0768
0.1638
0.0996
0.0301
0.0635
0.0370
0.0341
0.0505
0.0472
0.0711
0.0370
0.0472
*** : significant at 1 percent level (643 of 949)
** : significant at 5 percent level (64 of 949)
* : significant at 10 percent level (42 of 949)
’HOF’ indicates that the player is a member of the Basketball Hall of Fame
19
Table 10: Regression Results (400G; cut 4; α = 0.75)
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
1
Name
β
1
StandardError
Wanzer,Bobby (HOF)
Russell,Bill (HOF)
Kirilenko,Andrei
Senesky,George
Parker,Tony
Greer,Hal (HOF)
Olajuwon,Hakeem (HOF)
Phillip,Andy (HOF)
Macauley,Ed (HOF)
Sharman,Bill (HOF)
Thomas,Isiah (HOF)
Thompson,Lasalle
Prince,Tayshaun
Jones,Bobby
Mullin,Chris
Pettit,Bob (HOF)
Garnett,Kevin
Yardley,George (HOF)
Marion,Shawn
Nowitzki,Dirk
Catchings,Harvey
Rollins,Tree
Kersey,Jerome
Pollard,Jim (HOF)
Bird,Larry (HOF)
Bridges,Bill
Unseld,Wes (HOF)
Iverson,Allen
Lanier,Bob (HOF)
Smith,Phil
2.385169
1.786034
1.337327∗∗∗
1.224706
1.202966∗∗∗
1.160917∗∗
1.019381∗∗∗
1.017384
0.921746
0.916606∗∗
0.766716∗∗∗
0.721321∗∗∗
0.703031∗∗∗
0.685518∗∗∗
0.643933∗∗∗
0.638497
0.632584∗∗∗
0.607463∗∗∗
0.605214∗∗∗
0.587330∗∗∗
0.585985∗∗∗
0.579635∗∗∗
0.576088∗∗∗
0.562129∗∗∗
0.560646∗∗∗
0.560328∗∗∗
0.558477∗∗∗
0.549220∗∗∗
0.547607∗∗∗
0.542725∗∗∗
R2
0.9767
Adjusted R2
0.7511
41.3991
1.6458
0.3416
821.3991
0.1567
0.5874
0.1020
1.8497
0.8243
0.3811
0.0848
0.1297
0.0638
0.1042
0.0747
0.4276
0.0441
0.0915
0.0430
0.0729
0.1485
0.0901
0.0853
0.0914
0.0525
0.0989
0.0748
0.0446
0.0926
0.0552
*** : significant at 1 percent level (516 of 949)
** : significant at 5 percent level (77 of 949)
* : significant at 10 percent level (40 of 949)
’HOF’ indicates that the player is a member of the Basketball Hall of Fame
20
9
Criticism
Some problematic points have already been mentioned above. Since the number of players who have
played at least one NBA game far exceeds the number of team seasons, it was necessary to exclude all
players who have played less than 400 career games. Even though the impact on regular season victories
of most of those players can be expected to be sufficiently small, problems arise with the early seasons, the
ABA era and the seasons from 2004 on. In the early years, the number of regular season games was smaller
and several star players simply did not reach the mark of 400 games because they were already relatively
old when the league was founded. In the ABA era (1967-76), several very talented players spent significant
parts of their careers in the ABA and therefore did not record 400 NBA games. Finally, none of the players
who entered the league after the 2004 season is included in the analysis since in the four seasons from 2004
to 2008 only 328 games have been played per team. These exclusions made it necessary to skip 58 team
seasons in the preferred specification because only a few number of players included in the regression where
used by those teams.
Another limitation is the complete negligence of coaches. A savvy head coach certainly has a significant
impact on the number of regular season victories but the inclusion would have increased the number of
dependent variables by several hundred and made a sensible regression impossible. The multiple regression
takes into account that players sometimes change teams during a season. What it does not take into
account, however, is the success of the player’s old and new team before and after the trade. Trades during
a season are common but their number is small relative to the total number of players. The assumption
that individual values linearly add up to team strength is in this context not completely unrealistic but
simplifying. Therefore, decreasing marginal returns to added player value are assumed in the preferred
specification. Another possible shortcoming is the way the age adjustment is conducted. The method applied
assumes that a player’s performance is a function of his age (and talent, effort and training; see equation
1 in Section 2). The method, which yields quite reasonable results, could be improved by considering the
age at which a player has entered the league. On the one hand, athletes who start their professional career
at the age of 18 can be expected to be much more experienced at the age of 23 compared to others who
played college basketball for several seasons. On the other hand, since the number of games per season
on college is less than half the number of NBA regular season games, players who enter the league early
might face physical problems earlier than those who enter at a higher age. The career performance could
21
be expressed using the two variables (”age”, ”years as pro”). The effect of ”years as pro” is expected to be
strictly increasing. The effect of age on performance is expected to depend on ”years as pro” since many
players improve their physical condition in the first couple of years as professional athletes. From than on,
their body deteriorates due to natural aging and the high physical loading. The career performance function
estimated in Section 3, which estimates the combined effects of age and experience neglects the fact that not
all players enter the professional league at the same age and therefore differ in their experience at a certain
age.
Other basketball specific shortcomings are the differences in playing time per game and play-off performance which are completely disregarded in this analysis.
10
Conclusion
The focus of this work is on the value of individual athletes in a team sport. To analyze this value,
the effects of aging and gathering experience where jointly estimated and applied to adjust data of 949
professional basketball players which was then used in a multiple regression to determine what could be
called individual ’effective talent’ (which is constant over time). In this first step, constant marginal returns
to adding player value to a team were assumed. The regression results improve when this function is altered
in a way that recognizes the possibility, that the marginal value of a strong player is smaller in a strong
team than in a weak one. This is done by simply weighting the values of the dependent variable accordingly.
From the analysis in Section 7 it follows that the specification with a value of elasticity of success of 0.75
yields the highest overall fit for the multiple regression. The player with the highest ’effective talent’ in the
history of NBA basketball (given the limitations regarding players included mentioned in Section 9) seems
to be the French Tony Parker.
A possible extension to this paper could be a panel analysis of the effect of team success on franchise
revenue. Total franchise revenue can be expected to include a fixed component and variable components
such as ticket and merchandise sales, TV earnings for nation wide televised games and revenues generated
by playoff appearances. The revenue function will thus be a strictly increasing function of team success.
Assuming that players are remunerated according to their performance (which is a function of effective
talent, age and training), the team payroll is a convex and strictly increasing function of team success.
22
Depending on the characteristics of the revenue function, a situation with two equilibria is possible where
certain franchises choose to form mediocre teams and others try to build teams as strong as possible. This
phenomenon can be observed in the US professional leagues where franchises are not threatened by regulation.
Of course, the techniques applied in this paper can not be used to assess the effective talent of young players
who have played only a small number of games. For this purpose, the method used by Berri [1999] provides
acceptable estimates of individual effective talent. Considering the age performance function calculated in
section 4, these estimates can be used to determine the impact of a certain player on franchise revenue over
the time of his contract.
23
References
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Association. Managerial and Decision Economics, pages 411–427, 1999.
S. Chatterjee, M.R. Campbell, and F. Wiseman. Take that jam! An analysis of winning percentage for NBA
teams. Managerial and Decision Economics, pages 521–535, 1994.
R.C. Fair. How fast do old men slow down? The Review of Economics and Statistics, pages 103–118, 1994.
E. Gustafson, L. Hadley, and J. Ruggiero. Alternative econometric models of production in major league
baseball. Sports Economics: Current Research (Westport: CT, Praeger), 1999.
http://www.basketballreference.com/.
http://www.nba.com/.
L.H. Kahane. Production efficiency and discriminatory hiring practices in the National Hockey League: A
stochastic frontier approach. Review of Industrial Organization, 27(1):47–71, 2005.
G.W. Scully. Pay and performance in major league baseball. The American Economic Review, pages 915–930,
1974.
C.B. Sowell, W. Mounts, et al. Ability, Age, and Performance: Conclusions From the Ironman Triathlon
World Championship. Journal of Sports Economics, 6(1):78, 2005.
D. Stadelmann and R. Eichenberger. Wer ist der beste Formel 1 Fahrer? Eine okonometrische Talentbewertung. Perspektiven der Wirtschaftspolitik, 9(4):486–512, 2008.
24
Table 11: Results
Rank
25
11
129
902
236
511
482
335
579
361
650
311
777
86
399
936
709
595
646
432
600
702
730
190
900
142
689
384
373
767
387
831
114
180
470
463
165
713
604
572
349
552
26
783
771
319
856
799
581
305
524
Name
Abdul-jabbar,Kareem (HOF)
Abdul-rauf,Mahmo
Abdur-rahim,Shareef
Adams,Alvan
Adams,Don
Adams,Michael
Aguirre,Mark
Ainge,Danny
Allen,Lucius
Allen,Ray
Alston,Rafer
Anderson,Cadillac
Anderson,Derek
Anderson,Kenny
Anderson,Nick
Anderson,Richard
Anderson,Ron
Anderson,Shandon
Anderson,Willie
Anthony,Greg
Archibald,Nate (HOF)
Arenas,Gilbert
Arizin,Paul (HOF)
Armstrong,B.j.
Armstrong,Darrell
Arroyo,Carlos
Artest,Ron
Askew,Vincent
Askins,Keith
Atkins,Chucky
Attles,Alvin
Augmon,Stacey
Austin,Isaac
Awtrey,Dennis
Bagley,John
Bailey,James
Bailey,Thurl
Baker,Vin
Ballard,Greg
Banks,Gene
Bantom,Mike
Barkley,Charles (HOF)
Barnes,Jim
Barnett,Dick
Barnett,Jim
Barnhill,John
Barros,Dana
Barry,Brent
Barry,Jon
Barry,Rick (HOF)
*** : significant at 1 percent level
** : significant at 5 percent level
*
: significant at 10 percent level
β
cut 3.1
St.Err.
0.6234∗∗∗
0.2796∗∗∗
−0.2607∗∗∗
0.1831∗∗
0.0218∗
0.0417∗
0.1214∗∗∗
−0.0091
0.1063∗∗∗
−0.0497
0.1341∗∗∗
−0.1342∗∗∗
0.3281∗∗∗
0.0810∗∗∗
−0.4537∗∗∗
−0.0872∗∗∗
−0.0198
−0.0490∗∗∗
0.0649
−0.0220
−0.0835∗∗∗
−0.0999∗∗∗
0.2198∗∗∗
−0.2565∗∗∗
0.2698∗∗∗
−0.0745∗∗∗
0.0910∗∗∗
0.0964∗∗∗
−0.1277∗∗∗
0.0868∗∗∗
−0.1721∗∗∗
0.2926∗∗∗
0.2318∗∗∗
0.0475∗∗∗
0.0520∗
0.2524∗∗∗
−0.0887∗
−0.0246
−0.0061
0.1125
0.0003
0.4999∗∗∗
−0.1388∗∗∗
−0.1301∗∗∗
0.1307∗∗∗
−0.1937∗∗∗
−0.1502∗∗∗
−0.0105
0.1380∗∗∗
0.0143
0.1253
0.0343
0.0172
0.0917
0.0123
0.0219
0.0382
0.0126
0.0308
0.0336
0.0126
0.0164
0.0167
0.0222
0.0627
0.0144
0.0478
0.0085
0.0695
0.0178
0.0238
0.0180
0.0469
0.0391
0.0731
0.0205
0.0242
0.0180
0.0469
0.0309
0.0388
0.0248
0.0261
0.0151
0.0309
0.0239
0.0477
0.0207
0.0526
0.0711
0.0131
0.0340
0.0348
0.0151
0.0228
0.0377
0.0174
0.0141
0.0155
0.0266
β
cut 3.0
St. Err.
0.6504∗∗∗
0.2788∗∗∗
−0.2635∗∗∗
0.1528∗
0.0152
0.0285
0.1353∗∗∗
−0.0169
0.1074∗∗∗
−0.0475
0.1375∗∗∗
−0.1328∗∗∗
0.3234∗∗∗
0.0746∗∗∗
−0.4460∗∗∗
−0.0864∗∗∗
−0.0161
−0.0493∗∗∗
0.0535
−0.0231
−0.0738∗∗∗
−0.0983∗∗∗
0.1180∗∗∗
−0.2653∗∗∗
0.2738∗∗∗
−0.0701∗∗∗
0.0914∗∗∗
0.0902∗∗∗
−0.1335∗∗∗
0.0847∗∗∗
−0.1824∗∗∗
0.2857∗∗∗
0.2323∗∗∗
0.0525∗∗∗
0.0674∗∗
0.2571∗∗∗
−0.0916∗
−0.0232
−0.0071
0.1359∗
−0.0046
0.5088∗∗∗
−0.1516∗∗∗
−0.1298∗∗∗
0.1269∗∗∗
−0.1603∗∗∗
−0.1426∗∗∗
−0.0116
0.1400∗∗∗
0.0369
0.1262
0.0346
0.0174
0.0916
0.0123
0.0220
0.0384
0.0127
0.0308
0.0339
0.0127
0.0165
0.0168
0.0223
0.0632
0.0145
0.0480
0.0085
0.0699
0.0179
0.0237
0.0181
0.0168
0.0394
0.0737
0.0207
0.0244
0.0182
0.0473
0.0312
0.0391
0.0250
0.0264
0.0150
0.0311
0.0240
0.0481
0.0209
0.0530
0.0709
0.0131
0.0342
0.0351
0.0150
0.0229
0.0376
0.0175
0.0142
0.0156
0.0266
β
cut 3.3
St. Err.
0.6395∗∗∗
0.2707∗∗∗
−0.2470∗∗∗
0.1808∗∗
0.0232∗
0.0372∗
0.1094∗∗∗
−0.0130
0.1060∗∗∗
−0.0400
0.1396∗∗∗
−0.1308∗∗∗
0.3142∗∗∗
0.0708∗∗∗
−0.4916∗∗∗
−0.0890∗∗∗
−0.0111
−0.0506∗∗∗
0.0385
−0.0340∗
−0.0833∗∗∗
−0.0892∗∗∗
0.2186∗∗∗
−0.2635∗∗∗
0.3057∗∗∗
−0.0811∗∗∗
0.0653∗∗
0.0962∗∗∗
−0.1196∗∗
0.0751∗∗
−0.1733∗∗∗
0.2973∗∗∗
0.2288∗∗∗
0.0477∗∗∗
0.0650∗∗
0.2474∗∗∗
−0.0843∗
−0.0087
0.0039
0.1056
−0.0006
0.4979∗∗∗
−0.1404∗∗∗
−0.1298∗∗∗
0.1302∗∗∗
−0.1949∗∗∗
−0.1510∗∗∗
−0.0076
0.1513∗∗∗
0.0161
0.1267
0.0347
0.0178
0.0921
0.0123
0.0221
0.0388
0.0127
0.0309
0.0341
0.0128
0.0165
0.0173
0.0226
0.0672
0.0144
0.0482
0.0085
0.0718
0.0183
0.0239
0.0184
0.0471
0.0395
0.0772
0.0207
0.0262
0.0181
0.0473
0.0315
0.0390
0.0250
0.0263
0.0151
0.0315
0.0241
0.0480
0.0215
0.0531
0.0716
0.0132
0.0341
0.0350
0.0151
0.0229
0.0379
0.0175
0.0142
0.0161
0.0267
Table 11: Results
Rank
26
879
278
97
351
468
653
678
224
887
398
303
451
684
417
69
813
378
33
921
698
649
791
700
141
509
84
785
860
317
316
940
894
448
16
291
727
670
745
425
341
273
601
563
409
94
50
61
155
498
640
Name
Battie,Tony
Battier,Shane
Battle,John
Baylor,Elgin (HOF)
Beard,Butch
Beaty,Zelmo
Bell,Raja
Bellamy,Walt (HOF)
Benjamin,Benoit
Benoit,David
Benson,Kent
Best,Travis
Bianchi,Al
Bibby,Henry
Bibby,Mike
Billups,Chauncey
Bing,Dave (HOF)
Bird,Larry (HOF)
Birdsong,Otis
Blackman,Rolando
Blaylock,Mookie
Blount,Corie
Blount,Mark
Bockhorn,Arlen
Boerwinkle,Tom
Bogues,Muggsy
Bol,Manute
Boozer,Bob
Bowen,Bruce
Bowen,Ryan
Bowie,Anthony
Bowie,Sam
Boykins,Earl
Bradley,Bill (HOF)
Bradley,Dudley
Bradley,Shawn
Brand,Elton
Brandon,Terrell
Bratz,Mike
Braun,Carl
Breuer,Randy
Brewer,Jim
Brewer,Ron
Brickowski,Frank
Bridgeman,Junior
Bridges,Bill
Bristow,Allan
Brooks,Scott
Brown,Chucky
Brown,Dee
*** : significant at 1 percent level
** : significant at 5 percent level
*
: significant at 10 percent level
β
cut 3.1
St.Err.
−0.2288∗∗∗
0.1538∗∗∗
0.3124∗∗∗
0.1112∗∗∗
0.0496∗∗∗
−0.0515
−0.0688∗∗∗
0.1925∗∗∗
−0.2411∗∗∗
0.0812∗∗∗
0.1385∗∗∗
0.0549∗∗∗
−0.0730
0.0715∗∗∗
0.3917∗∗∗
−0.1568∗∗∗
0.0942∗∗∗
0.4762∗∗∗
−0.3371∗∗∗
−0.0805
−0.0496∗∗
−0.1451∗∗∗
−0.0815∗∗
0.2705∗∗∗
0.0236
0.3313∗∗∗
−0.1399∗∗∗
−0.1971∗∗∗
0.1309∗∗∗
0.1312∗∗∗
−0.4713∗∗∗
−0.2494∗∗∗
0.0567∗∗∗
0.5757∗∗∗
0.1476∗∗∗
−0.0987∗∗∗
−0.0648∗∗∗
−0.1091∗∗
0.0662∗∗∗
0.1164∗∗∗
0.1581∗∗
−0.0221
−0.0020
0.0758∗∗∗
0.3138∗∗∗
0.4344∗∗∗
0.4044∗∗∗
0.2601∗∗∗
0.0293∗∗
−0.0467∗
0.0209
0.0293
0.0536
0.0182
0.0066
0.0357
0.0147
0.0207
0.0225
0.0173
0.0287
0.0199
0.0688
0.0205
0.0252
0.0257
0.0213
0.0381
0.0629
0.1367
0.0225
0.0341
0.0390
0.0559
0.0303
0.0332
0.0301
0.0165
0.0303
0.0315
0.0313
0.0349
0.0214
0.1990
0.0171
0.0382
0.0127
0.0434
0.0217
0.0155
0.0698
0.0283
0.0308
0.0236
0.0924
0.0536
0.0439
0.0188
0.0125
0.0273
β
cut 3.0
St. Err.
−0.2301∗∗∗
0.1590∗∗∗
0.3070∗∗∗
0.1109∗∗∗
0.0462∗∗∗
−0.0654∗
−0.0680∗∗∗
0.2098∗∗∗
−0.2351∗∗∗
0.0834∗∗∗
0.1488∗∗∗
0.0584∗∗∗
−0.1147∗
0.0928∗∗∗
0.3918∗∗∗
−0.1513∗∗∗
0.1060∗∗∗
0.4558∗∗∗
−0.3767∗∗∗
−0.1133
−0.0488∗∗
−0.1459∗∗∗
−0.0846∗∗
0.2063∗∗∗
0.0268
0.3457∗∗∗
−0.1502∗∗∗
−0.1818∗∗∗
0.1303∗∗∗
0.1427∗∗∗
−0.4809∗∗∗
−0.2529∗∗∗
0.0529∗∗
0.4580∗∗
0.1606∗∗∗
−0.0868∗∗
−0.0619∗∗∗
−0.1097∗∗
0.0678∗∗∗
0.0947∗∗∗
0.1614∗∗
−0.0461
0.0038
0.0800∗∗∗
0.3173∗∗∗
0.4063∗∗∗
0.4452∗∗∗
0.2603∗∗∗
0.0270∗∗
−0.0312
0.0211
0.0295
0.0540
0.0183
0.0067
0.0359
0.0149
0.0205
0.0227
0.0175
0.0288
0.0200
0.0670
0.0204
0.0255
0.0259
0.0214
0.0382
0.0629
0.1375
0.0227
0.0344
0.0393
0.0550
0.0303
0.0332
0.0302
0.0163
0.0306
0.0317
0.0314
0.0352
0.0216
0.1960
0.0170
0.0385
0.0128
0.0438
0.0217
0.0149
0.0703
0.0283
0.0309
0.0237
0.0930
0.0528
0.0424
0.0189
0.0126
0.0274
β
cut 3.3
St. Err.
−0.2444∗∗∗
0.1675∗∗∗
0.2957∗∗∗
0.1092∗∗∗
0.0489∗∗∗
−0.0561
−0.0680∗∗∗
0.1867∗∗∗
−0.2420∗∗∗
0.0783∗∗∗
0.1342∗∗∗
0.0381∗
−0.0723
0.0667∗∗∗
0.3687∗∗∗
−0.1563∗∗∗
0.0909∗∗∗
0.4699∗∗∗
−0.3341∗∗∗
−0.0857
−0.0562∗∗
−0.1355∗∗∗
−0.0876∗∗
0.2734∗∗∗
0.0226
0.3253∗∗∗
−0.1338∗∗∗
−0.1972∗∗∗
0.1059∗∗∗
0.1304∗∗∗
−0.4652∗∗∗
−0.2531∗∗∗
0.0585∗∗∗
0.5795∗∗∗
0.1535∗∗∗
−0.0808∗∗
−0.0587∗∗∗
−0.0993∗∗
0.0720∗∗∗
0.1153∗∗∗
0.1466∗∗
−0.0230
0.0009
0.0807∗∗∗
0.3213∗∗∗
0.4346∗∗∗
0.4067∗∗∗
0.2643∗∗∗
0.0251∗∗
−0.0321
0.0217
0.0300
0.0547
0.0183
0.0066
0.0360
0.0148
0.0209
0.0226
0.0175
0.0289
0.0208
0.0691
0.0206
0.0269
0.0259
0.0215
0.0384
0.0633
0.1374
0.0228
0.0346
0.0393
0.0562
0.0305
0.0335
0.0304
0.0166
0.0323
0.0316
0.0315
0.0351
0.0215
0.2000
0.0173
0.0394
0.0129
0.0439
0.0219
0.0156
0.0705
0.0284
0.0310
0.0238
0.0930
0.0538
0.0442
0.0189
0.0126
0.0280
Table 11: Results
Rank
27
454
99
206
323
824
638
556
119
878
73
873
460
721
441
568
826
833
642
60
342
130
439
281
331
784
841
712
64
90
630
692
861
218
372
521
21
121
929
72
606
202
96
35
79
283
868
467
696
944
284
Name
Brown,Fred
Brown,John
Brown,Kwame
Brown,Mike
Brown,P.J.
Brown,Randy
Brown,Roger A.
Bryant,Emmette
Bryant,Joe
Bryant,Kobe
Bryant,Mark
Buckner,Greg
Buckner,Quinn
Buechler,Jud
Bullard,Matt
Burleson,Tom
Buse,Don
Butler,Caron
Caffey,Jason
Cage,Michael
Caldwell,Joe
Calhoun,Corky
Camby,Marcus
Campbell,Elden
Campbell,Tony
Carr,Antoine
Carr,Austin
Carr,Kenny
Carr,M.l.
Carroll,Joe Barry
Carter,Anthony
Carter,Fred
Carter,Vince
Cartwright,Bill
Cassell,Sam
Catchings,Harvey
Catledge,Terry
Cato,Kelvin
Causwell,Duane
Ceballos,Cedric
Chamberlain,Wilt (HOF)
Chambers,Tom
Chandler,Tyson
Chaney,Don
Chapman,Rex
Chappell,Len
Cheaney,Calbert
Cheeks,Maurice
Chenier,Phil
Chilcutt,Pete
*** : significant at 1 percent level
** : significant at 5 percent level
*
: significant at 10 percent level
β
cut 3.1
St.Err.
0.0540∗
0.3075∗∗∗
0.2031∗
0.1287∗∗∗
−0.1674∗∗∗
−0.0463∗∗
0.0000∗∗∗
0.2901∗∗∗
−0.2273∗∗∗
0.3759∗∗∗
−0.2159∗∗∗
0.0524∗∗∗
−0.0944∗∗∗
0.0605∗∗∗
−0.0040
−0.1694∗∗∗
−0.1735∗∗∗
−0.0475
0.4097∗∗∗
0.1164∗∗∗
0.2795∗∗∗
0.0606∗∗∗
0.1526∗∗∗
0.1241∗∗∗
−0.1392∗∗∗
−0.1832∗∗∗
−0.0886
0.4010∗∗∗
0.3175∗∗∗
−0.0401∗∗
−0.0774∗∗∗
−0.1999∗∗∗
0.1981∗∗∗
0.0971∗∗∗
0.0163
0.5352∗∗∗
0.2886∗∗∗
−0.3746∗∗∗
0.3761∗∗∗
−0.0280
0.2061∗∗∗
0.3126∗∗∗
0.4609∗∗∗
0.3465∗∗∗
0.1516∗∗∗
−0.2097∗∗∗
0.0497
−0.0799
−0.5431∗∗∗
0.1506∗∗∗
0.0322
0.0224
0.1118
0.0252
0.0478
0.0213
0.0000
0.0442
0.0256
0.0566
0.0300
0.0136
0.0226
0.0157
0.0168
0.0304
0.0539
0.0399
0.0205
0.0167
0.0318
0.0215
0.0263
0.0306
0.0231
0.0170
0.0692
0.0475
0.0498
0.0194
0.0200
0.0105
0.0498
0.0327
0.0340
0.0987
0.0249
0.0344
0.0783
0.0228
0.0195
0.0199
0.0245
0.0450
0.0528
0.0153
0.0355
0.0490
0.0651
0.0220
β
cut 3.0
St. Err.
0.0581∗
0.3012∗∗∗
0.2066∗
0.1228∗∗∗
−0.1619∗∗∗
−0.0543∗∗
0.0000∗∗∗
0.2574∗∗∗
−0.2330∗∗∗
0.3787∗∗∗
−0.2225∗∗∗
0.0607∗∗∗
−0.1057∗∗∗
0.0726∗∗∗
−0.0063
−0.1708∗∗∗
−0.1814∗∗∗
−0.0475
0.4154∗∗∗
0.1251∗∗∗
0.2851∗∗∗
0.0534∗∗
0.1465∗∗∗
0.1253∗∗∗
−0.1348∗∗∗
−0.1776∗∗∗
−0.1173∗
0.4134∗∗∗
0.3572∗∗∗
−0.0428∗∗
−0.0783∗∗∗
−0.2220∗∗∗
0.2004∗∗∗
0.0994∗∗∗
0.0112
0.5211∗∗∗
0.2962∗∗∗
−0.3774∗∗∗
0.3656∗∗∗
−0.0304
0.2242∗∗∗
0.3049∗∗∗
0.4609∗∗∗
0.3563∗∗∗
0.1455∗∗∗
−0.1615∗∗∗
0.0446
−0.0805
−0.5863∗∗∗
0.1563∗∗∗
0.0306
0.0223
0.1127
0.0253
0.0482
0.0215
0.0000
0.0442
0.0255
0.0570
0.0303
0.0137
0.0227
0.0157
0.0169
0.0294
0.0541
0.0403
0.0206
0.0168
0.0314
0.0215
0.0265
0.0308
0.0233
0.0170
0.0693
0.0478
0.0488
0.0194
0.0202
0.0103
0.0502
0.0330
0.0342
0.0992
0.0251
0.0347
0.0789
0.0230
0.0192
0.0198
0.0247
0.0440
0.0533
0.0140
0.0358
0.0493
0.0651
0.0222
β
cut 3.3
St. Err.
0.0509
0.3098∗∗∗
0.1585
0.1201∗∗∗
−0.1582∗∗∗
−0.0485∗∗
0.0000∗∗∗
0.2899∗∗∗
−0.2310∗∗∗
0.3698∗∗∗
−0.2186∗∗∗
0.0542∗∗∗
−0.0957∗∗∗
0.0563∗∗∗
−0.0063
−0.1729∗∗∗
−0.1664∗∗∗
−0.0608
0.3984∗∗∗
0.1150∗∗∗
0.2811∗∗∗
0.0602∗∗∗
0.1557∗∗∗
0.1370∗∗∗
−0.1452∗∗∗
−0.1893∗∗∗
−0.0796
0.4058∗∗∗
0.3180∗∗∗
−0.0434∗∗
−0.0630∗∗∗
−0.2020∗∗∗
0.2093∗∗∗
0.1024∗∗∗
0.0317
0.5228∗∗∗
0.2826∗∗∗
−0.3793∗∗∗
0.3448∗∗∗
−0.0216
0.2053∗∗∗
0.3156∗∗∗
0.4455∗∗∗
0.3493∗∗∗
0.1428∗∗∗
−0.2084∗∗∗
0.0477
−0.0877∗
−0.5453∗∗∗
0.1391∗∗∗
0.0324
0.0225
0.1182
0.0255
0.0483
0.0215
0.0000
0.0444
0.0258
0.0569
0.0302
0.0137
0.0227
0.0158
0.0169
0.0306
0.0544
0.0406
0.0209
0.0168
0.0320
0.0216
0.0264
0.0312
0.0234
0.0172
0.0698
0.0478
0.0500
0.0195
0.0207
0.0106
0.0504
0.0329
0.0348
0.0996
0.0252
0.0346
0.0816
0.0231
0.0196
0.0200
0.0253
0.0452
0.0533
0.0154
0.0357
0.0494
0.0654
0.0225
Table 11: Results
Rank
28
52
472
822
194
355
668
587
307
941
904
863
853
139
528
613
905
390
621
871
415
927
418
320
312
170
926
512
144
564
178
544
686
56
598
681
685
647
51
343
239
710
875
503
554
523
935
2
487
633
769
Name
Childs,Chris
Chones,Jim
Christie,Doug
Clark,Archie
Cleamons,Jim
Clemens,John
Clifton,Nat
Coleman,Derrick
Coleman,Jack
Coles,Bimbo
Collins,Doug
Collins,Jarron
Collins,Jason
Colter,Steve
Conlin,Ed
Conner,Lester
Cook,Darwin
Cook,Jeff
Cooper,Chuck
Cooper,Michael
Cooper,Wayne
Corbin,Tyrone
Corzine,Dave
Costello,Larry
Counts,Mel
Cousy,Bob (HOF)
Cowens,Dave (HOF)
Crawford,Jamal
Criss,Charlie
Croshere,Austin
Crotty,John
Cummings,Pat
Cummings,Terry
Cunningham,Billy (HOF)
Cureton,Earl
Curry,Dell
Curry,Eddy
Curry,Michael
Dailey,Quintin
Dalembert,Samuel
Dampier,Erick
Dandridge,Bob
Daniels,Antonio
Daniels,Mel
Dantley,Adrian (HOF)
Daugherty,Brad
Davies,Bob (HOF)
Davis,Antonio
Davis,Baron
Davis,Brad
*** : significant at 1 percent level
** : significant at 5 percent level
*
: significant at 10 percent level
β
cut 3.1
St.Err.
0.4276∗∗∗
0.0468
−0.1643∗∗∗
0.2128∗∗∗
0.1091∗∗∗
−0.0636∗∗∗
−0.0168
0.1366∗∗∗
−0.5151∗∗∗
−0.2672∗∗∗
−0.2015∗
−0.1919∗∗∗
0.2713∗∗∗
0.0108
−0.0312
−0.2681∗∗∗
0.0829
−0.0365
−0.2113∗∗∗
0.0731
−0.3700∗∗∗
0.0707∗∗∗
0.1305∗∗∗
0.1339∗∗∗
0.2472∗∗∗
−0.3681
0.0217
0.2681∗∗∗
−0.0021
0.2350∗∗∗
0.0040
−0.0732∗∗∗
0.4193∗∗∗
−0.0201
−0.0723∗∗∗
−0.0731∗∗∗
−0.0492
0.4284∗∗∗
0.1161∗∗∗
0.1813∗∗∗
−0.0874∗∗∗
−0.2181∗∗
0.0281∗∗
0.0000∗∗∗
0.0145
−0.4536∗∗∗
1.6047
0.0373∗
−0.0418∗∗
−0.1283
0.0552
0.0317
0.0186
0.0204
0.0235
0.0200
0.0563
0.0341
0.1226
0.0393
0.1044
0.0711
0.0393
0.0346
0.0231
0.0442
0.0655
0.0244
0.0320
0.0825
0.0297
0.0164
0.0155
0.0121
0.0120
0.2280
0.0617
0.0260
0.1259
0.0246
0.0192
0.0218
0.0544
0.0512
0.0161
0.0220
0.0300
0.0328
0.0434
0.0120
0.0150
0.1034
0.0132
0.0000
0.0259
0.1158
3.1000
0.0208
0.0213
0.1239
β
cut 3.0
St. Err.
0.4295∗∗∗
0.0483
−0.1692∗∗∗
0.2304∗∗∗
0.1252∗∗∗
−0.0844∗∗∗
−0.0643
0.1471∗∗∗
−0.0132
−0.2608∗∗∗
−0.2534∗∗
−0.1972∗∗∗
0.2741∗∗∗
−0.0127
−0.0117
−0.2865∗∗∗
0.0510
−0.0498∗∗
−0.0996∗∗∗
0.0789
−0.3950∗∗∗
0.0719∗∗∗
0.1323∗∗∗
0.1563∗∗∗
0.2564∗∗∗
−0.1010
0.0697
0.2689∗∗∗
0.0306
0.2325∗∗∗
−0.0059
−0.0659∗∗∗
0.4221∗∗∗
−0.0239
−0.0783∗∗∗
−0.0838∗∗∗
−0.0469
0.4379∗∗∗
0.1088∗∗
0.1828∗∗∗
−0.0822∗∗∗
−0.2112∗∗
0.0274∗∗
0.0000∗∗∗
0.0180
−0.4578∗∗∗
0.6233
0.0369∗
−0.0416∗
−0.1323
0.0557
0.0320
0.0188
0.0204
0.0228
0.0193
0.0528
0.0343
0.0321
0.0396
0.1042
0.0717
0.0397
0.0347
0.0223
0.0445
0.0657
0.0245
0.0277
0.0832
0.0294
0.0165
0.0156
0.0120
0.0120
0.2037
0.0594
0.0263
0.1255
0.0248
0.0193
0.0220
0.0548
0.0514
0.0162
0.0221
0.0302
0.0330
0.0437
0.0121
0.0151
0.1042
0.0133
0.0000
0.0259
0.1168
2.7625
0.0209
0.0215
0.1248
β
cut 3.3
St. Err.
0.4284∗∗∗
0.0527∗
−0.1600∗∗∗
0.2131∗∗∗
0.1093∗∗∗
−0.0663∗∗∗
−0.0182
0.1530∗∗∗
−0.5132∗∗∗
−0.2682∗∗∗
−0.2039∗
−0.2023∗∗∗
0.3000∗∗∗
0.0207
−0.0298
−0.2817∗∗∗
0.0770
−0.0396
−0.2089∗∗∗
0.0463
−0.3759∗∗∗
0.0776∗∗∗
0.1218∗∗∗
0.1333∗∗∗
0.2441∗∗∗
−0.3704
0.0249
0.2568∗∗∗
−0.0138
0.2339∗∗∗
0.0057
−0.0707∗∗∗
0.4318∗∗∗
−0.0229
−0.0705∗∗∗
−0.0637∗∗∗
−0.0487
0.4290∗∗∗
0.1140∗∗∗
0.1651∗∗∗
−0.1068∗∗∗
−0.2365∗∗
0.0227∗
0.0000∗∗∗
0.0063
−0.4476∗∗∗
1.6266
0.0431∗∗
−0.0913∗∗∗
−0.1281
0.0555
0.0320
0.0188
0.0205
0.0236
0.0201
0.0566
0.0350
0.1232
0.0395
0.1049
0.0718
0.0419
0.0351
0.0232
0.0450
0.0659
0.0245
0.0322
0.0850
0.0299
0.0166
0.0158
0.0122
0.0121
0.2291
0.0620
0.0265
0.1269
0.0247
0.0193
0.0219
0.0551
0.0515
0.0162
0.0224
0.0301
0.0330
0.0436
0.0128
0.0162
0.1049
0.0133
0.0000
0.0262
0.1165
3.1167
0.0210
0.0285
0.1245
Table 11: Results
Rank
29
132
255
123
738
280
916
438
65
27
583
226
322
231
253
212
360
266
893
217
488
314
558
392
265
340
872
306
922
821
609
760
111
244
659
445
688
214
38
37
175
302
238
794
154
191
406
447
541
855
882
Name
Davis,Charlie
Davis,Dale
Davis,Hubert
Davis,Jim
Davis,Johnny
Davis,Ricky
Davis,Terry
Davis,Walter
Dawkins,Darryl
Dawkins,Johnny
Day,Todd
Debusschere,Dave (HOF)
Declercq,Andrew
Dehere,Terry
Del Negro,Vinny
Dele,Bison
Delk,Tony
Dierking,Connie
Dietrick,Coby
Diop,Desagana
Dischinger,Terry
Divac,Vlade
Doleac,Michael
Donaldson,James
Dooling,Keyon
Douglas,Leon
Douglas,Sherman
Dreiling,Greg
Drew,John
Drew,Larry
Drexler,Clyde (HOF)
Duckworth,Kevin
Dudley,Chris
Dukes,Walter
Dumars,Joe (HOF)
Duncan,Tim
Dunleavy,Mike
Dunleavy,Mike
Dunn,T.r.
Eackles,Ledell
Eaton,Mark
Edwards,Blue
Edwards,James
Edwards,Kevin
Egan,Johnny
Ehlo,Craig
Eisley,Howard
Elie,Mario
Elliott,Sean
Ellis,Dale
*** : significant at 1 percent level
** : significant at 5 percent level
*
: significant at 10 percent level
β
cut 3.1
St.Err.
0.2786∗∗∗
0.1712∗∗∗
0.2870∗∗∗
−0.1053∗∗∗
0.1533∗∗∗
−0.3214∗∗∗
0.0611∗∗∗
0.3987∗∗∗
0.4931∗∗∗
−0.0118
0.1917∗∗∗
0.1297∗∗∗
0.1866∗∗∗
0.1730∗∗∗
0.2016∗∗∗
0.1075∗∗∗
0.1627∗∗∗
−0.2469∗∗∗
0.1985∗
0.0370
0.1332∗∗∗
−0.0009
0.0828∗∗∗
0.1631∗∗∗
0.1179∗∗∗
−0.2147∗∗∗
0.1366∗∗∗
−0.3407∗∗∗
−0.1637∗∗
−0.0285
−0.1213∗∗∗
0.2956∗∗∗
0.1781∗∗∗
−0.0552
0.0592
−0.0738
0.2005∗∗∗
0.4545∗∗∗
0.4547∗∗∗
0.2431∗∗∗
0.1402∗∗∗
0.1819∗∗∗
−0.1468∗∗∗
0.2608∗∗∗
0.2175∗∗∗
0.0767
0.0577∗∗∗
0.0054
−0.1937∗∗∗
−0.2355∗∗∗
0.0349
0.0376
0.0351
0.0370
0.0257
0.0208
0.0146
0.0498
0.0469
0.0156
0.0376
0.0211
0.0230
0.0277
0.0335
0.0221
0.0203
0.0171
0.1178
0.0375
0.0214
0.0339
0.0076
0.0513
0.0186
0.0512
0.0177
0.0742
0.0665
0.0350
0.0333
0.0440
0.0265
0.0412
0.0477
0.0778
0.0671
0.0459
0.0413
0.0498
0.0516
0.0139
0.0174
0.0422
0.0197
0.0496
0.0149
0.0294
0.0399
0.0179
β
cut 3.0
St. Err.
0.2854∗∗∗
0.1706∗∗∗
0.2841∗∗∗
−0.1554∗∗∗
0.1463∗∗∗
−0.3164∗∗∗
0.0594∗∗∗
0.4124∗∗∗
0.5235∗∗∗
−0.0152
0.1964∗∗∗
0.1572∗∗∗
0.1906∗∗∗
0.1614∗∗∗
0.2054∗∗∗
0.1083∗∗∗
0.1566∗∗∗
−0.2312∗∗∗
0.1998∗
0.0388
0.1107∗∗∗
−0.0020
0.0892∗∗∗
0.1844∗∗∗
0.1130∗∗∗
−0.2026∗∗∗
0.1418∗∗∗
−0.3547∗∗∗
−0.2066∗∗∗
−0.0211
−0.1259∗∗∗
0.3074∗∗∗
0.1837∗∗∗
−0.0524
0.0663
−0.0645
0.1969∗∗∗
0.4521∗∗∗
0.4905∗∗∗
0.2606∗∗∗
0.1700∗∗∗
0.1926∗∗∗
−0.1535∗∗∗
0.2687∗∗∗
0.2352∗∗∗
0.0680
0.0488∗∗∗
0.0029
−0.1872∗∗∗
−0.2295∗∗∗
0.0352
0.0380
0.0354
0.0357
0.0257
0.0210
0.0148
0.0501
0.0468
0.0157
0.0378
0.0208
0.0232
0.0279
0.0337
0.0223
0.0204
0.0167
0.1180
0.0378
0.0211
0.0342
0.0077
0.0515
0.0187
0.0516
0.0178
0.0748
0.0665
0.0351
0.0336
0.0444
0.0268
0.0397
0.0479
0.0785
0.0674
0.0463
0.0412
0.0502
0.0518
0.0140
0.0176
0.0424
0.0198
0.0500
0.0150
0.0296
0.0402
0.0180
β
cut 3.3
St. Err.
0.2772∗∗∗
0.1760∗∗∗
0.2618∗∗∗
−0.1055∗∗∗
0.1537∗∗∗
−0.3121∗∗∗
0.0662∗∗∗
0.4012∗∗∗
0.4945∗∗∗
−0.0161
0.1860∗∗∗
0.1304∗∗∗
0.1884∗∗∗
0.1646∗∗∗
0.2093∗∗∗
0.1076∗∗∗
0.1789∗∗∗
−0.2490∗∗∗
0.1878
0.0547
0.1324∗∗∗
−0.0230
0.0752∗∗∗
0.1579∗∗∗
0.1330∗∗∗
−0.2111∗∗∗
0.1357∗∗∗
−0.3397∗∗∗
−0.1631∗∗
−0.0232
−0.1160∗∗∗
0.3132∗∗∗
0.1738∗∗∗
−0.0586
0.0641
−0.0596
0.1899∗∗∗
0.4462∗∗∗
0.4514∗∗∗
0.2260∗∗∗
0.1314∗∗
0.1814∗∗∗
−0.1494∗∗∗
0.2421∗∗∗
0.2167∗∗∗
0.0607
0.0631∗∗∗
−0.0107
−0.2081∗∗∗
−0.2410∗∗∗
0.0351
0.0379
0.0371
0.0372
0.0259
0.0212
0.0148
0.0501
0.0472
0.0157
0.0378
0.0212
0.0231
0.0280
0.0338
0.0223
0.0211
0.0172
0.1187
0.0386
0.0215
0.0355
0.0078
0.0517
0.0194
0.0515
0.0178
0.0746
0.0669
0.0352
0.0335
0.0452
0.0267
0.0414
0.0480
0.0788
0.0678
0.0463
0.0415
0.0509
0.0521
0.0140
0.0175
0.0434
0.0198
0.0506
0.0150
0.0303
0.0407
0.0180
Table 11: Results
Rank
30
233
594
122
397
634
465
251
494
770
294
353
625
105
87
428
557
42
531
846
766
906
910
93
877
381
157
747
645
890
592
580
559
751
88
806
348
949
25
321
778
717
530
219
13
903
47
843
693
145
762
Name
Ellis,Joe
Ellis,Laphonso
Ellis,Leroy
Ellison,Pervis
Elmore,Len
Embry,Wayne
English,Alex (HOF)
Erickson,Keith
Erving,Julius (HOF)
Evans,Mike
Evans,Reggie
Ewing,Patrick (HOF)
Farmer,Mike
Felix,Ray
Ferrell,Duane
Ferry,Bob
Ferry,Danny
Finkel,Hank
Finley,Michael
Fisher,Derek
Fleming,Vern
Floyd,Sleepy
Ford,Chris
Ford,Don
Ford,Phil
Fortson,Danny
Foster,Fred
Foster,Greg
Foster,Jeff
Foust,Larry
Fox,Jim
Fox,Rick
Foyle,Adonal
Francis,Steve
Frazier,Walt (HOF)
Free,World
Fulks,Joe (HOF)
Gale,Mike
Gallatin,Harry (HOF)
Gambee,Dave
Gamble,Kevin
Garland,Winston
Garmaker,Dick
Garnett,Kevin
Garrity,Pat
Gasol,Pau
Gatling,Chris
Gattison,Kenny
Geiger,Matt
George,Devean
*** : significant at 1 percent level
** : significant at 5 percent level
*
: significant at 10 percent level
β
cut 3.1
St.Err.
0.1840
0.3428
−0.0195∗
0.0115
0.2876∗∗∗ 0.0198
0.0815∗∗
0.0399
−0.0421
0.0256
0.0505
0.0383
0.1735∗∗∗ 0.0231
0.0320∗
0.0171
−0.1285
0.3004
0.1464∗∗∗ 0.0321
0.1094∗∗∗ 0.0086
−0.0389
0.0825
0.2991∗∗∗ 0.0230
0.3281∗∗∗ 0.0343
0.0655∗∗
0.0306
−0.0006
0.0560
0.4418∗∗∗ 0.0505
0.0097
0.0768
−0.1854∗∗∗ 0.0339
−0.1244∗∗∗ 0.0127
−0.2720∗∗∗ 0.0367
−0.2795∗∗∗ 0.0501
0.3162∗∗∗ 0.0250
−0.2259∗∗
0.1048
0.0928∗∗
0.0408
0.2582∗∗∗ 0.0160
−0.1102∗∗∗ 0.0116
−0.0485∗∗
0.0243
−0.2451∗
0.1289
−0.0187
0.0673
−0.0100
0.0211
−0.0011
0.0176
−0.1130∗∗∗ 0.0336
0.3255∗∗∗ 0.0454
−0.1544
0.1054
0.1126∗∗∗ 0.0363
−6.3002
131.5695
0.5036∗∗∗ 0.0500
0.1299
0.3657
−0.1345∗∗∗ 0.0180
−0.0928∗
0.0532
0.0098
0.0125
0.1951∗∗∗ 0.0173
0.6041∗∗∗ 0.0303
−0.2666∗∗∗ 0.0413
0.4370∗∗∗ 0.0689
−0.1848∗∗∗ 0.0322
−0.0775∗∗∗ 0.0291
0.2664∗∗∗ 0.0366
−0.1232∗∗∗ 0.0254
β
cut 3.0
St. Err.
0.1978
0.3168
−0.0160
0.0116
0.2424∗∗∗ 0.0192
0.0636
0.0401
−0.0146
0.0255
0.0918∗∗ 0.0380
0.1769∗∗∗ 0.0227
0.0400∗∗ 0.0171
−0.1906
0.2984
0.1343∗∗∗ 0.0324
0.1104∗∗∗ 0.0086
−0.0480
0.0832
0.2798∗∗∗ 0.0229
0.3018∗∗∗ 0.0295
0.0688∗∗ 0.0308
−0.0392
0.0551
0.4488∗∗∗ 0.0509
−0.0714
0.0751
−0.1966∗∗∗ 0.0341
−0.1230∗∗∗ 0.0128
−0.2790∗∗∗ 0.0370
−0.2778∗∗∗ 0.0505
0.3064∗∗∗ 0.0245
−0.2672∗∗ 0.1048
0.0717∗
0.0410
0.2580∗∗∗ 0.0161
−0.0937∗∗∗ 0.0117
−0.0465∗
0.0245
−0.2424∗
0.1301
−0.0506
0.0669
−0.0310
0.0205
−0.0032
0.0177
−0.1138∗∗∗ 0.0339
0.3263∗∗∗ 0.0457
−0.1195
0.1058
0.1252∗∗∗ 0.0363
0.3915
30.3501
0.5223∗∗∗ 0.0503
0.2854
0.3414
−0.1759∗∗∗ 0.0173
−0.1050∗
0.0536
0.0044
0.0126
0.2008∗∗∗ 0.0164
0.5999∗∗∗ 0.0305
−0.2681∗∗∗ 0.0417
0.4385∗∗∗ 0.0695
−0.1841∗∗∗ 0.0325
−0.0866∗∗∗ 0.0293
0.2739∗∗∗ 0.0369
−0.1270∗∗∗ 0.0256
β
cut 3.3
St. Err.
0.1729
0.3448
−0.0143
0.0117
0.2861∗∗∗ 0.0199
0.0971∗∗
0.0408
−0.0428∗
0.0257
0.0505
0.0385
0.1725∗∗∗ 0.0232
0.0333∗
0.0172
−0.1307
0.3019
0.1462∗∗∗ 0.0323
0.1257∗∗∗ 0.0094
−0.0435
0.0830
0.3009∗∗∗ 0.0231
0.3254∗∗∗ 0.0345
0.0692∗∗
0.0308
−0.0011
0.0563
0.4479∗∗∗ 0.0508
0.0122
0.0772
−0.1766∗∗∗ 0.0343
−0.1162∗∗∗ 0.0129
−0.2712∗∗∗ 0.0368
−0.2738∗∗∗ 0.0504
0.3139∗∗∗ 0.0252
−0.2397∗∗
0.1059
0.0934∗∗
0.0410
0.2583∗∗∗ 0.0160
−0.1104∗∗∗ 0.0117
−0.0596∗∗
0.0248
−0.2420∗
0.1296
−0.0243
0.0678
−0.0115
0.0212
−0.0076
0.0178
−0.0950∗∗∗ 0.0347
0.3332∗∗∗ 0.0458
−0.1577
0.1060
0.1164∗∗∗ 0.0366
−6.4018
132.2524
0.4948∗∗∗ 0.0505
0.1337
0.3675
−0.1366∗∗∗ 0.0181
−0.0964∗
0.0535
0.0074
0.0126
0.1955∗∗∗ 0.0174
0.5965∗∗∗ 0.0306
−0.2544∗∗∗ 0.0420
0.4429∗∗∗ 0.0693
−0.1960∗∗∗ 0.0327
−0.0848∗∗∗ 0.0294
0.2567∗∗∗ 0.0370
−0.1109∗∗∗ 0.0259
Table 11: Results
Rank
31
768
675
249
810
817
338
222
942
469
754
160
779
205
345
328
240
421
519
78
167
367
332
171
172
588
66
108
724
715
618
151
55
546
823
462
565
204
507
383
742
484
413
464
911
100
149
91
486
449
118
Name
George,Jack
Gervin,George (HOF)
Gianelli,John
Gill,Kendall
Gilliam,Armen
Gilliam,Herm
Gilmore,Artis
Ginobili,Emmanuel
Glenn,Mike
Gminski,Mike
Gola,Tom (HOF)
Gondrezick,Glen
Gooden,Drew
Goodrich,Gail (HOF)
Graboski,Joe
Grant,Brian
Grant,Gary
Grant,Harvey
Grant,Horace
Grayer,Jeff
Green,A.c.
Green,Johnny
Green,Rickey
Green,Si
Green,Sidney
Greenwood,David
Greer,Hal (HOF)
Grevey,Kevin
Griffin,Adrian
Griffin,Paul
Griffith,Darrell
Gross,Bob
Grunfeld,Ernie
Guerin,Richie
Gugliotta,Tom
Guokas,Matt
Hagan,Cliff (HOF)
Hairston,Happy
Ham,Darvin
Hamilton,Richard
Hammonds,Tom
Hannum,Alex (HOF)
Hansen,Bob
Hanzlik,Bill
Hardaway,Anfernee
Hardaway,Tim
Harper,Derek
Harper,Ron
Harpring,Matt
Harrington,Al
*** : significant at 1 percent level
** : significant at 5 percent level
*
: significant at 10 percent level
β
cut 3.1
St.Err.
−0.1282∗∗
−0.0667
0.1747∗∗∗
−0.1559∗∗∗
−0.1594∗∗∗
0.1181∗∗∗
0.1938∗∗∗
−0.5158∗∗∗
0.0492∗∗
−0.1149∗
0.2560∗∗∗
−0.1355∗∗∗
0.2039∗∗∗
0.1157∗∗∗
0.1259
0.1802∗∗∗
0.0691
0.0173
0.3565∗∗∗
0.2504∗∗∗
0.0992∗∗∗
0.1236∗∗∗
0.2467∗∗∗
0.2461∗∗∗
−0.0172∗∗
0.3955∗∗∗
0.2969∗
−0.0977∗∗∗
−0.0896∗∗∗
−0.0340
0.2619∗∗∗
0.4241∗∗∗
0.0034
−0.1668∗∗∗
0.0522
−0.0024
0.2042
0.0270∗∗
0.0918∗∗∗
−0.1076
0.0396∗
0.0739∗∗∗
0.0513∗∗∗
−0.2810∗∗∗
0.3067∗∗∗
0.2627∗∗∗
0.3174∗∗∗
0.0391
0.0552∗∗∗
0.2904∗∗∗
0.0551
0.2056
0.0221
0.0198
0.0243
0.0123
0.0288
0.0638
0.0239
0.0662
0.0125
0.0337
0.0280
0.0183
0.1248
0.0312
0.0549
0.0426
0.0235
0.0330
0.0192
0.0263
0.0439
0.0301
0.0070
0.0417
0.1760
0.0369
0.0268
0.0382
0.0316
0.0610
0.0462
0.0199
0.0336
0.0124
0.1911
0.0128
0.0180
0.0747
0.0215
0.0068
0.0190
0.0697
0.0317
0.0306
0.0311
0.0476
0.0165
0.0131
β
cut 3.0
St. Err.
−0.0214
−0.1228
0.1837∗∗∗
−0.1460∗∗∗
−0.1561∗∗∗
0.1426∗∗∗
0.2204∗∗∗
−0.5166∗∗∗
0.0575∗∗
−0.1143∗
0.2679∗∗∗
−0.1503∗∗∗
0.2074∗∗∗
0.1278∗∗∗
0.2628∗∗∗
0.1853∗∗∗
0.0836
0.0189
0.3625∗∗∗
0.2399∗∗∗
0.1051∗∗∗
0.0361
0.2241∗∗∗
0.2520∗∗∗
−0.0198∗∗∗
0.4310∗∗∗
0.4261∗∗
−0.1048∗∗∗
−0.0903∗∗∗
−0.0143
0.2757∗∗∗
0.4309∗∗∗
−0.0095
−0.1307∗∗∗
0.0643∗
−0.0259∗∗
0.6115∗∗∗
0.0150
0.0872∗∗∗
−0.1256∗
0.0363∗
0.0654∗∗∗
0.0480∗∗
−0.3171∗∗∗
0.2933∗∗∗
0.2710∗∗∗
0.3183∗∗∗
0.0282
0.0560∗∗∗
0.2926∗∗∗
0.0417
0.2020
0.0223
0.0199
0.0245
0.0122
0.0286
0.0644
0.0240
0.0667
0.0119
0.0337
0.0282
0.0183
0.0494
0.0314
0.0553
0.0429
0.0236
0.0333
0.0191
0.0239
0.0441
0.0304
0.0070
0.0416
0.1703
0.0370
0.0270
0.0381
0.0318
0.0615
0.0466
0.0193
0.0339
0.0115
0.1403
0.0125
0.0181
0.0752
0.0217
0.0069
0.0192
0.0693
0.0319
0.0308
0.0314
0.0479
0.0166
0.0132
β
cut 3.3
St. Err.
−0.1292∗∗
−0.0561
0.1788∗∗∗
−0.1532∗∗∗
−0.1612∗∗∗
0.1185∗∗∗
0.1941∗∗∗
−0.5049∗∗∗
0.0455∗
−0.1323∗∗
0.2551∗∗∗
−0.1321∗∗∗
0.1834∗∗∗
0.1151∗∗∗
0.1221
0.2040∗∗∗
0.0604
0.0204
0.3609∗∗∗
0.2515∗∗∗
0.1015∗∗∗
0.1183∗∗∗
0.2448∗∗∗
0.2463∗∗∗
−0.0167∗∗
0.4024∗∗∗
0.3023∗
−0.0943∗∗
−0.0685∗∗
−0.0411
0.2584∗∗∗
0.4317∗∗∗
−0.0026
−0.1675∗∗∗
0.0567∗
−0.0011
0.2075
0.0298∗∗
0.0889∗∗∗
−0.1066
0.0365∗
0.0736∗∗∗
0.0496∗∗∗
−0.2723∗∗∗
0.3302∗∗∗
0.2643∗∗∗
0.3351∗∗∗
0.0458
0.0456∗∗∗
0.3404∗∗∗
0.0553
0.2069
0.0223
0.0199
0.0244
0.0124
0.0289
0.0645
0.0240
0.0674
0.0126
0.0339
0.0294
0.0184
0.1254
0.0330
0.0554
0.0428
0.0237
0.0332
0.0193
0.0265
0.0441
0.0303
0.0070
0.0421
0.1770
0.0372
0.0282
0.0385
0.0318
0.0615
0.0466
0.0200
0.0338
0.0125
0.1920
0.0129
0.0181
0.0750
0.0217
0.0069
0.0191
0.0703
0.0335
0.0308
0.0322
0.0479
0.0168
0.0204
Table 11: Results
Rank
32
245
566
39
388
424
543
869
477
179
429
818
192
46
628
476
444
246
529
148
884
242
914
695
106
870
143
200
706
840
729
827
682
247
757
648
755
59
419
697
708
731
539
324
624
851
270
293
45
691
40
Name
Harrington,Othella
Harris,Lucious
Harrison,Bob
Haskins,Clem
Hassell,Trenton
Hastings,Scott
Havlicek,John (HOF)
Hawes,Steve
Hawkins,Connie (HOF)
Hawkins,Hersey
Hawkins,Tom
Hayes,Elvin (HOF)
Haywood,Brendan
Hazzard,Walt
Heard,Garfield
Heinsohn,Tom (HOF)
Henderson,Alan
Henderson,Gerald
Henderson,Tom
Herrera,Carl
Hetzel,Fred
Higgins,Rod
Hill,Armond
Hill,Grant
Hill,Tyrone
Hillman,Darnell
Hinson,Roy
Hitch,Lew
Hodges,Craig
Hoiberg,Fred
Hollins,Lionel
Hornacek,Jeff
Horry,Robert
House,Eddie
Houston,Allan
Howard,Juwan
Howell,Bailey (HOF)
Hubbard,Phil
Hudson,Lou
Hudson,Troy
Hughes,Larry
Humphries,Jay
Hundley,Rod
Hunter,Lindsey
Huston,Geoff
Hutchins,Mel
Iavaroni,Marc
Ilgauskas,Zydrunas
Imhoff,Darrall
Issel,Dan (HOF)
*** : significant at 1 percent level
** : significant at 5 percent level
*
: significant at 10 percent level
β
cut 3.1
St.Err.
0.1759∗∗∗
−0.0034
0.4525∗∗∗
0.0847∗∗∗
0.0674∗∗∗
0.0040
−0.2100∗∗∗
0.0439
0.2341∗∗∗
0.0655∗∗∗
−0.1603∗∗∗
0.2175∗∗∗
0.4375∗∗∗
−0.0391∗∗
0.0448∗∗
0.0596
0.1759∗∗∗
0.0108
0.2651∗∗∗
−0.2399∗∗∗
0.1795∗∗∗
−0.3073∗∗∗
−0.0792
0.2989∗∗∗
−0.2110∗∗∗
0.2685∗∗∗
0.2085∗∗∗
−0.0860∗∗∗
−0.1825∗∗∗
−0.0993∗∗∗
−0.1695∗∗∗
−0.0724∗∗∗
0.1755∗∗∗
−0.1162∗∗∗
−0.0493∗
−0.1149∗∗∗
0.4106∗∗∗
0.0695∗
−0.0800∗∗∗
−0.0865∗∗∗
−0.1002∗∗∗
0.0056
0.1280
−0.0386∗∗∗
−0.1908∗∗∗
0.1601∗
0.1464∗∗∗
0.4398∗∗∗
−0.0762∗∗∗
0.4456∗∗∗
0.0172
0.0329
0.0451
0.0260
0.0134
0.0580
0.0335
0.0303
0.0217
0.0123
0.0144
0.0360
0.0539
0.0152
0.0193
0.0865
0.0212
0.0167
0.0413
0.0596
0.0185
0.0294
0.0526
0.0319
0.0303
0.0266
0.0340
0.0115
0.0236
0.0186
0.0308
0.0200
0.0326
0.0080
0.0298
0.0148
0.0276
0.0376
0.0212
0.0223
0.0169
0.0200
0.0804
0.0124
0.0186
0.0890
0.0355
0.0321
0.0268
0.1281
β
cut 3.0
St. Err.
0.1783∗∗∗
−0.0090
0.4632∗∗∗
0.0730∗∗∗
0.0743∗∗∗
−0.0050
−0.1353∗∗∗
0.0462
0.2397∗∗∗
0.0744∗∗∗
−0.1920∗∗∗
0.2146∗∗∗
0.4393∗∗∗
−0.0315∗∗
0.0533∗∗∗
0.0761
0.1808∗∗∗
−0.0026
0.2966∗∗∗
−0.2491∗∗∗
0.1778∗∗∗
−0.3162∗∗∗
−0.0628
0.3015∗∗∗
−0.2248∗∗∗
0.3113∗∗∗
0.2107∗∗∗
−0.1056∗∗∗
−0.1694∗∗∗
−0.1021∗∗∗
−0.1886∗∗∗
−0.0675∗∗∗
0.1841∗∗∗
−0.1174∗∗∗
−0.0481
−0.1178∗∗∗
0.3581∗∗∗
0.0857∗∗
−0.0531∗∗
−0.0848∗∗∗
−0.1028∗∗∗
0.0275
0.1974∗∗
−0.0392∗∗∗
−0.2013∗∗∗
0.1957∗∗
0.1562∗∗∗
0.4407∗∗∗
−0.0535∗∗
0.4903∗∗∗
0.0174
0.0331
0.0440
0.0250
0.0135
0.0584
0.0318
0.0305
0.0217
0.0124
0.0132
0.0362
0.0544
0.0153
0.0188
0.0870
0.0214
0.0167
0.0413
0.0601
0.0182
0.0296
0.0528
0.0322
0.0305
0.0260
0.0342
0.0110
0.0237
0.0188
0.0309
0.0201
0.0329
0.0081
0.0301
0.0150
0.0268
0.0377
0.0212
0.0225
0.0171
0.0200
0.0781
0.0125
0.0187
0.0861
0.0357
0.0324
0.0266
0.1286
β
cut 3.3
St. Err.
0.1741∗∗∗
0.0020
0.4523∗∗∗
0.0852∗∗∗
0.0650∗∗∗
−0.0004
−0.2119∗∗∗
0.0447
0.2331∗∗∗
0.0680∗∗∗
−0.1590∗∗∗
0.2206∗∗∗
0.4509∗∗∗
−0.0396∗∗∗
0.0447∗∗
0.0587
0.1854∗∗∗
0.0123
0.2668∗∗∗
−0.2483∗∗∗
0.1762∗∗∗
−0.3043∗∗∗
−0.0832
0.2975∗∗∗
−0.1948∗∗∗
0.2660∗∗∗
0.2104∗∗∗
−0.0855∗∗∗
−0.1930∗∗∗
−0.0861∗∗∗
−0.1773∗∗∗
−0.0670∗∗∗
0.1710∗∗∗
−0.1285∗∗∗
−0.0462
−0.1074∗∗∗
0.4080∗∗∗
0.0651∗
−0.0844∗∗∗
−0.0907∗∗∗
−0.0952∗∗∗
0.0085
0.1267
−0.0317∗∗
−0.1933∗∗∗
0.1587∗
0.1461∗∗∗
0.4410∗∗∗
−0.0753∗∗∗
0.4242∗∗∗
0.0173
0.0331
0.0453
0.0262
0.0135
0.0584
0.0337
0.0304
0.0218
0.0123
0.0145
0.0362
0.0547
0.0153
0.0194
0.0869
0.0216
0.0168
0.0415
0.0601
0.0186
0.0296
0.0529
0.0321
0.0312
0.0267
0.0342
0.0116
0.0241
0.0192
0.0311
0.0202
0.0328
0.0085
0.0300
0.0151
0.0277
0.0378
0.0214
0.0224
0.0171
0.0201
0.0808
0.0126
0.0187
0.0894
0.0356
0.0323
0.0270
0.1301
Table 11: Results
Rank
33
23
819
48
254
389
617
848
263
401
485
452
297
241
446
534
912
857
844
615
308
752
845
515
867
185
208
146
442
89
924
346
743
437
816
422
30
723
545
315
917
256
669
626
801
803
732
147
126
197
627
Name
Iverson,Allen
Jackson,Bobby
Jackson,Jaren
Jackson,Jim
Jackson,Lucious
Jackson,Mark
Jackson,Phil
Jackson,Stephen
James,Mike
Jamison,Antawn
Jefferson,Richard
Johnson,Anthony
Johnson,Avery
Johnson,Buck
Johnson,Charlie
Johnson,Clemon
Johnson,Dennis
Johnson,Eddie
Johnson,Eddie A.
Johnson,Ervin
Johnson,Frank
Johnson,George L.
Johnson,George T.
Johnson,Gus
Johnson,Joe
Johnson,John
Johnson,Kevin
Johnson,Larry
Johnson,Magic (HOF)
Johnson,Marques
Johnson,Mickey
Johnson,Ollie
Johnson,Steve
Johnson,Vinnie
Johnston,Neil (HOF)
Jones,Bobby
Jones,Caldwell
Jones,Charles
Jones,Damon
Jones,Dwight
Jones,Eddie
Jones,Jumaine
Jones,K.c. (HOF)
Jones,Popeye
Jones,Sam (HOF)
Jones,Wali
Jordan,Eddie
Jordan,Michael
Jordon,Phil
Kauffman,Bob
*** : significant at 1 percent level
** : significant at 5 percent level
*
: significant at 10 percent level
β
cut 3.1
St.Err.
0.5133∗∗∗
−0.1621∗∗∗
0.4357∗∗∗
0.1726∗∗∗
0.0835∗∗∗
−0.0337∗
−0.1856∗∗∗
0.1642∗∗∗
0.0796∗∗∗
0.0396∗∗
0.0543
0.1430∗∗∗
0.1800∗∗∗
0.0580∗
0.0079
−0.2811∗∗∗
−0.1939∗∗∗
−0.1851∗∗
−0.0334
0.1364∗∗∗
−0.1140∗∗
−0.1854∗∗∗
0.0189
−0.2096∗∗∗
0.2289∗∗∗
0.2026∗∗∗
0.2658∗∗∗
0.0601∗∗∗
0.3241∗∗∗
−0.3548∗∗∗
0.1131∗∗∗
−0.1077∗∗∗
0.0613∗∗∗
−0.1592∗∗∗
0.0687
0.4842∗∗∗
−0.0977
0.0037
0.1328∗∗∗
−0.3253∗∗∗
0.1711∗∗∗
−0.0643
−0.0389
−0.1518∗∗∗
−0.1530
−0.1027∗∗∗
0.2657∗∗∗
0.2842∗∗∗
0.2104∗∗∗
−0.0390∗∗
0.0300
0.0185
0.0277
0.0145
0.0219
0.0203
0.0180
0.0056
0.0133
0.0190
0.0339
0.0096
0.0215
0.0326
0.0381
0.0288
0.0244
0.0905
0.0255
0.0205
0.0463
0.0167
0.0394
0.0360
0.0811
0.0172
0.0767
0.0231
0.0712
0.1254
0.0165
0.0208
0.0226
0.0485
0.0557
0.0705
0.2336
0.1112
0.0072
0.0301
0.0280
0.0452
0.0532
0.0307
0.1534
0.0300
0.0811
0.0488
0.0169
0.0167
β
cut 3.0
St. Err.
0.5237∗∗∗
−0.1589∗∗∗
0.4379∗∗∗
0.1713∗∗∗
0.0966∗∗∗
−0.0245
−0.1717∗∗∗
0.1661∗∗∗
0.0788∗∗∗
0.0383∗∗
0.0497
0.1440∗∗∗
0.1736∗∗∗
0.0659∗∗
−0.0189
−0.2756∗∗∗
−0.1761∗∗∗
−0.1671∗
−0.0417
0.1369∗∗∗
−0.1205∗∗∗
−0.1877∗∗∗
0.0309
−0.1861∗∗∗
0.2282∗∗∗
0.1882∗∗∗
0.2855∗∗∗
0.0620∗∗∗
0.3172∗∗∗
−0.3407∗∗∗
0.1133∗∗∗
−0.1251∗∗∗
0.0491∗∗
−0.1656∗∗∗
−0.0711∗∗∗
0.4850∗∗∗
−0.0463
0.0345
0.1376∗∗∗
−0.3442∗∗∗
0.1660∗∗∗
−0.0658
0.0378
−0.1431∗∗∗
0.0520
−0.1209∗∗∗
0.2617∗∗∗
0.2783∗∗∗
0.1894∗∗∗
−0.0438∗∗∗
0.0302
0.0187
0.0280
0.0146
0.0218
0.0204
0.0181
0.0056
0.0134
0.0192
0.0342
0.0097
0.0216
0.0329
0.0383
0.0290
0.0244
0.0911
0.0257
0.0207
0.0466
0.0168
0.0396
0.0361
0.0817
0.0173
0.0772
0.0233
0.0718
0.1259
0.0165
0.0207
0.0227
0.0489
0.0148
0.0701
0.2348
0.1109
0.0073
0.0302
0.0282
0.0455
0.0518
0.0309
0.1414
0.0294
0.0816
0.0492
0.0157
0.0163
β
cut 3.3
St. Err.
0.5136∗∗∗
−0.1699∗∗∗
0.4220∗∗∗
0.1608∗∗∗
0.0831∗∗∗
−0.0440∗∗
−0.1860∗∗∗
0.1613∗∗∗
0.0852∗∗∗
0.0244
0.0256
0.1411∗∗∗
0.1872∗∗∗
0.0511
0.0116
−0.2825∗∗∗
−0.1922∗∗∗
−0.1911∗∗
−0.0320
0.1310∗∗∗
−0.1251∗∗∗
−0.1858∗∗∗
0.0135
−0.2062∗∗∗
0.2458∗∗∗
0.2056∗∗∗
0.2820∗∗∗
0.0550∗∗
0.3437∗∗∗
−0.3658∗∗∗
0.1108∗∗∗
−0.1084∗∗∗
0.0590∗∗∗
−0.1545∗∗∗
0.0694
0.4926∗∗∗
−0.0871
0.0003
0.1442∗∗∗
−0.3295∗∗∗
0.1507∗∗∗
−0.0909∗
−0.0369
−0.1437∗∗∗
−0.1525
−0.1022∗∗∗
0.2675∗∗∗
0.2988∗∗∗
0.2110∗∗∗
−0.0409∗∗
0.0301
0.0188
0.0284
0.0149
0.0220
0.0207
0.0181
0.0057
0.0135
0.0198
0.0365
0.0097
0.0217
0.0329
0.0384
0.0290
0.0245
0.0910
0.0256
0.0207
0.0469
0.0168
0.0396
0.0362
0.0823
0.0173
0.0778
0.0232
0.0727
0.1263
0.0166
0.0209
0.0227
0.0488
0.0560
0.0711
0.2351
0.1119
0.0076
0.0303
0.0294
0.0474
0.0534
0.0311
0.1542
0.0301
0.0816
0.0497
0.0170
0.0168
Table 11: Results
Rank
34
235
553
525
718
603
899
350
22
847
759
407
186
644
68
112
7
395
135
363
115
934
136
740
458
561
657
479
168
412
166
223
426
327
804
641
920
569
152
140
434
661
520
772
551
932
169
196
946
304
211
Name
Keefe,Adam
Keller,Billy
Kelley,Rich
Kemp,Shawn
Kenon,Larry
Kerr,Johnny
Kerr,Steve
Kersey,Jerome
Kidd,Jason
King,Albert
King,Bernard
King,George
King,Jim
King,Reggie
King,Stacey
Kirilenko,Andrei
Kite,Greg
Kittles,Kerry
Kleine,Joe
Knight,Billy
Knight,Brevin
Kojis,Don
Komives,Howard
Koncak,Jon
Krebs,Jim
Krystkowiak,Larry
Kuberski,Steve
Kukoc,Toni
Kunnert,Kevin
Kupchak,Mitch
Lacey,Sam
Laettner,Christian
Lafrentz,Raef
Laimbeer,Bill
Lambert,John
Landsberger,Mark
Lang,Andrew
Lanier,Bob (HOF)
Larusso,Rudy
Leavell,Allen
Leckner,Eric
Lee,Clyde
Lee,Ron
Lenard,Voshon
Leonard,Bob
Lever,Lafayette
Levingston,Cliff
Lewis,Freddie
Lewis,Rashard
Lewis,Reggie
*** : significant at 1 percent level
** : significant at 5 percent level
*
: significant at 10 percent level
β
cut 3.1
St.Err.
0.1832∗∗∗
0.0000∗∗∗
0.0142
−0.0931∗∗∗
−0.0223
−0.2556∗∗∗
0.1123∗∗∗
0.5258∗∗∗
−0.1856∗∗∗
−0.1192∗∗∗
0.0763∗∗
0.2250∗∗∗
−0.0478∗∗
0.3918∗∗∗
0.2952∗∗∗
0.7888∗∗∗
0.0820∗∗∗
0.2754∗∗∗
0.1022∗∗∗
0.2923∗∗∗
−0.4158∗∗∗
0.2751∗∗∗
−0.1069∗∗∗
0.0528∗∗
−0.0018
−0.0532∗∗∗
0.0435∗∗∗
0.2488∗∗∗
0.0746∗∗
0.2509∗∗∗
0.1935∗∗∗
0.0662∗∗∗
0.1264∗∗∗
−0.1534∗∗
−0.0472
−0.3345∗∗∗
−0.0044
0.2613∗∗∗
0.2706∗∗∗
0.0634
−0.0577∗∗
0.0167
−0.1310∗∗∗
0.0007
−0.4057∗∗∗
0.2484∗∗∗
0.2106∗∗∗
−0.6278
0.1383∗∗∗
0.2019∗∗∗
0.0205
0.0000
0.0236
0.0264
0.0436
0.0319
0.0278
0.0625
0.0279
0.0352
0.0384
0.0517
0.0233
0.0405
0.0400
0.0886
0.0236
0.0366
0.0308
0.0299
0.0267
0.0226
0.0269
0.0256
0.0917
0.0162
0.0103
0.0339
0.0353
0.0357
0.0237
0.0216
0.0241
0.0598
0.0766
0.0334
0.0251
0.0505
0.0298
0.0784
0.0249
0.1433
0.0145
0.0112
0.0168
0.0451
0.0158
0.4889
0.0283
0.0269
β
cut 3.0
St. Err.
0.1872∗∗∗
0.0000∗∗∗
0.0013
−0.0872∗∗∗
−0.0225
−0.2721∗∗∗
0.1071∗∗∗
0.5447∗∗∗
−0.1821∗∗∗
−0.1253∗∗∗
0.0533
0.1986∗∗∗
−0.0650∗∗∗
0.4278∗∗∗
0.3047∗∗∗
0.7889∗∗∗
0.0595∗∗
0.2818∗∗∗
0.0787∗∗
0.2999∗∗∗
−0.4144∗∗∗
0.2820∗∗∗
−0.0645∗∗
0.0637∗∗
−0.0631
−0.0502∗∗∗
0.0377∗∗∗
0.2625∗∗∗
0.0880∗∗
0.2567∗∗∗
0.1973∗∗∗
0.0749∗∗∗
0.1215∗∗∗
−0.1442∗∗
−0.0025
−0.3244∗∗∗
0.0017
0.2560∗∗∗
0.3172∗∗∗
0.0287
−0.0582∗∗
0.0950
−0.1520∗∗∗
0.0012
−0.4160∗∗∗
0.2615∗∗∗
0.2142∗∗∗
−0.8394∗∗
0.1381∗∗∗
0.2059∗∗∗
0.0206
0.0000
0.0237
0.0266
0.0439
0.0300
0.0280
0.0628
0.0281
0.0352
0.0385
0.0516
0.0208
0.0404
0.0403
0.0893
0.0235
0.0368
0.0307
0.0297
0.0269
0.0228
0.0265
0.0258
0.0906
0.0164
0.0103
0.0341
0.0352
0.0356
0.0238
0.0218
0.0243
0.0602
0.0758
0.0336
0.0253
0.0499
0.0272
0.0787
0.0252
0.1402
0.0145
0.0113
0.0164
0.0450
0.0159
0.4219
0.0286
0.0270
β
cut 3.3
St. Err.
0.1912∗∗∗
0.0000∗∗∗
0.0098
−0.0943∗∗∗
−0.0273
−0.2548∗∗∗
0.1207∗∗∗
0.5102∗∗∗
−0.2053∗∗∗
−0.1049∗∗∗
0.0739∗
0.2268∗∗∗
−0.0462∗∗
0.3960∗∗∗
0.3011∗∗∗
0.7906∗∗∗
0.0796∗∗∗
0.2799∗∗∗
0.0996∗∗∗
0.2880∗∗∗
−0.4056∗∗∗
0.2726∗∗∗
−0.1071∗∗∗
0.0471∗
0.0027
−0.0577∗∗∗
0.0413∗∗∗
0.2414∗∗∗
0.0754∗∗
0.2423∗∗∗
0.1924∗∗∗
0.0567∗∗
0.1121∗∗∗
−0.1538∗∗
−0.0576
−0.3403∗∗∗
0.0009
0.2597∗∗∗
0.2694∗∗∗
0.0506
−0.0511∗∗
0.0263
−0.1323∗∗∗
−0.0004
−0.4050∗∗∗
0.2487∗∗∗
0.2115∗∗∗
−0.6314
0.1130∗∗∗
0.1967∗∗∗
0.0207
0.0000
0.0237
0.0265
0.0439
0.0321
0.0282
0.0635
0.0292
0.0360
0.0386
0.0520
0.0235
0.0407
0.0403
0.0890
0.0237
0.0368
0.0309
0.0301
0.0271
0.0227
0.0270
0.0258
0.0922
0.0164
0.0104
0.0342
0.0355
0.0361
0.0238
0.0220
0.0248
0.0600
0.0773
0.0336
0.0253
0.0508
0.0300
0.0792
0.0252
0.1443
0.0146
0.0113
0.0169
0.0453
0.0159
0.4914
0.0304
0.0271
Table 11: Results
Rank
35
252
573
269
749
58
526
758
722
631
81
535
83
513
891
92
18
195
380
36
210
410
538
550
334
243
77
301
602
537
9
787
411
359
939
181
636
385
805
643
776
502
915
456
43
164
808
838
189
590
309
Name
Lister,Alton
Lloyd,Earl
Lohaus,Brad
Long,Grant
Long,John
Longley,Luc
Loscutoff,Jim
Loughery,Kevin
Love,Bob
Lovellette,Clyde (HOF)
Lucas,Jerry (HOF)
Lucas,John
Lucas,Maurice
Lue,Tyronn
Lynch,George
Macauley,Ed (HOF)
Macy,Kyle
Madsen,Mark
Maggette,Corey
Magloire,Jamaal
Mahorn,Rick
Majerle,Dan
Malone,Jeff
Malone,Karl
Malone,Moses (HOF)
Manning,Danny
Maravich,Pete (HOF)
Marbury,Stephon
Marin,Jack
Marion,Shawn
Marshall,Donyell
Martin,Darrick
Martin,Kenyon
Martin,Slater (HOF)
Mashburn,Jamal
Mason,Anthony
Mason,Desmond
Massenburg,Tony
Matthews,Wes
Maxwell,Cedric
Maxwell,Vernon
Mayberry,Lee
McAdoo,Bob (HOF)
McCarty,Walter
McCloud,George
McCormick,Tim
McCray,Rodney
McDaniel,Xavier
McDyess,Antonio
McElroy,Jim
*** : significant at 1 percent level
** : significant at 5 percent level
*
: significant at 10 percent level
β
cut 3.1
St.Err.
0.1731∗∗∗
−0.0062
0.1607∗∗∗
−0.1123∗∗∗
0.4173∗∗∗
0.0114
−0.1177∗∗∗
−0.0962∗∗∗
−0.0412
0.3398∗∗∗
0.0074
0.3315∗∗∗
0.0214
−0.2451∗∗∗
0.3173∗∗∗
0.5606∗∗∗
0.2128∗∗∗
0.0934∗∗∗
0.4566∗∗∗
0.2022∗∗∗
0.0752∗∗∗
0.0058
0.0007
0.1216∗∗
0.1791∗∗∗
0.3636∗∗∗
0.1410∗∗∗
−0.0222
0.0060
0.6545∗∗∗
−0.1408∗∗∗
0.0749∗∗∗
0.1084∗∗∗
−0.4674∗∗∗
0.2317∗∗∗
−0.0436
0.0874∗∗∗
−0.1538∗∗∗
−0.0475∗∗
−0.1337∗∗∗
0.0283
−0.3119∗∗∗
0.0535∗
0.4414∗∗∗
0.2534∗∗∗
−0.1550∗∗∗
−0.1795∗∗∗
0.2199∗∗∗
−0.0176
0.1358∗∗∗
0.0208
0.1926
0.0255
0.0348
0.0414
0.0314
0.0144
0.0330
0.0510
0.0334
0.0186
0.0194
0.0284
0.0156
0.0195
0.1796
0.0404
0.0278
0.0246
0.0196
0.0162
0.0239
0.0300
0.0568
0.0434
0.0442
0.0254
0.0136
0.0226
0.0336
0.0131
0.0225
0.0167
0.0877
0.0196
0.0280
0.0164
0.0198
0.0224
0.0348
0.0261
0.0270
0.0290
0.0298
0.0277
0.0323
0.0443
0.0126
0.0176
0.0447
β
cut 3.0
St. Err.
0.1843∗∗∗
0.1188
0.1418∗∗∗
−0.1100∗∗∗
0.4037∗∗∗
0.0148
−0.0894∗∗∗
−0.1153∗∗∗
0.0019
0.3318∗∗∗
0.0383∗∗
0.3361∗∗∗
0.0037
−0.2431∗∗∗
0.3235∗∗∗
0.2306
0.2274∗∗∗
0.0890∗∗∗
0.4587∗∗∗
0.2081∗∗∗
0.0830∗∗∗
0.0051
0.0048
0.1342∗∗
0.1905∗∗∗
0.3609∗∗∗
0.1396∗∗∗
−0.0169
0.0375∗
0.6572∗∗∗
−0.1439∗∗∗
0.0735∗∗∗
0.1066∗∗∗
−0.4460∗∗∗
0.2336∗∗∗
−0.0367
0.0923∗∗∗
−0.1649∗∗∗
−0.0520∗∗
−0.1720∗∗∗
0.0342
−0.3220∗∗∗
0.0225
0.4356∗∗∗
0.2501∗∗∗
−0.1652∗∗∗
−0.1656∗∗∗
0.2150∗∗∗
−0.0180
0.1402∗∗∗
0.0209
0.1843
0.0256
0.0351
0.0411
0.0317
0.0142
0.0325
0.0478
0.0335
0.0184
0.0194
0.0286
0.0158
0.0196
0.1428
0.0399
0.0280
0.0249
0.0198
0.0163
0.0241
0.0303
0.0573
0.0435
0.0446
0.0254
0.0137
0.0224
0.0339
0.0132
0.0227
0.0168
0.0867
0.0198
0.0282
0.0166
0.0200
0.0225
0.0342
0.0262
0.0272
0.0288
0.0300
0.0279
0.0325
0.0445
0.0127
0.0177
0.0451
β
cut 3.3
St. Err.
0.1716∗∗∗
−0.0051
0.1649∗∗∗
−0.1197∗∗∗
0.4144∗∗∗
0.0129
−0.1172∗∗∗
−0.0965∗∗∗
−0.0441
0.3404∗∗∗
0.0054
0.3270∗∗∗
0.0180
−0.2138∗∗∗
0.3261∗∗∗
0.5596∗∗∗
0.2272∗∗∗
0.0890∗∗∗
0.4450∗∗∗
0.2123∗∗∗
0.0700∗∗∗
0.0117
0.0016
0.1255∗∗
0.1804∗∗∗
0.3614∗∗∗
0.1449∗∗∗
−0.0280∗∗
0.0044
0.6545∗∗∗
−0.1422∗∗∗
0.0784∗∗∗
0.1221∗∗∗
−0.4710∗∗∗
0.2249∗∗∗
−0.0290
0.0875∗∗∗
−0.1554∗∗∗
−0.0572∗∗
−0.1364∗∗∗
0.0263
−0.3195∗∗∗
0.0524∗
0.4179∗∗∗
0.2581∗∗∗
−0.1641∗∗∗
−0.1989∗∗∗
0.2129∗∗∗
−0.0128
0.1434∗∗∗
0.0209
0.1936
0.0257
0.0352
0.0416
0.0316
0.0144
0.0331
0.0513
0.0336
0.0187
0.0196
0.0286
0.0186
0.0198
0.1805
0.0412
0.0280
0.0251
0.0200
0.0163
0.0241
0.0302
0.0572
0.0436
0.0444
0.0256
0.0137
0.0227
0.0338
0.0131
0.0226
0.0173
0.0882
0.0199
0.0288
0.0165
0.0199
0.0227
0.0350
0.0262
0.0273
0.0291
0.0315
0.0279
0.0327
0.0456
0.0128
0.0177
0.0451
Table 11: Results
Rank
36
763
103
371
158
781
457
215
453
292
725
296
834
161
104
330
267
654
400
798
492
228
874
680
137
17
471
405
98
607
501
374
376
416
138
436
593
632
250
258
575
474
773
134
237
300
402
560
286
116
369
Name
McGee,Mike
McGinnis,George
McGlocklin,Jon
McGrady,Tracy
McGuire,Dick (HOF)
McHale,Kevin (HOF)
McIlvaine,Jim
McInnis,Jeff
McKey,Derrick
McKie,Aaron
McKinney,Billy
McLemore,Mccoy
McMahon,Jack
McMillan,Nate
McMillen,Tom
McMillian,Jim
Meminger,Dean
Mengelt,John
Mercer,Ron
Meriweather,Joe
Meschery,Tom
Mihm,Chris
Mikan,George (HOF)
Mikkelsen,Vern (HOF)
Miles,Darius
Miles,Eddie
Miller,Andre
Miller,Brad
Miller,Mike
Miller,Oliver
Miller,Reggie
Mills,Chris
Mills,Terry
Ming,Yao
Mitchell,Mike
Mitchell,Sam
Mix,Steve
Mobley,Cuttino
Mohammed,Nazr
Mokeski,Paul
Moncrief,Sidney
Money,Eric
Monroe,Earl (HOF)
Montross,Eric
Moore,Johnny
Moore,Mikki
Moore,Otto
Morris,Chris
Mourning,Alonzo
Mueller,Erwin
*** : significant at 1 percent level
** : significant at 5 percent level
*
: significant at 10 percent level
β
cut 3.1
St.Err.
−0.1235∗∗∗
0.3027∗∗∗
0.0985∗∗∗
0.2568∗∗∗
−0.1383
0.0534
0.2003∗∗∗
0.0542∗∗∗
0.1470∗∗∗
−0.0981∗
0.1441∗∗∗
−0.1755∗∗∗
0.2557∗∗∗
0.2998∗∗∗
0.1241∗∗∗
0.1617∗∗∗
−0.0518∗∗∗
0.0807∗∗∗
−0.1500∗∗∗
0.0345
0.1884∗∗∗
−0.2162∗∗∗
−0.0705∗∗
0.2745∗∗∗
0.5713∗∗∗
0.0475∗∗
0.0777∗∗∗
0.3090∗∗∗
−0.0283
0.0284
0.0961
0.0953∗∗∗
0.0717∗∗∗
0.2736∗∗∗
0.0614∗∗∗
−0.0188
−0.0413∗∗
0.1736∗∗∗
0.1708∗∗∗
−0.0066
0.0463
−0.1312∗
0.2770∗∗∗
0.1819∗∗∗
0.1412∗∗
0.0794∗∗∗
−0.0012
0.1496∗∗∗
0.2921∗∗∗
0.0988∗∗∗
0.0191
0.0737
0.0285
0.0304
0.3708
0.0908
0.0176
0.0160
0.0295
0.0510
0.0327
0.0191
0.0231
0.0574
0.0249
0.0193
0.0152
0.0254
0.0217
0.0252
0.0413
0.0730
0.0350
0.0458
0.0724
0.0239
0.0142
0.0234
0.0427
0.0191
0.0740
0.0193
0.0159
0.0272
0.0169
0.0706
0.0201
0.0172
0.0144
0.0583
0.0932
0.0670
0.0258
0.0341
0.0612
0.0175
0.0067
0.0225
0.0201
0.0191
β
cut 3.0
St. Err.
−0.1168∗∗∗
0.3192∗∗∗
0.0559∗∗
0.2539∗∗∗
−0.2732
0.1035
0.1960∗∗∗
0.0581∗∗∗
0.1531∗∗∗
−0.1080∗∗
0.1511∗∗∗
−0.1969∗∗∗
0.0728∗∗∗
0.2933∗∗∗
0.1304∗∗∗
0.1748∗∗∗
−0.0623∗∗∗
0.0909∗∗∗
−0.1570∗∗∗
0.0249
0.2496∗∗∗
−0.2109∗∗∗
−0.0982∗∗∗
0.2950∗∗∗
0.5649∗∗∗
0.0764∗∗∗
0.0794∗∗∗
0.3172∗∗∗
−0.0275
0.0324∗
0.0873
0.0895∗∗∗
0.0692∗∗∗
0.2727∗∗∗
0.0731∗∗∗
−0.0264
−0.0299
0.1776∗∗∗
0.1734∗∗∗
−0.0238
0.0538
−0.1345∗∗
0.3040∗∗∗
0.1883∗∗∗
0.1304∗∗
0.0805∗∗∗
−0.0064
0.1475∗∗∗
0.2903∗∗∗
0.0846∗∗∗
0.0192
0.0740
0.0279
0.0307
0.3353
0.0908
0.0177
0.0161
0.0297
0.0514
0.0329
0.0190
0.0107
0.0578
0.0248
0.0192
0.0152
0.0244
0.0219
0.0254
0.0248
0.0736
0.0338
0.0453
0.0730
0.0237
0.0143
0.0236
0.0430
0.0193
0.0746
0.0195
0.0160
0.0274
0.0168
0.0711
0.0199
0.0173
0.0145
0.0587
0.0938
0.0673
0.0256
0.0343
0.0608
0.0177
0.0067
0.0227
0.0202
0.0192
β
cut 3.3
St. Err.
−0.1153∗∗∗
0.3078∗∗∗
0.1015∗∗∗
0.2295∗∗∗
−0.1451
0.0626
0.2002∗∗∗
0.0604∗∗∗
0.1564∗∗∗
−0.1033∗∗
0.1439∗∗∗
−0.1739∗∗∗
0.2554∗∗∗
0.3068∗∗∗
0.1266∗∗∗
0.1591∗∗∗
−0.0541∗∗∗
0.0815∗∗∗
−0.1509∗∗∗
0.0302
0.1862∗∗∗
−0.1778∗∗
−0.0701∗∗
0.2705∗∗∗
0.5339∗∗∗
0.0453∗
0.0698∗∗∗
0.3167∗∗∗
−0.0291
0.0209
0.0960
0.0882∗∗∗
0.0700∗∗∗
0.2761∗∗∗
0.0595∗∗∗
−0.0418
−0.0436∗∗
0.1698∗∗∗
0.1587∗∗∗
−0.0027
0.0581
−0.1346∗∗
0.2744∗∗∗
0.1750∗∗∗
0.1479∗∗
0.0792∗∗∗
0.0006
0.1512∗∗∗
0.2844∗∗∗
0.0991∗∗∗
0.0194
0.0742
0.0287
0.0327
0.3728
0.0915
0.0176
0.0162
0.0299
0.0513
0.0328
0.0192
0.0232
0.0579
0.0250
0.0194
0.0153
0.0255
0.0218
0.0254
0.0415
0.0776
0.0352
0.0461
0.0768
0.0241
0.0145
0.0237
0.0429
0.0194
0.0744
0.0196
0.0160
0.0273
0.0170
0.0725
0.0202
0.0173
0.0149
0.0586
0.0940
0.0674
0.0260
0.0344
0.0617
0.0176
0.0067
0.0226
0.0203
0.0192
Table 11: Results
Rank
37
15
789
800
852
608
62
612
101
522
836
497
711
268
382
274
885
574
753
555
394
567
201
662
74
95
404
329
313
517
10
275
450
71
124
318
663
637
5
481
701
901
344
473
414
876
611
651
850
719
820
Name
Mullin,Chris
Mullins,Jeff
Murdock,Eric
Murphy,Calvin (HOF)
Murphy,Troy
Murray,Lamond
Murray,Tracy
Mutombo,Dikembe
Najera,Eduardo
Nance,Larry
Nash,Steve
Nater,Swen
Natt,Calvin
Naulls,Willie
Neal,Lloyd
Nealy,Ed
Nelson,Don
Nesterovic,Radoslav
Netolicky,Bob
Neumann,Paul
Newlin,Mike
Newman,Johnny
Nichols,Jack
Nimphius,Kurt
Nixon,Norm
Noble,Chuck
Norman,Ken
Norris,Moochie
Norwood,Willie
Nowitzki,Dirk
O’Koren,Mike
O’neal,Jermaine
O’neal,Shaquille
Oakley,Charles
Odom,Lamar
Ohl,Don
Okur,Mehmet
Olajuwon,Hakeem (HOF)
Olberding,Mark
Ollie,Kevin
Olowokandi,Michael
Orr,Louis
Ostertag,Greg
Outlaw,Bo
Overton,Doug
Owens,Billy
Owens,Tom
Pack,Robert
Padgett,Scott
Palacio,Milt
*** : significant at 1 percent level
** : significant at 5 percent level
*
: significant at 10 percent level
β
cut 3.1
St.Err.
0.5824∗∗∗
−0.1433∗
−0.1509∗∗∗
−0.1918
−0.0284
0.4032∗∗∗
−0.0302
0.3055∗∗∗
0.0157
−0.1783∗∗∗
0.0294
−0.0885∗∗∗
0.1611∗∗∗
0.0928∗∗∗
0.1574∗∗
−0.2407∗∗∗
−0.0062
−0.1143∗∗∗
0.0000∗∗∗
0.0825∗∗∗
−0.0035
0.2085∗∗∗
−0.0599∗∗∗
0.3741∗∗∗
0.3129∗∗∗
0.0790∗∗∗
0.1258∗∗∗
0.1333∗∗∗
0.0179
0.6418∗∗∗
0.1560
0.0550
0.3816∗∗∗
0.2867∗∗∗
0.1307∗∗∗
−0.0610
−0.0443∗∗
0.8238∗∗∗
0.0417
−0.0816∗∗∗
−0.2587∗∗∗
0.1158∗
0.0464∗∗∗
0.0735∗∗∗
−0.2240∗∗∗
−0.0297
−0.0503
−0.1903∗∗∗
−0.0934∗∗∗
−0.1636∗∗∗
0.0517
0.0793
0.0192
0.2027
0.0527
0.0255
0.0191
0.0339
0.0212
0.0237
0.0372
0.0268
0.0437
0.0185
0.0740
0.0319
0.0313
0.0156
0.0000
0.0310
0.0771
0.0169
0.0107
0.0378
0.0537
0.0293
0.0196
0.0233
0.0268
0.0521
0.1907
0.0884
0.0530
0.0264
0.0468
0.0685
0.0214
0.0742
0.0268
0.0153
0.0470
0.0651
0.0159
0.0083
0.0282
0.0291
0.0429
0.0143
0.0120
0.0084
β
cut 3.0
St. Err.
0.5946∗∗∗
−0.2489∗∗∗
−0.1495∗∗∗
−0.1893
−0.0243
0.4034∗∗∗
−0.0261
0.2983∗∗∗
0.0187
−0.1809∗∗∗
0.0284
−0.0934∗∗∗
0.1558∗∗∗
0.1528∗∗∗
0.2429∗∗∗
−0.2504∗∗∗
0.0376
−0.1194∗∗∗
0.0000∗∗∗
0.0547∗
−0.0038
0.2087∗∗∗
−0.0393∗∗∗
0.3810∗∗∗
0.3035∗∗∗
0.0038
0.1259∗∗∗
0.1320∗∗∗
0.0052
0.6444∗∗∗
0.2235
0.0543
0.3797∗∗∗
0.2961∗∗∗
0.1279∗∗∗
0.0252
−0.0366∗
0.8402∗∗∗
0.0475∗
−0.0848∗∗∗
−0.2555∗∗∗
0.1145∗
0.0464∗∗∗
0.0715∗∗∗
−0.2179∗∗∗
−0.0240
−0.0594
−0.1931∗∗∗
−0.0907∗∗∗
−0.1644∗∗∗
0.0521
0.0754
0.0194
0.2036
0.0532
0.0258
0.0193
0.0342
0.0213
0.0239
0.0375
0.0258
0.0439
0.0172
0.0717
0.0322
0.0308
0.0157
0.0000
0.0294
0.0778
0.0170
0.0107
0.0380
0.0541
0.0272
0.0197
0.0235
0.0269
0.0525
0.1909
0.0892
0.0535
0.0266
0.0472
0.0659
0.0216
0.0748
0.0270
0.0154
0.0474
0.0655
0.0161
0.0083
0.0284
0.0294
0.0432
0.0145
0.0121
0.0085
β
cut 3.3
St. Err.
0.5707∗∗∗
−0.1463∗
−0.1467∗∗∗
−0.1957
−0.0130
0.4081∗∗∗
−0.0132
0.3038∗∗∗
−0.0160
−0.1837∗∗∗
0.0298
−0.0867∗∗∗
0.1582∗∗∗
0.0917∗∗∗
0.1615∗∗
−0.2500∗∗∗
−0.0069
−0.1062∗∗∗
0.0000∗∗∗
0.0806∗∗∗
−0.0047
0.2143∗∗∗
−0.0593∗∗∗
0.3743∗∗∗
0.3230∗∗∗
0.0768∗∗∗
0.1255∗∗∗
0.1209∗∗∗
0.0162
0.6281∗∗∗
0.1664
0.0211
0.3687∗∗∗
0.2845∗∗∗
0.1294∗∗∗
−0.0604
−0.0358∗
0.8387∗∗∗
0.0374
−0.0716∗∗∗
−0.2326∗∗∗
0.1036
0.0595∗∗∗
0.0760∗∗∗
−0.2350∗∗∗
−0.0133
−0.0455
−0.1872∗∗∗
−0.0936∗∗∗
−0.1689∗∗∗
0.0523
0.0797
0.0193
0.2038
0.0537
0.0257
0.0200
0.0341
0.0242
0.0239
0.0374
0.0269
0.0439
0.0186
0.0744
0.0324
0.0314
0.0158
0.0000
0.0311
0.0775
0.0170
0.0108
0.0380
0.0542
0.0295
0.0197
0.0238
0.0269
0.0529
0.1919
0.0922
0.0538
0.0265
0.0471
0.0688
0.0217
0.0752
0.0270
0.0156
0.0492
0.0659
0.0165
0.0083
0.0287
0.0301
0.0432
0.0144
0.0121
0.0085
Table 11: Results
Rank
38
159
948
3
886
585
945
811
925
57
221
63
736
690
703
75
913
892
746
354
815
532
225
480
370
53
687
352
163
765
19
188
864
889
660
679
672
368
365
666
652
734
117
12
761
430
733
923
656
802
391
Name
Parish,Robert (HOF)
Parker,Sonny
Parker,Tony
Parks,Cherokee
Patterson,Ruben
Paultz,Billy
Paxson,Jim
Paxson,John
Payton,Gary
Peeler,Anthony
Perdue,Will
Perkins,Sam
Perry,Curtis
Perry,Elliot
Perry,Tim
Person,Chuck
Person,Wesley
Petersen,Jim
Peterson,Morris
Petrie,Geoff
Pettit,Bob (HOF)
Phillip,Andy (HOF)
Phills,Bobby
Piatkowski,Eric
Pierce,Paul
Pierce,Ricky
Pinckney,Ed
Piontek,Dave
Pippen,Scottie
Pollard,Jim (HOF)
Pollard,Scot
Polynice,Olden
Poquette,Ben
Porter,Howard
Porter,Kevin
Porter,Terry
Posey,James
Potapenko,Vitaly
Pressey,Paul
Price,Brent
Price,Jim
Price,Mark
Prince,Tayshaun
Przybilla,Joel
Radmanovic,Vladimir
Rambis,Kurt
Ramsey,Frank (HOF)
Randolph,Zachary
Ransey,Kelvin
Rasmussen,Blair
*** : significant at 1 percent level
** : significant at 5 percent level
*
: significant at 10 percent level
β
cut 3.1
St.Err.
0.2567∗∗∗
−0.8629∗∗∗
1.1713∗∗∗
−0.2411∗∗∗
−0.0142
−0.5937∗∗∗
−0.1561∗∗∗
−0.3606∗∗∗
0.4173∗∗∗
0.1940∗∗∗
0.4015∗∗∗
−0.1047∗∗∗
−0.0752∗∗∗
−0.0848∗∗∗
0.3737∗∗∗
−0.2833∗∗∗
−0.2460∗∗∗
−0.1101∗∗
0.1092∗∗∗
−0.1591∗∗∗
0.0086
0.1922∗
0.0422
0.0986∗
0.4266∗∗∗
−0.0736∗∗
0.1100∗∗∗
0.2543∗∗∗
−0.1240∗∗
0.5529∗∗∗
0.2203∗∗∗
−0.2015∗∗∗
−0.2430∗∗∗
−0.0569∗∗
−0.0697∗∗∗
−0.0655
0.0988∗∗∗
0.1008∗∗∗
−0.0631
−0.0509∗∗∗
−0.1039∗∗∗
0.2921∗∗∗
0.6202∗∗∗
−0.1214∗∗∗
0.0650
−0.1032∗∗∗
−0.3541∗∗∗
−0.0529∗∗∗
−0.1525∗∗∗
0.0829∗∗∗
0.0705
0.1199
0.1053
0.0146
0.0148
0.1566
0.0360
0.0471
0.0282
0.0244
0.0382
0.0327
0.0232
0.0230
0.0530
0.0288
0.0200
0.0560
0.0147
0.0384
0.1816
0.1135
0.0603
0.0583
0.0242
0.0319
0.0186
0.0248
0.0505
0.0622
0.0266
0.0229
0.0262
0.0277
0.0223
0.0436
0.0143
0.0223
0.0749
0.0184
0.0156
0.0778
0.0483
0.0242
0.0425
0.0235
0.0467
0.0129
0.0307
0.0234
β
cut 3.0
St. Err.
0.2602∗∗∗
−0.8795∗∗∗
1.1741∗∗∗
−0.2367∗∗∗
−0.0143
−0.5995∗∗∗
−0.1658∗∗∗
−0.3663∗∗∗
0.4163∗∗∗
0.1976∗∗∗
0.4082∗∗∗
−0.0972∗∗∗
−0.0717∗∗∗
−0.0899∗∗∗
0.3633∗∗∗
−0.2860∗∗∗
−0.2545∗∗∗
−0.1198∗∗
0.1113∗∗∗
−0.1561∗∗∗
−0.3451∗∗
0.1991∗
0.0582
0.0995∗
0.4325∗∗∗
−0.0906∗∗∗
0.1114∗∗∗
0.2908∗∗∗
−0.1316∗∗∗
0.5425∗∗∗
0.2233∗∗∗
−0.2016∗∗∗
−0.2790∗∗∗
−0.0963∗∗∗
−0.0715∗∗∗
−0.0717
0.0994∗∗∗
0.1053∗∗∗
−0.0714
−0.0520∗∗∗
−0.1127∗∗∗
0.2941∗∗∗
0.6290∗∗∗
−0.1147∗∗∗
0.0643
−0.0926∗∗∗
−0.2422∗∗∗
−0.0509∗∗∗
−0.1722∗∗∗
0.0884∗∗∗
0.0711
0.1207
0.1061
0.0148
0.0149
0.1580
0.0363
0.0474
0.0284
0.0246
0.0385
0.0329
0.0233
0.0231
0.0534
0.0290
0.0201
0.0564
0.0148
0.0382
0.1436
0.1092
0.0608
0.0588
0.0244
0.0321
0.0187
0.0231
0.0509
0.0616
0.0268
0.0231
0.0257
0.0274
0.0224
0.0440
0.0144
0.0225
0.0755
0.0186
0.0157
0.0785
0.0487
0.0244
0.0428
0.0236
0.0427
0.0130
0.0308
0.0236
β
cut 3.3
St. Err.
0.2529∗∗∗
−0.8620∗∗∗
1.1504∗∗∗
−0.2354∗∗∗
−0.0121
−0.5863∗∗∗
−0.1555∗∗∗
−0.3693∗∗∗
0.4097∗∗∗
0.2054∗∗∗
0.3996∗∗∗
−0.1125∗∗∗
−0.0803∗∗∗
−0.0944∗∗∗
0.3555∗∗∗
−0.2974∗∗∗
−0.2563∗∗∗
−0.1080∗
0.1053∗∗∗
−0.1617∗∗∗
0.0022
0.1944∗
0.0168
0.0810
0.4356∗∗∗
−0.0857∗∗∗
0.1056∗∗∗
0.2521∗∗∗
−0.1292∗∗
0.5539∗∗∗
0.2414∗∗∗
−0.2184∗∗∗
−0.2452∗∗∗
−0.0588∗∗
−0.0713∗∗∗
−0.0751∗
0.0890∗∗∗
0.1077∗∗∗
−0.0630
−0.0500∗∗∗
−0.1056∗∗∗
0.2916∗∗∗
0.6120∗∗∗
−0.1082∗∗∗
0.0615
−0.1065∗∗∗
−0.3522∗∗∗
−0.0462∗∗∗
−0.1551∗∗∗
0.0799∗∗∗
0.0709
0.1205
0.1070
0.0148
0.0148
0.1575
0.0362
0.0475
0.0285
0.0249
0.0384
0.0330
0.0234
0.0233
0.0543
0.0295
0.0204
0.0562
0.0148
0.0386
0.1826
0.1141
0.0625
0.0595
0.0246
0.0325
0.0187
0.0249
0.0508
0.0625
0.0280
0.0239
0.0263
0.0279
0.0224
0.0441
0.0147
0.0226
0.0753
0.0185
0.0157
0.0782
0.0488
0.0248
0.0427
0.0237
0.0469
0.0131
0.0309
0.0235
Table 11: Results
Rank
39
829
279
347
862
493
182
364
408
193
866
310
333
508
177
918
705
741
897
260
809
70
774
540
203
28
909
187
478
109
261
120
677
577
786
547
828
796
41
865
605
420
156
888
133
506
174
4
49
658
427
Name
Ratliff,Theo
Ray,Clifford
Redd,Michael
Reed,Hub
Reed,Willis (HOF)
Reid,Don
Reid,J.r.
Reid,Robert
Restani,Kevin
Reynolds,Jerry
Rice,Glen
Richardson,Clint
Richardson,Jason
Richardson,Micheal Ray
Richardson,Pooh
Richardson,Quentin
Richmond,Mitch
Rider,Isaiah
Riordan,Mike
Risen,Arnie (HOF)
Rivers,Doc
Roberson,Rick
Roberts,Fred
Robertson,Alvin
Robertson,Oscar (HOF)
Robey,Rick
Robinson,Clifford R.
Robinson,Clifford T.
Robinson,David
Robinson,Flynn
Robinson,Glenn
Robinson,Truck
Robinzine,Bill
Robisch,Dave
Rodgers,Guy
Rodman,Dennis
Rogers,Rodney
Rollins,Tree
Rooks,Sean
Rose,Jalen
Rose,Malik
Roundfield,Dan
Rowe,Curtis
Royal,Donald
Ruffin,Michael
Rule,Bob
Russell,Bill (HOF)
Russell,Bryon
Russell,Campy
Russell,Cazzie
*** : significant at 1 percent level
** : significant at 5 percent level
*
: significant at 10 percent level
β
cut 3.1
St.Err.
−0.1703∗∗∗
0.1536∗∗∗
0.1129∗∗∗
−0.2003∗∗∗
0.0340
0.2313∗∗∗
0.1022∗∗
0.0763
0.2160∗∗∗
−0.2072∗∗∗
0.1350∗∗∗
0.1224∗∗∗
0.0257∗∗∗
0.2423∗∗∗
−0.3284∗∗∗
−0.0859∗∗∗
−0.1073∗∗∗
−0.2534∗∗∗
0.1682∗∗
−0.1553∗
0.3874∗∗∗
−0.1313∗∗∗
0.0054
0.2050∗∗∗
0.4885∗∗∗
−0.2786∗∗∗
0.2218∗∗∗
0.0435
0.2965∗∗∗
0.1676∗∗∗
0.2895∗∗∗
−0.0680∗∗∗
−0.0078
−0.1402∗∗∗
0.0016
−0.1702
−0.1490∗∗∗
0.4432∗∗∗
−0.2055∗∗∗
−0.0256∗
0.0694∗
0.2588∗∗∗
−0.2426∗∗∗
0.2772∗∗∗
0.0271
0.2457∗∗∗
1.1398
0.4348∗∗∗
−0.0538
0.0659∗∗∗
0.0179
0.0333
0.0219
0.0231
0.0300
0.0427
0.0411
0.0480
0.0303
0.0312
0.0247
0.0468
0.0099
0.0240
0.0673
0.0201
0.0219
0.0171
0.0836
0.0801
0.0322
0.0143
0.0124
0.0275
0.0469
0.0428
0.0364
0.0507
0.0695
0.0181
0.0251
0.0157
0.0279
0.0432
0.0351
0.1046
0.0197
0.0654
0.0154
0.0154
0.0414
0.0207
0.0278
0.0248
0.0175
0.0289
0.7621
0.0242
0.0538
0.0202
β
cut 3.0
St. Err.
−0.1673∗∗∗
0.1917∗∗∗
0.1107∗∗∗
−0.1664∗∗∗
0.0340
0.2226∗∗∗
0.1030∗∗
0.0743
0.2426∗∗∗
−0.2153∗∗∗
0.1362∗∗∗
0.1021∗∗
0.0279∗∗∗
0.2514∗∗∗
−0.3173∗∗∗
−0.0848∗∗∗
−0.1005∗∗∗
−0.2528∗∗∗
0.2262∗∗∗
−0.0730
0.3894∗∗∗
−0.1192∗∗∗
0.0082
0.2130∗∗∗
0.5226∗∗∗
−0.2715∗∗∗
0.2200∗∗∗
0.0305
0.3008∗∗∗
0.1919∗∗∗
0.2917∗∗∗
−0.0684∗∗∗
−0.0038
−0.1343∗∗∗
−0.0462
−0.1719
−0.1498∗∗∗
0.4363∗∗∗
−0.2077∗∗∗
−0.0263∗
0.0607
0.2539∗∗∗
−0.1944∗∗∗
0.2799∗∗∗
0.0281
0.2435∗∗∗
0.5608
0.4460∗∗∗
−0.0654
0.0811∗∗∗
0.0180
0.0325
0.0221
0.0218
0.0303
0.0430
0.0415
0.0484
0.0291
0.0313
0.0249
0.0468
0.0100
0.0242
0.0678
0.0203
0.0221
0.0173
0.0830
0.0782
0.0324
0.0143
0.0124
0.0277
0.0458
0.0428
0.0367
0.0504
0.0700
0.0179
0.0253
0.0158
0.0277
0.0429
0.0290
0.1055
0.0199
0.0654
0.0156
0.0155
0.0418
0.0207
0.0272
0.0249
0.0176
0.0276
0.6616
0.0244
0.0540
0.0199
β
cut 3.3
St. Err.
−0.1699∗∗∗
0.1538∗∗∗
0.0932∗∗∗
−0.2011∗∗∗
0.0353
0.2201∗∗∗
0.1089∗∗∗
0.0855∗
0.2186∗∗∗
−0.1972∗∗∗
0.1430∗∗∗
0.1137∗∗
0.0273∗∗∗
0.2443∗∗∗
−0.3193∗∗∗
−0.0844∗∗∗
−0.1125∗∗∗
−0.2470∗∗∗
0.1619∗
−0.1535∗
0.3891∗∗∗
−0.1340∗∗∗
0.0022
0.2091∗∗∗
0.4843∗∗∗
−0.2793∗∗∗
0.2289∗∗∗
0.0333
0.3048∗∗∗
0.1648∗∗∗
0.2657∗∗∗
−0.0678∗∗∗
−0.0120
−0.1428∗∗∗
0.0008
−0.1625
−0.1543∗∗∗
0.4538∗∗∗
−0.2141∗∗∗
−0.0189
0.0724∗
0.2556∗∗∗
−0.2411∗∗∗
0.2875∗∗∗
0.0301∗
0.2439∗∗∗
1.1353
0.4148∗∗∗
−0.0583
0.0645∗∗∗
0.0180
0.0335
0.0232
0.0232
0.0302
0.0433
0.0415
0.0485
0.0305
0.0316
0.0250
0.0473
0.0100
0.0242
0.0679
0.0202
0.0221
0.0173
0.0841
0.0805
0.0324
0.0144
0.0124
0.0277
0.0472
0.0430
0.0367
0.0512
0.0701
0.0182
0.0269
0.0158
0.0281
0.0434
0.0353
0.1052
0.0199
0.0661
0.0157
0.0156
0.0417
0.0209
0.0279
0.0253
0.0176
0.0290
0.7659
0.0255
0.0541
0.0203
Table 11: Results
Rank
40
207
366
635
835
287
234
788
596
6
570
356
379
230
110
325
832
455
623
571
728
1
943
582
20
298
232
125
665
209
931
504
589
357
54
895
113
199
153
32
150
536
928
290
257
549
584
807
34
229
29
Name
Sabonis,Arvydas
Salley,John
Salmons,John
Sampson,Ralph
Sanders,Mike
Sanders,Thomas
Sauldsberry,Woody
Schayes,Danny
Schayes,Dolph (HOF)
Schlueter,Dale
Schrempf,Detlef
Scott,Alvin
Scott,Byron
Scott,Charlie
Scott,Dennis
Scott,Ray
Sealy,Malik
Sears,Ken
Seikaly,Rony
Selvy,Frank
Senesky,George
Seymour,Paul
Share,Charlie
Sharman,Bill (HOF)
Shaw,Brian
Shelton,Lonnie
Short,Purvis
Shue,Gene
Sichting,Jerry
Siegfried,Larry
Sikma,Jack
Silas,James
Silas,Paul
Simmons,Connie
Simmons,Lionel
Skiles,Scott
Skinner,Brian
Sloan,Jerry
Smith,Adrian
Smith,Bingo
Smith,Charles
Smith,Derek
Smith,Don
Smith,Elmore
Smith,Greg
Smith,Joe
Smith,Kenny
Smith,Larry
Smith,Michael
Smith,Phil
*** : significant at 1 percent level
** : significant at 5 percent level
*
: significant at 10 percent level
β
cut 3.1
St.Err.
0.2029∗
0.1159
0.1002∗∗
0.0395
−0.0422∗∗∗ 0.0158
−0.1763∗∗
0.0780
0.1494∗∗∗ 0.0259
0.1839∗∗∗ 0.0263
−0.1432∗∗∗ 0.0118
−0.0198
0.0248
0.7928∗∗∗ 0.2666
−0.0053
0.0110
0.1089∗∗∗ 0.0340
0.0939
0.0860
0.1867∗∗∗ 0.0508
0.2957∗∗∗ 0.0243
0.1273∗∗∗ 0.0158
−0.1733∗∗∗ 0.0200
0.0537∗∗∗ 0.0169
−0.0380
0.0259
−0.0059
0.0548
−0.0989∗∗∗ 0.0220
6.0692
114.5482
−0.5314
0.4589
−0.0112
0.1277
0.5377∗∗∗ 0.1629
0.1425∗∗∗ 0.0146
0.1845∗∗∗ 0.0159
0.2856∗∗∗ 0.0601
−0.0622∗∗∗ 0.0213
0.2022∗∗∗ 0.0262
−0.3956∗∗∗ 0.0537
0.0277
0.0469
−0.0174
0.0389
0.1087∗∗∗ 0.0109
0.4262∗∗∗ 0.0625
−0.2506∗
0.1354
0.2946∗∗∗ 0.0296
0.2092∗∗∗ 0.0115
0.2609∗∗∗ 0.0623
0.4792∗∗∗ 0.0454
0.2625∗∗∗ 0.0376
0.0072
0.0363
−0.3741∗∗∗ 0.0464
0.1487∗∗∗ 0.0219
0.1710∗∗∗ 0.0221
0.0010
0.0191
−0.0125
0.0156
−0.1547∗∗∗ 0.0247
0.4662∗∗∗ 0.0466
0.1876∗∗∗ 0.0201
0.4858∗∗∗ 0.0368
β
cut 3.0
St. Err.
0.2108∗
0.1169
0.1000∗∗ 0.0398
−0.0457∗∗∗ 0.0159
−0.1534∗
0.0785
0.1445∗∗∗ 0.0261
0.2262∗∗∗ 0.0259
−0.1779∗∗∗ 0.0109
−0.0063
0.0250
0.6798∗∗ 0.2639
0.0334∗∗∗ 0.0104
0.1003∗∗∗ 0.0342
0.1057
0.0853
0.1807∗∗∗ 0.0511
0.3016∗∗∗ 0.0241
0.1340∗∗∗ 0.0159
−0.2037∗∗∗ 0.0198
0.0658∗∗∗ 0.0170
−0.0960∗∗∗ 0.0242
−0.0124
0.0552
−0.0654∗∗∗ 0.0217
−0.1990
24.7607
−0.5079
0.4557
0.1392
0.1217
0.4833∗∗∗ 0.1627
0.1410∗∗∗ 0.0147
0.2020∗∗∗ 0.0160
0.3187∗∗∗ 0.0602
0.0003
0.0195
0.1889∗∗∗ 0.0263
−0.2982∗∗∗ 0.0508
0.0368
0.0473
−0.0125
0.0390
0.1075∗∗∗ 0.0109
0.4528∗∗∗ 0.0538
−0.2614∗
0.1365
0.2979∗∗∗ 0.0297
0.2069∗∗∗ 0.0116
0.3593∗∗∗ 0.0591
0.4334∗∗∗ 0.0444
0.3006∗∗∗ 0.0370
0.0077
0.0366
−0.3987∗∗∗ 0.0465
0.1260∗∗∗ 0.0217
0.1828∗∗∗ 0.0222
−0.0055
0.0192
−0.0160
0.0157
−0.1616∗∗∗ 0.0249
0.4514∗∗∗ 0.0469
0.1992∗∗∗ 0.0202
0.4884∗∗∗ 0.0364
β
cut 3.3
St. Err.
0.2001∗
0.1165
0.0884∗∗
0.0401
−0.0361∗∗
0.0160
−0.1635∗∗
0.0789
0.1516∗∗∗ 0.0261
0.1819∗∗∗ 0.0264
−0.1425∗∗∗ 0.0119
−0.0243
0.0250
0.7785∗∗∗ 0.2686
−0.0068
0.0111
0.1169∗∗∗ 0.0343
0.0813
0.0869
0.1946∗∗∗ 0.0512
0.2961∗∗∗ 0.0244
0.1224∗∗∗ 0.0159
−0.1746∗∗∗ 0.0201
0.0455∗∗∗ 0.0172
−0.0372
0.0260
0.0066
0.0555
−0.1017∗∗∗ 0.0222
6.1584
115.1403
−0.5254
0.4613
−0.0110
0.1284
0.5327∗∗∗ 0.1638
0.1392∗∗∗ 0.0147
0.1842∗∗∗ 0.0160
0.3052∗∗∗ 0.0615
−0.0604∗∗∗ 0.0214
0.2047∗∗∗ 0.0263
−0.3914∗∗∗ 0.0541
0.0188
0.0474
−0.0149
0.0391
0.1107∗∗∗ 0.0110
0.4246∗∗∗ 0.0628
−0.2259
0.1378
0.3029∗∗∗ 0.0299
0.1955∗∗∗ 0.0121
0.2619∗∗∗ 0.0626
0.4820∗∗∗ 0.0457
0.2514∗∗∗ 0.0382
0.0052
0.0365
−0.3682∗∗∗ 0.0467
0.1477∗∗∗ 0.0220
0.1727∗∗∗ 0.0222
−0.0029
0.0192
0.0038
0.0164
−0.1543∗∗∗ 0.0248
0.4477∗∗∗ 0.0478
0.1889∗∗∗ 0.0202
0.4858∗∗∗ 0.0370
Table 11: Results
Rank
41
423
295
671
393
735
518
282
748
720
527
842
597
277
673
578
276
326
599
499
491
514
683
830
933
358
930
475
797
443
272
629
8
775
183
782
937
14
288
907
707
664
184
176
102
128
859
495
576
898
131
Name
Smith,Randy
Smith,Steve
Smith,Tony
Smits,Rik
Snow,Eric
Snyder,Dick
Sobers,Ricky
Sparrow,Rory
Spencer,Felton
Sprewell,Latrell
Stackhouse,Jerry
Stallworth,Dave
Starks,John
Steele,Larry
Stevenson,Deshawn
Stipanovich,Steve
Stith,Bryant
Stockton,John
Stojakovic,Peja
Stoudamire,Damon
Strickland,Erick
Strickland,Rod
Strong,Derek
Sundvold,Jon
Sura,Bob
Swift,Stromile
Szczerbiak,Wally
Taylor,Maurice
Teagle,Terry
Terry,Jason
Theus,Reggie
Thomas,Isiah (HOF)
Thomas,Kenny
Thomas,Kurt
Thomas,Tim
Thompson,David (HOF)
Thompson,Lasalle
Thompson,Mychal
Thorn,Rod
Thorpe,Otis
Threatt,Sedale
Thurmond,Nate (HOF)
Tisdale,Wayman
Tomjanovich,Rudy
Toney,Andrew
Traylor,Robert
Trent,Gary
Tresvant,John
Tripucka,Kelly
Tucker,Trent
*** : significant at 1 percent level
** : significant at 5 percent level
*
: significant at 10 percent level
β
cut 3.1
St.Err.
0.0685∗∗∗
0.1452∗∗∗
−0.0649
0.0826
−0.1044∗∗∗
0.0179
0.1523∗∗∗
−0.1123∗∗∗
−0.0939∗∗∗
0.0110
−0.1845∗∗∗
−0.0200
0.1545∗∗∗
−0.0656
−0.0079
0.1551∗∗∗
0.1267∗∗∗
−0.0209
0.0289
0.0365
0.0203
−0.0726∗∗∗
−0.1718∗∗∗
−0.4097∗∗∗
0.1086∗∗∗
−0.3887∗∗∗
0.0460
−0.1495∗∗∗
0.0597∗
0.1594∗∗∗
−0.0392
0.7300∗∗∗
−0.1336∗∗∗
0.2310∗∗∗
−0.1385∗∗∗
−0.4606∗∗∗
0.5914∗∗∗
0.1490∗∗∗
−0.2727∗∗∗
−0.0864∗∗
−0.0618∗∗∗
0.2289∗∗∗
0.2428∗∗∗
0.3034∗∗∗
0.2829∗∗∗
−0.1966∗∗∗
0.0307∗
−0.0068
−0.2545∗∗∗
0.2792∗∗∗
0.0163
0.0200
0.0409
0.0964
0.0166
0.0168
0.0280
0.0260
0.0127
0.0235
0.0205
0.0188
0.0447
0.0520
0.0096
0.0311
0.0284
0.0644
0.0233
0.0245
0.0206
0.0266
0.0285
0.0540
0.0201
0.0229
0.0317
0.0153
0.0355
0.0251
0.0288
0.0647
0.0282
0.0329
0.0164
0.0941
0.0825
0.0328
0.0248
0.0335
0.0151
0.0219
0.0405
0.1113
0.0614
0.0180
0.0177
0.0435
0.0537
0.0777
β
cut 3.0
St. Err.
0.0669∗∗∗
0.1423∗∗∗
−0.0626
0.0801
−0.1077∗∗∗
0.0384∗∗
0.1433∗∗∗
−0.0946∗∗∗
−0.0956∗∗∗
0.0087
−0.1965∗∗∗
−0.0474∗∗
0.1573∗∗∗
−0.0996∗
−0.0060
0.1642∗∗∗
0.1235∗∗∗
−0.0551
0.0255
0.0396
0.0212
−0.0749∗∗∗
−0.1678∗∗∗
−0.4479∗∗∗
0.1093∗∗∗
−0.3859∗∗∗
0.0481
−0.1475∗∗∗
0.0602∗
0.1602∗∗∗
−0.0618∗∗
0.7155∗∗∗
−0.1317∗∗∗
0.2377∗∗∗
−0.1426∗∗∗
−0.5142∗∗∗
0.6388∗∗∗
0.1641∗∗∗
−0.3188∗∗∗
−0.0867∗∗
−0.0689∗∗∗
0.2404∗∗∗
0.2548∗∗∗
0.3113∗∗∗
0.3186∗∗∗
−0.1963∗∗∗
0.0374∗∗
−0.1027∗∗
−0.2493∗∗∗
0.2817∗∗∗
0.0161
0.0201
0.0412
0.0973
0.0167
0.0163
0.0281
0.0261
0.0128
0.0237
0.0206
0.0187
0.0451
0.0521
0.0097
0.0313
0.0286
0.0646
0.0235
0.0248
0.0208
0.0268
0.0287
0.0540
0.0202
0.0231
0.0320
0.0154
0.0357
0.0254
0.0289
0.0650
0.0285
0.0332
0.0165
0.0929
0.0824
0.0330
0.0204
0.0338
0.0152
0.0174
0.0408
0.1118
0.0607
0.0182
0.0178
0.0402
0.0541
0.0783
β
cut 3.3
St. Err.
0.0655∗∗∗
0.1456∗∗∗
−0.0805∗
0.0743
−0.1063∗∗∗
0.0147
0.1556∗∗∗
−0.1153∗∗∗
−0.0956∗∗∗
0.0023
−0.1926∗∗∗
−0.0195
0.1714∗∗∗
−0.0688
−0.0086
0.1490∗∗∗
0.1308∗∗∗
−0.0217
0.0482∗∗
0.0366
0.0163
−0.0674∗∗
−0.2008∗∗∗
−0.4198∗∗∗
0.1040∗∗∗
−0.3849∗∗∗
0.0223
−0.1451∗∗∗
0.0629∗
0.1575∗∗∗
−0.0389
0.7324∗∗∗
−0.1175∗∗∗
0.2355∗∗∗
−0.1366∗∗∗
−0.4509∗∗∗
0.5964∗∗∗
0.1476∗∗∗
−0.2730∗∗∗
−0.0946∗∗∗
−0.0564∗∗∗
0.2283∗∗∗
0.2482∗∗∗
0.3040∗∗∗
0.2877∗∗∗
−0.1954∗∗∗
0.0255
−0.0057
−0.2569∗∗∗
0.2967∗∗∗
0.0164
0.0201
0.0418
0.0971
0.0166
0.0169
0.0282
0.0261
0.0128
0.0238
0.0208
0.0189
0.0458
0.0523
0.0096
0.0314
0.0285
0.0647
0.0245
0.0247
0.0208
0.0268
0.0311
0.0546
0.0202
0.0230
0.0335
0.0154
0.0357
0.0253
0.0290
0.0650
0.0291
0.0331
0.0164
0.0948
0.0829
0.0329
0.0250
0.0339
0.0152
0.0220
0.0408
0.1119
0.0618
0.0181
0.0178
0.0437
0.0540
0.0790
Table 11: Results
Rank
42
31
500
227
938
854
24
259
516
461
489
764
726
586
562
466
793
377
947
676
814
858
80
655
264
433
839
616
336
299
216
880
386
896
790
403
919
510
76
435
67
780
812
619
127
490
704
173
339
881
82
Name
Turkoglu,Hidayet
Turner,Elston
Turner,Jeff
Twyman,Jack (HOF)
Tyler,Terry
Unseld,Wes (HOF)
Valentine,Darnell
Van Arsdale,Dick
Van Arsdale,Tom
Van Exel,Nick
Vanbredakolff,Jan
Vandeweghe,Kiki
Vanhorn,Keith
Vanlier,Norm
Vaughn,Jacque
Vaught,Loy
Vincent,Jay
Voskuhl,Jake
Vranes,Danny
Walk,Neal
Walker,Antoine
Walker,Chet
Walker,Darrell
Walker,Foots
Walker,Jimmy
Walker,Kenny
Walker,Samaki
Walker,Wally
Wallace,Ben
Wallace,Rasheed
Walton,Bill (HOF)
Wanzer,Bobby (HOF)
Ward,Charlie
Warner,Cornell
Washington,Jim
Washington,Kermit
Watson,Earl
Watts,Slick
Weatherspoon,Clarence
Weatherspoon,Nick
Webb,Spud
Webber,Chris
Webster,Marvin
Wedman,Scott
Weiss,Bob
Wells,Bonzi
Wennington,Bill
Wesley,David
Wesley,Walt
West,Doug
*** : significant at 1 percent level
** : significant at 5 percent level
*
: significant at 10 percent level
β
cut 3.1
St.Err.
0.4841∗∗∗
0.0285
0.1886∗∗∗
−0.4629∗∗∗
−0.1935∗∗∗
0.5081∗∗∗
0.1692∗∗∗
0.0185
0.0523∗∗
0.0369∗
−0.1240
−0.0987∗∗
−0.0168
−0.0020
0.0504∗∗∗
−0.1465∗∗∗
0.0944∗∗∗
−0.7531∗∗∗
−0.0676
−0.1573∗∗∗
−0.1960∗∗∗
0.3407∗∗∗
−0.0526∗∗∗
0.1637∗∗∗
0.0636∗∗∗
−0.1804∗∗∗
−0.0334∗∗
0.1195∗∗
0.1420∗∗∗
0.1996∗∗∗
−0.2290∗∗∗
0.0872
−0.2522∗∗∗
−0.1445∗∗∗
0.0792∗∗∗
−0.3314∗∗∗
0.0225∗
0.3706∗∗∗
0.0619∗∗∗
0.3937∗∗∗
−0.1361∗∗∗
−0.1564∗∗∗
−0.0355∗∗∗
0.2830∗∗∗
0.0369
−0.0858∗∗∗
0.2459∗∗∗
0.1180∗∗∗
−0.2311∗∗∗
0.3335∗∗∗
0.0141
0.0237
0.0493
0.0618
0.0294
0.0369
0.0237
0.0286
0.0208
0.0209
0.1376
0.0417
0.0131
0.0270
0.0075
0.0297
0.0176
0.2084
0.0471
0.0255
0.0135
0.0448
0.0185
0.0488
0.0135
0.0600
0.0135
0.0492
0.0232
0.0473
0.0868
2.8034
0.0518
0.0193
0.0213
0.0409
0.0130
0.0242
0.0146
0.0238
0.0302
0.0269
0.0138
0.0262
0.0386
0.0328
0.0363
0.0362
0.0171
0.0591
β
cut 3.0
St. Err.
0.4895∗∗∗
0.0409∗
0.2011∗∗∗
−0.4677∗∗∗
−0.1722∗∗∗
0.5210∗∗∗
0.1825∗∗∗
0.0049
0.0807∗∗∗
0.0397∗
−0.1059
−0.1092∗∗∗
−0.0184
−0.0564∗∗
0.0470∗∗∗
−0.1463∗∗∗
0.0987∗∗∗
−0.7475∗∗∗
−0.0452
−0.1334∗∗∗
−0.1935∗∗∗
0.2815∗∗∗
−0.0524∗∗∗
0.1243∗∗∗
0.0472∗∗∗
−0.1746∗∗∗
−0.0303∗∗
0.1134∗∗
0.1432∗∗∗
0.2068∗∗∗
−0.2343∗∗∗
0.1940
−0.2542∗∗∗
−0.1424∗∗∗
0.0125
−0.3276∗∗∗
0.0234∗
0.3708∗∗∗
0.0609∗∗∗
0.3882∗∗∗
−0.1286∗∗∗
−0.1562∗∗∗
−0.0186
0.2952∗∗∗
0.0070
−0.0899∗∗∗
0.2461∗∗∗
0.1038∗∗∗
−0.2439∗∗∗
0.3275∗∗∗
0.0143
0.0238
0.0497
0.0612
0.0294
0.0371
0.0238
0.0288
0.0206
0.0210
0.1380
0.0421
0.0132
0.0262
0.0076
0.0300
0.0177
0.2102
0.0474
0.0251
0.0136
0.0423
0.0186
0.0482
0.0133
0.0605
0.0136
0.0496
0.0233
0.0477
0.0865
2.8229
0.0522
0.0193
0.0196
0.0413
0.0131
0.0244
0.0147
0.0237
0.0305
0.0271
0.0136
0.0263
0.0367
0.0330
0.0366
0.0363
0.0163
0.0596
β
cut 3.3
St. Err.
0.4784∗∗∗
0.0334
0.2091∗∗∗
−0.4688∗∗∗
−0.1951∗∗∗
0.5090∗∗∗
0.1644∗∗∗
0.0195
0.0512∗∗
0.0466∗∗
−0.1176
−0.0971∗∗
−0.0222∗
−0.0027
0.0477∗∗∗
−0.1314∗∗∗
0.1014∗∗∗
−0.7833∗∗∗
−0.0658
−0.1638∗∗∗
−0.1593∗∗∗
0.3351∗∗∗
−0.0508∗∗∗
0.1686∗∗∗
0.0630∗∗∗
−0.1962∗∗∗
−0.0372∗∗∗
0.1238∗∗
0.1345∗∗∗
0.2061∗∗∗
−0.2352∗∗∗
0.0595
−0.2573∗∗∗
−0.1471∗∗∗
0.0764∗∗∗
−0.3407∗∗∗
0.0361∗∗∗
0.3734∗∗∗
0.0620∗∗∗
0.3936∗∗∗
−0.1227∗∗∗
−0.1568∗∗∗
−0.0378∗∗∗
0.2839∗∗∗
0.0390
−0.0930∗∗∗
0.2419∗∗∗
0.1316∗∗∗
−0.2293∗∗∗
0.3472∗∗∗
0.0143
0.0239
0.0508
0.0622
0.0295
0.0370
0.0239
0.0288
0.0209
0.0212
0.1384
0.0420
0.0132
0.0272
0.0076
0.0305
0.0178
0.2121
0.0474
0.0257
0.0175
0.0451
0.0186
0.0491
0.0136
0.0610
0.0136
0.0495
0.0234
0.0476
0.0874
2.8197
0.0521
0.0194
0.0214
0.0414
0.0136
0.0244
0.0147
0.0239
0.0309
0.0270
0.0138
0.0264
0.0388
0.0331
0.0365
0.0369
0.0172
0.0600
Table 11: Results
Rank
43
362
639
431
213
837
162
107
699
542
795
908
396
667
591
337
220
739
248
496
289
44
271
883
825
375
694
505
483
849
459
716
85
714
533
548
622
285
750
620
737
610
614
440
744
792
262
756
198
674
Name
West,Jerry (HOF)
West,Mark
Westphal,Paul
White,Jojo
Whitehead,Jerome
Whitney,Chris
Wicks,Sidney
Wilkens,Lenny (HOF)
Wilkerson,Bob
Wilkes,Jamaal
Wilkins,Dominique (HOF)
Wilkins,Gerald
Wilkins,Jeff
Williams,Aaron
Williams,Alvin
Williams,Buck
Williams,Eric
Williams,Gus
Williams,Herb
Williams,Hotrod
Williams,Jason
Williams,Jayson
Williams,Jerome
Williams,John
Williams,Michael
Williams,Monty
Williams,Nate
Williams,Ray
Williams,Reggie
Williams,Ron
Williams,Scott
Williams,Walt
Williamson,Corliss
Willis,Kevin
Willoughby,Bill
Wilson,George
Winfield,Lee
Wingate,David
Winters,Brian
Wittman,Randy
Wolf,Joe
Wood,Al
Wood,David
Woodson,Mike
Woolridge,Orlando
Worthy,James (HOF)
Wright,Lorenzen
Yardley,George (HOF)
Young,Danny
C
*** : significant at 1 percent level
** : significant at 5 percent level
*
: significant at 10 percent level
β
cut 3.1
St.Err.
0.1056∗∗
−0.0463
0.0650∗∗∗
0.2011∗∗∗
−0.1786∗∗∗
0.2546∗∗∗
0.2982∗∗∗
−0.0814∗∗∗
0.0045
−0.1480∗∗∗
−0.2771∗∗∗
0.0817∗∗∗
−0.0633
−0.0187
0.1183∗∗
0.1942∗∗∗
−0.1055∗∗∗
0.1751∗∗∗
0.0294
0.1487
0.4404∗∗∗
0.1598∗∗∗
−0.2367∗∗∗
−0.1693∗∗∗
0.0958∗∗∗
−0.0786∗∗
0.0274∗∗
0.0402∗
−0.1896∗∗∗
0.0524
−0.0906∗∗∗
0.3295∗∗∗
−0.0892∗∗∗
0.0081
0.0012
−0.0370
0.1505∗∗∗
−0.1127∗∗∗
−0.0358
−0.1048∗∗
−0.0297∗∗
−0.0328
0.0605∗∗∗
−0.1090∗∗∗
−0.1459∗∗∗
0.1674∗∗∗
−0.1159∗∗∗
0.2097∗∗∗
−0.0667∗∗∗
3.6232
0.0487
0.0567
0.0193
0.0666
0.0210
0.0469
0.0219
0.0311
0.0136
0.0438
0.0821
0.0249
0.1054
0.0183
0.0557
0.0495
0.0391
0.0222
0.0367
0.1107
0.0273
0.0546
0.0269
0.0335
0.0156
0.0343
0.0121
0.0216
0.0622
0.0357
0.0183
0.0180
0.0125
0.0167
0.0318
0.0243
0.0237
0.0206
0.0547
0.0450
0.0144
0.0202
0.0200
0.0351
0.0124
0.0405
0.0252
0.0515
0.0141
6.7136
β
cut 3.0
St. Err.
0.0769
0.0487
−0.0563
0.0571
0.0866∗∗∗ 0.0193
0.1192∗
0.0648
−0.1760∗∗∗ 0.0211
0.2594∗∗∗ 0.0473
0.2789∗∗∗ 0.0219
−0.0662∗∗ 0.0313
0.0005
0.0136
−0.1358∗∗∗ 0.0434
−0.2933∗∗∗ 0.0825
0.0752∗∗∗ 0.0251
−0.0327
0.1055
−0.0172
0.0185
0.1143∗∗ 0.0561
0.1968∗∗∗ 0.0499
−0.0986∗∗ 0.0394
0.1442∗∗∗ 0.0216
0.0403
0.0370
0.1644
0.1114
0.4405∗∗∗ 0.0276
0.1467∗∗∗ 0.0550
−0.2370∗∗∗ 0.0271
−0.1720∗∗∗ 0.0337
0.1004∗∗∗ 0.0157
−0.0786∗∗ 0.0346
0.0204∗
0.0122
0.0266
0.0218
−0.1701∗∗∗ 0.0626
0.0368
0.0355
−0.0944∗∗∗ 0.0185
0.3298∗∗∗ 0.0181
−0.0865∗∗∗ 0.0126
0.0059
0.0168
0.0074
0.0320
−0.0255
0.0236
0.1516∗∗∗ 0.0239
−0.1122∗∗∗ 0.0208
−0.0486
0.0551
−0.0930∗∗ 0.0452
−0.0343∗∗ 0.0145
−0.0248
0.0203
0.0547∗∗∗ 0.0201
−0.1211∗∗∗ 0.0354
−0.1487∗∗∗ 0.0125
0.1630∗∗∗ 0.0408
−0.1204∗∗∗ 0.0254
0.1999∗∗∗ 0.0518
−0.0656∗∗∗ 0.0143
3.3707∗∗∗−0.9360
β
cut 3.3
St. Err.
0.1026∗∗
0.0490
−0.0710
0.0587
0.0619∗∗∗ 0.0194
0.1928∗∗∗ 0.0671
−0.1795∗∗∗ 0.0211
0.2344∗∗∗ 0.0483
0.2990∗∗∗ 0.0220
−0.0810∗∗∗ 0.0313
0.0045
0.0136
−0.1519∗∗∗ 0.0441
−0.2738∗∗∗ 0.0825
0.0824∗∗∗ 0.0250
−0.0459
0.1068
−0.0277
0.0187
0.0955∗
0.0574
0.1808∗∗∗ 0.0503
−0.1393∗∗∗ 0.0426
0.1742∗∗∗ 0.0223
0.0274
0.0369
0.1530
0.1113
0.4159∗∗∗ 0.0292
0.1448∗∗∗ 0.0555
−0.2271∗∗∗ 0.0273
−0.1652∗∗∗ 0.0337
0.0953∗∗∗ 0.0157
−0.0814∗∗
0.0345
0.0224∗
0.0123
0.0387∗
0.0217
−0.1887∗∗∗ 0.0625
0.0539
0.0359
−0.0979∗∗∗ 0.0186
0.3190∗∗∗ 0.0184
−0.0858∗∗∗ 0.0126
0.0045
0.0168
0.0047
0.0320
−0.0399
0.0244
0.1554∗∗∗ 0.0239
−0.1065∗∗∗ 0.0208
−0.0347
0.0550
−0.1064∗∗
0.0452
−0.0254∗
0.0145
−0.0316
0.0203
0.0666∗∗∗ 0.0202
−0.1102∗∗∗ 0.0353
−0.1498∗∗∗ 0.0125
0.1623∗∗∗ 0.0407
−0.1245∗∗∗ 0.0256
0.2097∗∗∗ 0.0518
−0.0608∗∗∗ 0.0143
0.9392∗∗∗ −0.2885
HHL-Arbeitspapiere / HHL Working Papers
102
Scherzer, Falk (2010)
On the Value of Individual Athletes in Team Sports
101
Wulf, Torsten; Brands, Christian; Meißner, Philip (2010)
A Scenario-based Approach to Strategic Planning: Tool Description –
360° Stakeholder Feedback
100
Viellechner, Oliver; Wulf, Torsten (2010)
Incumbent Inertia upon Disruptive Change in the Airline Industry: Causal Factors
for Routine Rigidity and Top Management Moderators
99
Wulf, Torsten; Meißner, Philip; Bernewitz, Friedrich Frhr. von (2010)
Future Scenarios for German Photovoltaic Industry
98
Wulf, Torsten; Meißner, Philip; Stubner, Stephan (2010)
A Scenario-based Approach to Strategic Planning – Integrating Planning and
Process Perspective of Strategy
97
Wulf, Torsten; Stubner, Stephan; Blarr, W. Henning; Lindow, Corinna (2010)
Erfolgreich bleiben in der Krise
96
Wulf, Torsten; Stubner, Stephan (2010)
Unternehmernachfolge in Familienunternehmen – Ein Untersuchungsmodell zur
Analyse von Problemfeldern bei der Übergabe der Führungsrolle
95
Zülch, Henning; Pronobis, Paul (2010)
The Predictive Power of Comprehensive Income and Its Individual Components
under IFRS
94
Zülch, Henning; Hoffmann, Sebastian (2010)
Lobbying on Accounting Standard Setting in a Parliamentary Environment –
A Qualitative Approach
93
Hausladen, Iris; Porzig, Nicole; Reichert, Melanie (2010)
Nachhaltige Handels- und Logistikstrukturen für die Bereitstellung regionaler
Produkte: Situation und Perspektiven
92
La Mura, Pierfrancesco; Rapp, Marc Steffen; Schwetzler, Bernard; Wilms,
Andreas (2009)
The Certification Hypothesis of Fairness Opinions
91
La Mura, Pierfrancesco (2009)
Expected Utility of Final Wealth and the Rabin Anomaly
90
Thürbach, Kai (2009)
Fallstudie sekretaria - Vom New Economy-Internet-Startup zum
Old Economy-Verlagsunternehmen
89
Wulf, Torsten; Stubner, Stephan; Blarr, W. Henning (2010)
Ambidexterity and the Concept of Fit in Strategic Management – Which Better
Predicts Success?
88
Wulf, Torsten; Stubner, Stephan; Miksche, Jutta; Roleder, Kati (2010)
Performance over the CEO Lifecycle – A Differentiated Analysis of Short and
Long Tenured CEOs
87
Wulf, Torsten; Stubner, Stephan; Landau, Christian; Gietl, Robert (2010)
Private Equity and Family Business – Can Private Equity Investors Add to the
Success of Formerly Owned Family Firms?
86
Wulf, Torsten; Stubner, Stephan (2008)
Executive Succession and Firm Performance – the Role of Position-specific Skills
85
Wulf, Torsten; Stubner, Stephan (2008)
Unternehmernachfolge in Familienunternehmen – Untersuchungsmodell zur
Analyse von Problemfeldern bei der Übergabe der Führungsrolle
84
Wulf, Torsten; Stubner, Stephan (2008)
Executive Departure Following Acquisitions in Germany – an Empirical Analysis
of Its Antecedents and Consequences
83
Zülch, Henning; Gebhardt, Ronny (2008)
Politische Ökonomie der Rechnungslegung - Empirische Ergebnisse und kritische
Würdigung des Forschungsansatzes
82
Zülch, Henning; Löw, Edgar; Burghardt, Stephan (2008)
Zur Bedeutung von IFRS-Abschlüssen bei der Kreditvergabe von Banken an
mittelständische Unternehmen
81
Suchanek, Andreas (2007)
Die Relevanz der Unternehmensethik im Rahmen der Betriebswirtschaftslehre
80
Kirchgeorg, Manfred; Jung, Kathrin (2007)
User Behavior in Second Life: an Empirical Study Analysis and Its Implications for
Marketing Practice
79
Freundt, Tjark (2007)
Neurobiologische Erklärungsbeiträge zur Struktur und Dynamik des
Markenwissens
78
Wuttke, Martina (2007)
Analyse der Markteintrittsstrategien chinesischer Unternehmen in Mitteldeutschland am Beispiel von chinesischen Unternehmen im MaxicoM in Leipzig
77
La Mura, Pierfrancesco; Swiatczak, Lukasz (2007)
Markovian Entanglement Networks
76
Suchanek, Andreas (2007)
Corporate Responsibility in der pharmazeutischen Industrie
75
Möslein, Kathrin; Huff, Anne Sigismund (2006)
Management Education and Research in Germany
74
Kirchgeorg, Manfred; Günther, Elmar (2006)
Employer Brands zur Unternehmensprofilierung im Personalmarkt :
eine Analyse der Wahrnehmung von Unternehmensmarken auf der Grundlage
einer deutschlandweiten Befragung von High Potentials
73
Vilks, Arnis (2006)
Logic, Game Theory, and the Real World
72
La Mura, Pierfrancesco; Olschewski, Guido (2006)
Non-Dictatorial Social Choice through Delegation
71
Kirchgeorg, Manfred; Springer, Christiane (2006)
UNIPLAN Live Trends 2006 : Steuerung des Kommunikationsmix im
Kundenbeziehungszyklus ; eine branchenübergreifende Befragung von
Marketingentscheidern unter besonderer Berücksichtigung der Live
Communication. – 2., erw. Aufl.
70
Reichwald, Ralf; Möslein, Kathrin (2005)
Führung und Führungssysteme
69
Suchanek, Andreas (2005)
Is Profit Maximization the Social Responsibility of Business? Milton Friedman and
Business Ethics
68
La Mura, Pierfrancesco (2005)
Decision Theory in the Presence of Uncertainty and Risk
67
Kirchgeorg, Manfred; Springer, Christiane (2005),
UNIPLAN LiveTrends 2004/2005 : Effizienz und Effektivität in der Live
Communication ; eine Analyse auf Grundlage einer branchen-übergreifenden
Befragung von Marketingentscheidern in Deutschland
66
Kirchgeorg, Manfred; Fiedler, Lars (2004)
Clustermonitoring als Kontroll- und Steuerungsinstrument für
Clusterentwicklungsprozesse - empirische Analysen von Industrieclustern in
Ostdeutschland
65
Schwetzler, Bernhard (2004)
Mittelverwendungsannahme, Bewertungsmodell und Unternehmensbewertung
bei Rückstellungen
64
La Mura, Pierfrancesco; Herfert, Matthias (2004)
Estimation of Consumer Preferences via Ordinal Decision-Theoretic Entropy
63
Wriggers, Stefan (2004)
Kritische Würdigung der Means-End-Theorie im Rahmen einer Anwendung auf
M-Commerce-Dienste
62
Kirchgeorg, Manfred (2003)
Markenpolitik für Natur- und Umweltschutzorganisationen
61
La Mura, Pierfrancesco (2003)
Correlated Equilibria of Classical Strategic Games with Quantum Signals
60
Schwetzler, Bernhard; Reimund, Carsten (2003)
Conglomerate Discount and Cash Distortion: New Evidence from Germany
59
Winkler, Karsten (2003)
Wettbewerbsinformationssysteme: Begriff, Anforderungen, Herausforderungen
58
Winkler, Karsten (2003)
Getting Started with DIAsDEM Workbench 2.0: A Case-Based Tutorial
57
Lindstädt, Hagen (2002)
Das modifizierte Hurwicz-Kriterium für untere und obere Wahrscheinlichkeiten ein Spezialfall des Choquet-Erwartungsnutzens
56
Schwetzler, Bernhard; Piehler, Maik (2002)
Unternehmensbewertung bei Wachstum, Risiko und Besteuerung –
Anmerkungen zum „Steuerparadoxon“
55
Althammer, Wilhelm; Dröge, Susanne (2002)
International Trade and the Environment: The Real Conflicts
54
Kesting, Peter (2002)
Ansätze zur Erklärung des Prozesses der Formulierung von
Entscheidungsproblemen
53
Reimund, Carsten (2002)
Internal Capital Markets, Bank Borrowing and Investment: Evidence from German
Corporate Groups
52
Fischer, Thomas M.; Vielmeyer, Uwe (2002)
Vom Shareholder Value zum Stakeholder Value? Möglichkeiten und Grenzen der
Messung von stakeholderbezogenen Wertbeiträgen
51
Fischer, Thomas M.; Schmöller, Petra; Vielmeyer, Uwe (2002)
Customer Options – Möglichkeiten und Grenzen der Bewertung von
kundenbezogenen Erfolgspotenzialen mit Realoptionen
50
Grobe, Eva (2003)
Corporate Attractiveness : eine Analyse der Wahrnehmung von
Unternehmensmarken aus der Sicht von High Potentials
49
Kirchgeorg, Manfred; Lorbeer, Alexander (2002)
Anforderungen von High Potentials an Unternehmen – eine Analyse
auf der Grundlage einer bundesweiten Befragung von High Potentials und
Personalentscheidern
48
Kirchgeorg, Manfred; Grobe, Eva; Lorbeer, Alexander (2003)
Einstellung von Talenten gegenüber Arbeitgebern und regionalen Standorten :
eine Analyse auf der Grundlage einer Befragung von
Talenten aus der Region Mitteldeutschland
(not published)
47
Fischer, Thomas M.; Schmöller, Petra (2001)
Kunden-Controlling – Management Summary einer empirischen Untersuchung in
der Elektroindustrie
46
Althammer, Wilhelm; Rafflenbeul, Christian (2001)
Kommunale Beschäftigungspolitik: das Beispiel des Leipziger Betriebs für
Beschäftigungsförderung
45
Hutzschenreuter, Thomas (2001)
Managementkapazitäten und Unternehmensentwicklung
44
Lindstädt, Hagen (2001)
On the Shape of Information Processing Functions
43
Hutzschenreuter, Thomas; Wulf,Torsten (2001)
Ansatzpunkte einer situativen Theorie der Unternehmensentwicklung
42
Lindstädt, Hagen (2001)
Die Versteigerung der deutschen UMTS-Lizenzen – eine ökonomische Analyse
des Bietverhaltens
41
Lindstädt, Hagen (2001)
Decisions of the Board
40
Kesting, Peter (2001)
Entscheidung und Handlung
39
Kesting, Peter (2001)
Was sind Handlungsmöglichkeiten? – Fundierung eines ökonomischen
Grundbegriffs
38
Kirchgeorg, Manfred; Kreller, Peggy (2000)
Etablierung von Marken im Regionenmarketing – eine vergleichende Analyse der
Regionennamen "Mitteldeutschland" und "Ruhrgebiet" auf der Grundlage einer
repräsentativen Studie
37
Kesting, Peter (2000)
Lehren aus dem deutschen Konvergenzprozess – eine Kritik des „Eisernen
Gesetzes der Konvergenz“ und seines theoretischen Fundaments
36
Hutzschenreuter, Thomas; Enders, Albrecht (2000)
Möglichkeiten zur Gestaltung internet-basierter Studienangebote im Markt
für Managementbildung
35
Schwetzler, Bernhard (2000)
Der Einfluss von Wachstum, Risiko und Risikoauflösung auf den
Unternehmenswert
34
No longer available. There will be no reissue.
33
Löhnig, Claudia (1999)
Wirtschaftliche Integration im Ostseeraum vor dem Hintergrund der
Osterweiterung der Europäischen Union: eine Potentialanalyse
32
Fischer, Thomas M. (1999)
Die Anwendung von Balanced Scorecards in Handelsunternehmen
31
Schwetzler, Bernhard; Darijtschuk, Niklas (1999)
Unternehmensbewertung, Finanzierungspolitiken und optimale Kapitalstruktur
30
Meffert, Heribert (1999)
Marketingwissenschaft im Wandel – Anmerkungen zur Paradigmendiskussion
29
Schwetzler, Bernhard (1999)
Stochastische Verknüpfung und implizite bzw. maximal zulässige
Risikozuschläge bei der Unternehmensbewertung
28
Fischer, Thomas M.; Decken, Tim von der (1999)
Kundenprofitabilitätsrechnung in Dienstleistungsgeschäften –
Konzeption und Umsetzung am Beispiel des Car Rental Business
27
Fischer, Thomas M. (2000)
Economic Value Added (EVA) - Informationen aus der externen
Rechnungslegung zur internen Unternehmenssteuerung?
(rev. edition, July 2000)
26
Hungenberg, Harald; Wulf, Torsten (1999)
The Transition Process in East Germany
25
Vilks, Arnis (1999)
Knowledge of the Game, Relative Rationality, and Backwards Induction
without Counterfactuals
24
Darijtschuk, Niklas (1998)
Dividendenpolitik
23
Kreller, Peggy (1998)
Empirische Untersuchung zur Einkaufsstättenwahl von Konsumenten
am Beispiel der Stadt Leipzig
22
Löhnig, Claudia (1998)
Industrial Production Structures and Convergence: Some Findings
from European Integration
21
Schwetzler, Bernhard (1998)
Unternehmensbewertung unter Unsicherheit – Sicherheitsäquivalentoder Risikozuschlagsmethode
20
Fischer, Thomas M.; Schmitz, Jochen A. (1998)
Kapitalmarktorientierte Steuerung von Projekten im Zielkostenmanagement
19
Fischer, Thomas M.; Schmitz, Jochen A. (1998)
Control Measures for Kaizen Costing - Formulation and Practical Use
of the Half-Life Model
18
Schwetzler, Bernhard; Ragotzky, Serge (1998)
Preisfindung und Vertragsbindungen bei MBO-Privatisierungen in Sachsen
17
Schwetzler, Bernhard (1998)
Shareholder-Value-Konzept, Managementanreize und Stock Option Plans
16
Fischer, Thomas M. (1998)
Prozeßkostencontrolling – Gestaltungsoptionen in der öffentlichen
Verwaltung
15
Hungenberg, Harald (1998)
Kooperation und Konflikt aus Sicht der Unternehmensverfassung
14
Schwetzler, Bernhard; Darijtschuk, Niklas (1998)
Unternehmensbewertung mit Hilfe der DCF-Methode – eine Anmerkung
zum „Zirkularitätsproblem“
13
Hutzschenreuter, Thomas; Sonntag, Alexander (1998)
Erklärungsansätze der Diversifikation von Unternehmen
12
Fischer, Thomas M. (1997)
Koordination im Qualitätsmanagement – Analyse und Evaluation im Kontext der
Transaktionskostentheorie
11
Schwetzler, Bernhard; Mahn, Stephan (1997)
IPO´s: Optimale Preisstrategien für Emissionsbanken mit Hilfe von
Anbot-Modellen
10
Hungenberg, Harald; Hutzschenreuter, Thomas; Wulf, Torsten (1997)
Ressourcenorientierung und Organisation
9
Vilks, Arnis (1997)
Knowledge of the Game, Rationality and Backwards Induction
(Revised edition HHL Working Paper No. 25)
8
Kesting, Peter (1997)
Visionen, Revolutionen und klassische Situationen – Schumpeters
Theorie der wissenschaftlichen Entwicklung
7
Hungenberg, Harald; Hutzschenreuter, Thomas; Wulf, Torsten (1997)
Investitionsmanagement in internationalen Konzernen
- Lösungsansätze vor dem Hintergrund der Agency-Theorie
6
Hungenberg, Harald; Hutzschenreuter, Thomas (1997)
Postreform - Umgestaltung des Post- und Telekommunikationssektors in
Deutschland
5
Schwetzler, Bernhard (1996)
Die Kapitalkosten von Rückstellungen zur Anwendung des ShareholderValue-Konzeptes in Deutschland
4
Hungenberg, Harald (1996)
Strategische Allianzen im Telekommunikationsmarkt
3
Vilks, Arnis (1996)
Rationality of Choice and Rationality of Reasoning (rev. Edition, September 1996)
2
Schwetzler, Bernhard (1996)
Verluste trotz steigender Kurse? - Probleme der Performancemessung
bei Zinsänderungen
1
Meffert, Heribert (1996)
Stand und Perspektiven des Umweltmanagement in der betriebswirtschaftlichen
Forschung und Lehre