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  • 标题:Born in the USA: national origin effects on time allocation in US and Spanish professional basketball.
  • 作者:Berri, David J. ; Deutscher, Christian ; Galletti, Arturo
  • 期刊名称:National Institute Economic Review
  • 印刷版ISSN:0027-9501
  • 出版年度:2015
  • 期号:May
  • 语种:English
  • 出版社:National Institute of Economic and Social Research
  • 摘要:Keywords: national origin discrimination; professional basketball
  • 关键词:Football teams;Sports associations;Time management

Born in the USA: national origin effects on time allocation in US and Spanish professional basketball.


Berri, David J. ; Deutscher, Christian ; Galletti, Arturo 等


This paper examines national origin bias in professional basketball in both the North American National Basketball Association (NBA) and Spanish professional league (Liga ACB). Past studies into racial bias find mixed results. In contrast, our study provides consistent evidence that players born in the USA receive preferential treatment in both the USA and Spain in terms of receiving additional time on the court.

Keywords: national origin discrimination; professional basketball

JEL Classifications: Z2; Z22; J7

1. Introduction

Basketball, like American football, is strongly identified with the United States, partly because of US dominance in recent international competitions. (1) But unlike American football, basketball is truly a global game. For example, the New York Times reported in 2012 that about 300 million people play basketball in China. (2) That same year, Forbes.com reported that fans from 215 different countries were expected to watch the National Basketball Association (NBA) Finals and the games were broadcast in 47 languages. Forbes.com noted that the NBA expected 278 million fans to follow the Finals via social media networks. (3) Henry Abbott of ESPN, com reported in 2010 that surveys show that basketball is now the most popular sport among young people around the world. (4)

The identification of basketball with the United States, though, might lead to a bias in the evaluation of talent in the sport. It is possible that people might conclude that players from the United States are simply better at the game invented in their country. (5) In other words, although the game is played worldwide, basketball might suffer from what is called 'national origin discrimination'. (6)

The US Equal Employment Opportunity Commission (USEEOC) (7) defines 'national origin discrimination' as treating employees "unfavorably because they are from a particular country or part of the world, because of ethnicity or accent, or because they appear to be of a certain ethnic background (even if they are not)." In addition, the USEEOC notes that US law "forbids discrimination when it comes to any aspect of employment, including hiring, firing, pay, job assignments, promotions, layoff, training, fringe benefits, and any other term or condition of employment."

[FIGURE 1 OMITTED]

This paper estimates national origin discrimination in two of the most prominent international basketball leagues: the National Basketball Association (NBA) and the Spanish Liga ACB. The majority of players in the NBA are from the United States. The number of players born outside the United States, though, has increased from about 1 per cent in the early 1980s to nearly 20 per cent today and, as figure 1 illustrates, playing time by players not born in the USA increased from around 10 per cent to more than 20 per cent in the 21st century. (8)

The Spanish Liga ACB is among the most prominent professional leagues in the world. Spain won the silver medal in both the 2008 and 2012 Olympic Games. And the ACB is often ranked as one of the top basketball leagues in Europe. Like the NBA, the ACB also employs players from around the world. As table 1 notes, only about one-third of minutes in the ACB go to players born in Spain. Meanwhile, about 25 per cent of minutes go to players born in the USA and about 40 per cent go to players born in places other than Spain or the USA. As the ACB is one of the most lucrative European leagues, many American players choose to play in Spain, something that is likely to happen if they have not been drafted or signed to a guaranteed contract by a team in the NBA.

So in both the NBA and the Spanish ACB we see that workers are drawn from places around the world. Do differences in national origin impact player evaluation?

Studies into discrimination frequently examine wages or hiring. Because we do not have salary data from the ACB and only three years of performance data, (9) we will focus our attention on 'job assignments'. Specifically, we analyse whether or not national origin impacts the allocation of minutes per game in the NBA and ACB.

Our inquiry will be organised as follows. The next two sections will discuss rules to limit discrimination in the European Union, as well as past studies into discrimination in professional sports. This will be followed by a discussion of a model designed to explain the allocation of minutes in the NBA and ACB. This model will offer evidence of national origin discrimination in both leagues. In the conclusion we speculate about the source of the discrimination our study has uncovered.

2. Anti-discrimination in European sports: laws and detours

Laws in the United States with respect to national origin discrimination are designed to apply to both sports and non-sports industries alike. And in the United States teams do not have rules in place to limit foreign participation in sports. In Europe, on the other hand, professional sports teams have historically limited foreign participation. In basketball only two out of five players (40 per cent) on the court could be from foreign countries (Primault, 2007). This all changed with the Bosman ruling. In 1995 the European court of justice ruled case number C-415/93 in favour of Jean-Marc Bosman and confirmed the freedom of movement for workers. In addition it banned restrictions on the number of foreign players from teams that were allowed to deploy. (10)

Concerns regarding an influx of low priced foreign players led leagues to introduce a so-called home-grown quota which set a minimum number of local players that clubs must include on their squad. Irrespective of nationality, players are referred to as being home-grown if they spend a predetermined period of time (usually three years) with the club itself or any club within the respective national association. Precise regulations depend on the respective sport and league. From an economic standpoint clubs are given incentives to invest in their own talent instead of acquiring talent from foreign leagues (Frick, 2007). So there is no minimum quota for American or Canadian players in the NBA whereas the Spanish ACB requires that a minimum of five players (on a roster with twelve players) must be registered with a Spanish club for three seasons between the youth and senior categories for every team in the Spanish ACB league (FIBA International Migration Report 2012). Hence, national origin 'discrimination' is explicitly part of professional sports in Spain.

The rules adopted by the Spanish ACB make it clear without the need for any further empirical analysis that national origin plays a role in who is named for the team. However, we focus on whether or not national origin discrimination can be found in the allocation of playing time. Before turning to this, we briefly review past research on discrimination in professional sports.

3. Research on discrimination in professional basketball

The literature on wage discrimination in professional sports suggests that the existence and magnitude of racial discrimination depends on the observation period, estimation technique and the use of control variables. (11) More relevant for our approach is a closer look at the literature on nationality and its impact on remuneration. Unlike race--which is often difficult to judge by simply looking at the player (12)--where a player was born is a matter of fact. (13)

The study of national origin with respect to salaries in basketball tends to provide mixed results. While Eschker et al. (2004) as well as Yang and Lin (2012) find a negative salary effect for foreign players in the NBA, Hoffer and Freidel (2014) estimate a wage premium of around $900,000 per season. The observation period appears to be critical as a) signing international players became more lucrative in recent years and b) the outside options for players improved as international leagues expanded. One difficulty with the study of salaries is that pay is a function of how the decision-maker thinks the player will perform in the future. The researcher, though, can only use past performance as a proxy of this forecast. (14)

Playing time should be more closely linked to current performance. Although past research has not looked at national origin and playing time, there has been investigation of the link between race and playing time. For example, Schroffel and Magee (2012) offer evidence of an own-race bias by coaches, suggesting that with respect to the allocation of playing time coaches favour players who are of the same race. (15) This result, though, is contrasted by earlier findings from McCormick and Tollison (2001), who find no difference between black and white coaches with regard to playing time allocation of black and white coaches.

It is possible that the study of race suffers from an inability to identify it properly. Hence, we turn to our study of national origin and the allocation of playing time in the NBA and Spanish ACB.

4. National origin and time allocation

In order to measure the impact of origin on playing time allocation we apply regression and Oaxaca-Blinder decomposition techniques, following two different approaches for regular season basketball in the NBA and the ACB.

We estimate equation (1) to study the allocation of minutes in the NBA. The dependent variable is minutes per game. A player had to play in at least 30 games (out of 82) and average six minutes per game to be included for a season in the data set (for the ACB a player had to be on the court in at least 15 out of 34 games). In other words, players who hardly ever played during a season were excluded for that season and that season only. Naturally injuries and suspensions of players can reduce the playing time they receive but missing games for these reasons does not impact on minutes per game in a dramatic fashion. Players being injured or suspended for a longer period are missing due to our data restrictions.

The list of explanatory variables employed is given in table 2 and includes performance statistics, talent indicators and players' characteristics. This list begins with a collection of box score statistics for the season being examined. Except for TOPER, ADJFG, and FT, each of these statistics is a per 48 minute measure. As player duties and statistics vary by their position on the court, all measures have been adjusted for position played. (16) By calculating performance on a 48 minute level, measures may exceed the maximum number of fouls allowed according to the rules (which stands at six in the NBA and five in the ACB).

In addition to a player's performance on the court, we also consider a player's height (also adjusted for position played), games played, (17) age and age squared, (18) a dummy variable for players born in the USA, and draft position. Equation (1) illustrates the specific model being estimated while table 2 notes the descriptive statistics of the dependent and independent variables employed. As one can see, about 82 per cent of our sample were born in the United States.

Minutes per Game = [[gamma].sub.j] + [[beta].sub.1] Productivity + [[beta].sub.2] DraftExperience + [[gamma].sub.1] Height + [[gamma].sub.2] Games Played + [[gamma].sub.3] Age + [[gamma].sub.4] SquaredAge + [[gamma].sub.5] DUSA + [[epsilon].sub.i] (1)

Where Productivity = Vector of player statistics including Points, Adjusted Field Goal Percentage, Free Throw Percentage, Rebounds, Turnover, Steals, Assists, Blocks and Personal Fouls.

Draft Experience = Vector of variables interacting Draft Position and Experience.

Draft position is an indicator for expected talent. Prior research (19) has found that decision-makers in the NBA have difficulty ignoring sunk costs. Specifically, research has shown that draft position influences minutes per game even after one controls for on-court performance. We address this issue by multiplying a player's draft position by a dummy variable for each year of experience. We account for up to ten years of experience, so DFTXP represents ten additional independent variables. If sunk costs are ignored, draft position should stop being significant after decision-makers have been able to see how well a player actually plays in the NBA.

Equation (1) was estimated across data from the NBA from 2001/2-2013/14. (20) The results are reported in table 3. Before discussing the issue of national origin we should note that these results indicate that draft position appears to impact playing time in the NBA until a player's sixth season in the league. In other words, decisionmakers in the NBA do not appear to let sunk costs be sunk. In addition, age follows the expected pattern (21) while players who are relatively tall (relative to position played) receive more minutes. And except for TOPER, the performance factors are statistically significant and of the expected sign. (22)

Turning to national origin, one can see that a player born in the USA sees an estimated 1.2 additional minutes after controlling for performance and the other factors we believe impact minutes per game.

The approach offered in table 3 is often seen in the sports economics literature. Non-sports studies, though, often employ the Oaxaca-Blinder (Oaxaca, 1973; Blinder, 1973) decomposition method. This decomposes the gap in minutes per game into explained and unexplained portions. Here, the explained portion of the gap is characterised by differences in observed endowments. The remaining unexplained part is characterised by differences in returns to performance and may therefore capture discrimination. Table 4 indicates that minutes per game are 1.3 higher for players with US nationality.

This difference cannot be explained by differences in endowments: indeed, endowments would lead non USA players to play 0.09 minutes more than their USA counterparts. Differences in playing time not ascribed to differences in performance are 1.39 minutes per game in favour of players born in the USA.

In sum, the analysis provided in both tables 3 and 4 is consistent with the proposition that coaches in the NBA favour players born in the USA.

Next we turn to the Spanish ACB. Again, the sample for this league only consists of three seasons. In addition, because European leagues do not employ a reverse order draft, this factor is not considered. Otherwise equation (1) is being estimated for the Spanish league.

Table 5 reports descriptive statistics. Games in Europe consist of four 10 minute quarters. So the average minutes per game--which were close to 24 minutes per game in the NBA--are only around 20 minutes per game in the ACB. And because the games are shorter, the box score statistics are per 40 minutes. The remaining statistics are calculated in the same fashion as for the NBA.

The estimation of equation (1) for the ACB is reported in table 6.23 Like the NBA, minutes per game are related to performance statistics like points, adjusted field goal percentage, steals, assists, and personal fouls. Although evidence of height bias doesn't appear, games played does impact minute per game. In addition, age does appear to matter and follows the predicted pattern. (24)

Two dummy variables were used to capture national origin bias. One considered whether or not a player was born in the USA. The second looked at whether or not a player was born somewhere other than Spain or the USA.

As table 6 notes, both factors are significant at the 1 per cent level. And the size of the effect is comparable to the NBA sample. In the ACB a player born in the USA receives about 1.8 additional minutes per game after we control for performance and the other factors that impact playing time.

So players from the USA receive a boost in playing time in both the USA and Spain. Apparently, decision-makers are biased in favour of USA players. The success of USA players in international competition suggests that the USA has 'better' players but we control for performance. The fact that USA players are still given preferential treatment controlling for performance is suggestive of coach bias in favour of US players. It is notable, however, that non-US players born outside Spain also spend more time on court than Spanish born players, ceteris paribus, though the effect is not as large as the effect of being a USA player.

Table 7 presents results from the Oaxaca-Blinder decomposition where we compare US players with those from Spain, then Spanish versus non-Spanish non-US players.

In a comparison of players from Spain and the USA, differences in performance and other observables explain around 53 per cent of difference in playing time, leaving 47 per cent of the difference unexplained. Around 25 per cent of the difference in playing time between Spanish and non-Spanish non-US players is explained by observable differences.

Table 8 offers an estimate of the economic significance of the player performance statistics employed. The rules of the game indicate that personal fouls will tend to have the largest impact on minutes per game. Beyond fouls we find that scoring dominates in both leagues, and factors such as shooting efficiency, rebounds, and turnovers are of less importance to determine playing time. Prior research from Berri (2008) (25) indicates that wins in basketball are primarily determined by a team's ability to gain and keep possession of the ball (i.e. rebounds, turnovers, and steals) and shoot efficiently. Furthermore scoring totals--which tend to be the focus of decision-makers in basketball--are a function of a player's shot attempts. Shot attempts, though, are often just taken from teammates.

Table 8 notes that minutes per game--in both the NBA and ACB--are primarily dictated by a player's per-minute scoring. So the evaluation of players in both leagues is not only similar with respect to the evaluation of players born in the USA, we also see the same bias in favour of scorers.

5. Concluding observations

Studies of racial bias in sport tend to provide very mixed results. In contrast, our study of national origin bias provides a surprisingly clear result. In both the NBA and ACB we see evidence that players born in the USA receive preferential treatment even after we control for performance. The magnitude of the difference ranges between 1.2 and 1.4 minutes for the NBA and the ACB. Since NBA games are 48 minutes while ACB games are 40 minutes the impact is larger for the ACB. Given the average playing time of players in our sample of around 20 minutes per game, the unexplained difference in playing time accounts on average for an extra 7 per cent playing time given identical performance.

Our empirical study into the allocation of minutes in the NBA and the Spanish ACB indicates that national origin plays a role in the decisions of coaches. But such a study doesn't tell us why coaches are making such decisions.

Different theories offer reasons for why people discriminate against others. Following Becker (1957), it is possible that discrimination simply reflects the employer's tastes. Specifically, employer taste discrimination can cause players of different national origins to be treated differently if the employer prefers (or dislikes) some players for a reason independent of on-court performance.

Phelps (1972) and Arrow (1973) focus on statistical discrimination. Here, the employers have limited access to information about the abilities of the workers. Hence employers use observable characteristics which they believe indicate productivity, often race or nationality. Hence preferred treatment of players can be credited to the athletes' origin (Bryson, Rossi and Simmons, 2014).

The discrimination might also reflect the preferences of customers. The literature notes that one can treat professional sports as a customer-based service sector (Kahn, 2012). Consequently, consumers may prefer certain types of players, resulting in differentiated willingness to pay for trading cards (Stone and Warren, 1999), varying sizes of TV audience (Kanazawa and Funk, 2001) and arena attendance (Brown, Spiro and Keenan, 1991). Customer discrimination can hence directly impact the economic performance of teams. General Managers may consider consumers' tastes not only during hiring and contract negotiation (Hamilton, 1997). Coaches, also hired by the general managers, could follow the general manager's lead to give extended minutes to certain players in response to fan preferences (Kahn, 2012). (26)

Although we cannot conclusively isolate the source of the discrimination, we can note that statistical discrimination is unable to capture the results presented in this paper. After all, decision-makers in sports have an abundance of data on player performance. We cannot, though, differentiate between the preferences of employers and customers. One can simply argue that the allocation of playing time reflects the preferences of coaches. But one could also argue that players from the USA draw more fans to the arena or are increasingly attractive to team sponsors.

We should note that coaches consider factors beyond the extensive list of box score statistics we control for. For example, taking charges, deflecting passes and intimidating shooters are factors a coach could consider that are not in the standard box score. If US-born players perform systematically better than non-US born players with respect to the non-box score factors, then coaches might be justified in giving additional minutes to US born players. In addition, it is possible that externalities between US-born and non US-born players would justify difference in how players are treated. And again, our data would not capture such an effect.

Perhaps further research could look into these non-box score factors. In addition, future research could examine whether the source of any discrimination uncovered is driven by employers' or customers' tastes. One could also expand this study to more leagues outside Spain and the USA. In sum, future research might examine whether or not leagues around the world exhibit a preference for players born in the nation that invented basketball.

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NOTES

(1) The United States dominated the first Olympic competitions, winning the gold medal in each of the summer games played from 1936 to 1968. In 1972 the Soviet Union managed to win a gold, an outcome repeated in the Soviet Union in 1988. And Yugoslavia won in 1980 (when the US boycotted the game). Professionals began playing in 1992, and with that change the USA again returned to dominance. Except for the 2004 Olympics (where the gold went to Argentina), the gold medal has gone to Team USA in each of the games played from 1992 to 2012. For more see http://www.sports-reference.com/olympics/sports/ BAS/mens-basketball.html

(2) http://www.nytimes.com/2012/02/05/magazine/NBA-in China. html?n=Top/Reference/Times per cent20Topics/People/Y/ Yardley, per cent20Jim?ref=jimyardley&_r=1&pagewanted=all.

(3) http://www.forbes.com/sites/aliciajessop/2012/06/14/the-surge-of-the-nbas-international-viewership-and- popularity/.

(4) http://espn.go.eom/blog/truehoop/post/_/id/19814/is-basketball-taking-over-the-world.

(5) During the winter of 1891-92, James Naismith invented the sport of basketball at Springfield College in Massachusetts. For the story of the origins of basketball one is referred to the website of Springfield College: http://www.springfieldcollege. edu/welcome/birthplace-of-basketball/index#.VFUn5_nF_h4.

(6) Discrimination against individuals due to their origin or religious beliefs remains a phenomenon in modern societies. Becker (1957) outlines potential sources of discrimination in labour markets where workers are treated differently despite being equally qualified. The availability of workers' performance, individual characteristics and salaries in professional sports data means it is a fruitful setting in which to test for labour market discrimination (Kahn, 2000).

(7) http://www.eeoc.gov/laws/types/nationalorigin.cfm.

(8) The international players employed by the NBA examined in our dataset are drawn from 54 different nations. European nations are the most common nationality observed (outside the USA). But players are also drawn from Africa, South America, Asia, Australia, and other North American nations. The international search for talent is likely related to the issue of the "short supply of tall people" (see Berri et al., 2005).

(9) For the ACB we are relying on data from RealGM.com. This site reports for the 2011/12-2013/14 seasons data on performance and personal characteristics of the players employed (including age and national origin). For the NBA we will be utilising data from basketball-reference.com. This site also reports performance data and personal characteristics.

(10) The liberalisation of the labour market in sports was followed in detail by the economic literature to analyse the impact on player migration and remuneration as well as its consequences for clubs and leagues (e.g. Milanovic, 2005; Frick, 2007, 2009; Groot, 2008; Lee & Fort, 2012).

(11) Berri (2006) offered an extensive literature review of past studies. This review illustrated that results varied in the literature. More recently, Robst et al. (2011) failed to find evidence of discrimination while Groothuis and Hill (2013)--in a comprehensive study of wage and employment discrimination in the NBA--noted that evidence of racial discrimination depended on how the model employed was constructed.

(12) This point was made by Fort and Gill (2000). It was also noted in a study of bias in the sports media by Berri, Van Gilder, and Fenn (2014).

(13) However, nationality can be changed by the player to circumvent restrictions for usage of foreign players. If a player has citizenship for the country he is playing in, he is treated as a domestic in our data.

(14) In addition--as noted in Berri and Schmidt (2010) and Berri, Schmidt and Brook (2006)--the evaluation of this past performance is inconsistent with how that performance actually impacted outcomes in the NBA.

(15) Price et al. (2013) do not find any own-race bias for players, showing that cooperative behaviour on the basketball court is independent of teammates' race.

(16) Performance relative to position is calculated following the approach taken originally in Berri, Schmidt, and Brook (2006). Specifically, the average per 48 minute performance in a player's primary position was subtracted from his per 48 minute performance. Then the overall average per 48 minute performance (across all positions) was added back to the outcome of the first step. This gives us a measure of performance that can allow comparisons across all positions. Without this step the inclusion of players at different positions is problematic, since performance varies systematically across position played. Specifically, big men tend to get rebounds and not many assists or steals. Smaller players tend to get fewer rebounds and more assists and steals.

(17) Games played are included as a proxy for injury. But it also reflects the evaluation of the coach.

(18) The squared term is necessary because we suspect a younger player might earn more minutes with experience while older players might see playing time decline with age (Fair, 1994).

(19) Prior research into this topic was offered by Staw and Hoang (1995) and Camerer and Weber (1999). Each of these authors examined the escalation of commitment in the NBA, defined by Camerer and Weber (pp. 59-60) as follows: "when people or organizations who have committed resources to a project are inclined to 'throw good money after bad' and maintain or increase their commitment to a project, even when its marginal costs exceed marginal benefits." Both of the aforementioned studies offer evidence that, after controlling for the performance of the player, where a player was chosen in the draft still impacts the amount of playing time the player receives after the first two years of the player's career and the completion of the rookie contract. Such a result indicates that NBA decision-makers cannot let sunk costs go.

(20) The estimation of this model included team specific fixed effects and robust standard errors.

(21) The model indicates minutes per game reach a maximum at 26.6 years of age.

(22) TOPER is found to be positive and significant. This means that players are rewarded for being more careless with the ball. Many 'stars' tend to score in large numbers but are also somewhat careless with the ball, a result that is consistent with a failure of decision-makers to evaluate inefficient stars (as detailed in Berri and Schmidt, 2010).

(23) The estimation of this model includes team specific fixed effects and robust standard errors.

(24) The model indicates that minutes per game peak around 32.1 years of age in the ACB.

(25) This was also noted in Berri, Schmidt, and Brook (2006), Berri, Brook, and Schmidt (2007), and Berri and Schmidt (2010). The focus on scoring has also been seen in past studies of the NBA draft (see Berri, Brook, and Fenn, 2010) and the media's voting for the NBA's MVP award (see Berri, Fenn, and Van Gilder, 2012).

(26) Even if no fan preference exists towards a certain type of player it can still be favourable to award additional playing time in response to referee bias. Such behaviour would be consistent with Price and Wolfers (2010), who offer evidence that calls made by referees are impacted by the race of the players in the game.

David J. Berri, Department of Economics and Finance, Southern Utah University. E-mail: berri@suu.edu.

Christian Deutscher, Department of Sport Science, University of Bielefeld. E-mail: christian.deutscher@uni-bielefeld.de.

Arturo Galletti, Senior Calibration Manager, Coopervision, Juana Diaz, and contributor to www.boxscoregeeks.com. E-mail: Arturo.galletti@gmail.com.
Table 1. Percentage of minutes played by players of
different national origins in the ACB: 2011 /12-2013/14

Season     Spain     USA     Non Spain/Non USA
            (%)      (%)            (%)

2013/14     34.5     22.8           42.6
2012/13     32.7     27.7           39.6
2011/12     32.0     26.9           41.0
Average     33.1     25.8           41.1

Source: RealGM.com.

Table 2. Descriptive statistics of variables in equation (1) for the
NBA 2001 /2-2013/14

Dependent variable                    Label      Mean

Minutes per game                      MGM         23.837
Independent variables
Points (a)                            PTS         18.486
Adjusted Field Goal Percentage (b)    ADJFG        0.486
Free Throw Percentage (a)             FT           0.738
Rebounds (a)                          REB          9.145
Turnover Percentage (d)               TOPER       13.764
Steals (a)                            STL          1.361
Assists (a)                           AST          2.894
Blocked Shots!3)                      BLK          1.149
Personal Fouls!3)                     PF           4.729
Draft Position, year 1                DFT1         3.337
Draft Position, year 2                DFT2         3.239
Draft Position, year 3                DFT3         2.839
Draft Position, year 4                DFT4         2.732
Draft Position, year 5                DFT5         2.332
Draft Position, year 6                DFT6         2.052
Draft Position, year 7                DFT7         1.844
Draft Position, year 8                DFT8         1.556
Draft Position, year 9                DFT9         1.426
Draft Position, year 10               DFT10        l.117
Height (a)                            Height      79.012
Age                                   Age         26.578
Age, Squared                          SQAge      724.409
Games Played                          GM          63.363
Dummy Variable, Bom in USA            DUSA         0.812

                                       SD           Minimum

Minutes per game                        8.993        6.091
Independent variables
Points (a)                              5.462        3.31
Adjusted Field Goal Percentage (b)      0.053        0.224
Free Throw Percentage (a)               0.112        0
Rebounds (a)                            2.005        1.32
Turnover Percentage (d)                 3.997        1.755
Steals (a)                              0.520        0.023
Assists (a)                             1.667       -3.792
Blocked Shots!3)                        0.707       -0.825
Personal Fouls!3)                       1.355        1.309
Draft Position, year 1                  1 1.415      0
Draft Position, year 2                 11.092        0
Draft Position, year 3                 10.322        0
Draft Position, year 4                  9.386        0
Draft Position, year 5                  9.386        0
Draft Position, year 6                  8.739        0
Draft Position, year 7                  8.350        0
Draft Position, year 8                  7.608        0
Draft Position, year 9                  7.287        0
Draft Position, year 10                 6.254        0
Height (a)                              1.617       70.182
Age                                     4.248       18
Age, Squared                          236.133      324
Games Played                           15.520       30
Dummy Variable, Bom in USA              0.391        0

                                      Maximum

Minutes per game                        43.7
Independent variables
Points (a)                              40.073
Adjusted Field Goal Percentage (b)       0.729
Free Throw Percentage (a)                1
Rebounds (a)                            19.537
Turnover Percentage (d)                 36.188
Steals (a)                               3.581
Assists (a)                             10.912
Blocked Shots!3)                         6.35
Personal Fouls!3)                       13.104
Draft Position, year 1                  61
Draft Position, year 2                  61
Draft Position, year 3                  61
Draft Position, year 4                  61
Draft Position, year 5                  61
Draft Position, year 6                  61
Draft Position, year 7                  61
Draft Position, year 8                  61
Draft Position, year 9                  61
Draft Position, year 10                 61
Height (a)                              85.754
Age                                     41
Age, Squared                          1681
Games Played                            82
Dummy Variable, Bom in USA               /

Notes: (a) Each of these variables is calculated relative to position
played, (b) Adjusted field goal percentage--or effective field goal
percentage--is calculated as follows: [(Points--Free Throws Made)-
Field Goal Attempts], This measure takes into account that players can
shoot from two point and three point range. It is detailed at
basketball-reference.com. (c) Free throw percentage = free throws
made-free throws attempted, (d) Turnover percentage is calculated as
follows: [(Turnovers)-(Turnovers + Field Goal Attempts + 0.44 * Free
Throw Attempts)] * 100. This measure--detailed at basketball-
reference.com--allows one to compare turnovers across a sample of
players who handle the ball more or less.

Table 3. Explaining minutes per game for the NBA: 2001 /2-2013/14

Independent variable              Label      Coefficient

Points                            PTS          0.413 ***
Adjusted Field Goal Percentage    ADJFG        8.802 ***
Free Throw Percentage             FT           8.109 ***
Rebounds                          REB          0.347 ***
Turnover Percentage               TOPER        0.049 *
Steals                            STL          0.841 ***
Assists                           AST          0.823 ***
Blocked Shots                     BLK          0.437 **
Personal Fouls                    PF          -2.478 ***
Draft Position, year 1            DFT1        -0.114 ***
Draft Position, year 2            DFT2        -0.088 ***
Draft Position, year 3            DFT3        -0.062 ***
Draft Position, year 4            DFT4        -0.057 ***
Draft Position, year 5            DFT5        -0.049 ***
Draft Position, year 6            DFT6        -0.039 ***
Draft Position, year 7            DFT7        -0.021
Draft Position, year 8            DFT8        -0.026 *
Draft Position, year 9            DFT9         0.009
Draft Position, year 10           DFT10       -0.009
Height                            Height       0.329 ***
Age                               Age          2.012 ***
Age, Squared                      SQAge       -0.038 ***
Games Played                      GM           0.134 ***
Dummy Variable, Bom in USA        DUSA         1.159 ***
Constant term                                -50.598 ***
Season Dummies                               included
Observations                      4521
R-squared                         0.67

Independent variable              Standard     t-statistic
                                  error

Points                            0.022         19.05
Adjusted Field Goal Percentage    1.862          4.73
Free Throw Percentage             1.098          7.38
Rebounds                          0.063          5.51
Turnover Percentage               0.027          1.78
Steals                            0.254          3.31
Assists                           0.072         11.39
Blocked Shots                     0.167          2.61
Personal Fouls                    0.083        -29.72
Draft Position, year 1            0.009        -12.82
Draft Position, year 2            0.008        -10.93
Draft Position, year 3            0.009         -6.73
Draft Position, year 4            0.008         -6.78
Draft Position, year 5            0.010         -5.01
Draft Position, year 6            0.011         -3.44
Draft Position, year 7            0.016         -1.31
Draft Position, year 8            0.014         -1.79
Draft Position, year 9            0.015          0.60
Draft Position, year 10           0.014         -0.61
Height                            0.067          4.90
Age                               0.312          6.45
Age, Squared                      0.006         -6.82
Games Played                      0.008         16.84
Dummy Variable, Bom in USA        0.226          5.12
Constant term                     5.484         -9.23
Season Dummies
Observations
R-squared

Notes: * significant at the 10 per cent level, ** significant at the 5
per cent level. ***significant at the 1 per cent level.

Table 4. Decomposition results for the NBA: 2001/2-
2013/14

Independent variable         Coeff.       Standard    z-stat.
                                            error

USA                        24.08 ***        0.15       160.40
Non USA                    22.77 ***        0.29       78.83
Difference                  1.31 ***        0.33        4.03
Explained                  -0.08            0.29        0.28
Unexplained                 1.39 ***        0.201       6.91
Observations total          4521
Observations USA            3671
Observations non USA        850

Notes: * significant at the 10 per cent level, ** significant at the 5
per cent level. *** significant at the 1 per cent level.

Table 5. Descriptive statistics of variables in equation (1) for the
ACB 2011/12-2013/14

Dependent variable                Label        Mean         SD

Minutes per game                  MGM          19.830         5.887
Independent variables

Points                            PTS           14.326        4.041
Adjusted Field Goal               ADJFG          0.506        0.07
  Percentage
Free Throw Percentage             FT             0.75         0.12
Rebounds                          REB            5.456        1.716
Turnover Percentage               TOPER         16.341        5.348
Steals                            STL            1.429        0.485
Assists                           AST            2.986        1.254
Blocked Shots                     BLK            0.375        0.535
Personal Fouls                    PF             4.096        1.359
Height                            Height        78.349        1.52
Age                               Age           27.76         4.277
Age, Squared                      SQAge        788.881      237.931
Games Played                      GM            31.348        6.994
Dummy Variable, Bom in USA        DUSA           0.231        0.422
Dummy Variable,
Born outside Spain (not USA)      DnonSpain      0.417        0.493

Dependent variable                Minimum      Maximum

Minutes per game                    5.135        33.815
Independent variables

Points                              2.548        26.133
Adjusted Field Goal                 0.211         0.728
  Percentage
Free Throw Percentage               0             1.000
Rebounds                           -0.641        11.813
Turnover Percentage                 0            41.91279
Steals                              0.059         3.078
Assists                            -1.746         8.28
Blocked Shots                      -0.843         3.234
Personal Fouls                      1.108        13.099
Height                             69.0          87.00
Age                                18.0          39.0
Age, Squared                      324.0        1521.0
Games Played                       15.0          46.0
Dummy Variable, Bom in USA          0.0           1.0
Dummy Variable,
Born outside Spain (not USA)        0.0           1.0

Table 6. Explaining minutes per game for the ACB: 2011/12-2013/14

Independent variable              Label           Coefficient

Points                            PTS                 0.397 ***
Adjusted Field Goal Percentage    ADJFG               5.592
Free Throw Percentage             FT                  3.076 *
Rebounds                          REB                 0.1556
Turnover Percentage               TOPER              -0.075
Steals                            STL                 1.047 **
Assists                           AST                 0.908 ***
Blocked Shots                     BLK                 0.247
Personal Fouls                    PF                 -1.429 ***
Height                            Height             -0.021 ***
Games Played                      GM                  1.554 ***
Age                               Age                 1.470 *
Age, Squared                      SQAge              -0.023 *
Dummy Variable, Born in USA       DUSA                1.791 ***
Dummy Variable, Born outside
  Spain (not USA)                 DnonSpain           0.963***
Constant Term                                        -0.464
Season dummies                                     included
Observations                      596
R-squared                         0.487

Independent variable                Standard      t-statistic
                                      error

Points                                 0.053        7.51
Adjusted Field Goal Percentage         3.23398      1.69
Free Throw Percentage                  1.549        2.04
Rebounds                               0.099        1.58
Turnover Percentage                    0.036       -2.11
Steals                                 0.391        2.68
Assists                                0.190        4.79
Blocked Shots                          0.311        0.79
Personal Fouls                         0.136      -10.70
Height                                 0.061       -3.42
Games Played                           0.034        4.56
Age                                    0.705        2.08
Age, Squared                           0.013       -1.79
Dummy Variable, Born in USA            0.445        4.02
Dummy Variable, Born outside
  Spain (not USA)                      0.289        3.33
Constant Term                     12,225            0.97
Season dummies
Observations
R-squared

Notes: * significant at the 10 per cent level, ** significant at the 5
per cent level. *** significant at the I per cent level.

Table 7. Decomposition results for the ACB: 2011/12-2013/14
Independent variable

                    Coeff.      Standard    z-stat.
                                 error

                           Spain vs non Spain

Spain             18.77 ***       0.42        43.9
non Spain         20.43 ***       0.28       71.83
Difference        -1.66 ***       0.51       -3.23
Explained           -0.42         0.41       -1.01
Unexplained       -1.24 ***       0.4        -3.14
Obs. total           596
Obs. Spain           209
Obs. non Spain       387

Independent variable

                    Coeff.      Standard    z-stat.
                                 error

                              Spain vs USA

Spain             18.77 ***       0.42        43.9
USA               22.05 ***       0.47       47.28
Difference        -3.28 ***       0.63       -5.18
Explained         -1.75 ***       0.5         -3.5
Unexplained       -1.53 ***       0.51       -3.01
Obs. total           347
Obs. Spain           209
Obs. USA             138

Notes: * significant at the 10 per cent level. ** significant at the 5
per cent level. *** significant at the 1 per cent level.

Table 8. Impact of one standard deviation on minutes per
game in NBA and ACB

Independent variable              Impact of a one     Impact of a one
                                     standard            standard
                                 deviation change    deviation change
                                   in NBA sample       in ACB sample

Personal fouls                         -3.22               -1.99
Points                                 2.48                1.65
Assists                                 1.3                1.08
Rebounds                               0.63           not significant
Free throw percentage                  0.47           not significant
Adjusted field goal percentage         0.43                0.46
Steals                                 0.34                 0.5
Turnover percentage                    0.28           not significant
Blocked shots                          0.22           not significant

Note: Estimated coefficient from above regressions must be significant
at the 10 per cent level or better to be included in table.
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