No pain, no gain? Effort and productivity in professional soccer.
Wicker, Pamela ; Prinz, Joachim ; Weimar, Daniel 等
Introduction
The recruitment of skilled personnel is a central role of firms
(Lazear, 2000) and can be regarded as a war of talent. Many firms do not
(only) advertise positions, they look for skilled people using scouts
and head hunters. Potentially new staff is selected based on a set of
selection criteria (Spence, 1973). This procedure is similar in
professional soccer where potential players are selected by talent
scouts. These scouts identify players based on specific selection
criteria. Typically, these selection criteria are related to human
capital, talent/ability (Bryson, Frick, & Simmons, 2012), leadership
skills (Deutscher, 2009), productivity (Franck & Nuesch, 2012),
cognitive skills (Deutscher, Frick, & Prinz, 2012), and effort (or
synonymously diligence). For example, one club may seek a young player
with good scoring performance that fits into the specific system of the
club. Yet, players cannot control all factors; human capital and talent
can hardly be changed by players and productivity may differ among
players of different positions. While this may not be an issue for
talented and productive players, less talented players may experience
problems in being recruited. Nevertheless, they can control the level of
effort they exert within the game. This effort may convince scouts to
recruit the players. For example, the players could put forth effort in
their endurance and sprint abilities. This could be a way of signaling
their quality to scouts. Since a player's quality is typically
reflected in his salary or market value, the question arises to what
extent a player's market value is determined by a player's
effort. If there is a positive relationship between effort and market
value, signaling effort could be a marketing strategy especially for
less talented players.
Therefore, the purpose of this study is to examine the influence of
effort on a player's market value in professional soccer. Two
aspects are important with regard to measuring effort. First, the
technical equipment must be available. In professional soccer, technical
advancements in recording routes and running distances represent an
innovative approach of measuring effort. Second, measurement costs have
to be low to ensure that pay (or market value) is a function of effort
(Lazear & Shaw, 2007). This study uses recent technological
innovations to measure player effort and advances the following main
research question: What impact does effort have on a player's
market value (controlling for human capital and ability)? Based on
signaling theory the relationship between effort and market value is
conceptualized. Secondary data on player characteristics from the
2011/2012 season and the first half of the 2012/2013 season of the
German Bundesliga were collected. Most importantly, this study tries to
disentangle ability and effort by providing an empirical discussion of
Nicholls's (1976) statement: "Effort is virtuous, but
it's better to have ability" (p. 306).
Literature Review
Previous research has identified individual and team-related
factors that influence the salary of players (Hakes & Sauer, 2006,
2007; Wallace, 1988; for an overview of soccer studies see Frick, 2007).
With regard to team factors, it was documented that they explain 14% of
the explained variance in salaries in the National Basketball
Association (NBA; Wallace, 1988). In soccer, a player's salary was
higher when his team qualified for a European competition (e.g., Huebl
& Swieter, 2002) or when he played in a European or World Cup
tournament, providing support for a shop window effect (Simmons &
Deutscher, 2012). Furthermore, players who were part of their respective
national teams received higher salaries (Franck & Nuesch, 2008;
Lehmann & Schulze, 2008; Lucifora & Simmons, 2003). Moreover,
some teams were found to pay their players a premium salary (Frick,
2008). For example, teams such as Real Madrid or the Los Angeles Lakers
tend to pay higher average salaries than other teams and therefore
athletes of these teams benefit financially, supporting the need to
include team dummies in salary equations (Garcia-del-Barrio & Pujol,
2007; Wallace, 1988).
The individual factors can be further divided into human capital
and productivity (e.g., performance, talent). Initiated by seminal work
on human capital theory (e.g, Becker, 1962; Mincer 1974; Schultz, 1961)
numerous studies attempted to explain salary dispersion using human
capital measures. In line with this theory, age and experience were
found to have an impact on salary (Franck & Nuesch, 2008, 2012;
Lehmann & Schulze, 2008; Lucifora & Simmons, 2003; Wallace,
1988). Rookies tend to earn less than experienced players (Hakes &
Turner, 2011) and players who are eligible for arbitration earn more
than their counterparts (Hakes & Sauer, 2006). Furthermore, month of
birth (Ashworth & Heyndels, 2007), position (Frick, 2007;
Garcia-del-Barrio & Pujol, 2007; Lehmann & Schulze, 2008;
Wallace, 1988), and nationality (Franck & Nuesch, 2008) were found
to influence player salaries. Also, a positive relationship between
contract duration and salary could be observed (Krautmann &
Oppenheimer, 2002); yet, it must be considered that a player's
performance seems to be associated with his contractual situation
(Stiroh, 2007). Previous studies found support for shirking because
players' performance improved significantly in the year before
signing a new contract, but declined afterwards (e.g., Krautmann &
Solow, 2009; Stiroh, 2007).
The pay-productivity relationship has been extensively investigated
in previous research supporting a positive relationship (e.g., Miceli
& Huber, 2009). In detail, playing time (Hakes & Sauer, 2006)
and offensive performance (Frick, 2008; Lehmann & Schulze, 2008;
Wallace, 1988) were found to have a positive impact on salary. Previous
research also showed that the number of appearances on the field per
season, games played for the team, and years employed by the club were
positively associated with a player's income (Franck & Nuesch,
2008, 2012; Frick, 2008; Garcia-del-Barrio & Pujol, 2007; Lucifora
& Simmons, 2003; Vincent & Eastman, 2009; Wallace, 1988). The
draft position, used as an indicator for a player's talent in North
American professional sports leagues, also contributed to the
explanation of salaries. The draft number has the expected negative
influence on player salaries: The earlier a player is selected in the
amateur draft by an NBA team, the higher his income (Vincent &
Eastman, 2009; Wallace, 1988).
The value of different player skills was also examined,
particularly in baseball. For example, Bradbury (2007) showed that the
value of pitchers is determined by individual run prevention, while the
defensive output of team production does not play a role. The
pay-performance relationship has received further attention after
Michael Lewis's (2003) controversially discussed publication
Moneyball suggesting that some skills of baseball players are
undervalued by the baseball labor market. His Moneyball hypothesis has
been tested empirically suggesting that some skills such as on-base
percentage, hitting for average, hitting for power, and plate discipline
have been undervalued in several seasons before the Moneyball
publication (Hakes & Sauer, 2006, 2007). While different skills are
critical in soccer, the finding that specific skills were undervalued
may also inform the present research.
In addition to the above mentioned factors, a player's effort
may influence his salary. General labor market research suggested that
individual effort was associated with salary. For example, theoretical
work by Lazear and Shaw (2007) supported an effect of effort on pay:
"Very large pay spreads induce high effort [...] If individuals are
working at a very high level of intensity [...] it will be necessary to
compensate those employees at a very high level" (p. 95). Moreover,
empirical studies document that effort (measured by e.g., absenteeism,
unpaid overtime work, and working more than others) was associated with
wages and the contractual situation (Engellandt & Riphahn, 2005;
Givord & Wilner, 2009; Riphahn, 2004; Treble, 2001). However,
evidence of the impact of effort on salary was rare. One reason for this
scarcity could be the difficulty to disentangle ability and effort.
While sound measures of ability have already been provided in previous
research, it was difficult to quantify an individual's effort.
These difficulties also apply to the professional sports sector.
Independent of the sport under investigation, the impact of effort on
income has been largely neglected in previous research. This study tries
to address these shortcomings by including effort in the income equation
and by using innovative effort measures. Moreover, it provides a
theoretical explanation as to why effort can be critical to the market
value of soccer players. In fact, effort could serve as a signaling
device which could be particularly relevant to less talented, less
productive, and less experienced players to compensate missing talent
(Hau & Salili, 1996).
Effort as a Potential Signaling Device
Although the professional team sport industry is characterized by
high performance visibility and trackability (Kahn, 2000; Rosen &
Sanderson 2000), it is unclear from the outset of the employer-employee
relationship whether the partnership becomes profitable. From the
firm's (principal) point of view it is risky to employ a new player
(agent); hence the firm might face an adverse selection problem.
Typically, the firm initializes some screening mechanism in order to
reveal a potential player's true productivity to make sure that the
individual is worth his potential salary. On the other hand, the player
faces the risk of being mismatched with the potential firm since his
expectations might not be met by the employer. As in many other (labor)
markets with imperfect information, an individual may seek the highest
wage among possible firms depending on given talent. Opposite to the
firm's point of view, the agent wants to maximize his income, while
the firm tries to motivate the agent to put forth more effort. As
outlined above, an agent's wage is presumably driven by his stock
of knowledge and abilities (Becker, 1962).
Signaling theory (Spence, 1973) offers an alternative explanation
as to why a player's income may depend to some extent on effort.
Individual effort forms the signal in the following argument which is
based on two main assumptions. First, there is some degree of imperfect
information among a football club and a potential agent offering his
service to the team. Second, the soccer club has good knowledge about an
incoming player's abilities from watching his past productivity in
a rival team. Yet, it is still questionable whether the (talented)
player finally fits into an existing team, that is, into the team's
production function. For example, it is highly uncertain whether a
player who performs excellent with team A is of similar value to team B.
Given this uncertainty, teams as well as agents have incentives to
reduce information deficits by searching for and sending additional
signals that are independent of teammates' quality. It is posited
that players' effort statistics, providing information about
running distance and sprints per match, serve as an independent and
simultaneously credible indicator. These indicators are of high interest
since a player's endurance level on the pitch is much more a
function of effort and discipline than talent/ability. Moreover,
improving individual physical fitness can be improved in training
sessions and again this is principally influenced by hours of running
input. Overall, it is believed that physical fitness is strongly
correlated with training hours (effort) and is consequently a costly
signal that provides inherent information about a player's
intrinsic motivation, which then improves quality.
In this context, the question arises if effort substitutes or
complements ability. It could be that players have the possibility to
compensate talent by putting forth over proportional effort. This may be
specifically the case for less talented players. For example, players
who are not able to read the game have to run more. This way they can
compensate missing talent by putting forth more (running) effort.
Conversely, any given level of performance can neither be reached solely
by talent nor effort supporting a complementary relationship of effort
and productivity. Both inputs (i.e., skill and hard work) are necessary
to produce reasonable outputs. Particularly in professional sports, it
might be argued that productivity itself is a necessary, but not
sufficient condition. A certain ability level is needed to play for a
Bundesliga team. Yet, once players play in the Bundesliga, effort may
complement their productivity on the field. Some athletes put a lot of
effort in reaching their full potential, while equally talented players
are not able (or willing) to retrieve their utmost potential because
they do not put sufficient effort into training. Yet, this is needed to
be in a position to put forth effort on the field. For example, if
players do not make intensive runs, they are not in the position to
strike the ball at the goal or to properly defend a counterattack
supporting the notion that effort complements productivity.
In summary, under the assumption of imperfect information and the
fact that individual productivity depends on teammates'
productivity (Idson & Kahane, 2000) effort should be a valid signal
to the team in order to select agents of different and unknown future
productive efficiency. Hence, this study assumes that team managers
observe the performance of players imperfectly, but view effort measures
as proxies for the potential willingness of players to put forth
extraordinary effort. In a world of imperfect information, players may
have an incentive to invest in painful effort parameters in an attempt
to signal their true ability. Importantly, these parameters are mostly
independent of teammates' quality. Consequently, putting forth more
(running) effort can be interpreted as signaling device for higher
productivity, which should in turn increase a player's market value
(Cahuc & Zylberberg, 2004).
Method
Data collection
The database contains information of players from the 2011/2012
season and the first half of the 2012/2013 season of the German
Bundesliga (round-robin competition). The panel unit of analysis is half
season (n=3). The data were collected from two different sources: (1)
Market values and individual information about players were taken from a
German transfer market website (www.transfermarkt.de); and (2)
performance and effort data were made available from the website of the
German Football League (Deutsche Fufiball Liga [DFL]; www.dfl.de).
Including earlier seasons in the sample is not possible since effort
data have only been available since the beginning of the 2011/2012
season.
The website www.transfermarkt.de is a German community-based
information site that provides performance statistics as well as market
values of soccer players. Market values are published at the end of each
half season. They are assessed and discussed by community members and
experts. Only proven and confident members are allowed to enter the
discussion forum about market values. At the end of the discussion the
head of the internet forum sets the final market value. While this
procedure seems subjective, previous research supports the validity of
market values because market values were highly correlated with salaries
(which are not disclosed in Germany; e.g., Franck & Nuesch, 2012;
Frick, 2007). Therefore, they should represent an adequate proxy of
salaries. In an attempt to avoid reverse causality problems between
market values and performance data (Angrist & Pischke, 2008), market
values were gathered at the end of each half season (February/July),
while performance variables represent average values from within each
half season, i.e., performance precedes compensation.
For the construction of the dataset, all players who appeared in
one club roster were considered (n=1,191). Players who were not listed
on the transfer market website or transferred during the running season
have not been included. Goal keepers were also excluded because their
effort measures are not comparable with players from other positions.
Altogether, 304 benchwarmers were excluded from the sample to reduce a
potential sample selection bias. Thus, the remaining database contains
only players who played in at least one of all possible matches (17 per
half season) given a total playing time of 90 minutes. Using only these
players is important because effort measures may be biased by fitter
players who did not play the full 90 minutes. Also, it is not possible
to determine the correct playing time of those players who enter the
field at the end of the match because the actual overtime is not added
to the playing time in the official statistics. Consequently, the final
sample of n=877 observations (equivalent to 446 players) should be
adequate to investigate the research question of this study.
Measures and variables
Table 1 provides an overview of the variables used in this study.
Given the skewed market value distribution, a player's logged
market value (Mincer, 1974) is computed (MV_LOG). In addition, the
percentage change in market value (MV_DIFF) between a player's
market value between the previous and the current half season is
calculated in order to investigate the effect of effort on a
player's market value development. Based on the arguments provided
by signaling theory (Spence, 1973), we assume that players are more
interested in future market value increases than in present value. As
changes are intuitively lower for higher market values, the VALUE_1
variable is used as a reference point (Kahnemann & Tversky, 1979)
that controls for players initial starting value.
Individual human capital is measured by a player's age (AGE),
nationality (GERMAN), height (HIGH), and position. Since the standard
inverse u-shape age-earnings-profile is expected (Maxcy, 1996), the
squared term of age is also included in the study (AGE2). The variable
APPEAR captures the number of appearances on the pitch in each observed
half season. The player's market value should also be influenced by
transfers to a new team since the same performance could result in a
lower/higher perceived performance in relation to the players of the new
team (Lazear & Shaw, 2007). Therefore, the variable TRANSFER covers
whether the player has been transferred after the half season.
Team-specific human capital is captured with the number of years the
player has played for the current club (CLUB_TIME).
Standard performance measures are used to proxy productivity.
Following Harder (1992), a player's scoring performance is
calculated (SP), which includes goals and assists divided by minutes.
Additionally, a player's efficiency is measured by computing the
efficiency of ball contacts (EFFIC). This variable represents the number
of passes completed, assists, and shots on goal related to the total
number of individual ball touches. Successful tackles are covered by the
tackling rate (TACK). Another productivity indicator relates to whether
the player is (productive enough to be) a member of his national team
(NAT, NAT_TOP). While NAT captures a player's participation in his
national team, NAT_TOP shows whether a player is a member of one of the
highest ranked national teams (Germany, Italy, France, Spain, England,
Netherland, Portugal, Brazil).
A player's effort is measured by his average running distance
standardized to the minutes played (DIST) and by the number of intensive
runs (i.e., faster than 20km/h) per minute (RUN). Since we expect
diminishing returns on effort, the squared terms of the two effort
measures are also applied (i.e., RUN2, DIST2). To test whether effort
either complements or substitutes productivity, the two effort measures
are interacted with the three productivity variables. Finally, team
dummies are included to control for unobservable team characteristics
such as management skills or team budget (TEAM01 to TEAM21).
Previous labor market research has indicated that it is difficult
to find an appropriate measure for effort (Givord & Wilner, 2009).
The advantage of this data sample of soccer players is that effort can
directly be separated from performance by minimizing the halo effect.
This effect was considered problematic since "higher effort led to
inferences of higher ability" (Nicholls, 1978, p. 808). This
problem can be neglected in the current study because a player can
choose the level of intensive runs without touching a ball and being
productive. To put it differently, an individual can reach his maximum
effort independent of his level of ability. This theoretical assumption
can be supported empirically: There is only a small correlation between
SP and DIST (r=0.12) as well as between SP and RUN (r=0.26). As can be
seen from the descriptive statistics in Table 2, players cover 120m per
minute and perform 0.58 intensive runs per minute on average. Among
those showing high effort are Tom Trybull (Werder Bremen) with 150m per
minute and Florian Trinks (Werder Bremen) with 1.11 intensive runs on
average (Table 2).
Data analysis
Regression analyses are run to test the impact of effort on market
value by controlling for productivity, human capital, and other
variables (Table 1). In analogy to the standard Mincer (1974) income
equation, the dependent variable is the natural log of a player's
market value (MV_LOG). To check for robustness, three regression
specifications are run. While model 1 identifies absolute logged market
values (MV_LOG), model 2 tests the robustness of model 1 by analyzing
percentage changes in market value (MV_DIFF). Model 1 is a fixed-effects
model, while model 2 uses random-effects estimates. The modeling
decision was based on the Hausman (1978) test, which was significant for
model 1 (p<.1) implying that unobserved player characteristics are
present. However, the test was insignificant for model 2 (p>.1)
supporting the choice of a random-effects model which allows the
inclusion of time-invariant variables (i.e., GERMAN, HIGH).
Since the two effort measures are highly correlated (r>.8),
separate estimates are necessary in order to avoid multicollinearity
issues. Models 1 and 2 present three different specifications in order
to disentangle the effects of effort and productivity on a player's
market value. The first specification displays the benchmark model,
i.e., it presents standard human capital and productivity variables
excluding effort. The second and third specification enlarges
specification 1 by our innovative effort measures. The second one
includes RUN, RUN2, and three interaction terms (RUN*SP, RUN*EFFIC,
RUN*TACK). Finally, specification 3 includes the respective distance
measures (i.e., DIST, DIST2, DIST*SP, DIST*EFFIC, DIST*TACK). The
following market value equation applies to the second specification
(including intensive runs as effort measure):
MV_LOG = [[alpha].sub.0] + [[alpha].sub.1] AGE+ [[alpha].sub.2]
AGE2 + [[alpha].sub.3] GERMAN+ [[alpha].sub.4] HIGH + [[alpha].sub.5]
APPEAR + [[alpha].sub.6] TRANSFER + [[alpha].sub.7] TIME_CLUB +
[[alpha].sub.8] SP + [[alpha].sub.9] EFFIC + [[alpha].sub.10] TACK +
[[alpha].sub.11] NAT + [[alpha].sub.12] NAT_TOP + [[alpha].sub.13] RUN +
[[alpha].sub.14] RUN2 + [[alpha].sub.15] RUN*SP + [[alpha].sub.16]
RUN*EEFIC + [[alpha].sub.17] RUN*TACK + [[summation].sup.8.sub.i=1]
[[alpha].sub.i]POSITION + [[summation].sup.21.sub.i=1] TEAM + [epsilon]
(1)
As in many labor markets that are part of the entertainment
industry (Franck, 1996), it is no surprise that some superstars have
extraordinarily high market values (Rosen, 1981). These superstars can
be regarded as outliers in the current sample and need to be controlled
for. One standard mechanism to mitigate the effects of outliers is using
logged values which reduce the standard deviation of the variable.
Altogether, 22 outliers (i.e., the 1st and 99th percentile of the
distribution; Vogel & Wagner, 2011) could be detected for MV_LOG and
9 for MV_DIFF. Models without these outliers were run. Results did
materially change supporting the robustness of the models.
Results and Discussion
The fixed-effects models (model 1) for logged market value (MV_LOG)
are summarized in Table 3. They show that many, but not all parameters
influence players' market values. The [R.sup.2] of model 1 reveals
that approximately 20% of the variance in market values is explained by
the independent variables. This relatively low percentage of explained
variance could be driven by the heterogeneity of the sample, i.e., it
includes both players who have played in most of the games and those who
appeared only once or twice on the pitch (for 90 minutes). The displayed
results of the standard human capital coefficients (FE1) are in
accordance with the findings from previous research using Mincer (1974)
income equations (e.g., Franck & Nuesch, 2008; Lehmann &
Schulze, 2008).
Most relevant for the underlying research question is the sign and
statistical significance of the two effort proxies RUN and DIST. The
second specification (FE2) shows that RUN is insignificantly related to
market values indicating that more diligent players are not able to
increase their individual market value by putting forth additional
effort. In fact, the squared term (RUN2) has a significant and negative
impact on market value. This means that running around like headless
chickens decreases the market value. Comparing FE1 and FE2, it can be
seen that scoring performance (SP) is now insignificant and negative in
the second specification, which is driven by the effort measures. Hence,
we may assume that effort substitutes productivity. Yet, the results
also suggest that effort may complement productivity given the
significant interaction of RUN*TACK. This means that players who perform
more intensive runs are more successful in winning tackles. One argument
could be that these players are one step quicker than the opponent and,
therefore, win the tackle. Independent of position, quicker players are
always one step ahead of their counterparts. This, in turn, signals
quality and increases a player's market value.
Turning to the third specification (FE3), it can be seen that
neither DIST nor its squared term (DIST2) have a significant impact on
the dependent variable. Thus, running far distances is not evaluated by
the market. Again, the interaction term between DIST*TACK is positively
and significantly sloped with market value. Given that RUN and DIST are
highly correlated, it seems that those players who perform more
intensive runs also run longer distances and win more tackles, which
positively affects their market value (Table 3).
The results of the random-effects models for MV_DIFF (model 2) are
presented in Table 4. These models are more adequately related to our
theoretical arguments with regard to signaling. One key issue of the
signaling idea is that usually workers signal their quality in hopes of
generating a pay increase in the future. Consequently, not absolute
market values, but relative increases in market values should be
important to them. Like in the previous set of specifications, we
concentrate on the specifications including the effort measures (RE2 and
RE3). Similar to the previous results, intensive runs (RUN) are not
regarded as a quality signal because those players performing more
intensive runs are not rewarded with pay increases in the future.
Consistent with our earlier findings (Table 3), scoring performance
(SP) turns insignificant when effort measures are added to the model
supporting the assumption that effort may partially substitute
productivity. Opposite to the fixed-effects model, the interaction
RUN*TACK is statistically not relevant in the random-effects model
(RE2). This finding suggests that effort may not be a complement of
productivity when looking at changes in market values. Since all
coefficients of the alternative effort variable DIST are also
insignificant, we assume that market progress cannot be explained by
putting forth additional effort.
Finally, the results show that a player's market value is
significantly determined by his previous market value (MV_1). Hence, we
included this variable to test for path dependence and a specific
reference point (Kahnemann & Tversky, 1979). After all, a player
will compare his future market value (salary) with his original market
value. As expected, those players with low absolute market values will
encounter higher relative increases in market value. This finding is in
accordance with option value arguments as proposed by Lazear (1998).
Since younger players typically have lower market values in comparison
to older players, younger players generally are more attractive to teams
(Table 4).
Conclusion
This study examined the impact of effort (measured by intensive
runs and total distance run per game) on the market value of soccer
players controlling for human capital and productivity. Panel regression
was used to investigate the influence of effort on logged market values
and relative increases in market values. The results of the
fixed-effects models displayed mainly insignificant and partially
negative effects of effort on logged market values. Only the interaction
between intensive runs and tackling rate had a significant positive
impact on logged market value. Yet, all effort measures were
insignificant in the random-effects models that analyzed changes in
market values.
These results seem interesting in the view of the classical
economic literature, which assumes that higher effort leads to higher
performance and subsequent higher payment (Lazear & Shaw, 2007). The
insignificant and negative effects of effort on player values seem
surprising, although it has already been found in previous research
(Treble, 2001). Moreover, several explanations for these findings can be
advanced. First, higher effort does not increase performance in a way
that can be perceived by managers. Second, effort is not an adequate
compensation for missing talent. It was shown in the literature that
managers are not the best personnel when translating individual
performance into suitable salary, even if all information is available
and visible to the public (Berri, Schmidt, & Brooks, 2007). Third,
some players may be able to read the game better, resulting in better
positioning and hence less meters to run.
Fourth, the role that was assigned to players by the coach may be
critical (although the study controlled for position). For example,
players like Mario Gomez of Bayern Munich predominantly have the task to
position close to the goal in anticipation of teammates' support to
score goals. Oppositely, there are other players who run a lot every
match because they fight for lost balls or have the task to prevent
counter attacks. These players are important for every team because they
sacrifice all their energy for the team and go long distances (like the
domestiques in professional cycling; Rebeggiani & Tondani, 2008). It
is suggested that these players usually do not score and, thus, their
contribution to the team's performance is less obvious (but
nevertheless very important).
Fifth, it could be that effort is undervalued by the soccer labor
market. This situation would be similar to the baseball labor market
where plate discipline was undervalued before the publication of
Moneyball (Hakes & Sauer, 2006, 2007; Lewis, 2003). To test this
assumption for professional soccer, the contribution of effort on the
team's performance must be estimated first. Afterwards, the
coefficients of the performance model have to be compared with those
from the value model (Hakes & Sauer, 2006) and differences in
coefficients would support the assumption that specific factors like
effort are undervalued by the labor market. However, estimating the
contribution of effort on team performance in soccer is more difficult
than the contribution of plate discipline in baseball.
This study was among the first to examine the impact of effort on
market values; yet, it has some limitations that also represent avenues
for future research. First, it must be taken into account that effort
can be influenced by drug cheating, although the effects may be smaller
in soccer compared with other sports like track and field or cycling.
Second, this study only looked at intensive effort margins; yet,
extensive effort margins could also influence players' market
values. For example, player's ability to be fit and ready for a
game every week of the playing season can also play a role. Future
studies may consider measures for extensive effort margins. Third, this
study is restricted to the player level. Yet, as noted above, effort may
also show up in team performance equations which could be estimated in
future research. Fourth, this study is limited to a relatively short
period of time in only one sport. Future research should check the
robustness of the present findings by using longer observation periods
and by looking at effort measures in other sports than soccer.
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Pamela Wicker [1], Joachim Prinz [2], Daniel Weimar [2], Christian
Deutscher [3], and Thorsten Upmann [2]
[1] German Sport University Cologne
[2] University of Duisburg-Essen
[3] University of Paderborn
Dr. Pamela Wicker is a senior lecturer in the Department of Sport
Economics and Sport Management. Her research interests include economics
of sport consumer behavior, sport participation, and financing of
non-profit sport clubs.
Prof. Dr. Joachim Prinz is a professor in the Department of
Managerial Economics in the Mercator School of Management. His research
interests include labor market and team sports economics.
Daniel Weimar is a PhD student in the Department of Managerial
Economics in the Mercator School of Management. His research interests
include empirical sports and media economics.
Christian Deutscher, PhD, is a scientific assistant in the Faculty
of Business Administration and Economics. His research interests include
salary determination and effort provision in tournaments.
Dr. Thorsten Upmann is a professor in the Department of Managerial
Economics in the Mercator School of Management. His research interests
include labor supply decisions, labor market negotiations, tax
competition, theory of taxation, as well as environmental and resource
economics.
Table 1. Overview of variables
Variable Description Scale
Market value
MV Market value at the end of half Metric
season (in [euro])
MV_LOG Logarithm of VALUE Metric
MV_DIFF Percentage change in market value Metric
from previous to current half season
MV_1 Market value in previous half Metric
season (in [euro])
Human capital
AGE Age in years Metric
AGE2 AGE squared Metric
GERMAN Nationality (1=German) Dummy
HIGH Height of player (in cm) Metric
POSITION Position of the player played in Dummy
most of the games of one half season
(central defense, left wing defense,
right wing defense, defense midfield,
right wing midfield, left wing midfield,
offense midfield, attack)
APPEAR Number of appearances in half season Metric
TRANSFER Transfer at the end of half season (1=yes) Dummy
TIME_CLUB Time played for the club (in years) Metric
Productivity
SP Scoring Performance (goals+assists) Metric
per minute
EFFIC Efficiency (rate) of ball contacts Metric
(=[flanks+ right passes+ shots]/ball
contacts)
TACK Tackling rate (=tackles won/all tackles) Metric
NAT National player in the player's Dummy
home country (2=yes)
NAT_TOP National player in one of the top Dummy
FIFA ranked nations (2=yes)
Effort
RUN Average number of intensive runs Metric
(i.e., >20km/h) per game (per minute)
RUN2 RUN squared Metric
DIST Average running distance per game
(km per minute) Metric
DIST2 DIST squared Metric
Controls
TEAM1-TEAM21 Team dummies Dummy
Table 2. Descriptive statistics (n=446 players)
Variable Obs. Mean SD Min Max
MV 877 5126739.00 6426055.00 200000.00 42000000.00
MV_LOG 877 14.95 0.97 12.21 17.55
MV_DIFF 867 0.1712654 0.6850334 -0.6428571 9.00
AGE 877 25.90 3.78 17.85 35.89
AGE2 877 684.84 200.74 318.55 1287.89
GERMAN 877 0.44 0.50 0.00 1.00
HIGH 877 183.42 6.26 164 198
APPEAR 877 7.44 4.92 1.00 17.00
TRANSFER 877 0.18 0.38 0.00 1.00
TIME_CLUB 877 2.78 2.79 0.01 14.41
SP 877 0.00 0.00 0.00 0.03
EFFIC 877 0.55 0.10 0.17 0.82
TACK 877 0.12 0.03 0.00 0.23
NAT 877 0.46 0.50 0.00 1.00
NAT_TOP 877 0.11 0.31 0.00 1.00
RUN 877 0.58 0.14 0.23 1.11
RUN2 877 0.36 0.17 0.05 1.23
DIST 877 0.12 0.01 0.06 0.15
DIST2 877 0.01 0.00 0.00 0.02
Table 3. Fixed-effects models for MV_LOG (model 1; n=877;
displayed are the coefficients, t-values in parentheses)
FE1 FE2
MV_1 / /
AGE 1.479 (9.09) *** 1.427 (8.83) ***
AGE2 -0.029 (-9.87) *** -0.028 (-9.70) ***
GERMAN / /
HIGH / /
APPEAR 0.021 (6.68) *** 0.021 (6.94) ***
TRANSFER -0.154 (-5.83) *** -0.157 (-5.97) ***
POSITION Incl. Incl.
TIME_CLUB 0.067 (1.94) * 0.078 (2.64) ***
SP 11.736 (3.55) *** -5.999 (-0.36)
EFFICIENCY 0.033 (0.17) -0.105 (-0.16)
TACK 0.750 (1.70) * -3.805 (-2.38) **
NAT 0.029 (0.19) 0.055 (0.40)
NAT_TOP 0.129 (0.83) 0.086 (0.60)
RUN / 0.390 (0.41)
RUN2 / -0.985 (-1.75) *
RUN*SP / 25.836 (1.02)
RUN*EFFIC / 0.256 (0.25)
RUN*TACK / 7.559 (2.81) ***
DIST / /
DIST2 / /
DIST*SP / /
DIST*EFFIC / /
DIST*TACK / /
TEAM1-TEAM21 Incl. Incl.
Constant -4.29 (-1.88) * -3.37 (-1.47)
R2 0.18 0.20
F 9.93*** 9.20 ***
FE3
MV_1 /
AGE 1.475 (8.97) ***
AGE2 -0.029 (-9.84) ***
GERMAN /
HIGH /
APPEAR 0.022 (7.03) ***
TRANSFER -0.152 (-5.78) ***
POSITION Incl.
TIME_CLUB 0.075 (2.24) **
SP 11.444 (0.27)
EFFICIENCY -0.673 (-0.48)
TACK -7.388 (-1.89) *
NAT 0.052 (0.39)
NAT_TOP 0.136 (1.00)
RUN /
RUN2 /
RUN*SP /
RUN*EFFIC /
RUN*TACK /
DIST -8.573 (-0.85)
DIST2 8.554 (0.17)
DIST*SP -1.625 (0.00)
DIST*EFFIC 5.913 (0.54)
DIST*TACK 66.148 (2.07) **
TEAM1-TEAM21 Incl.
Constant -3.20 (-1.36)
R2 0.20
F 9.08 ***
Note. * p<.1; ** p<.05; *** p<.01. Robust standard errors
are reported (White, 1980).
Table 4. Random-effects models for MV_DIFF (model 2; n=867;
displayed are the coefficients, t-values in parentheses)
RE1 RE2
MV_1 -0.000 (-5.76) *** -0.000 (-5.77) ***
AGE -0.619 (-5.38) *** -0.618 (-5.35) ***
AGE2 0.010 (4.86) *** 0.01 (4.81) ***
GERMAN 0.045 (0.68) 0.044 (0.68)
HIGH -0.003 (-0.59) -0.004 (-0.75)
APPEAR 0.028 (5.53) *** 0.027 (5.31) ***
TRANSFER -0.101 (-2.09) ** -0.101 (-2.12) **
POSITION Incl. Incl.
TIME_CLUB 0.002 (0.16) 0.002 (0.18)
SP 14.283 (3.25) *** -11.666 (-0.65)
EFFICIENCY -0.034 (-0.11) 0.425 (0.49)
TACK 0.617 (1.04) 0.473 (0.22)
NAT -0.029 (-0.54) -0.024 (-0.44)
NAT_TOP 0.277 (3.30) *** 0.278 (3.28) ***
RUN / 0.823 (0.72)
RUN2 / -0.726 (-0.96)
RUN*SP / 40.149 (1.42)
RUN*EFFIC / -0.808 (-0.65)
RUN*TACK / 0.39 (0.11)
DIST / /
DIST2 / /
DIST*SP / /
DIST*EFFIC / /
DIST*TACK / /
TEAM1-TEAM21 Incl. Incl.
Constant - -
[R.sup.2] 0.29 0.30
Wald-Chi 285.36 *** 315.05 ***
RE3
MV_1 -0.000 (-5.79) ***
AGE -0.618 (-5.38) ***
AGE2 0.01 (4.86) ***
GERMAN 0.046 (0.70)
HIGH -0.003 (-0.62)
APPEAR 0.028 (5.39) ***
TRANSFER -0.101 (-2.10) **
POSITION Incl.
TIME_CLUB 0.002 (0.14)
SP 67.426 (1.40)
EFFICIENCY 0.906 (0.54)
TACK 1.225 (0.23)
NAT -0.029 (-0.55)
NAT_TOP 0.280 (3.32) ***
RUN /
RUN2 /
RUN*SP /
RUN*EFFIC /
RUN*TACK /
DIST 3.985 (0.27)
DIST2 7.935 (0.11)
DIST*SP -432.552 (-1.12)
DIST*EFFIC -7.931 (-0.57)
DIST*TACK -4.618 (-0.11)
TEAM1-TEAM21 Incl.
Constant 9.14 (4.30) ***
[R.sup.2] 0.30
Wald-Chi -
Note. * p<.1; ** p<.05; *** p<.01. Robust standard errors
are reported (White, 1980).