Team Performance Management: An International Journal Team and individual performance in the Tour de Joachim Prinz Pamela Wicker Article information: To cite this document: Joachim Prinz Pamela Wicker, (2012),"Team and individual performance in the ", Team Performance Management: An International Journal, Vol. 18 Iss 7/8 pp. 418 - 432 Permanent link to this document: http://dx.doi.org/10.1108/13527591211281147 Downloaded on: 05 May 2015, At: 08:27 (PT) References: this document contains references to 48 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 1347 times since 2012* Users who downloaded this article also downloaded: Vidhi Agrawal, (2012),"Managing the diversified team: challenges and strategies for improving performance", Team Performance Management: An International Journal, Vol. 18 Iss 7/8 pp. 384-400 http:// dx.doi.org/10.1108/13527591211281129 Pilar Pazos, (2012),"Conflict management and effectiveness in virtual teams", Team Performance Management: An International Journal, Vol. 18 Iss 7/8 pp. 401-417 http:// dx.doi.org/10.1108/13527591211281138 Aruna B. Bhat, Neha Verma, S. Rangnekar, M.K. Barua, (2012),"Leadership style and team processes as predictors of organisational learning", Team Performance Management: An International Journal, Vol. 18 Iss 7/8 pp. 347-369 http://dx.doi.org/10.1108/13527591211281101

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TPM 18,7/8 Team and individual performance in the Tour de France Joachim Prinz 418 Department of Managerial Economics, Mercator School of Management, University of Duisburg-Essen, Duisburg, Germany, and Pamela Wicker Department of Tourism, Leisure, Hotel and Sport Management, Griffith University, Gold Coast Campus, Southport, Australia

Abstract Purpose – The purpose of this paper is to analyze the effects of an athlete’s body type, team characteristics, and pay on performance in the Tour de France (“the Tour”). Based on the concept of scaling and the concept of human capital, the paper aims to derive a set of hypotheses. Design/methodology/approach – Secondary data were collected about all riders that finished the Tour in the years from 2002 to 2005 leading to a total number of n ¼ 600 observations. Random effects regression models are estimated with rank as the dependent variable. Findings – The findings indicate that lighter riders perform better in the Tour than heavier cyclists. Better teammates were found to increase average riders’ performances, whereas top riders did not benefit from top teammates. Experience (rider, teammates, coach) was a significant driver of performance. Research limitations/implications – Team managers should pay attention to the composition of the team. Having only one strong team captain and several good coworkers was more effective than having several star riders (i.e. potential captains) in a team. Practical implications – The findings with regard to team composition can be transferred to other sports and professions where teamwork plays an important role. Successful teams should consist of only one captain and several good coworkers. Originality/value – The paper extends previous work on the determinants of performance in the Tour by using a longitudinal dataset that covers more variables than previous research. Keywords Team performance, Labour market, Cycling, Tour de France, Human capital, Experience, Team working Paper type Research paper Downloaded by Virginia Commonwealth University At 08:27 05 May 2015 (PT) Introduction “The sport industry is globally one of the fastest [growing] and largest industries” (Ratten, 2011, p. 680). The internationalization of sport becomes evident in the growth of major sport events that are watched worldwide (Ratten, 2011). One of these events is the Tour de France (or synonymously “the Tour”), which is the world’s premier bicycle race. It is an annual endurance competition through all the terrain of France held every year in July. Each year 21 teams consisting of nine riders, vie to win the 3,500 km distance race. The event is divided in 20 (daily) stages and winning one of them is a Team Performance Management prestigious victory for riders of different skills and abilities. The Tour is considered a Vol. 18 No. 7/8, 2012 pp. 418-432 highly stressful race and on average only 75 per cent of riders arrive in . At the q Emerald Group Publishing Limited end of the competition, the rider with the lowest (stage) cumulative time is the winner 1352-7592 DOI 10.1108/13527591211281147 of the Tour and allowed to keep the yellow jersey. Previous research investigating the determinants of performance in cycling has Team and focused largely on individual factors such as mechanic, metabolic, biomechanical, and individual physiological aspects of performance (e.g. Beneke and DiPrampero, 2001; Coyle et al., 1988; Coyle et al., 1990; Di Prampero et al., 1979; Neumann, 2000; Swain, 1994). performance However, performance in cycling is also determined by team characteristics (Torgler, 2007). The outcome of a cycling race is the result of a strong and united team. Teamwork is important as a rider’s success is reliant on his ability to conserve energy 419 at the correct times, and his team helps him to do this. Even good cyclists can hardly win a major cycling event without a strong team (Rebeggiani and Tondani, 2008). Cycling is a team sport; however, the fundamental difference to other team sports like football and basketball is that the winner of a cycling event is a single racer and not a team (Brewer, 2002). Cycling embodies a unique form of competition because some cyclists give away their individual chances of winning in the interest of the collective goal (Jutel, 2002). Thus, it can happen that some professional sportsmen spend their whole career helping others instead of pursuing their own goals (Rebeggiani and Tondani, 2008). Teams are important because a team is the “basic organizational unit of the professional cycling world” (Brewer, 2002, p. 281). These professional teams are strong alliances, as showcased in the road race of the 2000 Sydney Olympics where the riders from the Team Telekom revealed their “commercial identity” instead of the expected national identity (Jutel, 2002, p. 198). In other sports, there is also evidence on the importance of teammates’ abilities on compensation (e.g. Idson and Kahane, 2000; Kahane, 2001). Previous studies have analyzed the influence of financial characteristics such as player salaries or the annual team wage bill on performance (e.g. Forrest and Simmons, 2002; Lynch and Zax, 2000; Szymanski and Smith, 1997; Zimbalist, 1992). The evidence presented so far is mixed: While North American studies (Quirk and Fort, 1999; Zimbalist, 1992) did not find a significant correlation between pay and performance in baseball and football, the results presented from soccer studies (Forrest and Simmons, 2002; Szymanski and Smith, 1997) indicated a positive relation-ship between the annual team wage bill and club performance. The review of literature shows that the research focus has been on professional sports leagues up to now, whereas professional cycling has been largely neglected with only a few exceptions (e.g. Brewer, 2002; Cherchye and Vermeulen, 2006; Dilger, 2002; Dilger and Geyer, 2009; Morrow and Idle, 2008; Torgler, 2007). This is surprising given the popularity and financial success of the Tour and considering

Downloaded by Virginia Commonwealth University At 08:27 05 May 2015 (PT) cycling was one of the first sports ever practiced professionally (Rebeggiani and Tondani, 2008). As performance in the Tour is determined by individual ability and team characteristics, this interdisciplinary study combines characteristics of these two areas. This leads to the main research question: What is the influence of individual and team characteristics on performance in the Tour? The paper is organized as follows: In the next section the theoretical framework based on the concept of scaling and the concept of human capital is presented and hypotheses are formulated. To test the hypotheses, a database including team characteristics and individual characteristics is constructed. This dataset consists of all cyclists who have finished the Tour in the years 2002 to 2005 (n ¼ 600). Further years were not included in the dataset, since several (official and popular) doping cases occurred from the years 2006 that would lead to biased results. This does not mean that there was less doping before 2006; it has TPM just been less detected. Particularly the effects of an increased commercialization of 18,7/8 cycling were found to foster doping practices (Brewer, 2002) and are part of the economic forces that drive athletes to use drugs (Haugen, 2004). Maennig (2002) offered solutions to the problem of doping, for example by implementing sufficiently high financial penalties. Despite the issue of doping, performance management is a critical issue for sports teams and the current study addresses the call for more research on 420 performance management in the context of sports teams (Ratten, 2009). It builds on previous research on performance in the Tour (Torgler, 2007); however, it is based on longitudinal data (not only cross-sectional) that cover more variables. The findings contribute to the body of research on professional cycling and on team production. Studying professional road racing can extend the knowledge of compensation systems in general. Since cycling is team production won by a single racer (Brewer, 2002), it resembles all those professions where the laurels of success are mainly awarded to the responsible manager (i.e. investment bankers, cosmetic surgeons, etc.). Therefore, knowledge on the team production in cycling can also be transferred to other professions.

Theoretical framework and hypotheses Cycling races such as the Tour are subdivided into several stages that have different characteristics. While some legs emphasize climbing abilities, flat stages are tailored for sprinters, while individual and team trial sections are particularly made for the anaerobic threshold-maintaining athlete (Swain, 1994). Since various stages require different rider skills, the overall Tour winner is the athlete with the best all-rounder quality, usually the team’s captain. The outcome is not only a result of the rider’s abilities, but also a result of a strong and united team, where the ’ jobs are to shield their captain from the air by riding in front of him. In summary, both individual and team characteristics are important drivers of performance in the Tour. Drawing on the concept of scaling and the concept of human capital, a set of individual and team-related performance determinants is derived in the following paragraphs. When looking at endurance races such as the Tour, it emerges that light athletes are on average faster than comparable heavier athletes and that success in the Tour is typically determined in the mountains. While air resistance is the dominant force limiting a racer’s speed in flat terrain, most of the rider’s energy is used to overcome gravity while riding in the mountains. Because gravity is directly proportional to a

Downloaded by Virginia Commonwealth University At 08:27 05 May 2015 (PT) rider’s body weight, climbing performance and overall cycling success are a matter of body mass (Swain, 1994). The relationship between body mass and maximum oxygen uptake (VO2max, a direct physiological determinant of endurance performance; Coyle et al., 1988) can be explained by the concept of scaling. Scaling refers to how the dimensions of objects or people of different sizes compare. According to the concept, area scales with the square of linear dimensions, while volume and mass scale with the cube of linear dimensions. These facts from allometrics affect humans of different sizes as well. As long as body shape is similar, taller and heavier people have more surface area than shorter and lighter people, by a ratio of the square of their relative heights. Accordingly, the ability to process oxygen is limited by the surface area of the lungs, the muscle capillaries, and the aorta (Coyle et al., 1988) and therefore, it can be expected that a taller and heavier person has a higher maximum oxygen uptake than a shorter and lighter person. However, for hill climbing, the relative maximum oxygen uptake is relevant (i.e. maximum oxygen uptake divided by body mass) as it leads to a Team and disadvantage of the heavier athlete because the body mass is over-proportionately individual higher than the maximum oxygen uptake. Lighter riders have greater relative aerobic power output, because they have larger capillary surface areas in their lungs and performance muscles (Swain, 1994). This is a crucial advantage since the most difficult parts in the Tour are the six mountain stages. A similar analysis can be done for riding in the flats, when the task now is to 421 overcome the air resistance of one’s surface area. This analysis reveals that the advantage goes to the taller and heavier cyclist (Swain et al., 1987). Moreover, he benefits from a lower ratio of surface area to body weight. In summary, the large and heavy racer spends more energy costs than the small and light cyclist because he has more absolute air resistance. However, if expressed relative to body mass, the frontal drag for the light rider is greater than that for the heavy rider. Hence, the heavy rider has an advantage on flat terrain, because in relative terms his muscles have to overcome less (air) resistance, the more so as, in relative terms, the bike weight additionally favors the heavy athlete (Coyle et al., 1990; Di Prampero et al., 1979). Consequently, more massive cyclists should have a comparative advantage in the Tour on 14 (flat stages) out of 20 stages. However, the heavy rider’s advantages are effectively eliminated during level ground stages due to drafting (Swain, 1994), i.e. riders draft to minimize air resistance. This drafting technique is especially strong in the peloton where riders can save up to 30 per cent of energy costs (Neumann, 2000). In summary, as a result of scaling geometry light cyclists should be faster at mountain stages because they have – on average – greater relative aerobic power. Given the relative time intensive difficulty of steep inclines, light athletes should be better at the whole Tour. Moreover, since there is no equal distribution of up and downhill sections (there are three summit finishes meaning that heavier riders do not profit from Newton’s law of acceleration downhill), heavy riders cannot reduce the time they lost while ascending. This aspect is covered in the first hypothesis (H1) predicting that lighter cyclists are better performers than heavier cyclists in the Tour. It is suggested that human capital also plays a role in determining performance. Generally speaking, human capital (Becker, 1962; Mincer, 1974) includes an individual’s knowledge, ability, skills, and experience which have been acquired through education, formation, training, and practice. In the current study, particularly ex-ante Tour experience and tenure are important because they represent proxies for a

Downloaded by Virginia Commonwealth University At 08:27 05 May 2015 (PT) rider’s general and team-specific human capital (Mincer, 1974). More experienced cyclists are more familiar with the Tour circus and know the terrain of the parcours better than rookies. Also, as tenure increases, team-specific skills (i.e. in the team trial stage) are accumulated leading to better performance with the present employer in the Tour. In the current study, experience includes previous performance at the Tour (number of finishes in the top 20, number of stage wins), experience at the Tour (number of previous participations in the Tour), and experience with the team (tenure, i.e. number of years in the team). In summary, the second hypothesis (H2) suggests that more experienced cyclists perform better in the Tour. In addition to individual characteristics, team characteristics are important drivers of performance. The team production is most clearly observable when a team is protecting its captain. For example, the coworkers of the man wearing the yellow jersey want to prevent the leading contenders from gaining ground by escaping in a TPM breakaway, so they will cluster near the front of the field, control the action, work hard, 18,7/8 and shield their captain from the air (Rebeggiani and Tondani, 2008). Given that individual success in cycling depends to some extent on the quality and assistance of teammates, it is necessary to investigate the contribution of teammates’ abilities on individual performance. This relationship is insofar interesting since teams with some individual million dollar talents are not inevitably the strongest on the road. Too many 422 individuals have an incentive to ride for themselves and are not thinking team. This means they do not behave like good team players from who it is expected to pursue the team’s goals instead of their own ones. Self-interest can be detrimental in a team sport like cycling. Or as Lazear (1998, p. 307) puts it: “They [the teammates] focus too heavily on their own contributions and ignore teammates even when helping someone else might further the interests of the team.” In the current study, it is expected that team size matters because the force of the air opposes the motion of cyclists. Due to accidents and illness, usually about 25 per cent of riders drop-out from the competition, which affects a team’s winning probabilities, since riders of a small(er) team spend more energy costs (frontal drag) than racers of a large(r) group. Therefore, the third hypothesis (H3) predicts that the higher the number of teammates finishing the Tour, the better the rider’s final rank. In accordance with the concept of human capital (Mincer, 1974), not only the individual experience of a rider, but also the experience of his teammates and the coach should be relevant. Since teammates’ qualities are essential for winning the race, the impact of coworkers’ abilities on individual output is also explored. Prior research indicated that teammates’ abilities had an impact on performance in the Tour (Torgler, 2007). For this reason, the fourth hypothesis (H4) assumes that better teammates increase individual performance. However, while the hypotheses H2, H3 and H4 seem plausible, it is actually not clear whether human capital (H2), the number of finishers (H3), and the quality of co-workers (H4) are indeed responsible for better individual performance since these relationships might simply be the result of an “inherent” self-selection or survivor bias (Ashworth and Heyndels, 2007; Heckman, 1979; Stock and Watson, 2012), which in turn makes it difficult to determine causation (Angrist and Pischke, 2008). For example, riders who have accumulated more human capital select themselves into more productive teams and/or better teams attract better athletes and consequently

Downloaded by Virginia Commonwealth University At 08:27 05 May 2015 (PT) participate more often in the Tour and reach a better final ranking. Since this suspicion might certainly be true, it is virtually impossible to eliminate this selection problem without finding and using valid instruments in a two-stage least-squares regression model (2SLS). In addition to teammates’ abilities, the coach is also important to a team’s performance (Narcotta et al., 2009). The finding that some teams put forth low efforts in a high percentage of cycling events (Rebeggiani and Tondani, 2008) highlights the importance of the coach in cycling who is responsible for the team tactics and the effort each rider puts in the race. In the Tour, it is expected that quality coaching matters insofar that coaches with past Tour experience increase individual and team performance by selecting and motivating riders in a way that maximizes Tour success. Therefore, the fifth hypothesis (H5) suggests that coaches who have ridden the Tour themselves before increase performance. Moreover, a team’s financial endowment can affect the riders’ output. Since a team’s Team and budget also includes things that have nothing to do with what the athlete is paid, it is individual not possible to theorize a direct pay/performance relationship. A rider’s income includes both his salary plus other payments such as endorsement money or license performance deals (that are unfortunately not disclosed). While teams with higher budget have the opportunity to buy better cyclists for the team, the budget also contains expenses that are likely to affect riders’ performances, i.e. the quality and number of coaches, 423 mechanics, therapists, and doctors of the team, and the way that the team is managed. For example, sport managers can influence the performance of the team by changing its organizational structure (Foster and Washington, 2009). In this way, the budget represents a proxy for unobservable team-specific heterogeneity. This aspect is covered in the sixth hypothesis (H6) assuming that the higher the team’s budget, the better the rider’s final standing.

Method Data source To test the hypotheses empirically, the current study is based on all riders who finished the Tour in the years from 2002 to 2005. The database was primarily drawn from the official Tour de France homepage (www.letour.fr) and the internet portal www.radsport-news.com. Both web sites provided relevant rider characteristics and rider performance statistics. Although direct endurance measures such lung capacity (VO2max) are unfortunately not available, other detailed rider characteristics such as a cyclist’s height, weight, past performance, team tenure, Tour experience, rank, and stage wins were compiled from these two sources. Additionally, team information data were obtained from various issues of the Kicker (a German sports magazine) and the official Tour magazines (TOUR). The total number of observations is n ¼ 600, with some cyclists being active in the Tour in all four years and others in only a few years or in just one year. Over the four years, the teams employed 337 different riders, and from those 79 (23 per cent) participated at least three times. As shown in Table I, individual characteristics differ across cyclists. While the average athlete has participated in 2.4 prior Tours (TOUREXP) and stayed on average 3.3 years with his current team (TENURE), the six times winner of the climbing category jersey has already finished the Tour among the top 20 (TOP20) nine times, before retiring after the 2004 edition.

Downloaded by Virginia Commonwealth University At 08:27 05 May 2015 (PT) Following Idson and Kahane (2000), the database allows for estimating the contribution of teammates’ abilities on individual ability and their interactions. To test the impact of teammates on individual productivity, the racer’s quality is proxied by the number of his prior top 20 appearances in the Tour (Torgler, 2007). In doing so, the cyclist’s quality is transformed so that it is possible to isolate his productivity from team productivity by computing average team quality as a whole, but remove from this each individual rider’s number of appearances among the top 20 (Idson and Kahane, 2000; Torgler, 2007). This method reflects coworkers’ attributes (TEAMTOP20) and analogously teammates’ past Tour experience (TEAMTOUREXP) is generated. Given that the success of an athlete depends a lot on his body type, the profile of the average Tour de France participant reveals that he is 1.79m tall and weighs 69 kg (see Table I). In order to characterize body shape as theorized by the concept of scaling, the body mass index (BMI ¼ kg/m2) is used as the TPM 18,7/8 Variables Mean SD Min Max RANK 76 43 1 155 BUDGET (in million Euro) 6.1 2.7 1.8 18 BMI 21.36 1.29 17.57 26 HEIGHT (in m) 1.79 0.05 1.60 1.97 424 WEIGHT (in kg) 69 5.7 50 86 TOP20 0.45 1.2 0 9 TOUREXP 2.4 2.4 0 13 TENURE 3.3 2.3 1 13 STAGE WINS 0.49 1.7 0 22 Table I. COACH 0.63 0.48 0 1 Descriptive statistics of NOR 7.3 1.3 3 9 Tour de France cyclists TEAMTOP20 0.46 0.51 0 2.14 from 2002 to 2005 TEAMTOUREXP 2.3 1.2 0 7.5 (n ¼ 600) INTERTOP20 0.25 0.84 0 8

best measure available to proxy endurance skills, since there is a solid physiological relationship between maximum oxygen uptake and body mass.

Data analysis Regression analyses are estimated to answer the main research question of this study and to test the hypotheses. One way to address whether individual and team characteristics have an effect on a cyclist’s success is to measure output using a rider’s final rank as the dependent variable (RANK). Since not all four Tours have the same amount of finishers, these values are normalized to the interval [0,1] (Haan et al., 2005). This means that in a race with n finishers the rider coming in at place i, has a value for rank that equals (i-1)/(n-1). Consider for example the 2005 Tour edition where 154 cyclists survived the competition. One of them was the Spanish from the Liberty Seguros team who finished 45th. According to the previous formula the observation for rank for this rider equals 0.287 (44/153). Thus, the seven times consecutive Grand Champion of the Tour receives a value of zero, whereas the last contestant of the respective years gets a value of one. Given the panel characteristic of the database, it is possible to account for individual rider heterogeneity (i.e. motivation; degree of effort a rider puts forth while training,

Downloaded by Virginia Commonwealth University At 08:27 05 May 2015 (PT) inspiration etc.). Generally and specifically, the problem of unobserved heterogeneity among riders might be present and consequently an ordinary least squares model (OLS) will produce spurious estimates (Booth, 1993). In an attempt to correct for this omitted variable bias problem among riders (Kahn, 1993; Prisinzano, 2000), the fixed effects model and its alternative, the random effects model, are usually tested and compared. Normally, fixed effects models are preferred in panel data analyses and regarded as the most appropriate models (Greene, 2003), as implied by the significant Durban-Watson-Hausman test (x 2 ¼ 46:43; p , 0:01; Hausman, 1978). However, in this specific case fixed effects models are problematic, because they would suffer from the (automatic) elimination of two important (BMI, COACH), but time-invariant (Canella et al., 2008) independent variables. In particular, the influence of weight (H1) and coaching (H5) on performance could not be tested. Given the significant LM-Test (Breusch and Pagan, 1980), random effects models with robust standard errors (White, 1980) are estimated where RANK is used as the dependent variable. These models Team and which control for rider-specific effects are of the following general form: individual performance RANK ¼ b0 þ b1LNBUDGET þ b2BMI þ b3TOP20 þ b4TOUREXP þ b5TENURE

þ b6STAGEWINS þ b7COACH þ b8NOR þ b9TEAMTOP20 425 þ b10TEAMTOUREXP þ b11INTERTOP20 þ X&’CD þ X&’DL þ X&’YD

þ 1

where LN BUDGET is the natural log of the annual team payroll; BMI is the bicyclist’s body max index; TOP20 is how many times a rider was among the top 20 in prior Tours; TOUREXP refers to the racers’ number of previous Tour participations, TENURE is the duration of years the rider is with his current team; STAGE WINS is the number of former stage wins; COACH is a dummy variable reflecting whether the head coach was a former Tour participant or not; NOR is the number of riders per team finishing the Tour; TEAMTOP20 is teammates’ cycling Tour ability (i.e. number of top 20 finishes); and TEAMTOUREXP is teammates’ previous Tour experience (i.e. number of participations). In the past years it was often observed that teams with some million Euro riders were not necessarily better than teams with a single star. To test this argument an interaction term INTERTOP20 ( ¼ TOP20 *TEAMTOP20) is additionally entered into the model. Several control variables are integrated into the regression models. Controls for nationality are included, because it might be argued that budget expenses are not independent of nationality and that the earnings potential is different for cyclists in different national settings. To test for systematic country-specific differences, eleven country dummies (X’CD) are plugged into the equation. FRANCE is used as the reference category since this is the country with the most average (rider) performance over the whole four years examined (Haan et al., 2005). Likewise, it is necessary to control for the division of labor (X’DL). The division of labor takes different roles within the team into account (e.g. captain, servant) and also considers specific abilities of team members (e.g. time trialer, climber, ). As specified earlier, the captain can save energy through drafting behind teammates (Rebeggiani and Tondani, 2008).

Downloaded by Virginia Commonwealth University At 08:27 05 May 2015 (PT) Climbers might help bringing the captain over a high peak and sprinters protect him in a mass finish (Jutel, 2002). Servants have specified duties like avoiding breakaways, delivering food and water to the captain and other teammates (Rebeggiani and Tondani, 2008), or taking the thankless early lead in the final seconds of sprint finals which they do not win in most cases (Dilger and Geyer, 2009). In doing so, many team members cannot pursue their own goals and can thus not achieve their best rank possible. Therefore, it is important to control for the division of labor by including dummies for CAPTAIN, TIME TRIALER, CLIMBER, SPRINTER, and SERVANT (reference category). Finally, three year dummies (X’YD) are incorporated to capture year-specific differences (distance, weather, topography, etc.). Altogether, four models are estimated with the models 1 to 3 slightly differing regarding the entered variables: Model 1 contains BMI instead of WEIGHT and Model 2 WEIGHT instead of BMI. In Model 3, the variables BUDGET and WEIGHT are TPM removed to check the robustness of the first two models as these two variables are 18,7/8 correlated with other performance indicators. Model 4 is an ordered probit model to check the robustness of the effects of teammates’ ability of a rider’s performance (H4). The other models do not take into account that the dependent variable (RANK) mirrors a rank-order system and thus is not proportional. So far, they consider the difference between a ranking of 4 and 5 being equal to the difference of 25 and 26. However, it 426 could be argued that only the first ranks matter and that it is irrelevant whether a rider finishes on place 40 or 50. This issue is addressed by the ordered probit model.

Results and discussion The regression results are summarized in Table II. They show that most of the parameters influence cyclists’ performances in the predicted manner. Although these coefficients have the anticipated sign, not all are statistically significant. The BMI variable as well as the alternative WEIGHT variable of Model 2 have a significant positive influence on the dependent variable RANK. Independent of the model specification, it can be seen that lighter riders perform better in the Tour than otherwise comparable heavier athletes. Therefore, the first hypothesis (H1) suggesting that lighter cyclists are better performers than heavier cyclists in the Tour can be confirmed. This finding is in accordance with a prior study, where the BMI also lead to better performance in the Tour (Torgler, 2007), and also with the results presented by Coupe´ and Gergaud (2012). The regression estimates further show that a rider with a good prior Tour record (TOP20) is better placed in the current Tour than a rider who was not that successful in former years. Moreover, more experienced (TOUREXP) cyclists are better performers, while being longer with the present team (TENURE) and number of past stage wins (STAGE WINS) do not significantly affect a rider’s success. Thus, the human capital variables (Mincer, 1974) implicate that general, but not team-specific human capital plays a role in determining a cyclist’s overall final standing. Therefore, the second hypothesis (H2) suggesting that experienced riders perform better in the Tour can only partially be confirmed. Given the division of labor aspect, the findings display a strong negative correlation between RANK and being a team’s captain (CAPTAIN). This is not surprising since captains are drafted by teammates during level ground stages which reduces their energy expenditure (Neumann, 2000). Consequently, captains save energy, which is then readily available when encountering alpine regions.

Downloaded by Virginia Commonwealth University At 08:27 05 May 2015 (PT) The number of riders finishing the Tour (NOR) is not significant (see Table II). Therefore, H3 predicting that the higher the number of teammates finishing the Tour, the better the rider’s final rank, cannot be confirmed. However, although this variable was not significant, the negative sign of the team leader parameter (CAPTAIN) supports the expectation of the basic organizational aspect of a professional cycling race, the drafting mechanism. Since cycling is team production, it is important to test the contribution of teammates’ abilities on individual success. As indicated by the negative average team performance indicator (TEAMTOP20), good past coworkers’ performances helps the individual cyclist to excel during the Tour. While it pays the captain to employ strong coworkers, a team comprised of stars is counterproductive for each potential team favorite: The parameter estimate of the INTERTOP20 variable implies that ceteris paribus, a star who joins a team with another star, has a poorer ranking than a star in a usual team. This suggests that in a multi-million Euro team the Team and Model 1 Model 2 Model 3 Variable Coeff. z-value Coeff. z-value Coeff. z-value individual performance LN BUDGET 20.035 21.16 þ 20.034 21.10 þ –– BMI 0.032 3.06 *** – – 0.032 3.05 *** WEIGHT – – 0.007 3.16 *** –– TOP20 20.075 24.70 *** 20.074 24.67 *** 20.073 24.63 *** 427 TOUREXP 20.011 21.83 * 20.010 21.76 * 20.012 22.00 ** TENURE 0.003 0.58 þ 0.002 0.45 þ 0.003 0.60 þ STAGE WINS 0.004 0.50 þ 0.005 0.64 þ 0.004 0.48 þ CAPTAIN 20.236 26.11 *** 20.234 26.03 *** 20.234 26.06 *** TIME TRIALER 20.085 21.63 * 20.085 21.59 þ 20.088 21.63 * CLIMBER 20.064 22.10 ** 20.064 22.09 ** 20.064 22.11 ** SPRINTER 0.172 5.35 *** 0.176 5.50 *** 0.175 5.45 *** SERVANT REF REF REF COACH 20.044 22.18 ** 20.045 22.26 ** 20.044 22.23 ** NOR 20.000 20.04 þ 20.001 20.16 þ 20.001 20.16 þ TEAMTOP20 20.073 23.41 *** 20.074 23.47 *** 20.074 23.48 *** TEAMTOUREXP 0.007 0.66 þ 0.008 0.77 þ 0.004 0.40 þ INTERTOP20 0.051 3.43 *** 0.052 3.48 *** 0.050 3.36 *** USA 20.175 22.33 ** 20.176 22.34 ** 20.179 22.38 ** GERMANY 20.075 21.42 þ 20.086 21.64 * 20.088 21.71 * 20.015 20.27 þ 20.034 20.57 þ 20.023 20.38 þ NETHERLANDS 0.100 1.62 þ 0.086 1.39 þ 0.086 1.42 þ SPAIN 20.149 23.70 *** 20.148 23.63 *** 20.157 23.93 *** ITALY 20.065 21.65 * 20.067 21.69 * 20.070 21.72 * SCANDINAVIA 20.029 20.46 þ 20.040 20.63 þ 20.030 20.47 þ AUS/NZL 20.056 20.84 þ 20.041 20.63 þ 20.060 20.91 þ AUSTRIA/SWITZ 20.048 20.79 þ 20.044 20.72 þ 20.060 20.96 þ OTHERS 20.146 21.95 * 20.153 22.03 ** 20.156 22.08 ** GUS 20.134 22.91 *** 20.133 22.89 *** 20.143 23.13 *** FRANCE REF REF REF YEAR DUMMIES Included Included Included R 2 0.452 0.452 0.441 Wald chi 2 288.21 *** 289.14 *** 284.34 *** Table II. LM-Test 105.31 *** 106.58 *** 103.31 *** Determinants of No. of cases 595 595 595 performance in the Tour de France (from 2002 to Notes: Robust standard errors (White, 1980) are reported; +not significant; *p , 0.1; **p , 0.05; 2005; random effects ***p , 0.01 models) Downloaded by Virginia Commonwealth University At 08:27 05 May 2015 (PT)

individual determination to make the sacrifices necessary for the team’s and the leader’s success is considerably reduced. Or to put it differently: Why should self-interested teammates reduce their winning probability for an equally good colleague? These results indicate that hypothesis H4 predicting that better teammates increase individual performance can only be confirmed for teams with one single star rider. If several stars are in one team, the opposite effect occurs and the star riders have a worse rank. The robustness of this finding has been checked with an ordered probit model (see Table III). This model reveals – as expected – (Stock and Watson, 2012) similar results. Thus, we are, on econometrically grounds, confident that the current study draws a more detailed picture of the influence of teammates’ characteristics on performance. Therefore, the findings are only partially in accordance with a previous TPM Model 4 18,7/8 Variable Coeff. z-value

LN BUDGET 20.237 21.50 þ BMI 0.093 2.15 ** TOP20 20.364 24.54 *** 428 TOUREXP 20.014 20.62 þ TENURE 0.010 0.43 þ STAGE WINS 20.047 21.79 * CAPTAIN 21.285 27.10 *** TIME TRIALER 20.299 21.20 þ CLIMBER 20.412 23.14 *** SPRINTER 0.835 6.30 *** SERVANT REF COACH 20.179 21.83 * NOR 0.023 0.66 þ TEAMTOP20 20.208 22.02 ** TEAMTOUREXP 20.066 21.37 þ INTERTOP20 0.139 1.66 * USA 20.900 23.76 *** GERMANY 20.164 20.84 þ BELGIUM 0.124 0.53 þ NETHERLANDS 0.865 4.35 *** SPAIN 20.496 23.08 *** ITALY 20.270 21.80 * SCANDINAVIA 20.073 20.33 þ AUS/NZL 20.210 20.92 þ AUSTRIA/SWITZ 0.049 0.21 þ OTHERS 20.493 21.69 * GUS 20.389 22.16 ** FRANCE REF YEAR DUMMIES Included McFadden R 2 0.063 Wald chi 2 409.37 Table III. No. of cases 595 Results of the ordered probit model Notes: +not significant; *p , 0.1; **p , 0.05; ***p , 0.01

Downloaded by Virginia Commonwealth University At 08:27 05 May 2015 (PT) study where teammates’ abilities had a significant influence on performance in the Tour (Torgler, 2007). It was also hypothesized (H5) that coaches who have ridden the Tour themselves are more qualified to improve an individual cyclist’s and team’s output by developing racing strategies that increase a particular rider’s winning probability. The negative coefficient of the COACH variable supports this assumption (see Table III) and therefore, the fifth hypothesis (H5) can be confirmed. According to Model 1 and 2, riders from a rich team (LN BUDGET) perform better, but the effect is not statistically significant. However, interpreting this as being irrelevant might be biased and wrong, because teams with a higher budget are able to buy better riders, which signals higher quality. To check this assumption, the team’s budget and rank of each team’s top performer for every year were correlated (n ¼ 84). The significant and negative correlation (r ¼ 20:24; p , 0:05) indicates that more money attracts top performers since most of the money is allocated to the best riders. Team and Hence, although the budget is c.p. statistically insignificant, it is highly relevant for individual selecting the best individuals (but has no further value beyond that). Consequently, hypothesis H6 predicting that the higher the team’s budget, the better the rider’s final performance standing can be confirmed. Ratten (2011) has highlighted the importance of linking research results with practical implications for practitioners in international sport management. The 429 findings of this study have implications for various practitioners such as coaches, team managers, and all people involved in team production. First, the finding that weight and BMI had a significant impact on performance in the Tour should be of interest for coaches (it is self-explaining that riders are aware of the importance of their weight). It can be recommended that coaches choose light captains and teammates for races such as the Tour. Second, human capital was a significant driver of performance and therefore nominating experienced riders with past Tour participations can also contribute to success in the Tour. This finding is also interesting to other team sports and other professions with team production. It can thus be recommended that team managers take the experience and previous successes of players or workers into account when forming teams as these factors can be crucial to success. Third, the findings imply that the team’s budget is relevant to performance in the Tour. However, it is important that team managers spend the budget wisely and pay attention to the composition of the team. It was shown that having only one strong team captain and several good coworkers was more effective than having several star riders (i.e. potential captains) in a team. This finding is of interest to other team sports and other professions where teamwork plays an important role. For example, composing teams of several stars does often not work in football as well. It can therefore be recommended that teams should consist of only one captain and several good coworkers. While it is problematic to have several captains, it can also be problematic to have team members who are never allowed to pursue their own goals and might thus lose motivation. Therefore, it is necessary to have compensation systems in place that also reward and recognize the servants who do not earn the laurels of winning (Frick and Prinz, 2007).

Conclusion This study investigated the impact of individual and team characteristics on

Downloaded by Virginia Commonwealth University At 08:27 05 May 2015 (PT) performance in the Tour de France using data from the years 2002 to 2005. The results of random effects models showed that several factors significantly determine performance. First, a rider’s weight and BMI have a significant impact on performance which is explained by the concept of scaling. Second, the human capital of riders (previous top 20 finishes, previous participations in the Tour), teammates (previous top 20 finishes), and coaches (Tour experience as a rider) also has a significant influence on performance. Third, the team’s budget is crucial to buy good riders for the team and thus impacts performance, but it has no further value beyond that. Fourth, the results indicate that the composition of the team matters: composing a team of stars is counterproductive, since too many individuals have an incentive to ride for themselves and are not thinking like good team players. This problem is originated in the specifics of cycling: Cycling is team production won by a single racer (Brewer, 2002) thus leaving the servants without merits. TPM Given the length of the Tour, the different terrains, the specialization of riders and of 18,7/8 course the effect of wind resistance there is room for thinking strategically. Apart from the results of this study and the research presented by Coyle (2005, p. 2195) who argues that the seven-time consecutive winner of the Tour “embodies a phenomenon of both genetic natural selection and the extreme to which the human can adapt to endurance training”, being successful in the Tour might also be affected by tactics and strategy. 430 However, it has to be mentioned that this high regard of Lance Armstrong has to be treated with caution given his recently detected use of doping or at least medicine. Nevertheless, a promising avenue for rather unsuccessful teams would be to understand the principles of game theory. Certainly, given the nature of a repeated game (multiple stages), future research of the determinants of cycling success should implement a more game theoretical approach.

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Further reading Borjas, G.J. (2009), Labor Economics, McGraw-Hill, Boston, MA.

About the authors Professor Dr Joachim Prinz is a Professor in the Department of Managerial Economics at the University of Duisburg-Essen where he teaches Personnel and Organisational Economics. He has written articles about a wide array of individual and team sports including cycling, marathon running, biathlon, rowing as well as football and basketball. Furthermore, he looks at

Downloaded by Virginia Commonwealth University At 08:27 05 May 2015 (PT) the development and implementation of incentive compatible compensation systems. Additionally, he is highly interested in the economics of education and in media economics. Joachim Prinz is the corresponding author and can be contacted at: [email protected] Pamela Wicker is a Senior Lecturer in Sport Management at Griffith University, Australia. Previously she has been employed at the German Sport University Cologne where she was awarded a PhD in 2009. Her main areas of research are in the fields of sport management, sport economics, and sport finance where she looks at sport participation, economics of sport consumer behaviour, financial issues in sport, and the development of sports clubs. She has undertaken research in several professional and amateur sports.

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