Peer effects and individual human capital:

evidence from association football

Mariia Molodchik Associate Professor at National Research University Higher School of Economics, Perm Campus, Russian Federation

Sofiia Paklina Research assistant in International Laboratory of Intangible Driven Economy in National Research University Higher School of Economics, Perm Campus, Russian Federation

Petr Parshakov Associate Professor at National Research University Higher School of Economics, Perm Campus, Russian Federation

Abstract

The paper aims to investigate peer effects in the development of human capital in team sport.

Individual human capital of more than 8,000 soccer players from 281 teams between 2009 and 2015 is measured with the help of the FIFA video game simulator developed by EA

Sports. The study reveals non-linear effect of team quality and negative effect of team heterogeneity in terms of human capital. The learning effect is observed by the quality of newcomers, while share of new players is negatively associated with the development of individual human capital.

Keywords: peer effects, individual human capital, association football

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Introduction

Since Becker (1964) developed a construct of human capital (HC) and provided evidence of its importance for organizational performance, scholars have studied the antecedents of HC emergence. Among key areas for investigation are the types of groups that are most effective for individual learning and development, and how collective competence facilitates individual HC development. This paper follows studies of the peer-effect phenomenon, such as those of Manski

(1993), Jane, (2015) and Buechel et al. (2018), attempting to extend empirical knowledge of whether and how group members induce individual behavior and, consequently, form individual

HC.

Peer effects on individual HC development are still under scholars’ and practitioners’ debates, while both positive and negative effects were revealed (Jane, 2015). In contrast to previous studies, we focus our paper on a specific type of groups, which are characterized through high level of task complexity, interdependency and orientation to joint result. Such unique constellation is provided by team sport setting, in particular, for this study we use association football. In team sport the role of peer effects was established for team performance, in other words for group level (Depken and Haglund, 2011). To our knowledge team sport was not previously considered as a case for individual HC development through peer effects. Therefore, the impact of team quality on individual HC has not been investigated.

The peer-effect framework claims that group effects arise because of both knowledge transfer and social pressure (Buechel et al., 2018). This is one of the reasons of contradictory results obtained by the previous studies, because individuals can experience both positive and negative influence from peers, depending on task complexity, age, time period and other factors that moderate group effects. Taking team sport as a setting, we get one more opportunity to extend empirical knowledge on peer effects. Transferring in soccer allows at considering a group member mobility. This can be expressed in learning and pressure effects due to player mobility; in other

2 words, due to new group members team quality might change and, therefore, might influence individual HC. It seems relevant to include group member mobility in HC development discourse because of the rapid change in environments and increasing employee mobility during recent decades.

Consequently, the following research question is addressed in this study: Whether and how team quality contributes to individual HC development?

We measure individual HC of more than 8000 soccer players from 281 teams during 2009 and 2015 through their rankings available in fifa video game. We expect to reveal both learning and pressure effects of team quality, expressed in positive and negative impact on individual skills.

For this purpose, we measure team quality through four different perspectives. The first one presents the team average ranking of players, the second Gini coefficient of team players’ ranking reflecting talent disparity in particular team. Two last perspectives are devoted to player mobility and reflect the quantity and the quality of new commers. We suppose that in team sport individual

HC will benefit through low share of new commers, because of great importance of shared experience. At the same time, we expect, that knowledge transfer due to new players will balance such negative effect; and team quality growth due to new commers’ ranking will positively contribute to individual HC development.

We hope that the findings will contribute to empirical evidence of peer effect concept in a particular setting such as team sport. Additionally, the results might be interesting to individuals in terms of promoting understanding of self-efficacy determinants.

The rest of the study is organized as follows: firstly, the theoretical background to peer effects on individual HC is described. The following section presents the research design. The empirical part of the paper consists of database description and the results of econometric analysis.

The closing section is devoted to the conclusion.

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Theoretical background

Scholars recognize that individuals possess a stock of knowledge, skills and abilities or in other words, an individual human capital (HC), which can be leveraged to enhance organizational performance (Becker, 1964; Dawson and Dobson, 2002). This is why the antecedents of individual

HC attract scholarly attention. Beyond the traditional approach to predicting individual development and performance through within-person sources (Dalal et al., 2014), there are plenty of theoretical concepts that underline group or team contributions to individual development. One of such concept is the seminal approach of the peer-effects which ascertains the impact of peers or teammates on individual development (Cornelissen et al., 2017; Parboteeah et al., 2015).

Peer effects were investigated in different settings, such as education, the workplace, labor markets and in sport. These occur due to social interaction between the members of a group in a wide sense. Social pressure is induced by social incentives, for example, when an employee increases his or her efforts due to feelings of guilt. Simultaneously, interaction within the group may also lead to knowledge spillover whereby teammates learn from each other and improve their skills (Cornelissen et al., 2017). According to Martin (2010), peer support has a positive effect on knowledge transfer due to the ability of peers to arouse and motivate trainees.

Whether group effects are expected to be positive or negative is often theoretically ambiguous, and empirical studies report both positive and negative evidence. For example,

Emerson and Hill (2018) found negative peer effects in marathon racing, attributed to the self- sorting of runners by ability. We try to extend empirical evidence of the peer-effect concept by measuring teammate quality from different perspectives. In previous studies, peer group quality was measured by one indicator – the predetermined quality of peers. We attempt to find empirical evidence of team-induced individual development using four metrics; in particular, we consider team average rating, Gini coefficient of players’ ratings, share of new players and growth of team average rating due to new commers.

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In terms of human capital resource emergence, soccer team members function within a highly complex task environment, which means that interdependence and coordination among members are crucial to the team’s effectiveness (Ployhart and Moliterno, 2011). This influences the processes of interaction, communication, joint behavioral actions and co-development of teammates, which leads to complementarity of individual HC resources. Individual development is thus influenced by the behavior of other teammates (Franck and Nüesch, 2010).

During soccer training sessions and games, learning occurs due to joint play and opportunities to acquire additional skills from knowledge transfer, observing others and shared experiences, which players would not benefit from otherwise. Pressure can occur when players commit to regular practice and training together in particular combinations. According to Buechel et al. (2018), being observed by peers may have a positive effect on individual performance due to peer pressure because it increases perseverance or persistence when working on a demanding task. Peer pressure can also occur due to the position of individuals in relation to other group members. The relative position in terms of average skill level can cause negative tension if an individual considers that the skill level is particularly high or unachievable.

We consider two aspects of team players’ qualifications: the average rating of all players and team heterogeneity in terms of players’ ratings. Since an individual can acquire team skills, his achievements correlate with how strong a team is (Hartenian, 2003; Marks and Lockyer, 2004).

We, therefore, propose the following hypothesis:

H1: Average team rating has a positive impact on individual HC.

Another aspect of team quality concerns disparity of players’ HC within the team.

Heterogeneity in peer skills creates a learning pool where players share their knowledge and expertise, and those who are less skillful learn from their more talented and experienced teammates

(Franck and Nüesch, 2010; Haas and Nüesch, 2012; Rodan and Galunic, 2004). A gap in abilities may motivate players to catch up with the leaders, and, consequently, this gap will positively influence individual development and performance. At the same time, when the distance to the

5 leaders is extremely large, demotivation is more likely to result (Antonakis and Atwater, 2002;

Chen et al., 2011). Therefore, the second hypothesis is as follows:

H2: Heterogeneity of a team’s players’ rating has an impact on individual HC.

Why might the relative position of a player change? Team quality can be raised by new talent coming from the external market. The “fitting” process between the skills of different individuals, acquiring a synergy effect due to familiarity among team members, takes time. Such accumulated, shared experience is positively associated with team performance (Huckman et al.,

2009; Berman et al., 2002). However, Coates et al. (2018) found that, in eSports teams, employee turnover does not affect team performance if the skills are controlled. These studies are focused on team-level performance, whereas, in our paper, we focus on micro-level effects. We assume that shared experience is important not only at the level of the team but also at an individual level and is reflected in individual HC development. Following this logic, we can assume that a high proportion of new team members will have an adverse effect on improvement in individual HC.

Therefore, next hypothesis is:

H3: Higher proportion of new players negatively affects individual HC;

The presence of new “star” team members might also put additional pressure on other team members (Gibson et al., 2002). On the other hand, Campbell et al. (2014) found that mobility events have a significant adverse effect on the human capital of employees joining an organization, although they do not affect incumbent employees. It is, therefore, not clear whether the quality of new team members affect a team positively or negatively. Since new team members might vary in skill levels, we propose the following hypothesis in terms of the quality of newcomers:

H4: The increase of team average players’ rating due to external players affects individual HC.

Data and methodology

The study presented here is based on a sample of 8,833 soccer players from 281 teams between 2009 and 2015. In order to measure the HC of individual players, a metric provided by

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EA Sports was used, which represents players’ rating using a score ranging from 1 to 100. The dataset is based on data from the FIFA video game simulator developed by EA Sports. Sherif

(2016) and Coates et al. (2018) explain the process of evaluating skills for the FIFA video game.

In the first step of this multistage process, a “network of over 9,000 members reviews the player’s abilities, watch him play, and help assign him various ratings”. At the next stage, the data are reviewed “by 300 editors, which arrange the data into 300 fields and 35 attribute categories”. After this, EA “uses this feedback in conjunction with its own stats (scoured from other agencies) to determine ratings”.

Such data have the advantage of providing a free expert assessment of many actual soccer players from seven different perspectives (attacking, skill, movement, power, mentality, defending and goalkeeping). This is why soccer provides a unique setting for studying how individual HC development can be enhanced through a team environment.

The following regression equation was estimated:

푰풏풅풊풗풊풅풖풂풍 푯푪풊풋풕 = 훼 + 푻풆풂풎 풒풖풂풍풊풕풚풋풕−ퟏ ⋅ 훽 + 푿풊풕 ⋅ δ + ∑ 휃푡 ⋅ 푠푒푎푠표푛푡 + ∑휑푗 ⋅ 푡푒푎푚푗 + 휖푖푗푡

The dependent variable is based on the HC of individual players, measured by their ratings.

The team quality is expressed in team average rating, Gini coefficient of these ratings, the share of new players, and the increments in average team ratings due to new players. 푿 includes the following control variables: age, squared age, height, squared height, weight, squared weight, years in the team and formation variation. Finally, 푠푒푎푠표푛 and 푡푒푎푚 represent the season and team dummies used to catch unobservable factors. Table 1 shows how group characteristics were calculated.

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Table 1. Calculation of group characteristics

Variables Definition Calculation

푛 Avg_rating Average rating of ∑ 푗푡 푖=1 푟푎푡푖푛푔푖푗푡 − 푟푎푡푖푛푔푖푗푡 members of team 푗 = 푛 − 1 except for the observed 푗푡 player 푖 of this team at period 푡, n is the number of members in a team Gini_rating Team 푗 rating ∑ ∑ |푟푎푡푖푛푔 − 푟푎푡푖푛푔 | = 푖 푘 푖푗푡 푘푗푡 heterogeneity, measured 2푛 ∑ 푟푎푡푖푛푔 as a Gini coefficient 푖 푖푗푡 during the period 푡.

Growth_avg_rating_new The change in team 푗 = (푟푎푡푖푛푔푗푡 − 푟푎푡푖푛푔푗푡−1) players average rating during 푛푠푡푎푦푒푑 ∑ 푡 푠푡푎푦푒푑 푠푡푎푦푒푑 the period 푡, compared 푖=1 (푟푎푡푖푛푔푖푗푡 − 푟푎푡푖푛푔푖푗푡−1 ) − to 푡 − 1 due to new 푠푡푎푦푒푑 푛푡 players. Players who did not change team during period 푡 are labeled as 푠푡푎푦푒푑. Share _new players The number of new 푠푡푎푦푒푑 푛푗푡 − 푛푗푡 players in team 푗, = 푛 divided by the total 푗푡 number of team players during period 푡.

Table 2 shows descriptive statistics for all variables used in the study.

Table 2. Descriptive statistics

N Mean SD Min Max

Peers’ characteristics Avg_rating 13,875 57.82 5.003 43.45 73.03 Gini_rating 13,875 0.131 0.0198 0.0715 0.193 Growth_avg_rating_new_players 13,839 -0.604 4.054 -18.89 14.39 Share_new_players 13,875 0.406 0.137 0.0500 0.966

Players’ characteristics Rating 13,875 59.11 15.22 9.200 90 Years stayed in the team 13,875 1.584 1.433 0 7 Age 13,875 26.57 4.009 18 42 Height 13,875 182.0 6.408 160.0 208.3 Weight 13,875 76.86 6.822 54.88 110.2

Formation variation 13,875 2.902 1.508 0 7.240

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The first pool of variables represents the team quality. The first indicator (the average team rating except for the observed player) serves as a proxy for the team environment and can be a source to improve individual HC. In order to capture the effect of team skills’ heterogeneity, we evaluate the Gini coefficients for the ratings. The higher the value of the Gini, the more unbalanced the team is with respect to its rating. Observable teams are not very heterogeneous in terms of rating: the average Gini coefficient equals 0.131. Next, the group mobility is represented as external growth and the proportion of new players. To evaluate the effect of hiring new talent from the external market, we calculate the incremental rating of players who joined the team in the previous season. On average, such rating excess of new players rating is -1.604, meaning that teams invite new players who are not as strong as the existing members. Finally, the proportion of new players is included in the model because this reflects the opportunity for players with different backgrounds and experience to learn from each other. We observe a huge variation in this indicator, which varies from 0.05 to 0.96 with a mean value of 0.406.

The set of independent variables also includes control variables that reflect both individual and team characteristics. The former includes indicators of players’ physical abilities, such as age, height and weight. We also include the number of years each player stayed in the team. Regression equations include their squared terms, supposing possible nonlinear effects. The average player in the sample is 26 years old, weighs 76.8 kg, is 1.82 meters tall and stays in a particular team for a year and a half.

The second set of independent variables is related to team characteristics, represented by formation variation as an indicator of how conservative a team is in terms of its strategy. The idea is to see how the pursuit of a particular strategy by the whole team influences individual performance. Taking into account the dynamic nature of soccer, we assume that more agile teams create a more beneficial environment for particular players. For each game, we use pitch coordinates reflecting each player’s position in the starting squad, provided by Mathien (2016) and discussed by Coates et al. (2018). We average the y-axis coordinate for each player across matches

9 and evaluate the standard deviation of these averages across matches throughout the season in order to capture any variation in the strategy.

The dependent variable is presented among players’ characteristics and reflects the individual rating, giving an approximation of individual HC. A high rating for heterogeneity can be seen, ranging from 9.2 to 90 with an average value of 59.11.

Results

Table 3 reports the results of the regression analysis. All variables representing individual player characteristics (such as age, height and weight) and their squared terms are statistically significant at the 1% level. In each case, we observe the inverted U-shape relation, as one would expect. Individual HC is highest for 27-year-old players, with a height of 1.68 meters and a weight

64 kilos.

To see whether the effects of different group characteristics are nonlinear, we performed a set of tests. For each covariate, we included a squared term and tested whether both the linear and squared terms were jointly significant using an F-test. If they were found to be significant, we included both in the final regression equation, and, if not, only the linear term was included. This specification allows the marginal effect to vary across the different covariate values.

Team quality, measured as an average of the team players’ ratings (except for the observed player), influences individual human capital nonlinearly. We observe an inverted U-shape relation with a turning point of 45. Our findings generally indicate a declining curve since team average players’ rating vary from 43 to 73 in our sample. This means that players perform worse on average in a stronger team. This might be the result of high levels of competition or the psychological pressure effect. H1 is, therefore, rejected since we observe the negative impact of team average rating on the individual HC.

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Table 3. Empirical results Individual HC

Lag of Avg_rating 1.871*** (0.292) Lag of Avg_rating sq. -0.021*** (0.003) Lag of Gini_rating -56.566*** (6.611) Lag of Growth_avg_rating_new_players 0.439*** (0.027) Lag of Share_new_players -6.801*** (0.864) Lag of Years stayed in the team -0.027 (0.090) Lag of Formation variation 0.020 (0.080) Age 4.305*** (0.280) Age sq. -0.081*** (0.005) Height 8.070*** (0.821) Height sq. -0.024*** (0.002) Weight 1.915*** (0.288) Weight sq. -0.015*** (0.002) Rightfoot -2.253*** (0.232) Team dummies Included

Year dummies Included

Constant -747.070*** (69.580)

Observations 13,875 R-squared 0.420

The next part of the results is related to team heterogeneity in terms of players’ ratings, measured as the Gini coefficient. This is statistically significant and negative in the regression equation. An individual would, therefore, benefit from being in a team with a lower degree of team heterogeneity, in other words homogeneous team in terms of players’ ratings is more preferable for individual HC development. This is in line with H2. 11

In terms of H3 and H4, which are about the quality and the quantity of newcomers, we have found that the proportion of newcomers negatively influences individual HC, but the quality of newcomers improves these. This is in line with our hypotheses.

We also controlled for the number of years that players remained in one team. The variation in formation as a proxy for coaching strategy variability is not statistically significant. Coach efficiency is an important factor of team and individual performance (Matsuo, 2018), so the fact that our proxy is not statistically significant shows that our results are robust to the endogeneity problems cause by the impact of managerial coaching described in Rocha et al. (2018).

Conclusion

This study aimed to investigate the effect of team environment on learning and development of individual HC. Findings reveal significant peer effects in terms of team quality.

All estimations were done in the context of sport. Given that sport provides a good example of high task complexity and very intensive interaction within player groups, the results should be interpreted with this in mind. Contrary to expectations, the average level of team ratings negatively influences the development of individual HC. It appears that under conditions of high task complexity, individuals experience negative pressure if the average level of group skills is high.

At the same time, team heterogeneity has a negative impact on individual ratings, meaning that teams should be homogeneous in order to encourage players to grow.

In team mobility terms, two significant effects were discovered. Firstly, the quantity of newcomers impacts negatively on individual HC. This contradicts the findings of Campbell et al.

(2014) who found that mobility does not have an impact on incumbent employees. This study provides evidence that a higher proportion of newcomers might be associated with more limited improvement in individual HC. Such a conclusion is consistent with the shared experience paradigm when knowledge transfer is more successful in a group with a lower turnover. At the same time, this study shows that new knowledge in terms of newcomers is important for

12 development of individuals HC. Thus, if a team’s average rating is increased due to new players, this will stimulate individuals to improve their skills. It also means that new members should have higher HC (measured by individual ratings) than average team rating in order to contribute positively to players’ development.

To summarize, the current study provides some interesting insights concerning the improvement of individual HC through different aspects of a team environment. From a practical point of view, managers can use the results obtained to create an environment in which all members can develop skills and abilities, and fully realize their potential. In turn, development of this HC will enhance the performance of any organization.

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