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An econometric evaluation of the effect of firing a coach on team performance Koning, Ruud H.

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Download date: 29-09-2021 Applied Economics, 2003, 35, 555–564

An econometric evaluation of the effect of firing a coach on team performance

R. H. KONING Department of Econometrics, University of Groningen, PO Box 800, 9700 AV Groningen, The Netherlands E-mail: [email protected].

Firing the manager is a drastic measure employed by firms to deal with poor per- formance. However, data on within-firm dynamics are scarce, and the firing of individual managers is rarely recorded in the firm level data currently available. This makes the value of firing a manager difficult to assess. Data on sports offer a unique opportunity to study this phenomenon because the firing of a coach is usually well-publicized. Using data on soccer, the author evaluates the effect of the firing of a coach on team performance. As teams do not face the same opponents before and after a coach is fired, the issue of sample selectivity is addressed.

I. INTRODUCTION II. CHANGING MANAGERS AND COACHES: SOME FINDINGS IN THE Managers are usually held accountable for the perform- LITERATURE ance of the firm for which they work. If the results fall short of expectations, they may be fired or they may get Textbook economic theory would suggest that a firm another perhaps less prestigious position in the same firm. replaces a manager to increase profit (or reduce loss). If For economists it is very difficult to assess whether this this is indeed the case, then profits should increase after a action by the board improves the profitability (or any firing decision. In theory, one could compare profits under other measure of performance) of the firm since these the old manager to profits under the new manager to see if changes in management are not usually observable from performance has improved. However, there are three diffi- standard firm level data. Only changes in top-level manage- culties with this seemingly straightforward approach. ment are announced if the firm is required to do so because The first difficulty is the measurement of performance. In of the ownership structure. In sports, however, data on the theory, profit maximization may be the objective of a firm. performance of teams and on the dismissal of coaches is In practice, however, the performance of a firm is difficult publicly available. to measure, and in fact, different managers in an organ- Using data on soccer, the study examines whether firing ization can pursue different objectives. The intended effect a coach leads to improvement of the performance of the of firing a manager may be different from increasing profit. team. Folklore has it that replacement of a coach leads to a A manager may be fired to appease shareholders or to ‘shock’ effect; this improves the performance of a team. demonstrate that the firm can be ‘tough’ on poor perform- This article is set up as follows. Section II provides a ance. The second difficulty is observing if and when a discussion of related studies. Section III discusses data manager is fired. Firm-level data disclosing the duration and presents some preliminary evidence that indicates of employment of individual employees are difficult to that firing a coach improves performance. However, this find. Firm-level data are usually in such aggregated form preliminary evidence is misleading as it does not account that it is not possible to observe the firing of an individual for differences in the order of play. That is, the old and new manager. coaches do not face the same opponents. Two statistical Even if the performance of a firm can be precisely models that control for this type of sample selectivity are defined and measured and the firing of a manager is fitted later in the article. They reveal that firing a coach in recorded, there remains a third difficulty. The old and the fact does not improve team performance. new manager invariably face different conditions. This

Applied Economics ISSN 0003–6846 print/ISSN 1466–4283 online # 2003 Taylor & Francis Ltd 555 http://www.tandf.co.uk/journals DOI: 10.1080/0003684022000015946 556 R. H. Koning makes it difficult to assess what part of the change in player is hired and which player is put on the transfer performance is due to the change of manager, and what list. A coach’s responsibilities are to provide training and part is due to the change in the conditions faced by the two guidance to players that translates into more wins and a managers. higher ranking in the league. The issues mentioned above are addressed only partially There a number of reasons why a coach may be fired. in the literature on changes in performance and its relation- The reason cited most often is the existence of a ‘shock’ ship to changes in management. There exist in fact two effect: the new coach is able to motivate the players better, streams of the literature to which this article is related: and therefore is able to improve results. Coaches work one in finance and the other in the statistical analysis of under high media and fan pressure. If results fall short of sport outcomes. Most of the studies in the finance literature expectations, or if the quality of the play does not live up to on managerial change and performance focus on the expectations, pressure may mount to fire the coach. The reverse question: what are the factors that lead to a change general public is willing to pay to see winners, less so to see in management? For example, in a study on the relation losers. Moreover, it is important for teams to be successful, between a firm’s stock return and subsequent management because transfers of players from successful teams to other changes, Warner et al. (1988) found that a firm’s share teams are more valuable, and hence the return on invest- performance and the probability of a management change ments into these players is higher. are negatively related. They also examine whether the The effect of changing a coach has been studied before, announcement of a management change results in any see for instance Van Dalen (1994), Scully (1995) and Brown abnormal stock returns, but find little evidence of this (1982). Van Dalen’s study discussed whether firing the effect. While Warner et al. (1988) examined the effect of coach of a soccer team improves performance. He esti- the announcement of a new manager, they do not examine mated a model in which the dependent variable is the dif- whether the performance of stock returns actually improve ference between the goals scored by the two teams that play under the new manager. each other in a particular game. The independent variables The latter issue is addressed in studies by Denis and are a measure of the quality of the referee, a measure that Denis (1995) and more recently by Khurana and Nohria captures the difference in team quality, a dummy variable (2000). Using the ratio of operating income before depre- indicating a home game, the result of the previous game ciation to operating assets as the measure of performance, and a trend. He estimated this model for each team in the Denis and Denis found ‘that forced resignations of top competition, and he extended the model with a dummy managers are preceded by large and significant declines in variable that indicates that the new coach has taken over. operating performance and followed by large improve- Using data for the 1993/94 season only, he found that all ments in performance’. They conclude that there are valu- coach changes have a positive effect on the goal difference, able operating improvements associated with forced and the effect is significantly positive in three of the five resignations. Khurana and Nohria used a similar measure cases. of firm performance to assess whether forced management Van Dalen’s study has two drawbacks. The most import- turnover leads to improvements of performance. They pro- ant drawback is that the model used for the goal difference pose a model in which the departure of the existing man- depends on the ranking of both teams at the moment the ager and the origin of the incoming manager are analysed game is played. As one will argue later, this may bias the simultaneously. Using a random-effect panel data results because of the non-random order of play. Another approach to model firm performance, they find that natural drawback of the approach of Van Dalen is that he uses management turnover followed by an insider has little data for one season only. By extending the sample period effect on firm performance, but that forced turnover fol- to more seasons one can assess whether a firing effect, if lowed by an outside successor improves the performance any, is similar across seasons or not. significantly. Another contribution from the sports literature is Scully The studies just discussed use financial measures of per- (1995). In chapter 8, he examined changes of coaches in formance to assess the performance of a firm. For sport baseball and basketball between consecutive seasons. In his teams in general and soccer teams in particular, financial empirical analysis, Scully estimates binary choice models measures of success are less relevant. Only few soccer teams where the decision whether or not to terminate the contract are listed as publicly traded firms on stock exchanges. The of the coach is the dependent variable, and the ranking at aims of a team are usually more clear than the goals of a the end of the season is the (only) independent variable. He firm: teams want to end a competition as highly ranked as found that the probability that a contract is terminated is possible. significantly positively related to the ranking for almost all The coach of a team has an important role in determin- teams in baseball and basketball. On average, a one-rank ing the ranking of a team as he trains the team and deter- increase in club standing increases the average probability mines the line-up. Moreover, he is one of the managers of of firing by 0.066% and 0.077% in baseball (National the club who has an importance in determining which League and American League, respectively) and by Effect of firing a coach on team performance 557 0.11% in basketball. He then proceeded to regress the This article focuses on the results on the field as a meas- change in ranking between two seasons on the status of urement of team performance. It assumes that the board of the coach. He found that in almost all cases the decision soccer teams want their team to be as high as possible in to fire the coach was rational, with the average improve- the ranking. Dutch soccer teams are usually foundations ment in ranking being approximately 1 in baseball and 2/3 without a profit objective, and there are no franchises that in basketball. can be traded. The teams are managed by a board, and the Scully analyses the termination of the contract of a coach board appoints a coach. Considering the recent commer- between seasons. This makes his approach less relevant for cialization of soccer, one may wonder whether a high rank- soccer, as the composition of teams in soccer usually ing in the league is the only aim of the board of a soccer changes significantly between seasons. Moreover, in soccer, team. For instance, the board may want to maximize share- coaches are fired both during the season and between the holder value instead of the results on the field. Since no seasons. Dutch teams were listed on the stock exchange during the In contrast to Scully, in this article restricts attention to period considered, this issue is not addressed in this paper. coach changes during the season. By focusing on coach It focuses on the 18 teams in the Dutch Premier League. changes during the season one can measure the coach-effect We restrict ourselves to coach changes that are not caused better, since the pool of players that new coach can use is by outside offers to the coach. Instead, we focus on changes not very different from the one the old coach faced. that are initiated by the management of the club because Brown (1982) also analysed coach changes during sea- these changes are initiated to improve performance. At first sons and uses data from the National Football League glance, this may appear to introduce sample selection (NFL) over the 1970–1978 period. He estimated a random because it is known from the labour market literature effect panel data model in which performance (the percen- that an employee who anticipates being fired may quit to tage of wins) is explained by lagged performance, a suc- avoid any stigma effect of a firing. Therefore, it has been cession dummy, and a (random) individual effect. He argued that the distinction between quits and layoffs is found that a change of coach in the current season costs unclear. However, vacancies in coach positions are rare 11% in the percentage of games won. Since a season con- during the season, and a coach who anticipates being sists of 14 games, this means that it costs a little bit more fired is extremely unlikely to be able to generate an outside than one game won during the season. Because it is difficult offer. There exists a ‘class’ structure for coaches in the to hire new players during the season, he interpreted this Dutch premier league. That is, a coach who either quits finding as ritual scapegoating by the board of a team, or gets fired from one team in the premier league usually necessary to appease fans and press media. finds another position in the same league. This makes the pool of potential employers of a coach (who anticipates being fired) limited. Hence, outside offers can only be gen- erated if another team has a vacancy or just fired a coach, III. DATA AND DESCRIPTIVE MEASURES and that is a rare coincidence. On the other hand, the pool of potential coaches is big. Every team employs at least two This article uses data on the soccer teams that make up the coaches, so a fired coach can always be succeeded by his Dutch premier league (the highest division in The assistant. Moreover, there are a number of coaches without Netherlands) for the five seasons between 1993/94 and a team, and they can be hired as a successor. Hence, the 1997/98. The unit of observation is a game. The date supply of new coaches does not restrict teams in firing the when each game is played and the final score are recorded. current coach. If a coach was fired during a season, the date of firing is A first sketch of the number of coaches fired and their recorded as well. A detailed listing of all coach changes effects is given in Table 1. If one keeps in mind that only 18 within these seasons is listed in Appendix I. teams participate in the premier league, it is clear from the

Table 1. Descriptive statistics

APG N M Number of coaches fired Old New Old New Old New

1993/1994 4 0.50 1.30 0.90 1.28 2.26 1.61 1994/1995 7 1.01 1.26 1.31 1.68 1.84 1.88 1995/1996 8 0.98 1.04 1.43 1.10 1.91 1.80 1996/1997 4 1.03 1.43 1.19 1.36 1.70 1.39 1997/1998 5 0.66 1.08 1.26 1.46 2.26 1.48 558 R. H. Koning second column of Table 1 that coach dismissals are not First, one characterizes teams by two parameters: the uncommon. quality of the team and the home advantage of the team. Note that in soccer, a game won yields three points, a One will test whether these parameters change after the draw one point and a loss zero points. The number of coach is fired. Different methods have been proposed to points determines the ranking. The column labelled AGP measure such quality, see among others Stefani (1980), contains the average number of points per game for the old Clarke and Norman (1995), Kuk (1995) and Koning and new coach (one averages over teams that change (2000). The model used here is similar to that of Stefani, coaches). One sees that on average the new coach earns and Clarke and Norman. more points with his team. Moreover, one sees that in Consider a game between two teams indexed i and j.In most seasons the average number of goals per game what follows, the team indexed i will be the team that plays increases ðNÞ, and that the number of goals conceded a home game while team indexed j (the opponent of team i) ðM Þ decreases on average. Based on this eyeball interpret- is the away team.1 The number of goals scored by team i ation of the data, one would conclude that firing the coach against team j is denoted by Nij, and the number of goals of an underperforming team is a sensible strategy as results conceded by the home team is Mij. The goal difference is then improve. defined as Dij ¼ Nij À Mij. The quality of team i is denoted This conclusion, however, may not be correct. The old as i, and the goal difference is related to the difference of coach and the new coach do not play the same opponents. quality between both teams. The goal difference Dij is Coaches are not fired randomly throughout the season, but assumed to have the following form: usually after a spell of disappointing results. There can be D h two explanations for these losses: the team is underper- ij ¼ i þ i À j þ "ij ð1Þ forming or suffers from bad luck (in other circumstances where hi a parameter that denotes home advantage. The the team could have won some of these games), or the term hi may be interpreted as the expected win margin if other teams are simply better. Since the schedule of the team i would play a home game against a team of equal competition is fixed, it is possible that the old coach started quality (that is, if i À j ¼ 0). "ij is a mean zero error term the season by playing tough opponents. If he gets fired and that has constant variance. If Dij is positive, one expects a new coach takes over, the new coach faces the lesser team i to win, if it is negative, one expects team j to win. In teams in the competition, and wins his games. It is difficult model (1) team i can win against team j even if it is of to attribute the improvement in results to changes in the inferior quality ði À j < 0Þ if the home advantage of coach as there are quality differences among teams and the team i is big enough. Note that not all parameters in order of play is non-random. Hence, any precise measure- Equation 1P are identified, so one imposes the identifying ment of the coach effect should allow for randomness of restriction i i ¼ 0: the quality parameters can be inter- results, and for quality differences among the opponents of preted as deviations from a hypothetical average team with the team. This is the subject of the next section. quality 0. Hence, i is the expected goals difference if team i would play the average team on a neutral venue. The home advantage hi is allowed to vary between teams; in the IV. THE EFFECT OF FIRING A COACH empirical results we find that the restriction of constant (over teams) home advantage is rejected. As argued in the previous section, it is not satisfactory to It is now straightforward to measure the effect of a compare the average number of points gained (or other change of coach in Equation 1: one allows both the par- measures of team performance like the number of goals ameters that captures the home advantage and the quality scored or the percentage of games won) by the old coach parameter i to vary. We estimate the following two exten- with the average number of points gained by the new sions to model (1) for those teams that fire a coach during coach. This section uses two different approaches to correct the season: for this potential bias. First, it estimates a model that ranks hn ¼ ho þ k ; i 2F ð2Þ teams and second, it estimates a model for the number of i i i goals scored in home and away games. In both models it n o i ¼ i þ i; i 2F ð3Þ examines whether the presence of a firing effect can be detected. In both models the explanatory variables include with the superscript referring to either the new coach ðnÞ or the quality of the opposing team and therefore these mod- the old coach ðoÞ and F is the set of teams that fired a els correct for any bias introduced by the non-random coach during the season. ki measures the change in home schedule of play. advantage and i measures the change in team quality.

1 Home advantage is strong in soccer: approximately 50% of all games are won by the home team, 25% are won by the away team, and 25% end in a draw, see Koning (2000). Effect of firing a coach on team performance 559

Besides testing whether the kisand i differ jointly from 0, column (labelled i) allows for changing quality (Equation we also test whether they are constant over teams that fire 3), the fifth column (labelled ki, i) allows for both chang- their coach: ki ¼ k and i ¼ for all i. ing home advantage and team quality. Finally, in column Equations 2 and 3 makes a firing effect, if any, easily six (labelled k) one imposes the constraint that the change interpretable. Let team i face an opponent of equal quality in home advantage is equal for all teams ðki ¼ kÞ,andin o in a home game so that i À j ¼ 0. Under the old coach, column seven one imposes a fixed change in team quality o the expected goal difference is hi , under the new coach it is ð i ¼ Þ. The last column gives estimation results where i ho þ ki þ i, a change of ki þ i. Should this game be one assumes that the changes in home advantage and played away, the expected change of goal difference is i. team quality are similar across teams that changed the Hence, if there is a positive firing effect we would expect coach (ki ¼ k and i ¼ for all i 2F). The point estimates both i > 0 and ki > 0. One estimates Equation 1 and its for k and are also given where applicable. variants for each season separately. If one would pool the The picture that emerges from Table 2 is rather mixed. data over seasons, one would implicitly assume that the The results for the 1993/1994 season indicate that there is a quality of teams remains constant between seasons. This significant coach effect, that is, performance improves after is clearly an unreasonable assumption, consider for ex- a coach is fired. The detailed results in Table 2 show that ample all transfers that take place during the summer. By for all teams that changed coach, both the quality focusing on within-season coach changes, one keeps the improved ð i > 0Þ and the home advantage improved composition of the team constant as there are very few ðki > 0Þ. These results are in line with those documented within season transfers. Any change in performance can by Van Dalen. then be attributed to the change of coaches. This remarkable result, however, is specific to the 1993/ Summaries of the results of the estimation of model (1) 1994 season and we do not find such strong results for later with Equations 2 and 3 are given in Table 2. More detailed seasons. For all seasons one tested the restriction whether information on the estimation results by teams that chan- the change of home advantage and the change of quality is ged coaches can be found in Appendix II. Because of the constant among teams that fired a coach (that is, whether large number of parameters that are estimated for each ki ¼ k and i ¼ for all teams that change a coach). It is model,2 one only gives some summary statistics in only for the 1994/1995 season that this restriction is Table 2: the second column contains the R2 of the basic rejected with a p-value of 0.041, so one restricts the atten- Equation 1, the third column extends that model by allow- tion to the estimation results in the last column of Table 2. ing for changing home advantage (Equation 2), the fourth As concluded above, it is only for the 1993/1994 season

Table 2. Summary of estimation results ranking model, * indicates significance at 5%-level with the null model the model of the second column

h; ki i ki; i k k; 1993/1994 R2 0.312 0.337* 0.342* 0.349 0.328* 0.335* 0.336* k 7777 1.538* 7 1.261 77777 0.977* 0.343 1994/1995 R2 0.373 0.392 0.419 0.423 0.373 0.377 0.377 k 7777 0.089 770.312 77777 0.410 0.571 1995/1996 R2 0.484 0.503 0.482 0.506 0.484 0.485 0.485 k 777770.185 770.081 77777 70.175 70.139 1996/1997 R2 0.367 0.378 0.371 0.397 0.378* 0.367 0.380* k 7777 1.000* 7 0.386* 77777 0.272 70.423 1997/1998 R2 0.465 0.484 0.496* 0.496 0.468 0.476* 0.476 k 7777 0.600 770.157 77777 0.851* 0.932

2 The number of parameters that is estimated varies between 35 (basic model) and 51 (1995/96 season, unrestricted model). 560 R. H. Koning that one finds the result that the change of coach signifi- team i if it would play against the defence of every team in cantly improves both home advantage and team quality. the league (including its own) is now: For the 1996/1997 season one finds that home advantage X X changes significantly, but the change of team quality is e Nij ¼ ij ¼ Ii insignificant and negative. The other seasons do not show j j any significant improvements in home advantage and team because of the normalization mentioned above. Hence, one quality. The finding that a coach change may or may not can interpret i as the expected number of goals team i improve home advantage and/or team quality is corrob- would score if the identity of the opponent is unknown. orated by the detailed regression results listed in One assumes a similar model for Mij (the number of goals Appendix II: after a coach change, team quality decreases conceded by team i when it plays a home game against for 11 out of 28 changes and home advantage decreases 10 team j). Mij is also assumed to follow a Poisson distri- times. bution with parameter ij, and: These conclusions are at odds with the ones based on the descriptive statistics in Section III. There, the average goal ij ¼ ji ð5Þ difference improves when a new coach is appointed, except P Again one imposes I I. One assumes that N for the 1995/1996 season. When quality differences among i¼1 i ¼ ij and M are uncorrelated. In fact, the correlation over the opponents is corrected for, the coach effect disappears. The ij 1993/94–1997/98 period is slightly negative 70.19, but the conclusions based on the descriptive statistics in Table 1 analysis is simplified tremendously when we make the zero are based on sample selectivity. correlation assumption. It could be argued that a firing effect, if any, is only The approach to measuring whether a new coach has temporary. One tested for a temporary succession effect better results in terms of goals scored and goals conceded by letting the dummy variable that indicates the new is similar to the approach taken in the rating model dis- coach take value 1 for his first two or four games, and 0 cussed earlier. First, one estimates a model for scoring after those games. This temporary coach effect turned out intensities both in home and away games, and then exam- to be insignificant in almost all seasons. For the 1995/1996 ines whether these intensities have changed under the new season one finds that the team under the new coach per- coach. As argued earlier, again one estimates the model for forms significantly worse during the first two games. In that each season separately, so that one need not make the season, there is no significant coach effect during the first assumption that the parameters of the teams are constant four weeks. According to these results, a temporary between seasons. improvement in results can not be found. In the interest of fitting a parsimonious model, , , In the model used above, there is no distinction between i i i and was estimated without any restrictions, and then it goals scored and goals conceded: the dependent variable is i was tested whether any restrictions could be imposed. The the goal difference. According to popular belief, new only restriction that could not be rejected for all seasons at coaches try to improve the defence so that any losing streak the 5% level of significance was k : the average offen- is ended. It is possible that such an extra defensive effort i ¼ i sive capabilities of a team in an away game are propor- reduces the offensive efforts of the team. In this case one tional to its average offensive capabilities in a home game. may not observe any change of goal difference as the The parameter k can be interpreted as a measure of home decrease in goals conceded is offset by a decrease of goals advantage, as it is the ratio of the expected number of goals scored. Therefore, it is of interest to analyse the number of in a home game to the expected number of goals in an away goals scored and conceded separately and see if they are game. According to the restriction k , this ratio is the influenced by a change of coaches during the season. i ¼ i same for each team. Of course, k > 1 was found for all To answer this question, one uses a variant of the seasons. Defensive capabilities vary between teams between Poisson model for soccer scores developed by Maher home and away games: some teams defend better at home (1982). One assumes that N (the number of goals scored ij and some defend better in an away game. by team i against team j in a home game) follows a Poisson Using the specification and k , it was distribution with parameter . This parameter will be ij ¼ i j ij ¼ i j ij tested whether offensive and defensive capabilities vary referred to as the scoring intensity. One assumes that: between the old coach and the new coach by allowing the ij ¼ ij ð4Þ parameters i, i, and i to vary between the old coach and the new coach: where i measures the offensive skills of team i. The par- ameter captures the defensive skills of team j. The sta- n o j i ¼ i ; i 2F tistical model for the number of home goals is Nij PðijÞ. n o Again, not all parameters i, i ¼ 1; ...; I and j, i ¼ i i; i 2F j ¼ 1; ...; IPare identified. One imposes the identifying n o I i ¼ i i; i 2F restriction j¼1 j ¼ I. The expected number of goals of Effect of firing a coach on team performance 561 Table 3. p-value’s Poisson models, and the parameter estimates of V. CONCLUSION and , * indicates significance at 5% level This article examined whether firing a coach and hiring a i;i;i ; new one improves the performance of a team. It focused on 1993/1994 0.17 0.0033 1.39 0.65* soccer and in particular soccer in the Dutch Premier 1994/1995 0.046 0.25 1.24 0.96 League. The results illustrate that it is not sufficient to 1995/1996 0.15 0.052 0.79 0.89 1996/1997 0.24 0.28 1.19 0.82 simply compare goals scored, or some measure of goals 1997/1998 0.089 0.033 1.15 0.69* scored under the old and new coach. This simple approach does not control for the differences in the quality of the opponents faced by the new and old coach. The methodology used data from a five-year period and controls for quality differences in opponents. It finds that F is the set of teams that fired a coach. The estimation the performance of a team does not always improve when a results are summarized in Table 3 in the second column coach is fired. In some cases, new coaches perform worse (labelled i, i, i). In this column, the p-values are reported than their fired predecessors. This result is contrary to pre- that correspond to the hypothesis i ¼ i ¼ i ¼ 1 for all vious findings in the sports literature which indicate that teams that fired their coach. This is the null-hypothesis of firing a coach improves performance. no coach effect: the new coach is not able to improve either The model used in this article allows us separate changes the offensive skills or defensive skills of the team. One sees in performance to changes in defensive and offensive skills. that firing of coach has no effect is rejected marginally for By doing so, it is found that there is some evidence that the the 1994/95 season and not rejected for any other season. defensive skills of the teams show improvement when a It was also examined whether any firing effect is present coach is fired and a new coach takes over. This may indi- in this model by estimating a slightly more restricted alter- cate however, that the new coach adopts a strategy to avoid native model. This model tested again whether offensive losses rather than a more aggressive winning strategy. and defensive capabilities have changed, but now one Considering the empirical results, firing a coach occurs assumes that the effects (if any) are constant across teams: too often. Since it is not clear that the results on the field improve after a change of coach, it is likely that the board n o i ¼ i ; i 2F of a team intervenes for other reasons. It is likely that fan and media pressure are also strong determinants of the n o i ¼ i ; i 2F tenure of a coach. It would be interesting to address the question whether a n 0 i ¼ i ; i 2F new coach is more successful if he is an outsider than if he is an insider. Unfortunately, our data do not allow us to The restrictions i ¼ , i ¼ i ¼ could not be rejected for address this issue, we leave this for future research. any season at the usual 5% level. The p-values for testing the hypothesis ¼ ¼ 1 are reported in Table 3 in the column labelled , . The estimates for and are pre- sented in the last two columns of Table 3. In this specifica- ACKNOWLEDGEMENTS tion, the expected number of goals scored by team i when it The author thanks participants at the CEA meeting in plays a home game against team j is ij under the old Vancouver and Padma Rao Sahib for helpful comments. coach and ij under the new coach. Therefore, there would be any firing effect, one would expect to exceed 1 (offensive capabilities improve). Similar reasoning leads REFERENCES us to believe that is smaller than 1 (defensive capabilities Brown, M. C. (1982) Administrative succession and organiza- improve). It is seen that the hypothesis of no firing effect is tional performance: The succession effect, Administrative now rejected for two seasons: 1993/1994 and 1997/1998. In Science Quarterly, 27, 1–16. both cases, the rejection is caused by a significant improve- Clarke, S. R. and Norman, J. M. (1995) Home ground advantage ment in the defensive capabilities of the teams that fired a of individual clubs in English soccer, The Statistican, 4, 509– 21. coach: the expected number of goals conceded is reduced Denis, D. J. and Denis, D. K. (1995) Performance changes fol- by 35% and 31%, respectively. Note that these improve- lowing top management dismissals, Journal of Finance, 50, ments in defensive skills correspond to a drop in the aver- 1029–57. age number of goals conceded by 0.65 (1993/1994) and 0.78 Horn, M. (1999) Trainersontslag leidt zelden tot schokeffect, Voetbal International, 10, 24–26. (1997/1998) (see Table 1). In the rating model discussed Khurana, R. and Nohria, N. (2000) The performance conse- earlier, it was also found that in these two seasons team quences of ceo turnover, manuscript, MIT Sloan School of quality improved significantly. Management. 562 R. H. Koning Koning, R. H. (2000) Balance in competition in Dutch soccer, The relation The coach is fired because work relation between Statistician, 49, 419–31. either the coach and the team or the coach and the board Kuk, A. Y. C. (1995) Modelling paired comparison data with large numbers of draws and large variability of draw percen- have become strained. tages among players, The Statistican, 44, 523–8. voluntary The coach leaves his job voluntarily. Maher, M. J. (1982) Modelling scores, Statistica Neerlandica, 36, 109–18. When a coach is fired, a settlement has to be made Scully, G. W. (1995) The Market Structure of Sports, University between the team and the coach. Whenever is possible to of Chicago Press, Chicago. reach such a settlement before the actual date of firing, the Stefani, R. T. (1980) Improved least squares football, basketball, dismissal is listed as ‘voluntary’. and soccer predictions, IEEE Transactions on Systems, Man Sometimes the vacancy is filled by an interim coach first. and Cybernetics SMC-10, 116–23. Van Dalen, H. P. (1994) Loont het om een voetbaltrainer te ont- In that case both the interim coach and the new coach are slaan? Economisch Statistische Berichten, 79, 1089–92. listed, as for example in the case of (1993/1994): Warner, J. B., Watts, R. L. and Wruck, K. H. (1988) Stock pro- Korbach was succeeded by the interim coach Steegman on cess and top management changes, Journal of Financial 2 November 1993, and the interim coach was succeeded by Economics, 20, 461–92. the new head coach Rijsbergen on 27 February 1994. Note that in 1995/1996 the coach of Volendam was fired after the regular competition. The team played promotion/ relegation games under the new coach. Since we focus on APPENDIX I results during the regular competition only, we do not take this firing into account in the empirical results. Fired coaches 1993/1994, until 1997/1998 A complete overview of coaches that were fired during the period under consideration is given in Table A1. The first APPENDIX II column gives the team, in the second column the coach that was fired, in the third column the reason for the firing, and Detailed estimation results in the fifth and sixth column the new coach and the date In this appendix we give detailed estimation results of the respectively. The study distinguishes between four different models in Section IV. In Table A2 one gives the name of reasons for a termination of the contract between the coach the teams that changed coaches, with the quality and home and his team: parameters of the old and new coaches. Moreover, one results The coach is fired because the results fall short of gives the quality and home parameters of these teams expectations. based on estimation of the model for the four seasons pre- offer The coach leaves because he has a better offer from viously. One will denote these ‘long-term’ parameters by a another team. bar on top of the parameter as i and hi. Effect of firing a coach on team performance 563 Table A1. Fired coaches, source Horn (1999)

Team Fired coach Reason New Coach Date

1993/1994 FC Utrecht Fafie´ Results Van Veen 17 September 1993 Cambuur De Jong Results Korbach 2 November 1993 Volendam Korbach Offer Steegman 2 November 1993 Rijsbergen 27 February 1994 RKC Verel Results Jacobs 16 December 1993 FC Groningen Vonk Results Koevermans 19 March 1994 1994/95 PSV De Mos Relation Rijvers 29 October 1994 Advocaat 15 December 1994 Ten Cate Results Fafie´ 30 January 1995 FC Utrecht Van Veen Results Vonk/Du Chatinier 18 February1995 Dordrecht ’90 Van Zoghel Results Verslijen 4 March 1995 Sparta Berger Relation Van Stee 21 March 1995 Willem II Reker Relation De Jong 26 March 1995 MVV Vergoossen Voluntary Reker 16 May 1995 1995/96 Van Hanegem Results Meijer 2 October 1995 Haan 16 October 1995 Ko¨rver Relation Korbach 31 October 1995 NEC Van Kooten Results Looyen 7 November 1995 Koevermans 8 December 1995 Vitesse Spelbos Results Thijssen/Jongbloed 20 November 1995 FC Twente Ten Donkelaar Voluntary Rutten 20 November 1995 Meyer 15 January 1996 FC Utrecht Kistemaker Results De Ruiter 18 December 1995 Spelbos 18 January 1996 Willem II De Jong Relation Calderwood 19 March 1996 Go Ahead Eagles Fafie´ Results Maaskant/Maaskant 16 April 1996 FC Volendam Jacobs Results Brouwer/De Boer 6 May 1996 1996/1997 Roda JC Stevens Offer Achterberg 10 October 1996 Jol 1 November 1996 RKC Van Kooten Results Verkerk 12 October 1996 Jacobs Sparta Ten Cate Offer Brand 12 January 1997 Roks/Van Tiggelen 17 April 1997 Vitesse Beenhakker Voluntary Ten Cate 10 January 1997 FC Groningen Westerhof Results Van Dijk 25 February 1997 NEC Koevermans Results Looyen 3 March 1997 1997/1998 Fortuna S. Verbeek Voluntary Van Marwijk 3 September 1997 FC Utrecht Spelbos Results De Ruiter 27 October 1997 Wotte 4 January 1998 Feyenoord Haan Results Meijer 28 October 1997 Beenhakker 7 November 1997 Roda JC Jol Results Achterberg 6 March 1998 Vonk 17 March 1998 FC Groningen Rijsbergen Results Van Dijk 31 March 1998 564 R. H. Koning Table A2. Detailed results for the regression model o n o n i i i hi hi hi 1993/1994 Cambuur 70.966 70.933 770.411 1.265 7 Groningen 70.496 0.391 0.329 70.540 0.885 0.665 RKC 70.510 0.329 0.428 70.380 0.364 0.134 Utrecht 71.369 70.671 0.362 0.945 1.139 0.322 1994/1995 Dordrecht 70.938 0.616 70.624 0.458 0.246 0.062 Go Ahead Eagles 71.639 0.192 70.351 70.540 0.885 0.573 MVV 70.834 0.511 0.304 0.596 0.107 0.016 PSV 1.160 0.740 1.432 0.804 1.550 0.703 Sparta 70.698 71.525 70.303 1.711 2.036 0.938 Utrecht 70.902 0.722 0.131 1.361 70.760 0.625 Willem II 0.253 72.097 0.021 0.849 1.674 1.109 1995/1996 Feyenoord 70.436 1.246 1.359 3.601 70.013 0.040 Go Ahead Eagles 0.256 71.452 70.466 71.235 70.271 0.527 De Graafschap 70.612 71.564 71.162 70.051 2.578 1.519 NEC 70.458 70.246 70.330 70.881 71.037 1.107 FC Twente 0.300 0.030 0.545 70.632 0.078 0.697 Utrecht 70.858 70.208 70.043 0.139 70.273 0.525 Vitesse 0.219 0.842 0.855 0.542 70.489 0.369 Willem II 0.616 70.852 0.061 0.311 70.459 0.900 1996/1997 Groningen 70.426 1.806 70.155 70.137 71.760 0.437 NEC 70.838 71.346 70.215 0.412 2.489 70.103 RKC 0.247 70.926 0.371 70.928 0.961 70.282 Vitesse 0.674 70.799 0.785 70.416 2.249 0.265 1997/1998 Feyenoord 70.030 1.252 0.945 1.936 70.168 0.370 72.130 0.863 70.176 70.765 70.227 70.037 Groningen 70.650 70.885 70.061 0.934 1.521 0.230 Roda JC 0.312 0.553 0.451 0.058 0.256 1.011 Utrecht 71.121 0.263 70.516 0.581 1.064 0.667