Replacing College Football Coaches and Performance

7/30/2019 Replacing College Football Coaches and Performance

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Pushing Reset: The ConditionalEffects of Coaching Replacementson College Football Performance

E. Scott Adler, University of Colorado, Boulder

Michael J. Berry, University of Colorado, Denver

David Doherty, Loyola University Chicago

Objectives. We assess the effects of coaching replacements on college football team

performance. Methods. Using data from 1997 to 2010, we use matching techniquesto compare the performance of football programs that replaced their head coachto those where the coach was retained. The analysis has two major innovationsover existing literature. First, we consider how entry conditions moderate the effectsof coaching replacements. Second, we examine team performance for several yearsfollowing the replacement to assess its effects. Results. We find that for particularlypoorly performing teams, coach replacements have little effect on team performanceas measured against comparable teams that did not replace their coach. However, forteams with middling recordsthat is, teams where entry conditions for a new coachappear to be more favorablereplacing the head coach appears to result in worse

performance over subsequent years than comparable teams who retained their coach.Conclusions. The findings have important implications for our understanding of howentry conditions moderate the effects of leadership succession on team performance,and suggest that the relatively common decision to fire head college football coachesfor poor team performance may be ill advised.

Were in the era of PlayStation. If you dont like it, just hit reset.Former University of Colorado football coach Dan Hawkins responding

to a media question about the ease of replacing head football coaches

At the highest levels of competition, college athletics departments are ex-traordinarily dependent upon the revenue generated by their football programsto finance coaches, staff, facilities, and an array other athletic teams. Footballteam success affects television and bowl revenues, ticket and merchandise sales,and potentially alumnae contributions to athletics departments. When teamperformance is disappointing, athletics administrators and university officialsare often pressed to find ways to reverse the trend. The prominent leadershiprole held by head coaches means that, fairly or unfairly, they are often blamed

Direct correspondence to E. Scott Adler, Department of Political Science, UCB 333,Boulder, CO 80309-0333 [email protected].

SOCIAL SCIENCE QUARTERLY, Volume 94, Number 1, March 2013C 2012 by the Southwestern Social Science Association

DOI: 10.1111/j.1540-6237.2012.00929.x


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2 Social Science Quarterly

for poor records and are regularly fired or pressured to resign as a way offixing the program. Over the last decade, on average approximately 1 in 10teams annually replaced their coach for performance reasons.

The stakes involved in college football are high. A recent conservative esti-

mate finds that, on average, football accounts for almost half of the generatedrevenue in Football Bowl Subdivision (FBS; formerly Division I-A) athleticsdepartments (Fulks, 2009). Additionally, the costs of replacing a coach aretypically substantialoften involving hundreds of thousands, if not millionsof dollars in contract buyouts. However, there are no studies that empiricallyanalyze the consequences of this particular variety of leadership succession.Little is known about whether replacing the coach is an effective strategy forimproving performance.1 To date, studies investigating leadership successioneffects in sports focus almost exclusively on professional teams (for two ex-

ceptions, see Fizel and DItri, 1997, 1999). In this article, we present the firstanalysis of the effects of performance-based coaching replacements on theperformance of college football teams.

Our examination of the understudied environment of college football hasseveral innovations over previous research on leadership succession in sports.To start, we analyze a large numbers of teamsabout 120 programs in theFBS. We employ matching analysis to compare teams that undergo a treat-ment (coach replacement) with similarly performing teams that do not. Wealso examine whether replacement effects are conditioned on whether team

performance prior to the replacement was extremely poor or merely mediocre.This analysis allows for an assessment of the conditional effect of entry con-ditions on leadership succession and team performance. We find that whilecoaching replacements may provide a short-lived boost to performance amongteams that have been performing particularly poorly, they have a deleteriouseffect on mediocre teams (those that won approximately 50 percent of theirgames in the year prior to the replacement). Our final innovation is that wetrack postsuccession performance over the five years following the replacement

while all previous workwith the exception of Giambatistas (2004) study on

professional basketball coacheshas only examined performance in the yearimmediately following the replacement.The section that follows provides a brief survey of the research on sports

leadership succession and describes a number of possible ways coaching re-placement might be expected to affect team performance in college football.

We then describe our data. Next, we present our empirical analysis, whichreports our estimates of the effects of coaching replacements based on variousmatching techniques. We conclude with a discussion of the implications ofour findings.

1Brown, Farrell, and Zorn examine how well the qualities of individual football coachesmatch the characteristics of NCAA programs, but do not assess the effects of coaching successionon team performance (2007).


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Effects of Coaching Replacements on College Football Team Performance 3

Consequences of Leadership Replacement

The literature on leadership succession in organizations generally pursuesone of two lines of inquirythe causes of leadership succession or its effects

on organizational performance (Kesner and Sebora, 1994; Giambatista et al.,2005). An area of research that has been particularly fruitful examines leader-ship changes in sports teams (Allen et al., 1979; Cannella and Rowe, 1995;Fabianic, 1994; Fizel and DItri, 1997, 1999; Gamson and Scotch, 1964;Giambatista, 2004; Pfeffer and Davis-Blake, 1986; McTeer et al., 1995; Roweet al., 2005). There are a number of benefits to testing the impact of lead-ership succession on performance using sports teams. For one, unlike manyother organizational scenarios, the leadership hierarchy is very clearly defined.Second, sports provide an objective and broadly agreed upon way to mea-

sure organizational performancethat is, wins and losses. Though there areother goals that can also be part of the performance objectives in the sportsenterprisefor college athletics, these might be alumnae contributions, mediaattention for the university, or the ability to recruit higher quality student-athletesthese are generally considered ancillary to the primary objective ofteam performance. A final advantage of studying the effects of leadership ina sports environment is that all teams compete under uniform governing in-stitutions regarding recruitment of actors and participation in activities thatshould lead to improved performance. College football is a particularly ap-

pealing context to test for coaching replacement effects as an average of nearly20 FBS teams begin each fall with a new head coach.Performance-based replacement of head college football coaches is dom-

inated by the logic expressed by Army Athletic Director, Kevin Anderson,when he hired Rich Ellerson to replace fired head football coach Stan Brockin 2008: One of our primary goals of the search was to find someone capableof turning around our program immediately and we are confident Rich is theperfect individual to accomplish that (Associated Press [AP], 2008). That is,team underperformance is directly attributable to substandard coaching and

leadership, and replacement of the coach should result in quicker recoveryto satisfactory levels of achievement. In the organizational management lit-erature, this approach is known as improved management or the commonsense theory, and it asserts that the likely result of replacing failing leaders with

what should be a better fitting manager is greater success in the future (Grusky,1963; Gamson and Scotch, 1964; Huson et al., 2004). In the football context,this perspective would contend that schools that replace their head footballcoach due to poor performance will experience betterteam performance thansimilar teams that do not replace their coach. Previous research finds some

support for this notion (Gamson and Scotch, 1964; Fabianic, 1994; McTeeret al., 1995).However, another line of research contends that schools that replace their

head football coach due to poor execution on the field will perform worsethansimilar teams that do not replace their coach. The notion is that firing a head


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coach may be more of a symbolic act and that fluctuations in performance arebetter attributed to factors out of the control of coaches: changes in the qualityof opponents, loss of key players, or merely random shocks. Similar to thevicious cycle theory of managerial succession (Grusky, 1960, 1963; Brown,

1982), this perspective asserts further that coaching changes may disrupt theorganization by altering the staff, procedures, coaching style, recruitment net-works, and team culture. Ultimately, this may do more harm than good. Someempirical research also supports the contention that coaching replacementsare, more often than not, detrimental to team performance (Fizel and DItri,1999; Giambatista, 2004).

Some of the conflicting findings from previous research may be due tothe fact that this work has not adequately captured basic differences amongteams that replace their coach. One of the key contributions of our analysis is

that we consider the possibility that the effects of coaching replacements arenot homogeneous. Researchers exploring leadership success in other settingshave asserted that entry conditions can be consequential for understandingthe trajectory of unit performance over the course of their tenure (Hambrickand Fukutomi, 1991; Boal and Hooijberg, 2000; Pfeffer and Salancik, 2003).Thus, the effects of a coaching replacement may depend upon the context orenvironment faced by the new coach.

Improved management theory would contend that when an extremely poorperforming team replaces its head coach, it is not likely to see much im-

provement because the team does not have the raw materials in the form ofplayers, staff, and facilities to turn the program into a winner. However, amediocre team might have decent players. For these teams, a new coach mayrealistically be able to better manage these resources and, thus, improve teamoutcomes. In contrast, disruption theory might make the same assertion atthe lowest end of the performance scale, but suggests the opposite outcomefor better performing teams. That is, coaching replacements among extremelypoor performing teams are unlikely to affect performance both because thereis not much for the coach to work with andthe team does not have far to fall.

In other words, among these teams there may be little for the coach to dis-rupt. However, when an average team replaces its coach, it risks disruptingthe organization and harming performance. This leads us to two contrastinghypotheses:

Conditional Improved Management Hypothesis: Teams that replace their headfootball coach due to extremely poor performance will perform similarly to teamsthat do not replace their coach. Teams that replace their head football coach

due to mediocre performance will perform better than similar teams that do notreplace their coach.Conditional Disruption Hypothesis: Teams that replace their head football coach

due to extremely poor performance will perform similarly to teams that do notreplace their coach. Teams that replace their head football coach due to mediocre


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performance will perform worse than similar teams that do not replace theircoach.

Finally, Giambatista et al. (2005) note that most of the sports setting studiesused a one-year performance window, even though it is common knowledgeamong sports fans that most new high-profile coaches expect at least one yearbefore transformation begins to produce results (2005:966). This argumentis particularly applicable to college athletics, as the team fielded by the newlyhired head coach is largely comprised of the players recruited by the previouscoach. It may take several years for the new coach to recruit a set of incomingplayers that better fit into his system. To account for this phenomenon,

we examine the effects of coach replacement over the five years following thereplacement.

Data Sources

To assess the effect of coaching replacement on team performance, wecreated a primary data set that includes information on all FBS footballprograms from a contemporary period: the 1997 through 2010 seasons. This14-year time series captures the entire Bowl Championship Series (BCS) era,

which began in 1998, up to 2010. Modeling each year since the inceptionof the BCS is important since this system constituted a major reformationof the structure of college football. Accordingly, the analysis that follows usesthe team/year as the unit of analysis. There is an average of 117 universitiesfielding FBS football teams annually and a total of 1,643 cases in the dataset. The overall win-loss record, conference win-loss record, and informationabout bowl appearances for each team was collected from a database of collegefootball scores from 1985 to the present (Howell, 2009). To ensure accuracy,team records were checked by the authors against the records listed by ESPN.

The head coach for each team/year was identified using the College Foot-ball Data Warehouse, which facilitated identification of all instances where acoaching change took placea total of 263 coaching changes occur in ourdata set, affecting football programs at 115 universities (DeLassus, 2012).Two coders identified the reason for each coaching change by entering theuniversity and coachs name as well as year of replacement into Lexis-Nexisssearch engine to find AP articles covering the replacement. Coaching changes

were coded into five categories, which were then collapsed into replacementsthat were performance based and those that were not.2 To ensure coding re-liability, we randomly assigned nearly half of the 263 cases for examinationby both coders. In almost all cases the categorization of individual coaching

changes was straightforward (as indicated by the 94 percent rate of intercoder

2Nonperformance-based replacement categories (1) retired, (2) resigned to take a differentcoaching job, and (3) resigned for health reasons; performance-based categories (1) fired and(2) resigned amid pressure.


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agreement). One hundred and fifty-five replacements were coded as perfor-mance based, with the remaining 108 coded as unrelated to performance.Summary data of coaching replacements by year are presented in Table A1 ofthe Appendix.

The rationales for the vast majority of replacements were clear. Most re-placements unrelated to poor performance were either voluntary retirements(36 percent) or cases where a coach was hired away by another college orprofessional team (60 percent). There were also four cases (4 percent) wherea coach left for health reasons. Voluntary retirements were typically easy toidentify and most involved a coach leaving after at least eight seasons with theteam. Reports of these replacements did not mention team performance as areason for the coachs exit.

In most cases, identifying performance-based coaching changes was also

straightforward. Seventy-nine percent of these replacements were clear caseswhere the coach was fireda fact often mentioned in the headline of thearticle. The remaining performance-based replacements were cases where acoach left under pressure. In most cases, this type of replacement was easy toidentify as the report either explicitly mentioned performance as a reason forthe replacement or noted that the university would continue paying the coachafter his resignation. For example, an AP article discussing the resignation of

John Thompson as the East Carolina University head coach reported: EastCarolina coach John Thompson, who resigned under pressure earlier this

week, will be paid until Jan. 1, 2008, under the terms of a settlement withthe school. Thompson agreed Wednesday to quit effective the end of theseason after he was told he would be fired (AP, 2004). Although Thompsontechnically resigned, the report made it clear that this resignation was a directresult of East Carolinas dissatisfaction with the teams poor performance andintent to fire him if he did not resign.

Assessing the Effects of Performance-Based Coaching Changes

Although teams that perform poorly are more likely to replace their coach,coaching changes are not deterministically driven by team performance. Eachseason many teams finish with losing records, but only some replace theircoach. For example, of the 186 team/years in our data set where the team

won fewer than 20 percent of its games, only 39 led to a performance-basedcoaching replacement. We take advantage of the fact that among similarlyperforming teams, some opt to replace their coach while others do not. Thisdynamic allows us to compare teams that were similar apart from the coachingreplacement decision (or treatment).

To this end, we conduct three types of analysis. Each approach uses propen-sity scores (predicted probabilities of a coaching change) as a way of identifyingsets of teams that were similarly likely to replace their coach for performance-based reasons, but differed in that some replaced their coach while othersdid not. In the first approachstratified matchingwe conduct a series of


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t-tests to estimate the effects of a performance-based coaching change on sub-sequent team performance within blocks of teams that performed similarlyduring the previous season. Second, we conduct nearest neighbor matching.Here, we identify the most closely matching control case for each treated case

and compare the performance among this smaller set of control caseswhereentry conditions even more closely match those among the treated caseswith performance among the treated cases. In the third set of analyses, wealso use propensity scores, but rather than matching cases, we use them asanalytic weights in a series of ordinary least squares (OLS) regressions. Thisapproach allows for a further examination of the possibility that the effect ofperformance-based coaching replacements depends on the entry conditions.In the next section, we describe the method we use to calculate our propensityscores and then present each of our analyses in turn.

Calculating the Propensity to Replace a Coach

Our proposed analysis requires that we identify sets of teams that were sim-ilarly likely to replace their coach for performance-based reasons. To identifysimilar teams, we adopt a timeline where Tis the first year that we evaluate theeffects of coaching replacement. For the treatment (coaching replacement)cases, this is the first year of the new coachs tenure. We compare treated

cases with control cases that were similar at T 1that is, in the year thatprecipitated the replacement among the treated cases. Assuming that we haveaccounted for all of the factors that are likely to be correlated with performanceandthe decision to replace a coach, we can treat the decision to fire a coach asrandomly assigned within these blocks of teams and assess the impact of thisdecision as though it were an experimental treatment (Rubin, 1973; Dehejiaand Wahba, 1999).

Because the outcome of interest is team performance at time T, we mustensure that the treated and control teams we compare are similar at T 1 on

any factor that may affect performance at time Tand also be correlated withcoaching replacement. Past performance is likely to be the strongest predictorof future performance. This past performance captures a variety of relativelystable factors that may influence performance at time T, including institutionalsupport for the team, reputation (which may improve recruiting capabilities),player quality, and other unidentified factors that may affect team success. Wefocus on four measures of absolute team performance: overall win percentageat T 1, conference win percentage at T 1, average win percentage fromT 10 to T 2, and whether the team appeared in a bowl game at T

1.3

We also account for the possibility that the decision to expel a coach,as well as future performance, may be affected by whether a team meets

3We substitute overall win percentage for conference win percentage for the small numberof independent teams (47 of the 1,205 team/years used in the analysis presented below).


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performance expectations. For example, in 2004, the University of Wyomingand the University of Florida each won 58 percent of their games. However,over the previous four years Wyoming only won 19 percent of its games whileFlorida won 70 percent. While the two teams performed similarly in 2004 in

absolute terms, accounting for historical performance, 2004 was a good yearfor Wyoming and a disappointing year for Florida. As such, Florida may bemore likely to replace its coach than Wyoming. Importantly, if we expect thatteams are likely to regress to their mean performance, we would expect Floridato outperform Wyoming in 2005. We calculated measures of the differencebetween overall win percentage (at T 1) and average win percentage overthe previous five years (T 6 through T 2). We also calculated a similarmeasure comparing performance to the average over the previous 10 years (T 11 through T 2). These measures capture how well teams performed

relative to both recent and fairly long-term historical performance.4

In orderto ensure that treatment and control teams are as similar as possible, we alsoinclude a measure of how many years the coach had been in place at thebeginning of the season.

We calculate propensity scores, which are estimates of the likelihood thata team will replace its coach in a given year, using a logistic regression modeland regressing the dichotomous performance-based coaching replacementvariable on the covariates discussed above using Becker and Ichinos (2002)

pscoreroutine for STATA. The predicted values for each case generated from

this model are the propensity scores. Among the control cases, these scoresrange from 0.001 to 0.658. Scores among the treated cases range from 0.022to 0.470.5 There is a great deal of overlap in the range of propensity scoresin these two groups. However among control cases, there are a substantialnumber of propensity scores close to zero. Because there are not any treatmentcases that performed similarly to these teams, we exclude these cases from ouranalysis and use only those control cases that fall within the range of scoresobserved among the treated cases (the region of common support).6

Columns 1 and 2 of Table 1 compare the mean values of the covariates used

in the analysis for those teams that replaced their coach for performance reasonsand those that did not, highlighting the differences in performance between

4A number of teams entered the FBS at a time that made it impossible to calculate averageperformance over the previous 5 or 10 years. In these cases, we used average performance overthe teams full history in the FBS.

5The propensity score model is presented in Table A2 of the Appendix. Note that this modelis a means of identifying propensity scores, which can be used to identify sets of treatment andcontrol cases that are similar on the covariates used in this model. It is not intended to identify

which factors are independently most predictive of coaching replacement. The distribution ofpropensity scores among treated and control cases is presented in Figure A1 in the Appendix.

6Specifically, there are 413 nontreated cases with propensity scores lower than the lowestpropensity score among the treated cases. Among these control cases, the average win percentageat T 1 was 76 percent. Almost 93 percent of these high-performing teams went to a bowlgame in year T 1. We also exclude the two untreated cases with propensity scores higherthan the highest propensity score among the treated cases (Penn State in 2004 and 2005 haspropensity scores of 0.658 and 0.598, respectively).


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TAB

LE1

BalanceofCovariatesinTre

atedandNontreatedCases

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

N

earestNeighbor

FullSample

Comm

onSupport

Block1

Block2

Block

3

Block4

Matching

No

No

No

No

No

No

No

Change

Change

Change

Change

Change

Change

Change

Change

Change

Change

Change

Change

Change

Change

Winpercentage

0.5

28

0.3

31

0.4

36

0.3

31

0.5

93

0.5

71

0.4

56

0.4

48

0.3

00

0.2

96

0.1

52

0.1

55

0

.334

0.3

31

(T

1)

[0.2

213]

[0.1

669]

[0.1

814

]

[0.1

669]

[0.0

922]

[0.0

949]

[0.1

127]

[0.0

959]

[0.1

14]

[0.1

015]

[0.1

153]

[0.1

201]

[0

.1769]

[0.1

669]

Winpercentage

0.5

05

0.4

62

0.4

84

0.4

62

0.5

27

0.5

64

0.4

79

0.4

64

0.4

44

0.4

42

0.4

46

0.4

51

0

.456

0.4

62

(T

2toT1

0)

[0.1

59]

[0.1

531]

[0.1

579

]

[0.1

531]

[0.1

585]

[0.1

807]

[0.1

507]

[0.1

65]

[0.1

519]

[0.1

461]

[0.1

506]

[0.1

24]

[0

.1728]

[0.1

531]

Winpct.different

ial

0.0

18

0.1

12

0.0

40

0.1

12

0.0

63

0.0

11

0.0

25

0.0

06

0.1

24

0.1

34

0.2

49

0.2

48

0

.109

0.1

12

(5years;T1

)

[0.1

953]

[0.1

682]

[0.1

782

]

[0.1

682]

[0.1

463]

[0.1

1]

[0.1

425]

[0.1

498]

[0.1

568]

[0.1

423]

[0.1

44]

[0.1

337]

[0

.1773]

[0.1

682]

Winpct.Differential

0.0

24

0.1

36

0.0

49

0.1

36

0.0

68

0.0

10

0.0

23

0.0

21

0.1

49

0.1

49

0.3

02

0.3

05

0

.125

0.1

36

(10years;T

1)

[0.1

993]

[0.1

625]

[0.1

708

]

[0.1

625]

[0.1

285]

[0.1

27]

[0.1

167]

[0.1

21]

[0.1

297]

[0.1

177]

[0.1

144]

[0.1

176]

[0

.1683]

[0.1

625]

Conf.winpct.

0.5

21

0.2

88

0.4

10

0.2

88

0.5

89

0.5

82

0.4

37

0.4

37

0.2

51

0.2

31

0.0

91

0.0

93

0

.303

0.2

88

(T

1)

[0.2

567]

[0.1

893]

[0.2

065

]

[0.1

893]

[0.1

207]

[0.0

713]

[0.1

197]

[0.0

935]

[0.1

212]

[0.1

124]

[0.1

172]

[0.1

153]

[0

.2089]

[0.1

893]

Bowlappearance

0.4

98

0.1

55

0.3

30

0.1

55

0.7

20

0.6

25

0.2

04

0.3

25

0.0

27

0.0

00

0.0

11

0.0

28

0

.153

0.1

55

(T

1)

[0.5

002]

[0.3

629]

[0.4

703

]

[0.3

629]

[0.4

495]

[.5]

[0.4

036]

[0.4

743]

[0.1

609][

0]

[0.1

054]

[0.1

667]

[0

.3611]

[0.3

629]

Coachtenure

4.1

93

4.4

07

4.1

72

4.4

07

4.3

03

3.8

13

4.7

13

4.3

50

3.4

64

4.0

64

4.3

89

5.3

33

3

.863

4.4

07

(T

1)

[5.3

39]

[3.8

72]

[5.7

37]

[3.8

72]

[4.4

02]

[3.7

28]

[6.7

75]

[2.6

07]

[5.7

58]

[3.0

58]

[7.1

89]

[5.8

85]

[6

.867]

[3.8

72]

Propensityscore

0.0

87

0.1

78

0.1

15

0.1

78

0.0

39

0.0

42

0.0

89

0.0

93

0.1

79

0.1

87

0.3

08

0.3

19

0

.165

0.1

78

[0.0

885]

[0.0

995]

[0.0

857

]

[0.0

995]

[0.0

115]

[0.0

124]

[0.0

176]

[0.0

185]

[0.0

365]

[0.0

388]

[0.0

405]

[0.0

562]

[0

.0908]

[0.0

995]

Observations

1,4

65

155

1,0

50

155

393

16

265

40

302

63

90

36

131

155

Cellentriesarem

eans.

Standarddeviationsinbrackets.


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these groups and validating our coding of performance-based replacements.Columns 3 and 4 report means for each of the covariates restricting the sampleto cases within the region of common support. The sizable differences in thesemeasures observed in the full sample are reduced substantially among this

restricted set of cases. However, some differences remain. We address thesedifferences using both stratified and nearest neighbor matching.

Stratified Matching

After calculating propensity scores, thepscoreroutine uses a simple process toidentify ranges of propensity scoresor blockswhere the mean propensityscore among treatment and control cases is similar. This process yielded fourblocks (propensity score ranges: Block 1: 0.022 p < 0.062; Block 2: 0.062

p < 0.125; Block 3: 0.125 p < 0.250; Block 4: 0.250 p). Note thatthese blocks delineate sets of teams where entry conditions varied from mostfavorable (Block 1) to least favorable (Block 4). After identifying blocks ofcases, the routine tests for significant differences in covariate means betweentreatment and control cases within each block because balanced propensityscores do not guarantee balance on these underlying variables.7 Summarystatistics within each block are reported in columns 512 in Table 1. Thetable illustrates that the treated and control teams in each block performedremarkably similarly within each block.

This matching process allows us to confidently attribute any observed dif-ferences between control and treatment cases in performance at time Tandbeyond to the coaching change. At this point, we proceed with analysis muchas we would in the context of a randomized experiment. We can also pro-vide preliminary evidence regarding whether the effects of performance-basedcoaching replacement differs across entry conditions by comparing the treat-ment effect across blocks.

The entry conditions were most favorable for teams in Blocks 1 and 2.These teams performed best at T 1, on average winning 59 and 45 percent

of their games, respectively. In contrast, entry conditions in Block 3 were lessfavorable, and those in Block 4 were particularly unfavorable. The teams inBlocks 3 and 4 won only 30 and 15 percent of their games at T 1. Wefocus our analysis here and below on the effects of coaching replacements onchange in overall win percentagehow a teams performance changed fromtime T 1 to time T. The findings are reported in Figure 1.

The bars in Figure 1 indicate the estimated difference in change in perfor-mance between treated and control teams in each block. Because there are a

7Given the number of statistical tests involved in checking for balance on all covariates (here,28 tests, seven within each of four blocks), we applied a standard of balance of differencesbetween treated and control cases not being significant at the p < 0.01 level. Our analysisfound that this level of balance was achieved for all individual independent variables withineach of the blocks. In fact, the only case where the more stringent standard of differences notbeing significant at the p < 0.05 level was violated was the five-year historical win percentagedifference measure in Block 1.


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FIGURE 1

Effects of Coaching Replacement on Change in Win Percentage

(Stratified Matching)

limited number of treated cases in each block (ranging from 16 in Block 1 to63 in Block 3), the standard errors (95% confidence intervals are indicatedby the whiskers on each bar) on the estimates are quite large. Nonetheless,the pattern of estimated effects is telling. Among the teams with the mostfavorable entry conditions in Blocks 1 and 2, teams that replaced their coach

won approximately 7 percent fewer games than those teams that did not re-place their coach (p = 0.19 and 0.03, respectively). Additionally, contrary toclaims that new coaches need a couple of years to rebuild a team, we find littleevidence that Block 1 and 2 teams that replace their coach see a substantialimprovement in performance after the new coach has had a season or twoto rebuild. Instead, the pattern of effects suggests a regression to the mean

where the negative impact of introducing a new coach diminishes over timefor these teams. In contrast, there is some evidence that teams that replacedtheir coach with particularly unfavorable entry conditions (Block 4) saw largerimprovements in performance at T+ 1 and T+ 2 than their counterparts

who did not (p < 0.05 for each difference). However, this benefit evaporatesat T+ 3 and T+ 4.8

The pattern of effects reported in Figure 1 suggests that the effects ofcoaching replacement depend on the entry conditions. In summary, the keyfindings are that coaching replacements, on average, appear to provide short-term benefits to teams that are performing extremely poorly. However, ifanything, they have a deleterious effect on performance among teams where

8In additional analysis of the effects of coaching replacement on conference win percentage,we find a similar pattern of effects (see Appendix Figure A2 ).


12/28


entry conditions are most favorable. Importantly, this dispels the commonrationale used by university athletic directors when firing the head coach,namely, that replacing the incumbent coach is a necessary step to improveon-field performance. Our findings demonstrate that the actual effects of such

replacements are generally the opposite of what is intended.

Nearest Neighbor Matching

To check the robustness of these findings, we also conduct nearest neighbormatching. For each treated case, we identify the control case with the closestpropensity score. Columns 11 and 12 of Table 1 report summary statistics forthe cases used in this analysis and demonstrate that the treatment and control

cases identified through this approach are nearly identical on all measures.9

T-tests indicated that the performance-based coaching change treatment didnot have an overall effect on subsequent win percentage. However, our expec-tations, as well as the findings presented in Figure 1, suggest that the effectsof coaching replacements depend on the entry conditions a new coach faces.

We examine this possibility using a series of OLS regressions, presented inTable 2.

In Table 2 column 1, we estimate the conditional effect of a coaching changeon change in performance from T 1 to time Tby entering a linear inter-

action between the propensity score for a case and the treatment indicator. Wealso control for the covariates used in the propensity model to account for anyremaining minor differences between treated and control cases. Consistent

with the findings discussed above, the model indicates that among teams withmore favorable entry conditions (those with propensity scores approachingzero), a coaching change leads to a decrease in change in wins between T1 and T of approximately 12 percent (p < 0.01). The positive, statisticallysignificant coefficient on the interaction term (p < 0.01) indicates that thisnegative effect decreases among teams where entry conditions are less favorable

(i.e., those with higher propensity scores).In column 2, we estimate the conditional effect of coaching replacementusing an interaction between the replacement treatment and indicators for eachentry condition block. The negative coefficient on the coaching replacementvariable indicates that among teams in Blocks 1, the estimated effect of acoaching change is an approximate 10 percent decrease in win percentage,relative to teams that did not replace their coach (p= 0.052). The coefficientson the interaction terms are positive, which indicates that the negative effectof coaching replacement among Block 1 teams is mitigated among teams with

less favorable entry conditions. In columns 35 of Table 2, we estimate theconditional effect of coaching replacement on performance at T+ 1, T+

9The difference in the number of treated and control cases stems from the fact that in somecases, the same control case is the closest match for more than one treated case.


13/28


TAB

LE2

EffectsofCoaching

ReplacementonChangeinWinPercentage(Nearest

NeighborMatching)

(1)

(2)

(3)

(4)

(5)

ChangeinWinPercentag

e

(TminusT

1)

(TminusT

1)

(T+

1minusT

1)

(T+

2minusT

1)

(T+

3minusT

1)

Performance-b

asedreplacement

0.1

22

0.1

02

0.0

72

0.0

09

0.0

63

(1=

yes)

[0.0

37]a

[0.0

52]

[0.0

95]

[0.0

85]

[0.0

69]

Propensityscore

0.3

65

[0.5

51]

Propensityscore

PBchange

0.5

99

[0.1

98]a

Block2(1=ye

s)

0.0

29

0.1

32

0.0

20

0.1

16

[0.0

58]

[0.0

72]

[0.0

83]

[0.0

77]

Block3(1=ye

s)

0.0

74

0.1

69

0.0

28

0.1

00

[0.0

88]

[0.1

03]

[0.1

01]

[0.1

00]

Block4(1=ye

s)

0.1

56

0.2

96

0.1

38

0.2

09

[0.1

23]

[0.1

29]b

[0.1

39]

[0.1

44]

Block2

PBc

hange

0.0

17

0.0

52

0.0

20

0.1

17

[0.0

72]

[0.1

01]

[0.1

06]

[0.0

77]

Block3

PBc

hange

0.1

05

0.1

04

0.0

00

0.1

06

[0.0

60]

[0.1

04]

[0.0

91]

[0.0

82]

Block4

PBc

hange

0.1

65

0.1

83

0.1

19

0.1

25

[0.0

68]b

[0.1

05]

[0.0

99]

[0.1

00]

Winpercentage(T

1)

1.6

39

1.5

78

0.8

79

0.6

39

0.1

48

[0.6

83]b

[0.6

54]b

[0.9

84]

[0.9

28]

[0.9

20]


14/28


TABLE2

continued

(1)

(2)

(3)

(4)

(5)

ChangeinWinPercentage

(Tm

inusT

1)

(TminusT

1)

(T+

1minusT

1)

(T+

2minusT

1)

(T+

3minusT

1)

Winpercentage(T

2T

10)

1.3

67

1.2

61

0.4

61

0.2

64

0.2

41

[0.7

17]

[0.6

62]

[0.9

94]

[0.8

99]

[0.8

90]

Winpct.differe

ntial(5years;T

1)

0.1

52

0.2

10

0.2

09

0.0

46

0.0

13

[0.2

01]

[0.1

65]

[0.2

55]

[0.2

34]

[0.2

48]

Winpct.differe

ntial(10years;T

1)

0.8

21

0.6

45

0.2

62

0.2

19

0.9

76

[0.8

14]

[0.6

72]

[0.9

47]

[0.8

36]

[0.9

06]

Conf.winpercentage(T

1)

0.0

08

0.0

22

0.3

09

0.2

59

0.2

05

[0.1

52]

[0.1

40]

[0.1

48]b

[0.1

72]

[0.1

84]

Bowlappearan

ce(T

1)

0.0

05

0.0

00

0.0

29

0.0

23

0.0

21

[0.0

36]

[0.0

40]

[0.0

44]

[0.0

54]

[0.0

60]

Coachtenure(

T

1)

0.0

01

0.0

02

0.0

03

0.0

03

0.0

00

[0.0

03]

[0.0

03]

[0.0

03]

[0.0

04]

[0.0

04]

Constant

0.1

76

0.2

01

0.4

04

0.3

03

0.3

18

[0.1

45]

[0.1

12]

[0.1

18]a

[0.1

12]a

[0.1

28]b

Observations

286

286

267

246

224

R-squared

0.2

49

0.2

54

0.3

50

0.3

54

0.3

94

Robuststandarderrors(clusteredbyteam)inbrac

kets.

aSignificantat1

%.

bSignificantat5

%.


15/28


2, and T+ 3. The pattern of coefficients suggests that the negative effects ofcoaching replacements among teams with the most favorable entry conditionsdissipate with the passage of time. Notably, the analysis in columns 35 showsno evidence that coaching replacements positively affect performance after an

adjustment period.

Weighted Analysis

Finally, Barsky et al. (2002) propose an alternative approach to usingpropensity scores for this type of analysis. They suggest using OLS regres-sion where control cases are weighted by the inverse of their propensity scoreand treatment case weights are set to one. In effect, this weighting scheme

produces balance between treated and control groups on the measures usedto calculate the propensity scores. This approach gives greater weight to con-trol cases with higher propensity scores and lower weight to those with lowpropensity scores (i.e., those team/years where a coaching replacement neitheroccurred, nor was likely to occur).10 Because we can include all of the casesacross all blocks in this analysis (rather than analyzing cases within each blockseparately), this approach substantially improves estimate efficiency.11

In Table 3 columns 15, we replicate the models presented in Table 2 usingall of the cases in the range of common support and the weighting approach

proposed by Barsky et al. (2002), clustering standard errors by team. Thefindings are consistent with those presented and discussed above. As seen incolumn 1, for teams with propensity scores approaching zero, the decisionto replace a coach leads to an almost 10 percent decrease in win percentagerelative to the change in win percentage among similar teams that did notreplace their coach (p < 0.01).

In column 2, we replace the linear interaction with a series of interactionsbetween the coaching replacement indicator and indicators for Blocks 24.

Again, the analysis indicates that among the teams in Block 1 where the

entry conditions a new coach would face seem to be most favorable, theeffect of coaching replacement is negative and statistically significant. Amongthese teams the model estimates that, on average, a coaching replacementresults in an 8.5 percentage point decrease in win percentage (p < 0.05).The small coefficient on the interaction between the coaching replacementand Block 2 indicators indicates that the effect among teams in Block 2

was similara 7.3 percentage point decrease in win percentage (p < 0.05

10We demonstrate the effectiveness of this weighting approach in Table A3 of the Appendix.

Specifically, we regress each of the covariates used in the propensity score model on the coachingchange variable using these weights (and clustering standard errors by team). The statisticallyinsignificant coefficient on the coaching change variable shows that these weights successfullybalance the treated and control cases on these measures.

11For the sake of consistency, we restrict the sample to cases that fall within the range ofcommon support.


16/28


TAB

LE3

EffectsofCoac

hingReplacementonChan

geinWinPercentage(WeightedAnalysis)

(1)

(2)

(3)

(4)

(5)

ChangeinWinPercentag

e

(TminusT

1)

(TminusT

1)

(T+

1minusT

1)

(T+

2minusT

1)

(T+

3minusT

1)

Performance-b

asedreplacement

0.0

99

0.0

85

0.0

63

0.0

43

0.0

63

(1=

yes)

[0.0

34]a

[0.0

41]b

[0.0

66]

[0.0

57]

[0.0

51]

Propensityscore

0.1

35

[0.3

78]

Propensityscore

PBchange

0.4

38

[0.1

99]b

Block2(1=ye

s)

0.0

09

0.0

44

0.0

47

0.0

60

[0.0

32]

[0.0

35]

[0.0

39]

[0.0

42]

Block3(1=ye

s)

0.0

15

0.0

98

0.0

79

0.0

94

[0.0

56]

[0.0

62]

[0.0

66]

[0.0

68]

Block4(1=ye

s)

0.0

32

0.2

02

0.1

74

0.1

77

[0.0

87]

[0.0

89]b

[0.0

94]

[0.1

09]

Block2

PBc

hange

0.0

12

0.0

02

0.0

09

0.0

54

[0.0

51]

[0.0

79]

[0.0

70]

[0.0

61]

Block3

PBc

hange

0.0

88

0.1

00

0.0

58

0.1

08

[0.0

47]

[0.0

76]

[0.0

66]

[0.0

60]

Block4

PBc

hange

0.1

12

0.1

64

0.1

56

0.1

11

[0.0

58]

[0.0

76]b

[0.0

72]b

[0.0

78]

Winpercentage(T

1)

1.2

16

1.0

43

0.8

53

0.4

09

0.1

11

[0.5

15]b

[0.5

32]

[0.7

12]

[0.7

21]

[0.7

12]


17/28


TABLE3

continued

(1)

(2)

(3)

(4)

(5)

ChangeinWinPercentage

(Tm

inusT

1)

(TminusT

1)

(T+

1minusT

1)

(T+

2minusT

1)

(T+

3minusT

1)

Winpercentage(T

2T

10)

1.0

18

0.7

88

0.4

24

0.0

70

0.3

98

[0.5

09]b

[0.5

23]

[0.7

04]

[0.6

97]

[0.6

82]

Winpct.differe

ntial(5years;T

1)

0.0

44

0.1

41

0.2

26

0.0

50

0.0

83

[0.1

68]

[0.1

26]

[0.1

81]

[0.1

75]

[0.1

91]

Winpct.differe

ntial(10years;T

1)

0.7

07

0.3

46

0.3

04

0.3

98

1.0

27

[0.5

85]

[0.5

43]

[0.7

08]

[0.6

84]

[0.7

06]

Conf.winpercentage(T

1)

0.0

45

0.0

15

0.2

57

0.2

62

0.1

84

[0.1

33]

[0.1

06]

[0.1

27]b

[0.1

45]

[0.1

58]

Bowlappearan

ce(T

1)

0.0

02

0.0

09

0.1

03

0.0

69

0.0

36

[0.0

27]

[0.0

33]

[0.0

37]a

[0.0

42]

[0.0

42]

Coachtenure(

T

1)

0.0

02

0.0

01

0.0

00

0.0

01

0.0

01

[0.0

03]

[0.0

02]

[0.0

02]

[0.0

03]

[0.0

03]

Constant

0.0

89

0.1

49

0.3

33

0.3

77

0.3

64

[0.0

98]

[0.0

73]b

[0.0

80]a

[0.0

79]a

[0.0

88]a

Observations

1,2

05

1,2

05

1,1

17

1,0

29

940

R-squared

0.2

34

0.2

35

0.3

46

0.3

44

0.3

72

CellentriesareOLSregressioncoefficientsusingw

eightsasdescribedintext.Robuststandarderrors(clusteredbytea

m)inbrackets.

aSignificantat1

%.

bSignificantat5

%.


18/28


for linear combination of coefficients). The coefficients on the interactionsbetween coaching replacement and Blocks 3 and 4 suggest that these negativeeffects are mitigated among teams that were performing particularly poorlybefore the coaching replacement (p = 0.066 and 0.057, respectively).

The pattern of findings in columns 3 through 5 are broadly consistent withthose presented in Figure 1. The unfavorable effect of coaching replacementsin situations where entry conditions appear to be most favorable diminishesgradually over time but does not reverse. Teams that performed particularlypoorly at T 1 (Block 4) appear to see a brief improvement in performancetwo and three years into the new coachs tenure relative to comparable teamsthat did not replace their coach. The linear combinations of the coefficient onthe coaching replacement variable and the interaction between this variableand the Block 4 indicator are 0.101 (p < 0.01) and 0.113 (p < 0.01) in

columns 3 and 4. However, this positive effect wanes the fourth year of thenew coachs tenure (column 5; linear combination of coefficients = 0.049,p = 0.431).

Rolling the Dice?

One additional possibility is that poorly performing teams are playing agame of high risk, high reward. That is, that coaching replacement may

harm team performance on average, but in a small number of teams thatare particularly fortunate, a new hire may improve performance substantially.In contrast, teams that do not replace their coach may find themselves ina predictable pattern of disappointing performance year after year. If thisexplanation is valid, it would be manifest in larger variance in performanceamong teams that replace their coach, a possibility we test by comparingvariance in win percentage among control and treatment cases at time T.

We make this comparison within the full region of common support as wellas within each of the three blocks using Levenes (1960) test of the equality

variances in two groups as well as the alternative formulations of this testproposed by Brown and Forsythe (1974). The p-values range from 0.096to 0.987, indicating that variance in performance is similar for treated andcontrolled teams (full results presented in Appendix Table A4). Put simply, wefind little support for the notion that performance-based coaching replacementis a high-risk/high-reward strategy.

Leadership Succession and College Football

This article addresses the question of whether leadership succession inunderperforming college football programs is a good strategy for improvingteam performance. Through an examination of the performance of elite-level


19/28


college football programs between 1997 and 2010, we demonstrate that thecondition of the organization at the time of leadership replacement is criticallyimportant in determining the consequences of replacement. These findingssupport the conditional disruption hypothesis. Specifically, they demonstrate

that coaching replacements do not immediately affect performance amongteams where entry conditions are particularly unfavorable (though we findsome evidence of a boost in performance in a new coachs second and thirdyear among these teams). However, among teams where entry conditionsappear to be most favorable, the choice to replace leadership is detrimental,rather than helpful, to team performance.

The innovations in this study offer new insights into our understanding ofleadership succession. For instance, our work suggests an explanation for themixed findings in previous research on leadership succession. Most prior work

does not account for the possibility that the effects of changes in leadership areconditional. Our work demonstrates the need to consider organizational entryconditions as an important explanatory factor when examining the effects ofleadership succession. By properly accounting for entry conditions at the timeof leadership succession, scholars may be better able to estimate the effectsof such changes and also gain theoretical traction for understanding how thistype of organizational change affects performance.

While our results are robust to a number of different analytic approaches,like all research, the analysis presented here has limitations. Perhaps most

importantly, although the various measures of past performance we usein our analysis are likely to account for the factors that most strongly af-fect both performance-based coaching replacements and subsequent perfor-mance, we are not able to account for (or even identify) every potentialconfound.

One possibility is that coaching replacements affect the quality of playerrecruitment that, in turn, affects performance. Similarly, fluctuations in thefinancial resources available to a team may be associated with both team perfor-mance and decisions about coaching replacements. We explore these possibil-

ities in supplementary analysis (presented in Appendix Table A5). Specifically,we compared recruitment class ratings from 2002 to 2007 from Rivals.comand do not find any statistically significant differences in recruitment ratings atT 1 and Tbetween treated and control teams within any block (p > 0.10for all comparison). Similarly, examining logged team expenses at Tand T1 using data from 2001 to 2007 (Department of Education, Office of Post-secondary Education, 2009), we do not find any differences between treatedand control teams within any blocks.

An additional possibility is that the quality of replacement coaches varies

with the performance of the team. If this is the case, it seems reasonable toexpect that teams in Block 1 would be able to attract higher quality coachesthan the lower performing teams in the other three blocks. To the extentthat this is the case, our findings may underestimate the actual difference in


20/28


the effect of replacement between these classes of teams. A variety of otherfactors may also affect the likelihood of coaching replacement, includingthe amount of time left on a coachs contract, whether a university has justmade a sizable expenditure hiring a coach in a different sport, and patterns

of alumni giving. However, in order to bias our estimates of the effects ofcoaching replacement, these factors would also have to affect subsequent teamperformance independent of the performance measures we use in our analysis.

We also acknowledge that we cannot directly identify the mechanismsthat drive the aggregate effects we observe. The effects we find may stemfrom the time it takes for a new coach to adapt to his new environment,the disruption caused by changes in on-field strategies, or a number ofother factors. Examining these mechanisms is an important avenue for futureresearch.

As with any statistical analysis, we cannot rule out the possibility thatsome specific instances of coaching replacements truly benefit a team. This iscertainly a possibility and there is little doubt that many commentators, schooladministrators, and other observers believe that coaching changes are oftenresponsible for turnarounds in team performance. However, it is importantto bear in mind that the fact that a teams performance improves followinga coaching replacement does not necessarily mean that the coach should begiven credit for the improvement. Many teams that perform poorly one yearimprove the following year without replacing their coach. In fact, while 3 of

the 16 teams in Block 1 (19%) that replaced their coach saw improvementsin win percentage at time T, 158 of the 393 teams in this block that didnot replace their coach (40%) improved. In summary, to the extent thatsome coaching changes are more effective than others, our evidence suggeststhat on average teams are either unable to determine whether the coach isresponsible for unsatisfactory performance or do a poor job of selecting areplacement.

Our findings have important practical implications for the high stakes envi-ronment that is contemporary college football. When a college football teams

performance is disappointing, the first and often only remedy administrators,fans, and sports writers turn to is firing the coach. This is usually an expen-sive approach to solving the problem.12 In fact, the concern of sky-rocketinghead coaching salaries was the key finding in a 2009 Knight Commission onIntercollegiate Athletics report based on interviews with 95 FBS university

12Fulks (2009) finds that coaching salaries and benefits constitute about one-third of totalathletic department expenditures, with head football coaches comprising nearly one-third ofall salaries. In 2009, the average head coaching salary for all FBS teams was more than $1.3

million annually, which corresponds to a 46 percent increase over the prior three years (Wiebergand Upton, 2007; Wieberg et al., 2009). Moreover, in recent years, it has become commonpractice for poorly performing teams to fire the head coach prior to the expiration of hiscontract, forcing the university to buyout the remainder of the fired coachs contract oftenat considerable expense (Wieberg, 2008).


21/28


presidents (Knight Commission on Intercollegiate Athletics, 2009). Despitethe fanfare that often accompanies the hiring of a new coach, our researchdemonstrates that at least with respect to on-field performance, coach replace-ment can be expected to be, at best, a break-even antidote. These findings,

coupled with the significant costs universities typically incur by choosing toreplace a head football coach, suggest that universities should be cautious intheir decision to discharge their coach for performance reasons.

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Appendix FIGURE A1

Distribution of Propensity Scores


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FIGURE A2

Effects of Coaching Replacement on Change in Conference Win Percentage

(Stratified Matching)

TABLE A1

Effects of Coaching Replacement (Stratified Matching)

Year

No. ofFBS

Teams

No. ofFBS

TeamsChangingCoaches

No. ofPerformance-

BasedCoachingChanges

No. ofNonperformance-Based Coaching

Changes

Percent ofFBS

TeamsChangingCoaches

1997 112 24 11 13 21.41998 112 15 8 7 13.41999 114 20 9 11 17.52000 116 13 9 4 11.22001 117 25 17 8 21.42002 117 13 10 3 11.12003 117 19 13 6 16.22004 120 14 12 2 11.72005 119 22 15 8 18.52006 119 13 5 8 10.92007 120 23 10 13 19.22008 120 18 10 8 15.02009 120 22 15 7 18.32010 120 22 11 11 18.3

Total 1643 263 155 109 16.0

Data gathered by authors.


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26/28


TABL

EA3

DemonstrationofBa

lanceUsingWeights

(1)

(1)

(2)

(3)

(4)

(5)

(6)

Win

Win

Winpct.

WinPct.

Conf.Win

Bowl

Coach

Percentage

Percentage

Diffe

rential

Differential

Percentage

Appearan

ce

Tenure

(T

1)

(T

2T

10)

(5yea

r;T

1)

(10year;T1)

(T

1)

(T

1)

(T

1)

Performance

-based

0.0

22

0.0

04

0.0

15

0.0

18

0.0

24

0.0

34

0.3

64

replaceme

nt(1=

yes)

[0.0

15]

[0.0

12]

[0.0

16]

[0.0

16]

[0.0

16]

[0.0

29]

[0.6

34]

Constant

0.3

09

0.4

58

0.1

26

0.1

53

0.2

64

0.1

21

4.0

43

[0.0

13]a

[0.0

15]a

[0.0

08]a

[0.0

09]a

[0.0

14]a

[0.0

17]a

[0.6

60]a

Observations

1,2

05

1,2

05

1,2

05

1,2

05

1,2

05

1,2

05

1,2

05

R-squared

0.0

04

0.0

00

0.0

02

0.0

03

0.0

04

0.0

02

0.0

01

CellentriesareOLSregressioncoefficients.

Robuststandarderrors(cluste

redbyteam)inbrackets.

aSignificantat1%.


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TABL

EA4

EffectofCoachingReplacementonVariance

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Commo

nSupport

Block1

Block2

Block3

Block4

NoPB

Change

PB

Change

NoPB

Change

P

B

Cha

nge

NoPB

Change

PB

Change

NoPB

Change

PB

Change

NoP

B

Chan

ge

PB

Change

Standardde

viation

(winperce

ntageatT)

0.2

14

0.2

10

0.1

95

0.206

0.2

04

0.2

13

0.1

95

0.1

99

0.18

6

0.2

20

Measureofc

entraltendency

Te

stsoftheequalityofvarian

ces(p)

Mean

0.9

11

0.9

05

0.6

91

0.8

61

0.0

96

Median

0.9

87

0.9

49

0.6

93

0.9

77

0.1

09

10%

trimm

edmean

0.9

11

0.9

72

0.6

91

0.9

27

0.1

26

Cellentriesin

firstrowarestandarddeviatio

nsofwinpercentageattimeT.

Othercellentriesarep-valu

esoftestsoftheequalityofv

arianceacross

groups.


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TABL

EA5

RecruitmentClassRatings(2002

200

7)andAthleticExpenses(2001

2007)

Block

1

Bloc

k2

Blo

ck3

Block4

NoPB

PB

NoPB

PB

NoPB

PB

NoPB

PB

Variable

Change

Change

Change

Change

Change

Change

Change

Change

AverageRival.com

starratings(T

1)

2.5

85

2.8

10

2.4

67

2.5

52

2.3

37

2.3

72

2.4

19

2.3

54

[0.5

155]

[0.6

419]

[0.4

724]

[0.5

551]

[0.4

748]

[0.3

918]

[0.4

571]

[0.3

771]

AverageRival.com

starratings(T)

2.5

84

2.6

31

2.4

90

2.3

51

2.3

06

2.3

35

2.3

68

2.2

26

[0.5

337]

[0.5

378]

[0.4

98]

[0.4

074]

[0.4

246]

[0.4

254]

[0.4

122]

[0.4

055]

Footballexpenses

(logged;T

1)

15.7

91

15.7

23

15.6

72

15.7

76

15.6

01

15.5

59

15.7

05

15.7

46

[0.5

979]

[0.6

175]

[0.5

408]

[0.6

786]

[0.5

836]

[0.5

499]

[0.4

748]

[0.4

599]

Footballexpenses

(logged;T)

15.7

84

15.8

54

15.6

79

15.7

09

15.5

19

15.5

87

15.6

37

15.8

16

[0.5

486]

[0.6

792]

[0.5

524]

[0.6

917]

[0.5

784]

[0.5

65]

[0.5

071]

[0.5

53]

Observations

393

16

265

40

302

63

90

36

Standarddeviationsinbrackets.

Nostatistic

allysignificantdifferencesin

recruitmentratingsorlogged

expensesatT

1orT.

Logg

edchangesin

expensesare

significantlylargerfortreatedgroupsinBlock3(p=

0.0

16).

Numberofobservationsvariesacrossrows.

Replacing College Football Coaches and Performance

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