An Efficiency Evaluation of Men's College Basketball ... - SAGE Journals

652037 research-article2016

AEXXXX10.1177/0569434516652037The American EconomistHorowitz

Article

An Efficiency Evaluation of Men’s College Basketball Coaches

The American Economist 2017, Vol. 62(1) 77–98 © The Author(s) 2016 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0569434516652037 journals.sagepub.com/home/aex

Ira Horowitz1

Abstract An ESPN panel of experts ranked the top 51 men’s college basketball coaches as of June 2014. I evaluate the production efficiency of those coaches, along with (a) 25 others who received honorable mention and (b) 86 whose teams earned either an NCAA or National Invitation Tournament (NIT) bid in the three then-most-recent seasons, and (c) 27 that were fired at the end of the 2013-2014 season. The evaluation uses data envelopment analysis and its three measures of efficiency: technical, overall technical, and scale. The measures do not necessarily yield the same rankings, with the efficiency-based rankings modestly corresponding to those of the panel when based on the coach’s entire career and somewhat less so when based on the 2012-2014 data. An ordered-logit analysis highlights the correspondingly modest role efficiency played in determining what it took to get into the sample and, by contrast, the dominant role that winning played in the process.

JEL Classifications: Z2, Z22 Keywords college basketball, coaches, efficiency, data envelopment analysis

Introduction Academic research into the various business and economic aspects of sports, other than the legal issues related to Major League Baseball’s reserve clause and assorted antitrust issues, dates back over half a century to the seminal articles of Rottenberg (1956) and Neale (1964). With the publication of Noll (1974), sports research was given the imprimatur of the Brookings Institution, and with Noll taking pride of place, several of the contributors to the book continue to work and publish in the area. One particular line of inquiry has looked into the evaluation and, by extension, the ranking of coaches/managers. Porter and Scully (1982) and Horowitz (1994) dealt with Major League Baseball managers, and Horowitz (1995) involved managers from the Nippon Leagues as well, while Fizel and D’Itri (1996) evaluated the efficiency of college basketball coaches. In a subsequent article (Fizel & D’Itri, 1997), they showed that winning percentage rather than efficiency is the main determinant of whether a coach keeps his job (although his successor may well be less efficient). Focusing on the 2005-2006 season and the Atlantic 10 Conference, Rimler, Song, and 1University

of Florida, Gainesville, FL, USA

Corresponding Author: Ira Horowitz, Information Systems and Operations Management, Warrington College of Business, University of Florida, Gainesville, FL 32611-77169, USA. Email: [email protected]

78

The American Economist 62(1)

Yi (2010) concluded, in essence, that all the coaches in the league were technically efficient, quality coaches, and that the principal differences between their degrees of success could be attributed to the skill levels of their players and, hence, their success in recruiting the better players—that is, the resources available to them. Most recently, Maxcy (2013) evaluated the head coaches of the 120 schools in the NCAA’s Football Bowl Subdivision, and if one broadens the scope to focus on teams rather than their coaches or managers, professional basketball comes into play in Zak, Huang, and Siegfried (1979), baseball resurfaces in Ruggerio, Hadley, and Gustafson (1996), the National Football League in Hadley, Poitras, Ruggerio, and Knowles (2000), and Major League Soccer in Haas (2003a). Haas (2003b), Barros and Leach (2006), and Barros and Garcia-del-Barrio (2008) looked into English Premier football teams, after which Barros and Garcia-del-Barrio (2011) crossed the Channel, at least figuratively, to look at the Spanish First Division football league. Stretched out over a 6-week period that began on June 2, 2014, the ESPN Forecast Panel revealed their answers to the question: Which 50 (college) coaches are doing the best job right now? The panel comprised “nearly 100 ESPN writers, editors, broadcasters and researchers to rate college basketball coaches on all aspects of running a program, on a scale of 1-10. The crucial distinction here is that the prompt was not career-oriented” (http://espn.go.com/ncb/notebook/_/page/top50coaches50-25/top-coaches). The rankings were published for the top 51 coaches, because Randy Bennett (Saint Mary’s) and Scott Drew (Baylor) tied for the final spot on the list. ESPN also published, in alphabetical order, the names of 25 coaches that just missed the final cut. There were 351coaches of Division 1 basketball-playing schools among whom the panel could choose. The choices could not have been easy. In this article, I evaluate the efficiency of these 76 NCAA men’s college basketball coaches using data envelopment analysis (DEA). I further extend the sample of coaches to include (a) the 52 additional coaches whose teams either qualified for the 2012 through 2014 64-team NCAA men’s basketball tournaments or lost in that year’s eight so-called play-in games, (b) the 34 coaches not included by dint of either the poll or the tournaments whose teams accepted invitations to play in the three National Invitation Tournaments (NIT), and (c) the otherwise not-yetincluded 27 coaches that were fired following the 2014 season. Excluded from the sample were the 14 coaches that survived the test of at least one of the latter three criteria, but who did not coach for the entire 2013-2014 season. In principle, then, all the coaches not named in the poll would have qualified for it. Moreover, most assuredly, there is some overlap in the sense that Cuonzo Martin, in particular, completed the hat trick of being one of the 25 barely failing to meet the cut in the poll, while his Tennessee Volunteers competed in both tournaments.The principal contributions of this latest look at the coaches are as follows. First, three different if related production efficiency measures qua perspectives are considered: notably, technical efficiency (TE), overall technical efficiency (OTE), and scale efficiency. Second, it is shown that their ranking is dependent on the efficiency measure that is employed, although some coaches are indeed in the upper (or lower) echelons regardless of the measure used to evaluate them. Third, it is shown that the measures and rankings based on how the coaches are doing “right now,” interpreted here as over the most recent two seasons, can differ substantially from those based on their “legacies” or career performance. Fourth, it is shown that the rankings based on career-long TE most closely mirror those of the ESPN panel, producing a Spearman rank-correlation coefficient of ρTE = .49 over the list of 51 ranked coaches. On one hand, this suggests that career TE provides a reasonable way to rank basketball coaches, “intangibles” such as recruiting ability and the production of NBA-ready players aside. On the other hand, a Spearman coefficient of ρW% = .60 based on the rankings by win percentage “right now” suggests that a ranking by win percentage might be even more reasonable; or, as was apparently true of Fizel and D’Itri’s (1997) athletic directors, ESPN’s experts were more focused on the coaches’ ability to win “right now” than on their managerial efficiency. Finally, and the article is also unique in this respect, I first estimate the parameters of an ordered-logit regression model that attempts to determine the role that efficiency played in

79

Horowitz

how the coaches entered the sample. The estimation, however, soundly rejects the proportional-odds assumption of the model, which led to the estimation of a series of binary logits. The latter estimates to turn the spotlight on (a) the very limited, but interesting role that efficiency can be inferred to have played in the panel’s evaluations; (b) the critical role that win percentage particularly, but not exclusively, win percentage “right now” played in its evaluations; (c) the panel’s seeming failure to take into account, except by omission, coaching in a Mid-Major or less renowned conference; (d) the panel’s ignoring experience, while giving respect to reputation-enhancing invitations to participate in “March Madness,” and not just here and now; and (e) the fact that coaches that fail to win “right now” show a distinct tendency to get themselves fired, regardless of where they are coaching and how efficiently they carry out the task.

The DEA Efficiency Measures Larson and Maxcy (2013) discussed various aspects of coaching, several with specific respect to basketball where a team has a high emphasis on skill practice, a sufficient measure of physical and cardiovascular conditioning (and) a high degree of planned team strategy . . . (with) . . . tactical efficiencies gained during coordinated practices and the rehearsal of specific planned plays. (p. 365)

Organizational skills are required, which include the delegation of authority to, for example, a conditioning coach, assistant coaches in charge of specific geographic areas for recruiting, persons to ensure that the players retain their academic eligibility, and so forth. When all is said and done, however, a basketball coach puts on the court the teams that he has recruited and trained, and, acknowledging the possibility that he will accept some losses for strategic purposes, by and large exercises whatever skills he possesses to get the maximum number of wins in the N games played in a given sample period, given point differentials and strength of schedule. With this view of the coach’s production process, let Wi = I1i denote the number of wins produced by team i over a period in which it has played Ni games (including post-season games) and recorded an average point differential of SRSi = I2i for a schedule whose magnitude of difficulty is measured by SOSi = I3i.1 Following Banker, Charnes, and Cooper (1984), the input-oriented measure of OTE in the DEA is determined by solving the following linear programming problem for each Coach j as the decision-making unit (DMU): OTE j = min λ j (1) subject to : ∑i µiWi ≥ W j (2) ∑i µi I ki ≤ λ j I kj ( k = 1, …, 3) (3) ∑i µi = 1 (4) 0 ≤ µi , λ j ≤ 1. (5) Because in effect strength of schedule is a negative input into the production process, the direction of the k = 3 inequality in the second set of constraints, Equation 3, is reversed. When OTEj = 1, Coach j is “producing” wins on the efficient production frontier. Removing Equation 4 converts the constant-returns-to-scale model into a variable-returns-toscale model whose solution yields the input-oriented measure of TE. The former is necessarily at least as large as the latter for any given coach qua DMU, because it includes an additional

80


constraint in a minimization problem. The ratio 0 ≤ TE / OTE = SE ≤ 1 yields what is commonly referred to as scale efficiency. The interpretation of the latter measure is somewhat obtuse and is the subject of some debate, but in essence, it reflects the ability of management to choose the optimal scale to optimize production (Kumar & Gulati, 2008). In the present context, this would translate into the number of games to play, which may not be up to the coach whose team plays in post-season tournaments, and whether to emphasize margin of victory or difficulty of schedule. Collier, Johnson, and Ruggerio (2011) provided an excellent and insightful discussion on the measurement of TE in sports.

The Sample of 189 Coaches The 189 coaches comprising the sample, along with their 2013-2014 affiliations, are listed in both Tables 1 and 2. The first 51 coaches are listed in the order in which the panel ranked them (i.e., Billy Donovan, now the coach of the NBA’s Oklahoma City Thunder, ranked first and John Calipari second, through Bennett and Drew tying for the final spot). The next 25 coaches, who received honorable mention, are then listed in alphabetical order. The 52 coaches that qualified for the sample by virtue of an NCAA Tournament appearance follow, again in alphabetical order. The next bloc of 34 coaches qualified via the consolation prize of a birth in the NIT. The remaining 27 coaches on the list had the distinction of being fired at the end of the season. With several notable exceptions, the overwhelming majority have never coached in an NCAA Tournament Championship game, no less won one. Rick Pitino (2013) and Kevin Ollie (2014) coached the most recent two “right now” champions, beating teams coached by John Beilein and Calipari, respectively. With Duke’s having won the 2015 tournament, Mike Krzyzewski-Coach K-has now won five. Donovan, Pitino, and Roy Williams follow with two apiece. Beilin, Jim Boeheim, Larry Brown, Calipari, Tom Izzo, Bill Self, and Toby Smith won one apiece. Moreover, although many of the coaches brought at least one team to the Final Four, only Coach K (11), Petino and Williams (seven each), Izzo (six), Calipari (five), and Boeheim and Donovan (four each) had more than three appearances in the Final Four. Most of the coaches have coached for more than a decade and at more than one school. James Johnson, Danny Manning, Ollie, Jack Perri, Chico Potts, Richard Pitino (Rick’s son), Joseph Price, Bennie Seltzer, and Travis Williams have only coached during the “right now” 2-year period; Roman Banks, John Becker, Jamion Christian, Bryce Drew, Andy Endfield, Ray Harper, Todd Howard, Clemon Johnson, Steve Masiello, Archie Miller, Lewis Preston, Steve Prohm, David Rice, and Michel White have only 1 earlier year under their belts. Mike Brennan, Craig Neal, and Brad Underwood were neophytes during the 2013-2014 season. In essence, for these coaches, right now represents their careers. In assessing the coaches’ “intangibles,” however, it is difficult to believe that the panel of experts was able to completely block out some of their legacy achievements—or failings. As a final parenthetic note, none of the aforementioned coaches of recent vintage entered the sample by way of an invite to the NIT, which hints at a latent criterion for such an invite.

The Efficiency Results When publishing the results of its poll, ESPN provided explanations, some in considerable detail, of what each coach brought to the panel’s table, and not just right now. Indeed, Bruce Pearl had not coached since being fired after Tennessee’s loss in the 2011 NCAA Tournament, following the university’s own investigation of the NCAA’s allegations of rules violations and its subsequent sanctions. As was true of Donovan, cited for making basketball relevant at a “football” school, and Calipari, cited for his salesmanship, there were apparently somewhat more positive aspects to Pearl’s vita, aspects that resulted in his having been hired by Auburn in March 2014.

81

Horowitz Table 1. Efficiency Results Based on “Right Now” (2012-2014) Statistics. Efficiency measure Coach (1) B. Donovan J. Calipari T. Izzo M. Krzyzewski Rick Pitino B. Self B. Ryan G. Marshall J. Beilein K. Ollie S. Miller J. Boeheim S. Smart T. Bennett F. Hoiberg R. Williams M. Few S. Fisher J. Wright T. Matta L. Brown T. Amaker G. McDermott B. McKillop J. Dixon A. Miller M. Cronin J. Larranaga J. Crews B. Huggins L. Kruger T. Miles F. McCaffery T. Boyle P. Martelli S. Alford R. Byrd B. Williams T. Smith B. Weber E. Cooley J. Pastner C. Mack

Rank by efficiency measure

Affiliation

Bloc

TEj

OTEj

SEj

RTEj

ROTEj

RSEj

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Florida Kentucky Michigan St. Duke Louisville Kansas Wisconsin Wichita St. Michigan Connecticut Arizona Syracuse VCU Virginia Iowa St. UNC Gonzaga San Diego St. Villanova Ohio St. SMU Harvard Creighton Davidson Pittsburgh Dayton Cincinnati Miami Saint Louis West Virginia Oklahoma Nebraska Iowa Colorado Saint Joseph’s UCLA Belmont Virginia Tech Texas Tech Kansas St. Providence Memphis Xavier

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 0.8868 1 0.9936 1 0.9951 0.9799 1 1 1 1 0.9771 0.8730 0.9016 0.9244 0.9438 0.9956 0.9641 0.9626 0.9448 0.8288 0.9404 0.9268 0.8832 0.8638 0.8619 0.8928 0.9137 0.9787 0.7565 0.9349 1 0.7938 0.9891 0.9067 1 0.9457 0.9189 1 0.9595 0.9114 0.9978 0.8631

1 0.8972 1 0.9978 1 1 1 1 1 1 1 0.9779 0.8904 0.9153 0.9326 0.9509 0.9962 0.9725 0.9709 0.9507 0.8597 0.9648 0.9339 0.8895 0.8763 0.8864 0.9148 0.9260 0.9883 0.8139 0.9518 1 0.8183 0.9906 0.9273 1 0.9484 0.9366 1 0.9686 0.9223 1 0.8940

1 0.9884 1 0.9958 1 0.9951 0.9799 1 1 1 1 0.9992 0.9805 0.9850 0.9912 0.9925 0.9994 0.9914 0.9915 0.9938 0.9641 0.9747 0.9924 0.9939 0.9857 0.9724 0.9760 0.9867 0.9903 0.9295 0.9822 1 0.9701 0.9985 0.9778 1 0.9972 0.9811 1 0.9906 0.9882 0.9978 0.9654

1 88 1 21 1 20 24 1 1 1 1 26 104 76 59 43 19 29 30 42 142 49 57 92 112 117 79 70 25 168 52 1 154 22 74 1 40 63 1 33 72 18 114

1 102 1 24 1 1 1 1 1 1 1 31 116 84 66 46 26 32 33 47 143 37 64 118 132 123 85 73 28 176 45 1 172 27 70 1 51 58 1 34 74 1 108

1 61 1 32 1 35 86 1 1 1 1 22 84 82 51 46 20 50 49 39 130 102 47 41 67 105 99 64 56 165 75 1 118 24 95 1 28 79 1 55 63 26 128

(continued)

82


Table 1. (continued) Efficiency measure Coach (1) R. Barnes M. Brey J. Thompson III B. Hoffman S. Morrill Richard Pitino R. Bennett S. Drew D. Altman T. Cluess T. Crean K. Dambrot J. Dawkins F. Dunphy L. Hamilton B. Jacobson D. Kellogg L. Krystkowiak M. Lonergan C. Martin C. Mooney Craig Neal M. Painter D. Paulsen B. Pearl S. Prohm D. Rose H. Sendek K. Stallings A. Toole B. Underwood B. Wardle M. White C. Alexander R. Banks J. Becker M. Brady M. Brennan W. Brown J. Callero Jamion. Christian K. Davis M. Davis


Affiliation

Bloc

TEj

OTEj

SEj

RTEj

ROTEj

RSEj

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3

0.9170 0.8636 0.9214 0.9368 0.8786 0.9314 0.9085 0.8846 0.9423 0.8404 0.8298 0.9478 0.8752 0.8485 0.9167 0.6701 1 0.8396 0.8387 0.8463 0.8406 1 0.7755 0.9164 0.9311 0.9308 0.8736 0.8721 0.8301 0.9406 1 0.8769 0.9534 0.7970 0.9411 0.8595 0.8229 0.7919 0.9030 0.8138 0.8834 0.9508 0.8505

0.9261 0.8892 0.9412 0.9551 0.9167 0.9352 0.9265 0.8922 0.9507 0.8715 0.8456 0.9606 0.8927 0.8846 0.9220 0.7044 1 0.8734 0.8783 0.8674 0.8697 1 0.8251 0.9319 0.9381 0.9400 0.8909 0.8918 0.8747 1 1 0.9073 0.9542 0.8198 0.9418 0.8672 0.8565 1 0.9033 0.8525 0.9022 0.9555 0.8500

0.9902 0.9712 0.9894 0.9808 0.9584 0.9959 0.9806 0.9915 0.9912 0.9643 0.9813 0.9867 0.9804 0.9592 0.9943 0.9513 1 0.9613 0.9549 0.9757 0.9665 1 0.9969 0.9834 0.9925 0.9902 0.9806 0.97797 0.9490 0.9406 1 0.9665 0.9992 0.9722 0.9993 0.9911 0.9608 0.7919 0.9997 0.9546 0.9792 0.9951 0.9947

65 113 60 51 96 53 73 90 45 132 141 39 101 127 66 184 1 133 134 128 131 1 159 67 54 55 102 105 140 48 1 98 36 153 47 120 144 155 75 145 91 37 126

72 119 55 42 80 60 71 110 47 135 154 40 109 124 75 189 1 134 131 139 136 1 165 68 57 56 113 111 133 1 1 90 43 171 54 140 145 1 96 151 98 41 148

58 113 92 80 140 31 68 91 52 129 77 66 85 139 38 149 1 134 145 101 125 1 159 71 45 57 81 94 152 158 1 126 23 107 21 53 137 183 19 146 90 46 37

Texas Notre Dame Georgetown Mercer Utah St. Minnesota Saint Mary’s Baylor Oregon Iona Indiana Akron Stanford Temple Florida St. Northern Iowa U. Massachusetts Utah George Wash. California Richmond New Mexico Purdue Bucknell Auburn Murray St. Brigham Young Arizona St. Vanderbilt Robert Morris S. F. Austin Wisconsin–GB Louisiana Tech N.C. A & T Southern Vermont James Madison American Albany California Poly Mt. St. Mary’s Middle Tennessee Texas Southern

(continued)

83

Horowitz Table 1. (continued) Efficiency measure Coach (1) B. Drew C. Ellis A. Endfield L. Eustachy A. Evans J. Ferry T. Ford J. Giannini M. Gottfried A. Grant J. Groce F. Haith R. Harper S. Hawkins S. Heath R. Jeter A. Kennedy D. Layer D. Manning R. Marlin F. Martin S. Masiello R. McCallum M. McConathy M. Menzies D. Monson M. Montgomery L. Moton S. Nagy J. Neubauer J. Patsos J. Perri S. Phillips R. Rahe B. Reed D. Rice L. Rice M. Ross M. Schmidt W. Tinkle S. Woods M. Young M. Anderson


Affiliation

Bloc

TEj

OTEj

SEj

RTEj

ROTEj

RSEj

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4

0.8876 0.8796 0.8731 0.8688 0.9399 0.6938 0.8355 0.8890 0.9306 0.8408 0.9539 0.8904 0.9209 0.9607 0.8959 0.8766 0.8625 0.6814 0.8537 0.8269 0.7685 0.8629 0.7751 0.9161 0.9142 0.9185 0.8978 1 0.9190 0.9679 0.8882 0.8105 0.9456 0.9429 0.8135 0.8619 0.8793 0.9565 0.7678 1 0.8582 0.8095 0.8041

0.9065 0.8971 0.8879 0.8947 0.9500 0.8263 0.8529 0.9136 0.9348 0.8681 0.9541 0.9074 0.9333 0.9651 0.9144 0.8979 0.8833 0.7387 0.8832 0.8578 0.8202 0.8951 0.8259 0.9321 0.9209 0.9351 0.9179 1 0.9288 0.9974 0.8948 0.8552 0.9494 0.9444 0.8547 0.8890 0.9054 0.9634 0.8273 1 0.8826 0.8455 0.8428

0.9792 0.9805 0.9833 0.9711 0.9894 0.8397 0.9796 0.9731 0.9955 0.9686 0.9998 0.9813 0.9867 0.9954 0.9798 0.9763 0.9765 0.9224 0.9666 0.9640 0.9370 0.9640 0.9385 0.9828 0.9927 0.9823 0.9781 1 0.9895 0.9704 0.9926 0.9477 0.9960 0.9984 0.9518 0.9695 0.9712 0.9928 0.9281 1 0.9723 0.9574 0.9541

86 94 103 109 50 178 138 82 56 130 35 81 61 31 78 116 99 182 124 143 162 115 160 68 69 64 77 1 62 28 85 147 41 44 146 118 95 102 163 1 122 149 150

92 103 122 106 49 163 150 87 62 137 44 89 65 36 86 126 100 188 127 144 170 104 164 67 76 61 78 1 69 25 105 147 50 52 149 120 93 113 162 1 129 155 156

48 83 72 115 60 181 88 104 33 121 18 78 65 34 87 97 98 171 124 132 162 131 161 73 43 74 93 1 59 116 44 153 30 25 148 120 114 81 167 1 106 142 147

Valparaiso Coastal Carolina USC Colorado St. Florida International Duquesne Oklahoma St. La Salle NC State Alabama Illinois Tulsa Western Kentucky Western Michigan South Florida Milwaukee Ole Miss Liberty Tulsa Louisiana Lafayette South Carolina Manhattan Detroit Mercy Northwestern St. New Mexico St. Long Beach St. UC Berkeley NC Central South Dakota St. Eastern Kentucky Siena LIU North Dakota St. Weber St. Lehigh UNLV Boise St. Delaware St. Bonaventure Montana. Morehead St. Wofford Arkansas

(continued)

84


Table 1. (continued) Efficiency measure Coach (1) H. Broadnax B. Brownell D. Carter S. Cherry Jim. Christian B. Coen S. Cross J. Dooley B. Flint M. Fox T. Herrion D. Hunsaker R. Hunter D. Jones Joe Jones Johnny. Jones D. Kaspar T. Kowalczyk G. Lansing S. Lavin A. Major J. Mihalich S. Pikiell B. Radenbaugh L. Romar J. Scott S. Sutton M. Turgeon R. Turner D. Tyndall R. Walters G. Waters K. Willard F. Allen T. Barbee K. Bone B. Braun J. Bzdelik J. Capel S. Donahue M. Good T. Howard B. Huse


Affiliation

Bloc

TEj

OTEj

SEj

RTEj

ROTEj

RSEj

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Savannah St. Clemson Nevada High Point Ohio Northeastern Texas–Arlington Florida Gold Coast Drexel Georgia Marshall Utah Valley Georgia St. Central Florida Boston University LSU Texas St. Toledo Indiana St. St. John’s Charlotte Hofstra Stony Brook Charleston Southern Washington Denver Oral Roberts Maryland California – Irvine Southern Miss. San Francisco Cleveland St. Seton Hall Maryland – Eastern Shore Auburn Washington St. Rice Wake Forest Appalachian St. Boston College Loyola Marymount IUPUI Montana St.

4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5

0.8381 0.7801 0.88860 0.8696 0.9115 0.7822 0.8641 0.78462 0.7658 0.8608 0.8583 0.9261 0.8379 0.8649 0.8786 0.8908 0.8101 0.9714 0.9605 0.8764 0.9506 0.7297 0.9417 0.7620 0.8873 0.8001 0.8012 0.8352 0.8804 0.9836 0.8887 0.8886 0.7694 0.4343 0.6821 0.6873 0.5421 0.8714 0.7808 1 0.7561 0.6072 0.8531

0.8621 0.8215 0.9356 0.8880 0.9180 0.8332 0.8977 0.8838 0.8335 0.8903 0.8829 0.9345 0.8678 0.9051 0.8993 0.9168 0.8524 0.9865 0.9642 0.9023 0.9678 0.7879 0.9438 0.8070 0.9113 0.8485 0.8279 0.8559 0.8909 1 0.9161 0.9163 0.8161 0.8235 0.8364 0.8941 0.9156 0.9067 0.8409 1 0.8132 0.8807 0.8913

0.9722 0.9496 0.9470 0.9793 0.9929 0.9388 0.9626 0.9575 0.9188 0.9669 0.9721 0.9910 0.9656 0.9560 0.9770 0.9716 0.9504 0.9846 0.9962 0.9713 0.9822 0.9261 0.9978 0.9424 0.9737 0.9430 0.9678 0.9758 0.9882 0.9836 0.9701 0.9697 0.9428 0.5274 0.8155 0.7687 0.5921 0.9611 0.9285 1 0.9298 0.6895 0.9571

136 158 89 107 71 156 111 129 164 119 78 58 137 110 97 80 148 27 32 100 38 173 46 166 87 152 151 139 158 23 83 84 161 188 181 180 187 106 157 1 169 186 125

142 169 59 121 77 160 101 125 159 117 86 63 138 99 94 79 152 30 38 97 35 182 53 178 88 153 161 146 135 1 82 81 174 166 158 107 83 91 157 1 177 130 112

108 151 154 89 40 160 133 141 172 123 87 54 127 144 110 96 150 69 29 112 76 168 27 155 103 156 122 100 174 70 117 119 157 189 182 184 187 136 166 1 164 186 143

(continued)

85

Horowitz Table 1. (continued) Efficiency measure Coach (1) J. James M. Jarvis C. Johnson J. Johnson F. Mitchell L. Orr B. Peterson C. Potts L. Preston J. Price C. Robinson B. Seltzer C. Warren T. Williams D. Wojcik T. Woodward J. Yarbrough


Affiliation

Bloc

TEj

OTEj

SEj

RTEj

ROTEj

RSEj

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5

0.7356 0.6967 0.7611 0.7361 0.6697 0.6773 0.7010 0.7067 0.4144 1 0.7438 0.7623 0.8694 0.6890 0.8385 0.7102 0.8549

0.8229 0.7708 0.8224 0.9876 0.7569 0.7542 0.7898 0.8042 0.7715 1 0.8059 0.8151 0.9045 0.7692 0.8632 0.8169 0.8909

0.8939 0.9039 0.9255 0.7453 0.8848 0.8980 0.8876 0.8788 0.5371 1 0.9229 0.9363 0.9612 0.8957 0.9714 0.8694 0.9596

188 177 167 171 185 183 176 175 189 1 170 165 108 179 135 184 123

189 184 29 168 186 187 181 180 183 1 179 175 95 185 141 185 113

183 173 169 185 178 174 177 179 188 1 170 163 135 175 111 124 138

Tennessee – Martin Florida Atlantic Florida A&M Virginia Tech Coppin St. Bowling Green NC–Wilmington Miss. Valley St. Kennesaw St. Grambling Oregon St. Samford Jacksonville Tennessee St. College of Charleston Maine SE Louisiana

Note. Three coaches, Brennan, Neale, and Underwood, debuted in the 2013-2014 season, while for Johnson, Manning, Ollie, Perri, Potts, Richard Pitino, Price, Seltzer, and Travis Williams, it was only their second season. Fourteen others, Banks, Becker, Jamion Christian, Bryce Drew, Endfield, Harper, Howard, Clemon Johnson, Masiello, Archie Miller, Preston, Prohm, David Rice, and White, Donovan’s successor at Florida, logged a third season—2011-2012. After a 2-year hiatus from coaching subsequent to being fired by Tennessee for several rules violations, both NCAArelated and non–NCAA related, Pearl signed on at Auburn for 2014-2015. For purposes of computing his “right now” efficiency measures, the data are used from his two most recent seasons, which spanned 2009-2011. TE = technical efficiency; OTE = overall technical efficiency.

The first two columns of Table 1 list the sample of 189 coaches and their affiliations as of the 2013-2014 season. Column 3 indicates the means by which they entered the sample: 1 = ranked, 2 = closest also-ran, 3 = NCAA invitee, 4 = NIT invitee, and 5 = fired. Columns 4 through 6 give the results for the three efficiency measures, TEj, OTEj, and SEj, based on data from the 20122013 and 2013-2014 seasons. Columns 7 through 9 give the ranks for the coaches, as determined through the respective measures. Thus, for example, given the difficulty and number of games played by his Gators, along with their score differentials, Donovan produced sufficient wins as to place him on the efficient production frontier. His technical efficiency, TE1 = 1, guaranteed an OTE1 = 1, and hence, a scale efficiency of SE1 = 1. Ten other coaches proved to be equally efficient. In the case of ties, all the tied coaches are listed at the highest available rank. Thus, Donovan’s ranking in the first and third categories was RTE1 = RSE1 = 1, tying him with, for example, Izzo, Pitino, and George Marshall. Imposing a constant-returns-to-scale wins-production process, three additional coaches were also technically efficient (OTEj = 1). Table 2 uses the same format, based on data encompassing each coach’s entire coaching career. As the Note to the table indicates, the data were modified to exclude (a) the seasons prior to 1979-1980 when adjusted-point-differential and strength-of-schedule data were unavailable (five coaches) and (b) partial seasons (two coaches).

86


Table 2. Efficiency Results Based on Career Statistics. Efficiency measure Coach (1) B. Donovan J. Calipari T. Izzo M. Kryzysewski Rick Pitino B. Self B. Ryan G. Marshall J. Beilein K. Ollie S. Miller J. Boeheim S. Smart T. Bennett F. Hoiberg R. Williams M. Few S. Fisher J. Wright T. Matta L. Brown T. Amaker G. McDermott B. McKillop J. Dixon A. Miller M. Cronin J. Larranaga J. Crews B. Huggins L. Kruger T. Miles F. McCaffery T. Boyle P. Martelli S. Alford R. Byrd B. Williams T. Smith B. Weber E. Cooley J. Pastner C. Mack


Affiliation

Bloc

TEj

OTEj

SEj

RTEj

ROTEj

RSEj

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0.9092 0.9787 0.9193 0.9925 0.9478 0.9542 0.8973 0.9125 0.8023 1 0.9119 0.9512 0.9330 0.8327 0.8462 1 1 0.8530 0.8163 0.9687 0.9057 0.7736 0.7836 0.8096 0.9479 0.7997 0.8099 0.7748 0.6944 0.9314 0.7947 0.6835 0.7512 0.7789 0.7684 0.8433 0.8256 0.8247 0.8794 0.8629 0.7365 0.9374 0.8476

0.9115 0.9794 0.9341 1 0.9505 0.9551 0.9001 0.9138 0.8043 1 0.9180 1 0.9402 0.8477 0.8641 1 1 0.8572 0.8186 0.9691 0.9169 0.7819 0.7906 0.8147 0.9482 0.8578 0.8281 0.7789 0.6958 0.9364 0.8018 0.6894 0.7520 0.8155 0.7725 0.8463 0.8262 0.8405 0.8817 0.8656 0.7652 0.9490 0.8733

0.9975 0.9993 0.9842 0.9925 0.9972 0.9991 0.9969 0.9986 0.9975 1 0.9934 0.9512 0.9923 0.9923 0.9793 1 1 0.9951 0.9972 0.9996 0.9878 0.9894 0.9915 0.9937 0.9997 0.9323 0.9780 0.9947 0.9980 0.9947 0.9911 0.9914 0.9989 0.9551 0.9947 0.9965 0.9993 0.9812 0.9974 0.9969 0.9625 0.9878 0.9706

25 9 22 8 15 12 27 23 76 1 24 13 18 51 42 1 1 38 61 11 26 103 91 68 14 80 67 101 144 19 82 152 112 96 107 44 56 58 31 35 123 16 41

31 13 23 1 16 15 33 27 88 1 25 1 19 54 44 1 1 50 75 14 26 110 100 77 18 49 68 113 153 21 91 158 126 76 119 55 70 58 38 42 122 17 39

40 21 118 81 44 25 47 34 38 1 75 163 84 120 125 1 1 62 43 14 19 98 92 73 13 167 127 66 37 68 93 90 30 160 67 53 23 122 41 48 151 108 146

Florida Kentucky Michigan St. Duke Louisville Kansas Wisconsin Wichita St. Michigan Connecticut Arizona Syracuse VCU Virginia Iowa St. UNC Gonzaga San Diego St. Villanova Ohio St. SMU Harvard Creighton Davidson Pittsburgh Dayton Cincinnati Miami Saint Louis West Virginia Oklahoma Nebraska Iowa Colorado Saint Joseph’s UCLA Belmont Virginia Tech Texas Tech Kansas St. Providence Memphis Xavier

(continued)

87

Horowitz Table 2. (continued) Efficiency measure Coach (1) R. Barnes M. Brey J. Thompson III B. Hoffman S. Morrill Richard Pitino R. Bennett S. Drew D. Altman T. Cluess T. Crean K. Dambrot J. Dawkins F. Dunphy L. Hamilton B. Jacobson D. Kellogg L. Krystkowiak M. Lonergan C. Martin C. Mooney Craig Neal M. Painter D. Paulsen B. Pearl S. Prohm D. Rose H. Sendek K. Stallings A. Toole B. Underwood B. Wardle M. White C. Alexander R. Banks J. Becker M. Brady M. Brennan W. Brown J. Callero Jamion Christian K. Davis M. Davis


Affiliation

Bloc

TEj

OTEj

SEj

RTEj

ROTEj

RSEj

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3

0.8489 0.8351 0.8543 0.7340 0.8848 0.8368 0.8583 0.7830 0.9232 0.8280 0.7856 0.8272 0.7461 0.8392 0.7169 0.8031 0.7660 0.7360 0.7817 0.7696 0.7302 1 0.8462 0.7849 0.8894 0.9706 0.9343 0.7594 0.8038 0.8501 1 0.7187 0.8653 0.6946 0.9283 0.8355 0.7030 0.7366 0.6486 0.6464 0.8114 0.7888 0.7749

0.8525 0.8380 0.8635 0.7389 0.8925 0.9583 0.8643 0.7893 0.9232 0.8605 0.7877 0.8301 0.7641 0.8413 0.7724 0.8242 0.7843 0.7673 0.7893 0.8026 0.7485 1 0.8488 0.7971 0.9025 1 0.9305 0.7602 0.8043 0.8697 1 0.8044 0.9181 0.699 0.9357 0.8605 0.7112 1 0.6490 0.6992 0.9135 0.7893 0.7834

0.9958 0.9965 0.9894 0.9934 0.9914 0.9750 0.9931 0.9920 1 0.9622 0.9973 0.9965 0.9896 0.9975 0.9924 0.9744 0.9767 0.9592 0.9904 0.9589 0.9756 1 0.9969 0.9847 0.9855 0.9706 0.9945 0.9990 0.9994 0.9775 1 0.8935 0.9425 0.9929 0.9921 0.9710 0.9885 0.7336 0.9994 0.9245 0.8882 0.9994 0.9892

40 49 37 126 29 46 36 92 21 53 89 54 144 45 133 74 108 124 93 105 127 1 43 90 28 10 17 111 73 85 1 132 32 143 20 48 137 122 164 166 66 86 100

51 61 45 137 35 48 43 101 24 46 105 65 123 57 142 72 108 121 101 90 128 1 53 94 32 1 20 124 88 40 1 87 30 150 22 47 144 1 173 151 28 103 109

57 50 99 74 91 134 77 175 1 152 42 52 132 39 83 136 131 155 95 157 133 1 46 116 114 145 71 29 20 130 1 176 166 79 86 144 104 188 107 170 178 21 100

Texas Notre Dame Georgetown Mercer Utah St. Minnesota Saint Mary’s Baylor Oregon Iona Indiana Akron Stanford Temple Florida St. Northern Iowa U. Massachusetts Utah George Wash. California Richmond New Mexico Purdue Bucknell Auburn Murray St. Brigham Young Arizona St. Vanderbilt Robert Morris S. F. Austin Wisconsin–GB Louisiana Tech N.C. A&T Southern Vermont James Madison American Albany California Poly Mt. St. Mary’s Middle Tennessee Texas Southern

(continued)

88


Table 2. (continued) Efficiency measure Coach (1) B. Drew C. Ellis A. Endfield L. Eustachy A. Evans J. Ferry T. Ford J. Giannini M. Gottfried A. Grant J. Groce F. Haith R. Harper S. Hawkins S. Heath R. Jeter A. Kennedy D. Layer D. Manning R. Marlin F. Martin S. Masiello R. McCallum M. McConathy M. Menzies D. Monson M. Montgomery L. Moton S. Nagy J. Neubauer J. Patsos J. Perri S. Phillips R. Rahe B. Reed D. Rice L. Rice M. Ross M. Schmidt W. Tinkle S. Woods M. Young M. Anderson


Affiliation

Bloc

TEj

OTEj

SEj

RTEj

ROTEj

RSEj

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4

0.8118 0.7895 0.7010 0.7927 0.7164 0.6466 0.7297 0.6798 0.8249 0.8136 0.7701 0.8028 0.7917 0.7396 0.6747 0.8118 0.6883 0.6290 0.6227 0.7949 0.8044 0.7694 0.6794 0.6920 0.8003 0.7290 0.8801 0.8808 0.7806 0.7748 0.6931 0.7558 0.8081 0.8630 0.7861 0.8649 0.7450 0.6088 0.6446 0.8358 0.6241 0.6766 0.8263

0.8992 0.7961 0.7926 0.7935 0.7189 0.6473 0.7424 0.6802 0.8312 0.8358 0.8097 0.8060 0.9132 0.7490 0.6772 0.8230 0.7084 0.6433 0.7727 0.7958 0.8119 0.8423 0.6880 0.6923 0.8261 0.7324 0.8849 0.8064 0.7984 0.7759 0.7008 0.7758 0.8081 0.8668 0.7888 0.8915 0.8081 0.6373 0.6497 0.8401 0.6403 0.6800 0.8395

0.9028 0.9917 0.8844 0.9990 0.9965 0.9989 0.9829 0.9994 0.9924 0.9734 0.9511 0.9960 0.8670 0.9875 0.9963 0.9864 0.9716 0.9778 0.8059 0.8059 0.9908 0.9135 0.9875 0.9996 0.9688 0.9954 0.9946 0.9931 0.9777 0.9986 0.9890 1 1 0.9956 0.9966 0.9702. 0.9219 0.9553 0.9922 0.9949 0.9747 0.9950 0.9843

64 85 138 83 134 165 128 154 57 63 104 75 84 120 159 150 64 174 177 81 72 106 155 147 79 129 30 78 94 101 146 99 69 34 88 33 117 179 168 47 176 156 55

34 95 99 98 143 175 134 162 64 62 80 85 29 127 165 147 74 177 118 96 78 56 160 155 71 138 37 84 92 115 149 116 82 41 104 36 82 180 172 59 179 163 60

87 88 179 28 51 31 119 18 82 139 64 56 180 100 54 143 111 128 186 32 94 173 109 15 148 133 69 78 129 33 101 1 1 58 49 147 171 159 85 65 135 63 117

Valparaiso Coastal Carolina USC Colorado St. Florida International Duquesne Oklahoma St. La Salle NC State Alabama Illinois Tulsa Western Kentucky Western Michigan South Florida Milwaukee Ole Miss Liberty Tulsa Louisiana Lafayette South Carolina Manhattan Detroit Mercy Northwestern St. New Mexico St. Long Beach St. UC Berkeley NC Central South Dakota St. Eastern Kentucky Siena LIU North Dakota St. Weber St. Lehigh UNLV Boise St. Delaware St. Bonaventure Montana. Morehead St. Wofford Arkansas

(continued)

89

Horowitz Table 2. (continued) Efficiency measure Coach (1) H. Broadnax B. Brownell D. Carter S. Cherry Jim Christian B. Coen S. Cross J. Dooley B. Flint M. Fox T. Herrion D. Hunsaker R. Hunter D. Jones Joe Jones Johnny Jones D. Kaspar T. Kowalczyk G. Lansing S. Lavin A. Major J. Mihalich S. Pikiell B. Radenbaugh L. Romar J. Scott S. Sutton M. Turgeon R. Turner D. Tyndall R. Walters G. Waters K. Willard F. Allen T. Barbee K. Bone B. Braun J. Bzdelik J. Capel S. Donahue M. Good T. Howard B. Huse


Affiliation

Bloc

TEj

OTEj

SEj

RTEj

ROTEj

RSEj

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Savannah St. Clemson Nevada High Point Ohio Northeastern Texas – Arlington Florida Gold Coast Drexel Georgia Marshall Utah Valley Georgia St. Central Florida Boston University LSU Texas St. Toledo Indiana St. St. John’s Charlotte Hofstra Stony Brook Charleston Southern Washington Denver Oral Roberts Maryland California–Irvine Southern Miss. San Francisco Cleveland St. Seton Hall Maryland–Eastern Shore Auburn Washington St. Rice Wake Forest Appalachian St. Boston College Loyola Marymount IUPUI Montana St.

4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5

0.5482 0.8106 0.7452 0.6897 0.7870 0.6311 0.7381 0.7344 0.7290 0.8183 0.8065 0.8293 0.7457 0.7639 0.6696 0.7412 0.8081 0.6987 0.8137 0.8347 0.6984 0.7246 0.6906 0.6073 0.7780 0.6567 0.7806 0.7785 0.6752 0.8228 0.6801 0.7421 0.6454 0.5095 0.6640 0.6961 0.6947 0.6474 0.6399 0.6397 0.5631 0.4991 0.6170

0.5513 0.8115 0.7769 0.7086 0.7982 0.6619 0.7467 0.7735 0.7319 0.8333 0.8283 0.8301 0.7461 0.7948 0.6733 0.7447 0.8083 0.7086 0.8517 1 0.7541 0.7250 0.6927 0.6166 0.7794 0.6622 0.7808 0.7876 0.7403 0.8266 0.7401 0.7477 0.6778 0.5100 0.6906 0.7226 0.6960 0.6904 0.6904 0.6471 0.5748 0.5884 0.6350

0.9944 0.9878 0.9592 0.9733 0.9860 0.9535 0.9885 0.9495 0.9961 0.9820 0.9737 0.9990 0.9995 0.9611 0.9945 0.9953 0.9998 0.9860 0.9554 0.8347 0.9261 0.9995 0.9970 0.9849 0.9982 0.9917 0.9997 0.9885 0.9121 0.9954 0.9189 0.9925 0.9522 0.9990 0.9615 0.9633 0.9981 0.9269 0.9269 0.9886 0.9797 0.8482 0.9717

185 77 116 149 87 173 121 125 129 60 71 52 115 110 160 119 69 139 62 50 140 131 148 180 98 162 94 97 158 59 153 118 167 186 161 141 142 170 171 172 183 187 178

186 79 114 145 93 170 131 117 139 63 67 65 132 97 133 166 81 145 52 1 125 140 154 182 112 169 111 106 135 69 136 129 164 187 156 141 152 174 157 176 184 183 181

72 106 156 140 113 161 103 165 55 121 137 26 16 61 70 154 11 112 158 185 169 17 45 115 35 89 12 105 174 59 172 80 162 27 153 150 36 97 168 102 123 184 142

(continued)

90


Table 2. (continued) Efficiency measure Coach (1) J. James M. Jarvis C. Johnson J. Johnson F. Mitchell L. Orr B. Peterson C. Potts L. Preston J. Price C. Robinson B. Seltzer C. Warren T. Williams D. Wojcik T. Woodward J. Yarbrough


Affiliation

Bloc

TEj

OTEj

SEj

RTEj

ROTEj

RSEj

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5

0.3891 0.7464 0.5859 1 0.6757 0.6553 0.7048 0.6932 0.2794 1 0.6443 0.7113 0.6261 0.5716 0.7642 0.5521 0.6838

0.4521 0.7470 0.6562 1 0.6825 0.6692 0.7084 0.8052 0.5031 1 0.6662 0.8239 0.6431 0.7699 0.7861 0.5637 0.6885

0.8607 0.9992 0.8929 1 0.9900 0.9792 0.9949 0.8609 0.5554 1 0.9671 0.8633 0.9736 0.7424 0.9721 0.9794 0.9932

188 113 181 1 157 163 136 145 189 1 169 135 175 182 109 184 151

189 130 171 1 161 167 147 86 188 1 168 73 178 120 107 185 159

183 24 177 1 96 126 64 182 189 1 149 181 138 187 141 124 76

Tennessee–Martin Florida Atlantic Florida A & M Virginia Tech Coppin St. Bowling Green NC–Wilmington Miss. Valley St. Kennesaw St. Grambling Oregon St. Samford Jacksonville Tennessee St. College of Charleston Maine SE Louisiana

Note. In addition to the caveats provided in the Note to Table 1, (a) the career statistics of Boeheim (1976-1979), Krzyzewski and Ellis (1975-1979), and Rick Pitino and Montgomery (1978-1979) were excluded from their data, because SRS and SOS data are unavailable prior to the 1979-1980 season; (b) the data for the 2005-2006 season were excluded from Scott Drew’s statistics, because his Baylor Bears were under NCAA sanction that barred them from playing non-conference games; and (c) the six NCAA Championship games through which Fisher guided Michigan to the 1989 National Championship, the only games he coached that season, were excluded from his statistics. Fisher replaced Bill Frieder, who accepted the Arizona State job effective at the end of the season, but who was not allowed to coach in the NCAA Tournament by the distinctly unhappy Michigan Athletic Director, Bo Schembechler. TE = technical efficiency; OTE = overall technical efficiency.

Focusing first on Table 1, it is apparent from even a casual glance at the efficiency measures that regardless of the measure at which one glances, Donovan would be more highly rated than Matt Painter. However, based on any one or all these measures could one really make an argument that he ought to be more highly rated than Coach K? In point of fact, regardless of the measure that one prefers, the strongest impression given is that, at least insofar as on-the-court production efficiency is concerned, there does not seem to be a dime’s worth of difference between most of the coaches. Indeed, the respective Spearman rank correlations, allowing for ties (Kendall, 1955), between the three efficiency measures, TE, OTE, and SE, and the panel’s ranking are ρTE = .40, ρOTE = .38, and ρSE = .39. In each case, the hypothesis of a negative correlation can be soundly rejected in a one-tailed t test (p < .005). The fact that scale efficiency performs as well as the other two measures comes out of the blue, since, insofar as I am aware, this aspect of efficiency has been neglected in sports studies and, moreover, appears to indicate the smallest differences among the coaches. One can be reasonably confident that none of the panelists took any of these efficiency measures into consideration in their ranking processes. It is not unreasonable, however, to suspect that each took into consideration, if with varying degrees of precision, the measures’ underlying elements— most particularly, win percentage (W%). It should therefore come as no surprise that the accordant

91

Horowitz

correlation is ρW% = .6014 (p < .0001). It is also not surprising that none of the measures aligns more closely with the panel’s judgments than another, because there is no difference between how well these measures correlate with W%, as indicated in the following correlation matrix:

TE OTE SE W%

TE

OTE

SE

W%

1 .9908 .8375 .5960

.9715 1 .7564 .5758

.8543 .7704 1 .5806

.6129 .5961 .5416 1

In the matrix, the Spearman correlations are given above the diagonal and the corresponding product–moment correlations are given below it. The inferences are the same whether one looks above or below the diagonal: TE and OTE convey the same message, but scale efficiency would appear to add some new information. Although none of this is meant to suggest that the criterion for choosing among the measures for the purpose of evaluating the coaches, at least in regard to on-the-court production efficiency, ought to be to select that which most closely ranks them in accordance with the panel’s ranking, it is nonetheless interesting to consider the coaches for whom the disparities in the rankings tend to be the greatest, taking all three measures into consideration. Specifically, at the time the panel ranked them, Steve Alford and Tubby Smith had just completed their first seasons at UCLA and Texas Tech, respectively, while Tim Miles had had only 2 years under his belt at Nebraska; Josh Pastner has only coached at Memphis, having succeeded Calipari in 2009. At the opposite end of the spectrum, Calipari, Larry Brown, Bob Huggins, and Shaka Smart, who has now succeeded Rick Barnes at Texas, would seem to have been treated much more kindly by the panel than their most recent 2 years of production efficiency would warrant. Smart, however, brought his Mid-Major VCU team into four NCAA Tournaments in his 5 years of coaching, winning seven of 11 games and reaching the Final Four in 2011 by beating top-seeded Kansas in the Elite Eight. Brown was lauded for, after having last coached at Charlotte (NBA) for the first 28 games of the 2010-2011 season before being fired and having not coached in college since the 1987-1988 season, coming out of retirement at the age of 71 to rejuvenate a moribund SMU program. Huggins was said to be “a well-respected basketball mind if not always the paragon of virtue . . . among colleagues and media members,” while Calipari, beside his salesmanship, was hailed for “bringing his team along at the right moment.” In general, then, it seems evident that the panel members did not necessarily restrict themselves to the 2-year time horizon when ranking the coaches, nor to strictly quantifiable measures. Legacy counted. Indeed, my efforts to determine and infer weights to assign to a set of quantifiable factors such as winning percentage, number of first-round and second-round NBA draft selections, and total number of players coached who went on to play the equivalent of a full season of 82 games in the NBA, to accord with the panel’s ranking in a linear-programming-based policy-capturing process (see, for example, Horowitz & Zappe, 1995) went for naught. There was simply no feasible solution. How, for example, does one quantify Huggins’s mind or Calipari’s salesmanship? By the same token, and turning to Table 2, which is based on the coaches’ career statistics, it is immediately seen just by looking at the efficiency measures for Donovan that there is no way he would be ranked at the top based on his career efficiency in producing wins. Rather, Calipari, Coach K, Roy Williams, Mark Few, and Thad Matta, all of whom are household names (at least in the households of basketball aficionados), stand at the head of the class. Two fired coaches, James Johnson and Joseph Price, had 2-year careers, in each of which they lost more than they won, although by any of the measures, they were exceptionally efficient while doing so.

92


Given the “right now” aspect of the panel’s rankings, it is quite surprising that the respective career-based Spearman correlations of ρTE = .49 and ρOTE = .46 exceed those of their 2-year counterparts, while ρSE = .30 and ρW% = .49 are lower, as one would expect, with ρSE having the highest p value = .015 in a one-tail test of the negativity hypothesis. Finally, the following correlation matrix replicates the previous one, but uses the coaches’ career statistics:

TE OTE SE W%

TE

OTE

SE

W%

1 .9891 .2254 .9822

.9706 1 .0801 .9769

.3645 .2234 1 .2049

.9865 .9478 .3566 1

The latter matrix is included as it shows that only scale efficiency is barely correlated with win percentage; the other two efficiency measures are again highly correlated with each other, as well as with win percentage, regardless of the correlation measure to which we refer. The full sample of coaches yields the following triangular product–moment correlation matrix between the efficiency measures and win percentage, both “right now” and career:

TERN OTERN SERN W%RN TEC OTEC SEC W%C

TERN

OTERN

1 .8450 .8207 .7888 .6006 .5385 .3623 .5420

1 .3912 .6396 .5233 .5035 .2049 .4172

SERN

1 .6751 .4888 .4032 .4206 .4937

W%RN

1 .6328 .5817 .3290 .7814

TEC

1 .9428 .4450 .8050

OTEC

1 .1325 .7399

SEC

W%C

1 .4231

1

Looking at the first four rows and the first column, it is seen that “right now” TE is highly correlated (r > .82) with both of the other efficiency measures and trivially less so (r = .79) with win percentage. Similarly, OTE is modestly correlated (r = .39) with scale efficiency and less modestly (r = .64) with win percentage. Moreover, the correlation between scale efficiency and win percentage is in the same neighborhood (r = .68). Looking at the last four rows and the last four columns provides the same information for the career statistics. What stands out are the rather low correlations of career scale efficiency with both TE (r = .45) and particularly OTE (r = .13). Moreover, while win percentage correlates a bit more strongly with both technical (r = .81) and OTE (r = .74), its correlation with scale efficiency drops severely (r = .42). Comparing the “right now” measures with their career counterparts, it is seen that TE “right now” is modestly correlated with its career counterpart (r = .60), somewhat less so for OTE (r = .50), and much less so for scale efficiency (r = .42). On a more positive note, regardless of the efficiency measure employed, coaches tend to be as efficient in the short term as they are in the long term, and the coaches that tend to win in the short term also show a strong tendency to win in the long term (r = .78). Finally, a casual glance at the tables might suggest that the coaches ranked by the panel tend to be the more efficient coaches, while those that were fired tend to be the less efficient coaches,

93

Horowitz

again regardless of the efficiency measure employed. The less casual application of ordinary least squares (OLS), however, does much to dispel the suggestion. Specifically, let Ej denote the efficiency measure filling the role of the dependent variable in the equation to be estimated and let Dkj denote a binary indicator variable that is equal to unity when Coach j (j = 1, . . . ,189) enters the sample via bloc k (k = 1, . . . ,5) and is zero otherwise. The OLS estimates were obtained for six equations of the form E j = γ + Σγ k Dk + Σγ n Z n + ε j , (6) where εj, is a random-error term with the usual normality properties and the Zn includes both “right now” and career win percentages for Coach j, indicator variables for the level of the conference in which his team plays (i.e., Major vs. Mid-Major), and his experience (as measured by the number of complete seasons that he has logged as a head coach). Win percentage aside, the estimates have the following major implications (α ≤ .05): First, as far as the career measures are concerned, after controlling for career win percentage, on average, the ranked coaches have the higher technical and OTE, by g1 = 0.0303 and 0.0391, respectively, where g1 is the estimate of γ1 when Ej = TEj and OTEj, respectively. The more experienced coaches tend to be less technically efficient, by 0.0030, but more scale efficient, by 0.0021. Second, living in the “right now” moment, on average, the ranked coaches again have the higher OTE, by 0.0203, while the coaches that were fired tend to have been less scale efficient, by 0.0467. Third, and this might well be the most interesting aspect of the OLS results, the conference classification has nothing at all to do with it. That is, a coach in one of the less prominent conferences is as likely to manage his team on the efficient frontier as is a coach at a power-conference school. Which brings us to what I view to be the more interesting questions of whether, and the extent to which, coaching efficiency affected the panel, the tournament selection committees, and the Athletic Directors that made the decision to replace their coach?

The Ordered-Logit Approach Let Ojk, denote the odds that Coach j will have entered the sample at any point k' ≤ k. The orderedlogit model is then written as Lik ′ = ln ( Oik ′ ) = α k ′ + Σβm X m . (7) That is, the procedure estimates a series of k − 1 non-decreasing intercepts, αk’, and M fixed regression coefficients that are attached to each of the M independent variables, Xm (m = 1, . . . , M), that are hypothesized to “explain” the point of entry into the sample. Only four intercepts are estimated, because the probability that a coach will enter the sample as a result of having been fired is determined by subtracting from unity the probability that he will enter the sample by any of the first four criteria. After minor algebraic manipulation, that probability is determined to be exp(αk’ + ΣβmXm) / [1 + exp(αk’ + ΣβmXm.)]. For present purposes, I focused solely on the six efficiency metrics, “right now” and career, the two corresponding win percentages, four binary indicator variables defined by the level of the conference in which coach’s team competed in the 2013-2014 season (with the notable exception of Pearl), and the coach’s experience and NCAA Tournament appearances. The estimated equation also included the set of 32 interaction variables between the four conference-level variables and the eight metric variables. As to the former, these are defined as the five power conferences (Atlantic Coast, Big 10, Big 12, Pac 12, and Southeastern), the Mid-Majors (Atlantic 10, Colonial

94


Table 3. The Binary Logit Regressions. Independent variables

k 1 2 3 4

Intercept −11.6332 (