George Minoso2025-03-27T07:20:41-05:00

Losing Matters: A European Association Football Model for NCAA Men’s Basketball

George Minoso2025-03-27T07:20:41-05:00June 6th, 2025|Contemporary Sports Issues, Sport Education, Sports Studies|

Tricia Muldoon Brown, PhD, is a Professor of Mathematical Science at Georgia Southern University in Savannah, GA.  Her research interests include recreational mathematics associated with games and sport.

Andrew M. Heroy is employed by the Metis Foundation.  His area of specialty is data science. Eric B. Kahn, PhD, is a Professor of Mathematics as Commonwealth University of Pennsylvania. His research interests are in mathematics in sport and IBL in mathematics.

Losing Matters: A European Association Football Model for NCAA Men’s Basketball

ABSTRACT

Purpose: The purpose of this study is to examine the impact of applying a European association football model to American men’s college basketball.

Methods: Structures for NCAA men’s basketball and European association football are analyzed, and an Association Football (AF) model is proposed which utilizes features of the European system of promotion and relegation and a mathematical model for tournament bids and seeding.  

Results: Data were collected for NCAA men’s basketball teams from 2010 through 2019 and we identify teams which would be affected by promotion and relegation each year and compare the AF-modeled NCAA tournament to the actual tournament brackets finding 85% similarity.

Conclusions: Geographic conferences and relegation models where losing matters could be applied to men’s college basketball to decrease costs and increase excitement and fairness, while still having strong agreement with data from recent years.

Applications in Sport: American sports should consider adopting some selection and organizational practices from European sports, particularly objective selection and seeding and regional groupings.

Key Words: college basketball, tournament model, relegation, geographic conferences

INTRODUCTION

European and American sports cultures have many dissimilar features, but none are more notable than the disparate consequences for losing. European football associations follow an open model where teams can be moved between tiers based on their performance that season. On the other hand, American sports leagues use a closed model that does not allow for movement between major and minor leagues. An attempt was made in the 2010’s and early 2020’s to apply a more American system to European football, forming a super league that would bypass the Premier League’s current system of promotion, relegation, and championship selection. When asked about this idea, as reported in Sports Illustrated (1) Man City coach Pep Guardiola famously says, “It is not a sport when it doesn’t matter if you lose.” In contrast, the American version of these opinions is fictionalized in the popular television series Ted Lasso (20). Ted is the new head coach of FC Richmond, and one of his players questions what happens to losing teams if there is no relegation in America. Ted says, “You know, they play out the rest of the schedule, go through the motions in meaningless games contested in lifeless, half-empty stadiums, and everyone is pretty much fine with that.” The negative ramification of a poor season is not the only difference in European and American sports. We also wish to apply the regional nature of the clubs and teams in the European system and the objective selection process for both promotion and relegation, and postseason selection and seeding. These features have benefits in cost, transparency, and enthusiasm.

Taking advantage of these three association football characteristics, an open system, objective ranking process, and regional structure, we propose a new model for National Collegiate Athletic Association (NCAA) basketball conferences and tournaments. We begin by describing the current NCAA conference model and tournament selection process as well as that used by the Union of European Football Associations (UEFA) and the English Football League. Then we introduce the Association Football model for men’s basketball which realigns the conferences, creates a system of promotion and relegation, and objectively selects and seeds the NCAA tournament. Finally, we use the proposed model to identify teams for promotion and relegation and retroactively determine the tournament field using data collected for each year from 2010 through 2019.

METHODS

Current NCAA Model

Basketball is the most sanctioned sport in the NCAA with over 350 colleges and universities fielding Division I (DI) men’s basketball teams. While the precise number of participating teams varies slightly from year to year as teams move up from or down to Division II, every Division I school has a men’s basketball team.

Conference geography and alignment

Division I teams are organized into 32 conferences. The first college basketball conferences were developed in the early 20th century in an attempt to standardize rules and regulations and protect student athletes. These were primarily geographic in nature, as evidenced by many of the naming conventions such as the Southern Intercollegiate Athletic Association, the Missouri Valley Intercollegiate Athletic Association, or the Pacific Coast Conference. While versions of these conferences still exist in the SEC, the Big 12, and the PAC 12, the participating schools have expanded to include those outside of the original geographic region. For example, the Big 12, originally covering the Missouri Valley, added West Virginia University in 2012 and expanded in 2023 to include Brigham Young University, University of Houston, and University of Central Florida extending the border of the conference from Utah to the gulf of Texas to Florida to West Virginia. Additionally, the Big 10 added University of California Los Angeles and University of Southern California in 2024, stretching the conference from the coast of Maryland to the coast of California. While more feasible today than in 1915, such expansions have an increased cost in dollars and time for teams traveling longer distances.

Moreover, conference affiliation is not necessarily stable, many of the realignments mentioned above were catalyzed by the University of Texas and University of Oklahoma agreeing to move to the SEC for the 2025 season.  Conference movement causes ripple effects as losing a university (or two) creates a void, leading conferences losing a team to invite teams from other conferences subsequently leaving other holes to be filled. Further, conferences not directly impacted by a team leaving or entering will often feel the need to realign to stay competitive. Moriarty (13) explores how some conferences benefit while others are harmed by this domino effect. Although conference realignment is often spurred by football, the other sports, including basketball, are also impacted. Conference realignment is not a recent or transitory phenomenon, Daughters (5) finds that 78 teams switched conferences during the period 1998-2013. Conference realignment, studied in the context of college football, has varied effects including increased attendance and profits for the athletic department while lessening regional rivalries. (See Hoffer and Pincin (10) or Dennie (6) for example.) The movement of schools from one conference to another has been and will likely remain a part of the landscape of college sports.

Tournament selection and seeding

Men’s basketball teams typically play around 30 regular season games each year in a mix of conference and non-conference games. The outcomes of these games and the conference tournament help determine selection for and seeding in the NCAA Men’s basketball tournament. Since 2011, 68 college basketball teams have participated in the tournament; the selection process includes 32 automatic bids going to the conference tournament winner for each conference, as well as 36 at-large bid which are selected by a committee to participate in the tournament. The NCAA selection committee is composed of 12 members, expanded from 10 in 2022 (24), and consists of collegiate athletic directors, conference commissioners, and other conference officials. The committee uses a mix of metrics, discussion, and balloting to seed the field (14). While the teams’ win/loss record is the first consideration, many other factors are considered including distances to event locations, expected attendance numbers, television viewership, player injuries, and recency of strong wins. While objective metrics are applied, tournament selection and seeding are essentially subjective processes and come under fire when things like the “eye test” are used to determine whether teams should be in or out. Personal bias can creep in, and despite recent attempts to increaase transparency, the process remains secretive.

Studies on bias in the selection process have produced mixed results. Criticism has been levied toward the Ratings Performance Index (RPI) which was used as a major determining factor until 2018. The RPI is a blunt metric solely determined by a team’s wins and losses as well as their opponents’ wins and losses. Research into bias of the RPI includes Sanders (19) who, building on previous work on bias in the RPI, divides teams into four different types according to the performance of the conference and the performance of the team within its conference, finding a bias against lower performing teams in higher ranked conferences compared with higher performing teams in lower ranked conferences. Coleman (3) looked at bias in both selection and seeding, finding bias towards teams with a conference member on the selection committee and against mid-major teams. On the other hand, Paul and Wilson (16) asserted the RPI is inherently biased as it omits the margin of victory in its calculation. When attempting to correct for this, (as the committee has access to the margin of victory data) they found no bias. The NCAA introduced a newer ranking system in the 2018-2019 season, hoping to correct some of these issues. This system, called NET, classifies wins by quadrants and incorporates variables such as the location of the game and margin of victory. Some recent analysis is available on the effectiveness of part of this newer system. Reinig and Horowitz (18) use a linear programming approach to incorporate the use of the quadrant system, finding results more consistent with the actual tournament selection.

Because of these flaws with subjectivity and bias, we argue a better selection and seeding method should be free of personal biases and transparent. It is not a new idea to apply non- subjective models to the NCAA Tournament selection process. For example, Matthews et. al. (12) applied the PageRank algorithm to rank all teams, selecting the top 64 teams for the tournament; this method has the advantage of incorporating more variables than RPI including venue, date, and point margin. Also, Dutta and Jacobson (7) made use of a decision tree with 11 stages to pairwise compare all NCAA teams. Those with the most pairwise wins were selected for the tournament, and this method successfully selects almost all the participating teams from 2012-2016. Both of these methods have an implied rank so seeding could be affected with the traditional S-curve (and possibly a tie-breaking mechanism). Further, Reinig and Horowitz (17) used seven variables such as RPI and BPI but also polling ranks to generate a final ranking of the teams that respects domination, that is, one team being equal or ahead of another team in all seven variables.

We note that other works are attempting to predict the selection committees’ choices, such as Coleman, DuMond, and Lynch (4) and while we compare our selections with the past selections, this is as validation not prediction. Furthermore, it is understandably a popular topic to predict the outcomes of games in the tournament, but that is not the purpose of this work.

To summarize, the current NCAA model is flawed by its subjective and semi-transparent process of tournament seeding and selection, the higher costs of moving away from a regional conference alignment, and the instability of movement within these conferences. To correct these flaws, we will propose a model adapted from European association football.

European association football as a model

Association football or soccer has been played in Europe since the 1800’s. The football clubs are organized regionally, primarily by nation, with a few exceptions. The modern international governing body is the Union of European Football Associations (UEFA) while the clubs in England are regulated by the English Football Association. Here we discuss the promotion and relegation of the English clubs in their national association and selection and seeding for the UEFA tournament.

Promotion and relegation in the English Football League System

The English football league is structured as a pyramid with eleven defined levels where the 20-team Premier League sits on the top tier of the pyramid. At the end of each season, teams are moved between tiers based on their performance. Higher performing teams are promoted up the pyramid to the next level and lower performing teams are relegated down the pyramid to the previous level. In the English system, the number of teams promoted and relegated depends on the level with typically between one and four teams moving into and out of each league. In theory, this system allows any local club the opportunity to move up to the national stage over time. Similar relegation and promotion systems are in place in all the major national football associations. Positive benefits have been seen in the European promotion and relegation systems such as increases in both net attendance and player compensation Noll (15).

In particular, the national teams competing in UEFA Champions League (UCL) or UEFA Europa League (UEL) are almost always selected from the top tier of the pyramid. As we are using the UCL as a model for the NCAA tournament, we also wish to include a series of promotions and relegations. Here we model a shorter pyramid with the so-called “power conferences” above mid-major conferences. Because precedence is given in the tournaments to teams from power conferences in college basketball and premier leagues in association football, the promotion system allows high-performing teams from mid-major conferences the opportunity to move up to play against stronger competition but also have increased opportunity for tournament selection. Next, we look at European tournament selection and seeding.

UEL tournament selection and seeding

The UEFA consists of over 50 members and is composed primarily of national football associations in Europe, with some exceptions for associations formed from groups of nations and nations not having their own association. Each member association falls in one of four categories which determine how many representatives will compete from that association in the UCL and UEL championships. The UCL is considered the premier competition with high-ranking teams failing to qualify for the UCL group stage being selected to compete in the secondary UEL.

We are interested in the UEFA championship as a model for the NCAA tournament because this process is objective. Association representatives for the championship are chosen entirely by mathematical rank within the national championship with between one and four of the top finishing teams chosen from that league (with a rare possibility of five teams if the previous year’s UCL or UEL winner fails to qualify). The number of teams is determined by the association’s five-year club coefficient, a metric that measures the average number of points earned by a qualifying team in UCL and UEL competitions, where points are awarded for wins and ties. Specifically, 2 points are allocated for a win, 1 point for a tie, and 0 points for a loss. Each association is ranked by association coefficient (with a tie-breaking system in place when coefficients are identical). Once these teams enter the field, under typical circumstances 26 teams directly qualify for the group stage. These are the UCL and UEL title holders, the champions from associations ranked 1-10, the second-place teams from members ranked 1-6, and the third and fourth place teams from associations ranked 1-4. (If the UCL or UEL title holder qualifies in one of the other categories, then the champion from the eleventh ranked member or the third placing team from the fifth ranked member association, respectively, is selected for the group stage.) The remaining teams are chosen from two pathways: the Champions path and the League path.

The playoffs allow lower ranking teams a chance to play their way into the UCL group stage. The qualifying phase consists of a preliminary round, and three qualifying rounds along with a final playoff round. Fifty-five teams qualify for the playoffs consisting of 44 champions, 9 runners-up, and 2 third place teams from the remaining highest ranking member associations. The champions play along the Champions path and the second and third place teams play in the League path. The teams earn byes for earlier stages based on their ranking.  For example, the top two ranking champions enter in the final play-off round, but the lowest four must begin play in the preliminary round, and the top three runners-up begin playing in the third qualifying round while the next six begin playing in the second qualifying round. Teams are paired based on seeding (determined by an individual club coefficient) and a draw with (after the preliminary round) each pair playing two matches or legs, one on each home field, with the winner determined by total score differential. Any team eliminated before the UCL group stage gains automatic entry in the UEL with byes determined by progression through the qualifying phase and playoff. Furthermore, seeding in the tournament is also determined by coefficients except in extreme instances. Thus, the European system of seeding and selection is based purely on points and is simple, transparent, and unbiased.  To make use of these advantages, we propose the Association Football (AF) model for NCAA men’s basketball.  Our method provides conference alignments, a tournament selection process, and a promotion and relegation system.

Geographic conference alignment

To create new conference alignments, we classified seven conferences[1] in men’s college basketball (ACC, American, Big East, Big 10, Big 12, SEC, and PAC-12) as top-level basketball conferences, because, although there is variability from year to year, these seven conferences typically have the highest non-conference win percentages. For example, in the 2019-20 season these conferences were each winning over 70-80% of their games outside of their conferences according to Wittry (22). In the AF model, the 87 teams from these conferences will form a league analogous to UEFA’s Premier League and as such, they will receive a higher proportion of automatic bids into the tournament. Our proposal of geographic redistricting condenses these seven conferences down to five (East, Central, Midwest, South, and West), with each conference being sub-divided into two divisions. Figure 1 illustrates the top teams grouped into 10 new divisions within five geographic top conferences which we call the Premier-5 (P5) conferences.  (Table 7 in the Appendix lists the teams by conference and division.)

Figure 1: Premier-5 teams grouped by divisions

The remaining D1 teams are placed in Mid-major (MM) conferences (also named East, Central, Midwest, South, and West) that are also subdivided into divisions. Universities are assigned to conferences and divisions with regional proximity as the main factor, and as a secondary factor we tried to keep teams from the same state in the same division. Figure 2 illustrates the distribution of Mid-major teams into 18 divisions. Full lists of these divisions with additional images can be found Tables 8-12 in the Appendix. (Note, the Ivy League with its differing athletic philosophy is exempt from this realignment.)

As expected, universities are not evenly distributed throughout the United States. A lower density of universities in the western states causes those conferences to be geographically larger while a higher density on the eastern seaboard causes those conferences to be geographically smaller but to consist of a larger number of teams. There is precedence for this variation of size

Figure 2: Mid-major teams grouped by divisions (excluding Hawai’i)

within UEFA’s national associations; for example, Scotland has 12 teams while England has 20 in their top leagues. Current DI conferences averaging about 12 teams: the smallest conference is the Ivy League with 8 teams and largest is the Big 10 with 18 teams. The seven major conferences contributing to the AF realignment have expanded in recent years and now average 14 teams per conference. Our five new Premier-5 conferences are comparable in size, but slightly bigger averaging 17 teams per conference and 9 teams for each division. The 13 new mid-major divisions are larger than current conferences averaging about 14 teams per division with a minimum of 8 teams and a maximum of 21 teams in a division.

NCAA tournament

As in association football, some mechanism of promotion and relegation is needed to help the Premier-5 conferences maintain their premier status and deservedly earn the extra automatic bids in the NCAA tournament. Following the association football model, associated with each conference will be a numerical conference coefficient and with each team a numerical team coefficient. Let team P5 wins be the number of games a team wins against Premier-5 teams, and similarly team MM wins are the number of wins the team had against Mid-major teams. The team games are the total number of games played by the team in the regular season.  We define team coefficient as follows:

Conference coefficients are then the mean of the team coefficients in that conference.  Likewise, division coefficients are the mean of the team coefficients in that division. Mimicking association football, we use a simple 2-1-0 point system. The difference being, as college basketball games do not end ties, we keep track of the typical expected difficulty of the game by awarding 2 points for a Premier-5 win, 1 point for a Mid-major win, and 0 points for a loss.

With the team and division coefficients in place, the NCAA tournament can be restructured into two-tiers where top teams are automatically entered into the final tournament of 64 teams and lower-tier teams are given the opportunity to win a one-game playoff or “play-in” game for entry into the tournament. The teams are ranked within their conference by their team coefficients.  We say TC1 is the team with the highest coefficient, TC2 the team with the second highest coefficient, and so on. Then we rank the Premier- 5 conferences from 1 to 5 based on their conference coefficients, and we rank the Mid-major conferences from 1 to 19 based on their conference coefficients. Similarly, here PC# represents the #th ranked premier conference and MC# represents the #th ranked mid-major conference. Along with a tiebreaker, the coefficients completely determine seeding and region for each team. All teams labeled TC1 get an automatic bid, but teams TC2, TC3, and TC4 in PC1 also get an automatic bid as well as teams TC2 and TC3 from PC2 and PC3. This allocated the first 32 spots in the tournament. Further, the order of the locations may be rotated each year so each part of the country has the option to host the top-ranked team, as is done with the FBS national tournament, or alternately, as has been proposed for the NCAA, the teams given one seeds could choose their region with the PC1 TC1 team choosing first, the PC2 TC2 teams choosing second, and the PC3 TC1 team choosing third. The seeding of the teams with automatic bids is found in Table 1.

Table 1: Tournament Seeding

SEED	EAST	SOUTH	MIDWEST	WEST
1	PC01 TC1	PC02 TC1	PC03 TC1	PC04 TC1
2	PC03 TC2	PC02 TC2	PC01 TC2	PC05 TC1
3	PC01 TC3	PC02 TC3	PC03 TC3	PC01 TC4
4	MC03 TC1	MC02 TC1	MC01 TC1	PC01 TC5
5	MC04 TC1	MC05 TC1	MC06 TC1	MC07 TC1
6	MC11 TC1	MC10 TC1	MC09 TC1	MC08 TC1
7	MC12 TC1	MC13 TC1	MC14 TC1	MC15 TC1
8	MC19 TC1	MC18 TC1	MC17 TC1	MC16 TC1

The remaining 32 teams are determined by play-in games and are seeded by S-curve with the winner of the top-ranked play-in game seeded 9^th in the East to the winner of the lowest-ranked play-in game seeded 16^th in the East. To determine the teams in these play-in games we rank all unseeded premier conference teams by their team coefficients. The top 36 Premier-5 conference teams from any division will play in the preliminary round of the tournament. Here we identify the teams rather than the conferences with P#. Similarly, we rank all unseeded Mid-major conference teams by their team coefficients labeling them M#. Here the top 28 Mid-major teams will get a spot in the play-in round. The play-in games are seeded as shown in Table 2. The choice of 36 Premier-5 conference teams and 28 Mid-major conference teams may seem somewhat arbitrary, but when comparing the team coefficients of the 36th Premier-5 conference team and the 1st Mid-major team we saw consistency and so believe this strikes the correct balance. (See the discussion in Section 5.)

Table 2: Play-in games

Game

1

2

3

4

5

6

7

8

9

10

11

12

Home

P01

P02

P03

P04

P05

P06

P07

P08

P09

P10

P11

P12

Away

M28

M27

M26

M25

M24

M23

M22

M21

M20

M19

M18

M17

Game

13

14

15

16

17

18

19

20

21

22

23

24

Home

P13

P14

P15

P16

P17

P18

P19

P20

P21

P22

P23

P24

Away

M16

M15

M14

M13

M12

M11

M10

M09

M08

M07

M06

M05

Game

25

26

27

28

29

30

31

32

Home

P25

P26

P27

P28

P29

P30

P31

P32

Away

M04

M03

M02

M01

P36

P35

P34

P33

Promotion and relegation

An obvious difficulty with applying the model of promotion and relegation from a professional sports league to a collegiate setting is travel. Professional teams are composed of adults whose primary relationship to the team is that of an employee making extended travel simply a component of the work environment. On the other hand, college athletes have the additional responsibilities of being students, and the negative impact of extensive travel demands on scholastic responsibilities should be minimized if possible. With teams being promoted and relegated between Premier-5 and Mid-major conferences, coordinating geographic regions between the conferences is one way to achieve lower travel costs on the student athletes. There is evidence that geographic redistricting can be advantageous in more ways than the direct savings in time of travel. Lawrence (11) suggests rearranging the divisions around regional competition and rivalries will save in operational costs but also help ease overall inequalities between institutions. Additionally, Featherston (8) argues that geographic conferences are central to a student- centered conference alignment approach over the current model driven by financial incentives of national exposure.

With these coefficients in place, the promotion and regulation are simple processes. At the end of each season, within each conference teams are ranked by their team coefficients. The lowest ranking team in each of the ten Premier-5 divisions is identified. Within each mid-major conference, the divisions with the two highest division coefficients are selected and the team with the largest team coefficient from that division is chosen. Then the premier teams and mid-major teams move down and up respectively. Geography is used to determine how the Premier-5 and Mid-major teams are paired and swapped. A total of four teams will be directly affected by the promotion and relegation process in each of the five conferences.

We note, while it may seem unfair to have almost twice as many team vying for the promotion in the East, very talented winning teams in lower-ranked Mid-major East divisions will still have the option to move up in the next season’s promotion. Further, given the large number of mid-major teams in the West it may make sense to promote without relegation for the first two years to increase the size of the Premier-5 West conference to 16 teams to more closely parallel the other four conferences. Finally, the teams from the Ivy League conference will lie outside of this system and remain at the Mid-major level.

DISCUSSION

We have specified how the geographic AF model has the potential to lower costs for the schools and the athletes in terms of travel time and travel expenses as well as potentially balancing operational expenses. The AF model of promotion and relegation also provides stability. There is still movement and excitement or disappointment among the fan bases, but in a controlled manner, so conferences are not left floundering if a team leaves. Objective tournament selection and seeding also has the advantage of reducing bias by removing subjectivity. It should be acknowledged that the weighting system for wins in Premier-5 versus Mid-major divisions will benefit from further study to evaluate if it unfairly favors certain types of teams. The geographic model also has the advantage of increasing fan attendance and revenue as fans of opposing teams will be more able to travel to see the games. These competitive atmospheres could increase and build regional rivalries leading to higher excitement about the game. Most historic college basketball rivalries are geographic in nature, so there is unlikely to be a net negative effect here as well, for example Duke and North Carolina or Kentucky and Louisville, will either remain in the same conference or division, or switch from being in different conferences to being in the same conference or division.

We also posit an increased incentive to win nearer the end of the season for certain teams. With the current system, teams at the bottom of their league in a premier conference are only incentivized to win regular season games through seeding in the conference tournaments and the seeding in the NCAA should their long shot of winning the conference tournament occur. With the AF model, these lower-performing teams will be fighting against relegation, trying to keep their spot in premiere conferences. For Mid-major teams near the top of the conference, current incentives to win the conference and get top seeding in their conference tournament are already strong. Earning promotion will provide another tangible payout for winning the conference title.

Along with these general advantages, we consider potential impacts to the specific teams who get promoted or relegated at the end of the season. These schools will likely see changes in revenue, especially regarding media contracts and possibly apparel and licensing, depending on whether the team was promoted or relegated, changes could be positive or negative. In either case, schools would have to deal with the logistics of rebranding for their new conference. As discussed, conference realignment is already a frequent process so these impacts likely will not be worse in most cases, but there is the possibility a team could move up and down frequently. (For example, Grimsby Town F.C. has been promoted or relegated at least 30 times among different English club tiers over its 100+ year history (9)). Using data from the English Football League, Wilson (21) finds that 75% of teams survive promotion for at least one year with an average rate of 3.1 years before relegation. The English model has five tiers and thus four possible avenues of promotion and the rates of surviving that first promotion vary widely among the tiers.

There is also uncertainty remaining. One major difference between professional sports leagues and college athletics is the recruitment process and choices to attend or transfer from a school that are made by the college athlete. Would teams on the edge of either promotion or relegation see positive, negative, or neutral net impacts on recruiting and transfer decisions? Another uncertainty is the effect on competitive balance. Regular conference realignment can help balance the power within the conference, but geographical divisions do not consider the strength of the program, currently or historically.

RESULTS

We now retroactively apply the AF model to past NCAA tournaments. Data was collected on regular season wins and losses for NCAA Division I men’s basketball teams for the years 2010 through 2019. These data were collected from the website Basketball Reference (2) during the months of May and June in 2021 and used to calculate the previously described team and conference coefficients for each team, conference, and year.

Retroactive promotion and relegation

First, we recognize the teams who are promoted or relegated by the Association Football model from 2010 to 2019 using the AF model conference and division alignments.  (See Table 3.)

Table 3: Teams Promoted or Relegated under the Model by Year

Year	Relegated	Promoted
2010	Nebraska, Tulane East Carolina, Penn State DePaul, Indiana Auburn, Central Florida UCLA, Utah	Missouri State, Sam Houston State Rhode Island, Richmond Marshall, Northern Iowa Murray State, UAB Gonzaga, UT El Paso
2011	Oklahoma, TCU Providence, Wake Forest DePaul, Indiana Auburn, South Florida Oregon State, Utah	Missouri State, North Texas Fairfield, Richmond Marshall, Northern Iowa Belmont, Coastal Carolina Gonzaga, Brigham Young
2012	Nebraska, Texas Tech Boston College, East Carolina DePaul, Butler Auburn, South Carolina Southern California, Utah	Rice, Saint Louis Davidson, Wagner Dayton, South Dakota State Murray State, Southern Mississippi Gonzaga, New Mexico
2013	TCU, Tulsa Penn State, Virginia Tech DePaul, Xavier Auburn, South Florida Oregon State, Washington State	S. F. Austin, Saint Louis Davidson, Massachusetts Akron, North Dakota State Middle Tennessee, Southern Mississippi Gonzaga, New Mexico
2014	TCU, Tulsa Boston College, Virginia Tech Butler, DePaul Mississippi State, South Florida Southern California, Washington State	S. F. Austin, Saint Louis North Carolina Central, VCU Green Bay, Toledo Middle Tennessee, Southern Mississippi Gonzaga, New Mexico
2015	Missouri, Texas Tech Rutgers, Virginia Tech DePaul, Michigan Auburn, South Florida Southern California, Washington State	Sam Houston State, S. F. Austin Old Dominion, Wofford Dayton, Northern Iowa Georgia Southern, Murray State Colorado State, Gonzaga
2016	Missouri, Tulane Boston College, East Carolina Minnesota, Ohio State Auburn, South Florida UCLA, Washington State	Texas A&M Corpus Christi, S. F. Austin Monmouth, James Madison/VCU Dayton, South Dakota State Chattanooga, UNC Wilmington Gonzaga, Hawai‘i
2017	Tulane, Wichita State Boston College, North Carolina State DePaul, Indiana/Ohio State South Florida, Mississippi State Oregon State, Washington	Louisiana Tech, UT Arlington VCU, Winthrop Dayton, Illinois State Middle Tennessee, UNC Wilmington Gonzaga, Nevada
2018	Oklahoma State, Tulane East Carolina, Pittsburgh DePaul, Indiana Mississippi, South Florida California, Washington State	Louisiana Lafayette, Southern Illinois Saint Bonaventure, UNC Greensboro Loyola (IL), South Dakota State College of Charleston, Middle Tennessee Gonzaga, New Mexico State
2019	Oklahoma State, Tulane East Carolina, Pittsburgh Illinois, Notre Dame Georgia, Vanderbilt California, Washington State	Abilene Christian, Louisiana Lafayette VCU, Wofford South Dakota State, Toledo Belmont, College of Charleston UC Irvine, Nevada

While examining this table, unsurprisingly Gonzaga, a mid-major team who has been a national title contender in multiple tournaments, is predicted to be promoted in each of the years except 2019 and in that year their division coefficient was lower than the other two divisions in their conference. Other often-promoted teams are Dayton, Middle Tennessee, Stephen F. Austin, South Dakota State who were promoted four times. Further, DePaul is outclassed in their proposed division and would be predicted to be relegated in 8 of the 10 years. Auburn, East Carolina, Tulane, and Washington State are also relegated in five of the ten years and USF would have been relegated six times. More remarkably, we note that it is possible for quite good teams to get relegated. The Midwest Great Lakes conference was so strong that either Indiana or Ohio State, with 17-14 records, were relegated in 2017. Finally, there is plenty of variety, 42 different teams had seasons bad enough to earn relegation and 56 different teams were promoted.

Comparison of the Association Football model to past NCAA tournament selections

We can also look at the model through the lens of the seeding of the NCAA tournament. We have chosen the 2016 tournament as an example. Tables 5 and 6 found in the Appendix illustrate how the tournament could have been seeded that year.  Between the 32 automatic bids and the 32 play-in bid winners the AF model allows for 96 teams to make the NCAA tournament field, that is 32 pre-first round play-in games followed by the traditional 63 game single-elimination tournament. Given the on-and-off discussion for expanding the tournament field, we argue that this model increases the satisfaction of teams involved, allowing for more teams to participate as well as improving the selection process. Since the AF model has 28 more teams than the current model for tournaments from 1999 through 2010 and 24 more teams for tournaments after 2011, we examine teams that would not participate in the tournament as determined by the AF models but did receive a bid under the current system. These are the teams that potentially object to the change in system.  Table 4 lists the teams not selected by the AF model from 2010 to 2019. Except for 2011, only one or two premier teams were not selected by our model each of the ten years, with a total of sixteen Premier-5 teams being selected in the actual tournaments, but not by the AF model. There was a wider range of Mid-major teams that were not selected: between six and twelve each year and a total of 83 over the ten years. Therefore, the AF model agrees with 85% of the tournament committees’ selections.

We can also look at a basic analysis of the automatic seeding in the model as compared to the committee’s seeding. We consider 13 top teams automatically seeded from the premier conferences. Because the committee is much more likely to take a top premier conference team that does not win its conference tournament, these are less likely to be affected by upsets in a conference tournament than the remaining 19 teams selected after these top-ranked teams.  Thirty-nine of the 40 number 1 seeds in our model were selected for the tournament by the committee, with the one missing being Oregon in 2012. Seeding by the committee ranged from 1 to 6 with the average seeding over these 39 teams being 2.205. There were also 39 of the 40, number 2 seeds selected for the tournament by the committee. The excluded team being SMU in 2016 that was ineligible for the tournament due to NCAA violations. The range here of actual seeds was from 1 to 9, and the average seeding was 3.000. Regarding number 3 seeds, there were again 39 of 40 teams selected by the committee. (SMU in 2014 is the excluded team in this case.) The range is broader, from 1 to 12, with an average seed of 4.203. Overall, the committee and model seedings for teams selected by both was off by an average of 1.110 in those 13 top-ranked teams.

We also used the data to justify the play-in team ranking of 36 Premier-5 teams followed by 28 Mid-major teams. Over the ten years, the 36th-ranked Premier-5 team had a mean team coefficient of 0.9139 while the following top-ranked Mid-major play-in team had a team coefficient of 0.9246. This is less than one one-hundredth of a point advantage to the premier conference team.

We recall that the committee only chooses the at-large teams, with 32 teams chosen automatically by winning their conference tournament. The AF model is unable to account for the situation where a team that performed poorly in the regular season won their conference tournament and received an automatic bid. In fact, many of the current Mid-major conferences have such weak strength of schedules that even teams with reasonably good records won’t be selected unless they win their conference tournament. So, discounting the teams whose records were not strong, but received and automatic bid by winning their conference tournaments, there were only twelve teams that that the AF model omitted. These are: Penn State (2011), Marquette (2011), Southern California (2011), Xavier (2012), Temple (2013), Texas (2014), Purdue (2015), Wichita State (2016), Vanderbilt (2017), Alabama (2018), and Butler (2018).

CONCLUSIONS

We hypothesize some reasons for these teams being selected by the committee despite their poorer performance in the AF model. First, some teams may have been selected based on the “star-power” of that team. The primary example is the 2011 Marquette team that had both Jae Crowder

Table 4: NCAA Tournament teams not selected by the AF model from 2010 to 2019

Year	Teams	Premier-5	Mid-major	Total
2010	New Mexico State (12), Houston* (13), Ohio (14), Robert Morris (15), UC Santa Barbara (15), East Tennessee (16), UA Pine Bluff (16), Lehigh (16)	1	7	8
2011	Butler* (8), Penn State* (10), Marquette* (11), USC* (11), Indiana State (14), Wofford (14), Saint Peter’s (14), Northern Colorado (15), UC Santa Barbara (15), Akron (15), UT San Antonio (16), Alabama State (16), UNC Asheville (16), UA Little Rock (16), Boston University (16)	4	11	15
2012	Xavier* (10), Saint Bonaventure (14), Detroit Mercy (15), Mississippi Valley State (16), Western Kentucky (16), Vermont (16)	1	6	7
2013	Temple* (9), New Mexico State (13), Pacific (15), Iona (15), Albany (15), James Madison (16), Long Island (16), Western Kentucky (16), Liberty (16), North Carolina A&T (16)	1	9	10
2014	Texas* (7), Tulsa* (13), Lafayette (14), Milwaukee (15), American (15), Wofford (15), Coastal Carolina (16), Weber State (16), Albany (16), Mount Saint Mary’s (16), Cal Poly (16), Texas Southern (16)	2	10	12
2015	Purdue* (9), UC Irving (13), Valparaiso (13), UAB (14), Northeastern (14), Belmont (15), Texas Southern (15), New Mexico State (15), Lafayette (16), North Florida (16), Robert Morris (16), Manhattan (16), Hampton (16)	1	12	13
2016	Wichita State* (11), Florida Gulf Coast (16), Fairleigh Dickinson (16), Holy Cross (16), Southern (16), Austin Peay (16), Hampton (16)	1	6	7
2017	Vanderbilt* (9), Kent State (14), Iona (14), Troy (15), Jacksonville State (15), Mount Saint Mary’s (16), South Dakota State (16), Texas Southern (16), UC Davis (16)	1	8	9
2018	Alabama* (9), Butler* (10), Davidson (12), Marshall (13), CSU Fullerton (15), Iona (15), LIU Brooklyn (16), Radford (16), North Carolina Central (16), Texas Southern (16)	2	8	10
2019	Mississippi State* (5), Mississippi* (8), Saint Mary’s (11), Colgate (15), North Carolina Central (16), North Dakota State (16), Fairleigh Dickinson (16), Prairie View A&M (16), Iona (16)	2	7	9

and Jimmy Butler, two current NBA stars, and Darius Johnson-Odom who were all drafted in 2011 or 2012. Another example, is Colin Sexton on Alabama in 2018 who was the No. 2 ranked point guard coming out of high school and drafted 8^th that year. Other teams with future NBA draft picks include USC in 2011 with Nikola Vusevic, or 2016 Wichita State with Landry Shamet. Even undrafted players can excite notice: for example Fred VanVleet on that same Wichita State team twice won the Missouri Valley Player of the Year and the 2017 Vanderbilt team with Luke Kornet breaking school shot-blocking records. Although impossible to precisely measure the impact these future NBA players had on the national conversation, it is quite natural that committee members, also being fans, would be excited to see what some of these players could do in the tournament.

The committee might have also placed extra weight on runs in the conference tournament even if the team did not end up winning the championship game. Penn State and Xavier lost in their conference championship game while USC, Texas, Purdue, Wichita State, Vanderbilt, Alabama, and Butler all lost in their conference semifinals. In fact, 6 of the 9 lost to the eventual winner of the conference tournament. The AF model does not include data from the conference tournaments, so these wins may have helped propel these teams into the tournament if included.

Another explanation for the omission of some teams is strength of schedule as based on the Premier-5 and Mid-major teams in our model. Take the 2016 Wichita State team as an illustration. Overall, their record of 26-9 is favorable, but Wichita State did not move into the American Athletic Conference until the 2017-18 season. So, while they are considered a Premier-5 conference school in AF rankings, they almost exclusively played Mid-major schools in the 2015-16 season precipitating a low team coefficient. There may also be a recency bias; the committee may have felt that based on the past success of Wichita State in the Missouri Valley Conference the 26-9 record deserved a bid, even though our metric disagrees. Wichita State may not be the only team that was selected because of recent tournament success; a weak 2018 Butler was granted a spot in the tournament after stronger Butler teams had appeared in the previous three tournaments and nine of the last eleven.

Finally, we suggest that the committee composed of humans is fallible and some of these teams simply should not have been selected. Especially since our model selected an additional 28 (31 in 2010) teams, team omissions from our model suggests we ranked them significantly lower than the tournament committee who placed them in the field of 68 (65 in 2010). In particular, we point to the selection of Mississippi in 2019. Mississippi had a mediocre record of 20-12 and exited the SEC tournament in the first round. They had no notable players and no tradition of playing in the tournament with only two appearances (2013, 2015) in the previous 15 years. We see also in this year; Mississippi State was selected by the committee and not by the AF model. This decision is also questionable as Mississippi State had a mediocre record of 23-10 and quickly exited the SEC tournament reaching only the quarterfinals. They did however have some second-round future NBA draft picks in Quinndary Weatherspoon who was drafted 49th overall in the 2019 draft and Robert Woodard and Reggie Parry who were drafted 40th and 57th, respectively, in the 2020 draft.

To conclude, geographic conferences and relegation models where losing matters could be applied to men’s college basketball to decrease costs and increase excitement and fairness, while still having strong agreement with data from recent years.

APPLICATIONS IN SPORT

American sports should consider adopting some selection and organizational practices from European sports, particularly objective selection and seeding and regional groupings.

REFERENCES

1. Allen,  N. (2021, April 20). “It’s not a sport when it doesn’t matter if you lose!” – Pep Guardiola  sends  out  fiery  message  to  European  Super  League.  SI.com  Retrieved from            https://www.si.com/soccer/manchestercity/news/its-not-a-sport-when-it-doesnt-matter-if-you-lose-pep-guardiola-sends-out-fiery-message-to-europea
2. Sports Reference LLC (2021). Basketball-Reference.com – Basketball Statistics and History. Retrieved from https://www.basketball-reference.com.
3. Coleman, B. J., DuMond, J. M., and Lynch, A. K. (2010). Evidence of bias in NCAA tournament selection and seeding. Managerial and Decision Economics, 31 (7), 431-452.
4. Coleman, B. J., DuMond, J. M., and Lynch A. (2016). An easily implemented and accurate model for predicting NCAA tournament at-large bids. Journal of Sport Analytics, 2 (2), 121-132.
5. Daughters, A. (2014, July 2). A Timeline of Conference Football Conference Realignment over the BCS Era. Bleacher Report. Retrieved from https://bleacherreport.com/articles/ 2115606-a-timeline-of-college-football-conference-realignment-over-the-bcs-era.
6. Dennie, C. (2011). Conference realignment: from back-yard brawls to cash cows. Mississippi Sports Law Review, 1, 249-79.
7. Dutta, S. and Jacobson, S. (2018). Modeling the NCAA basketball tournament selection process using a decision tree. Journal of Sports Analytics, 4(1), 65-71.
8. Featherston, S (2017). Building an Alternative NCAA Division I Competition Model. Thesis University of North Carolina at Chapel Hill, ProQuest Dissertations Publishing.
9. F.C.H.D., Football Club History Database. Retrieved Aug 2022 from https://www.fchd.info/ GRIMSBYT.HTM
10. Hoffer, A. J. and Pincin, J. A. (2015). The effects of conference realignment on NCAA athletic departments. Applied Economics Letters, 22(15) 1209-1223.
11. Lawrence, H. (2013). The Impact of Intercollegiate Athletics Financial Inequalities. Journal of Intercollegiate Sport, 6, 25-43.
12. Matthews, N. R., McLain, A., Smith, C. M. L., Tennant, and A. G. (2021). Application of PageRank Algorithm to Division I NCAA men’s basketball as bracket formation and outcome predictive utility. Journal of Sports Analytics, 7, 1-9.
13. Moriarty,  M. (2021, December 21). Winners and Losers of College Football Conference Realignment. Bleacher  Report. Retrieved from https://bleacherreport.com/articles/2951683-winners-and-losers-of-college-football-conference-realignment
14. NCAA.com. (2021). How the field of 68 teams is picked for March Madness.  Retrieved from https://www.ncaa.com/news/basketball-men/article/2021-01-15/how-field-68-teams-picked-march-madness
15. Noll, R. G. (2002). The Economics of Promotion and Relegation in Sports Leagues: The Case of English Football. Journal of Sports Economics, 3(2), 169-203.
16. Paul, R. J. and Wilson, M. (2015). Political Correctness, Selection Bias, and the NCAA Basketball Tournament. Journal of Sports Economics, 16(2), 201-213.
17. Reinig, B. A. and Horowitz, I. (2018). Using Mathematical Programming to Select and Seed Teams for the NCAA Tournamen., INFORMS Journal on Applied Analytics, 48(3), https://doi.org/10. 1287/inte.2017.0939
18. Reinig, B. A. and Horowitz, I. (2019). Analyzing the impact of the NCAA Selection committee’s new quadrant system. Journal of Sports Analytics, 5,325-333.
19. Sanders, S. (2007). A Cheap Ticket to the Dance: Systematic Bias in College Basketball’s Rating Percentage Index. Economics Bulletin, 4 (34), 1-7.
20. B. Hunt (Writer), J. Kelly (Writer), J. Sudeikis (Writer), and M. J. Delaney (Director) (2020). The Hope That Kills You [Television series episode]. In J. Sudeikis (Creator), B. Lawrence (Creator), B. Hunt (Creator), and J. Kelly (Creator). Ted Lasso. Apple TV+.
21. Wilson, C. (2020, October 8). The data behind promotion and relegation in the EFL. TFA, 42. Retrieved  from  https://totalfootballanalysis.com/article/ how-difficult-is-it-to-climb-through-the-english-football-league-system
22. Wittry, A. (2020, February 24). This is how every conference has done through 2,424 non- conference men’s basketball games in 2019-20. NCAA.com. Retrieved from https://www.ncaa.com/news/basketball-men/article/2020-01-07/how-every-conference-has-done-so-far-through-2424-non

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[1] Conference membership as of 2023

George Minoso2025-05-23T11:23:16-05:00

Effect of a Lace Locking Device on Skate Lace Tension

George Minoso2025-05-23T11:23:16-05:00May 30th, 2025|General, Sport Training|

Effect of a Lace Locking Device on Skate Lace Tension

ABSTRACT

Ice hockey skate boots traditionally use laces to secure the foot inside the boot, limit slippage, customize fit and enhance performance. A lace locking device was proposed to enhance the role of laces, maintain lace tension and customize tension zones throughout the lacing pattern. Applied sport research is often challenged by the lack of portable measurement instrumentation that can quantitatively assess the merits of athletic equipment and the effectiveness of specific equipment components. With a measurement apparatus developed in the previous research¹, the merits of skate laces can be easily assessed. The purpose of the study was to investigate the effect of without versus with the lace locking device installed on lace tension pre-post a bout of skating in two skating conditions: on a skating treadmill and on real ice. No significant differences were revealed with the lace locking device installed, potentially suggesting that lace tension was maintained by the device (p<0.05). Outcomes of the study suggested that the lace locking device provided a resistance to lace slippage, had the ability to maintain pre-established tension specific to the location where the device was installed, and further supported the athletes in customizing their skate setup.

Keywords: footwear, lace tension, lace locking device

INTRODUCTION

Athletic footwear is the vehicle by which mechanical function is translated to human motion and sport performance. The athlete-equipment interaction in gliding and sliding sports has been recognized as a fundamental component of performance success^2–5. However, quantitatively assessing the merits of equipment, namely athletic footwear on the execution of technique, is technically challenging and has been handcuffed by the lack of portable, reliable, and sensitive research instrumentation that can be used in a real-world sport environment.

Previous research has provided proof of concept that laces can help limit foot-shoe movement and achieve better fit⁶. Different lacing patterns are often employed in an attempt to address anatomical differences in individual foot morphology⁷ and customize tension preferences in footwear. In the sport of ice hockey, skate boots traditionally use laces to secure the foot inside the boot and further enhance fit and comfort. It is common for an athlete to customize their skate lacing setup by using different types of laces or lacing strategies to create varying tension zones at different locations along the lacing pattern to optimize fit, comfort and ultimately support skating performance. Some practices have included using different materials of laces (e.g., waxed versus non-waxed), different lacing patterns (e.g., skipping or doubling up on eyelets), or by installing ad hoc lace locking devices. These practices are somewhat temporary band-aid solutions since laces are continuous; tension may disperse across the length of the laces and lacing pattern. Furthermore, the material properties of the laces allow for stretch¹ and as a result, lace tension loosens. All these factors may result in changes in the original or intended effect of securing and customizing footwear setup.

A novel practice that has emerged recently to enhance lace tension and secure footwear is the use of a self-lacing technology. One innovative method utilizes a motorized cable instead of the traditional lace permitting automated and precise control of lace tension. A recent study by Myers et al. (2022)⁸ investigated the impact of self-lacing technology on foot containment during dynamic motion. Results revealed significantly less movement of the foot within the footwear that potentially translates to related improvements in athletic performance and reduced injury potential, with the technology at low (-10% preferred), preferred, and high (+10% preferred) tensions. A subjective measure of athlete’s perception also revealed an increase in the athlete’s confidence level when performing dynamic motion with higher tension. However, the technology described above is currently unavailable in specialized footwear, such as ice skates.

Previous research has established relationships between lacing practices, tension and athletes’ confidence and performance^7–9. Quantitatively assessing the merits of lace locking practices is technically challenging. A portable apparatus was designed, built and reliability assessed previous research¹, specifically for the purpose of quantifying lace tension parameters [applied force (N) and displacement from skate tongue (mm)] while the footwear is secured on the foot. The apparatus included a customizable platform to accept different types of footwear (e.g., running shoes, hiking boots, ski boots, ice skates), allowing lace tension to be assessed when the footwear was secured on the foot, pre and post a bout of activity in both the controlled environment of a laboratory and when subjected to a sport-specific, real-world environment.

The current study proposed an alternative lace locking device to maintain lace tension during activity and to further support comfort and fit of the athletes’ equipment in sport-specific environments. The piece of equipment under investigation was a patented and commercially available after-market lace locking device (Figure 1). The lace locking device can be installed on the existing lace of athletic footwear with the intention to reduce lace slippage, permit the user to create distinct zones of lace tension, and reduce the need of retying the footwear during extended use. Therefore, the purpose of the study was to confirm these claims by comparing lace tension pre and post a bout of skating without and with the lace locking device both in a lab environment on a skating treadmill and in a real-world environment on ice.

Figure 1

Note. Illustrations of the lace locking device in three positions (a) the Lace Locking Device, (b) Device open with laces, (c) Device closed with laces secured.

METHODS

Study Design

The study was a mixed methods experimental design including two interventions, namely without and with the lace locking device installed on the skate boot laces. The intervention was repeated in two experimental environments; (i) in a controlled laboratory environment on a skating treadmill, and (ii) in the real-world sport environment, on the ice during game play. Ethical clearance was obtained from the Office of Research Ethics Board at Brock University (File #21-251).

Instrumentation

A portable apparatus (Figure 2) designed and built specifically for the purpose of quantifying lace tension parameters¹ was used for the current study. Data collection procedures and data analyses were consistent with procedures developed in the previous study¹.  Output measures of the apparatus included force (N) and displacement (mm) collected by the load cell and the calliper when a force is applied to the lace. To facilitate this, the hook secured to the end of the cable was attached to the lace at the desired eyelet location. Once a force was applied to the lever, the force was transferred to the lace via the cable and pulled the lace away from the tongue of the footwear¹.

Figure 2

Note. Different views of the apparatus. (a) View at 45 degrees (b) Frontal view. The base of the apparatus was designed to secure all types of footwear including ice skates. There are adjustable stoppers located at the front and the two sides of the base. There is a slot in the middle of the base plate which can allow skate blade to fit in to provide constrain to footwear movement. The lever on the top coming out of the back provided a loading mechanism through the cable while the load cell and calliper attached at the other end measure force and displacement of the force applied respectively. The microcontroller was used to initiate and stop the data collection while recording measurements from the load cell and the calliper.

Phase 1 – Investigation of lace locking device installed on a skate boot in a controlled laboratory environment while skating on a skating treadmill

The purpose of Phase 1 was to investigate the effect of without the lace locking device installed versus with the lace locking device installed on lace tension pre and post a bout of skating on a skating treadmill.

Participants

A total of eight participants (n=8) were recruited (four males and four females). The eligibility criteria included: participants were treadmill trained defined by having completed a minimum of eight sessions of previous skating treadmill training, participants were self-reported as injury free, and participants were actively playing hockey at a competitive level.

Equipment

Participants wore their own skates. Skates were sharpened to game play conditions and a new pair of non-wax laces (Howies Hockey Tape, MI, USA) were installed in all skates. A previous study suggested that laces stretch under tension loads and depending on the material of the laces, some stretches more than the other¹. For the purpose of consistency across all participants, non-waxed laces were selected for the study. The lacing pattern was consistent. Each participant tied their own skates to their own preferred tension for game-like conditions. Plantar pressure insoles (XSENSOR® Technology Corporation, AB, Canada) were inserted into the skate boots prior to lacing the skates. The insoles were used in the study to provide a baseline measure of fit of the skate and lace tension for each intervention. Baseline pressure measures (psi) were defined as the pressure exerted on the insoles from the foot due to the tension of the laces securing the foot to the footbed. The baseline pressure measures were collected prior to the skating bout in both interventions. Participants were required to sit on a bench with knees extended and feet lifted from the ground to unweight the skates for five seconds. The baseline pressure measures were calculated as the average pressure over five seconds for each section of the foot (forefoot, midfoot, heel and total foot). Paired samples t-tests of the baseline pressures (psi) recorded by the insoles at the forefoot, midfoot, heel and total foot were conducted between interventions. An analysis of the pressure data revealed consistent pre skate baseline pressures for each section of the foot and total foot between interventions. This consistency in the pressure data can be interpreted as a consistent fit of the skate and lace tension prior to the bout of skating in both interventions. 

Warmup

A skating treadmill in a controlled laboratory environment was used for the purpose of this study. The surface of the treadmill is 3.6 m² (200cm by 180cm) covered by a series of parallel polyethylene slats prepared with silicon to simulate the frictionless surface of real ice. A harness was fit and secured to each participant and connected to an overhead track system as a safety measure. A standardized and supervised warm up protocol consisted of three 20-second skates at a speed of 7.5mph (12.07km/h) and an incline of 5° on the skating treadmill.

Treadmill Skating Protocol

Participants completed a standardized protocol of moderate intensity consisting of five 30-second bouts of skating at 7.5mph (12.07km/h) speed and 5° incline, followed by three 30-second bouts of skating at 7mph (11.27km/h) speed and 10° incline. Moderate intensity was dictated by participant’s previous treadmill training experience and calibre of play and was consistent and repeated for both interventions. A work-to-rest ratio of 1:3 was implemented to control for the effect of fatigue while skating on the treadmill.

Without lace locking device – pre and post lace tension measurement procedures

Pre: Following the warmup and prior to the skating protocol described above, participants were instructed to sit in a chair to loosen and retie the laces of the skates to ensure the pre skate tension measures were not influenced by any skating prior to the start of the skating bout. The lace tension apparatus (Figure 2) was used to measure the force (N) required to pull the lace as well as its displacement (mm) away from the tongue of the skate boot (Figure 3). Lace tension measurements were recorded at the second (below the lace locking device hold zone), the fourth (between the lace locking device hold zone), and the sixth (above the lace locking device hold zone) eyelet of the left skate.

Post: Following the skating protocol described above, participants were again required to sit in a chair and lace tension measurements were repeated at the same eyelet locations (two, four, and six) as the pre measurement on the left skate.

With lace locking device

Participants were instructed to loosen their laces so that the lace locking device could be installed. Lace locking devices were installed on the laces aligned with the third and the fifth eyelets on the left skate during the retying process. The hold zone referred to the space between the two lace locking devices (Figure 3). The skating protocol and measurement procedures were consistent with those performed for the previous condition, without the lace locking device.

Figure 3

Note. Locations for the lace locking devices in Phase 1 with the red box indicating hold zone.

Figure 4

Note. Setup of the apparatus during data collection of Phase 1.

Phase 2 – Investigation of lace locking device installed on a skate boot in a real-world environment while skating on the ice

The purpose of Phase 2 was to investigate the effect of without the lace locking device installed versus with the lace locking device installed on lace tension pre and post a bout of skating on the ice.   

Participants

A total of 14 female participants (n=14) were recruited. Eligibility criteria included; participants were self-reported as injury free and were actively playing competitive hockey. All participants were instructed to wear their own skates sharpened for game like conditions.

On Ice Skating Protocol

Participants warmed up with their own choices of movements for the first 5 minutes of the skate. A 45-minute bout of on ice skating simulated the movement patterns, speeds, and intensities of competitive game play.

Pre and post lace tension measurement procedures

Pre: Participants were required to sit in a chair with their knees flexed at 90°. The lace locking device was installed on the seventh eyelet from the toe of each participant’s left skate. No lace locking device was installed on the right skate. The measurement apparatus was used to collect force (N) and displacement (mm) of a pull on both left (representing with) and right (representing without) skates of each participant at the sixth eyelet (one eyelet below the lace locking device).

Post: Following the on-ice bout of skating, participants were required to sit in a chair with their knees flexed at 90° in order to repeat tension measurements (force (N) required to pull the lace and lace displacement (mm) away from the tongue of the skate boot) at the same eyelet location (eyelet 6).

Figure 5

Note. Apparatus setup during Phase 2 data collection.

DATA ANALYSIS & RESULTS

Phase 1 – Results of lace tension with lace locking device installed on a skate boot in a controlled laboratory environment while skating on a skating treadmill

At each of the measurement sites (eyelets 2, 4, and 6), a force (N) versus displacement (mm) plot was generated for both pre and post skate data. A linear line of best fit was generated for each data set; the slope of the line represents the stiffness property of the lace materials and the translation value represents the shift in displacement (mm) for pre and post measurements. The shift in displacement (mm) was compared and used as a quantitative metric for lace tension. Paired-comparison t-tests were performed to compare means of the lace tension parameter between pre and post skate measurements without and with lace locking device.

Results revealed significant differences in lace tension parameter pre and post a bout of treadmill skating for both interventions (without and with the lace locking device) at the fourth and the sixth eyelets, but no significant differences at the second eyelet (Table 1). An observation of graphs revealed less change was observed between pre and post measurements for the second (see Figure 6) and the fourth eyelet (Figure 7) with the lace locking device equipped, but not in the sixth eyelet (Figure 8). Measured displacements were negative as they reflected the direction the calliper was moving when the lever of the apparatus was pulled. Less magnitude in the displacement measured by the apparatus indicates that there was less “give” in the lace which means there was less likelihood of foot shift and discomfort caused by loosening of the lace.

Table 1

Paired comparison t-test results for treadmill data collection.

Compared Pair	Mean ± SD	p-value
Without Eyelet 2 Pre – Post	0.56 ± 0.97	0.076
With Eyelet 2 Pre – Post	1.30 ± 2.84	0.119
Without Eyelet 4 Pre – Post	0.63 ± 0.69	0.018
With Eyelet 4 Pre – Post	2.16 ± 2.74	0.031
Without Eyelet 6 Pre – Post	1.44 ± 1.33	0.009*
With Eyelet 6 Pre – Post	3.01 ± 1.90	0.001*

*p<0.05 for indication of statistical significance.

Figure 6

Note. Pre and post skate comparison of force – displacement plots for in-lab skating session at eyelet 2. (a) Result without the lace locking device equipped (b) Result with the lace locking device equipped. X and Y axis of the plots were scaled to the same values. There is a larger change in displacement measurements pre versus post skate without the lace locking device equipped meaning there is more change to lace tension without the device equipped at eyelet 2.

Figure 7

Note. Pre and post skate comparison of force – displacement plots for in-lab skating session at eyelet 4. (a) Result without the lace locking device equipped (b) Result with the lace locking device equipped. X and Y axis of the plots were scaled to the same values. There is a larger change in displacement measurements pre versus post skate without the lace locking device equipped meaning there is more change to lace tension without the device equipped at eyelet 4.

Figure 8

Note. Pre and post skate comparison of force – displacement plots for in-lab skating session at eyelet 6. (A) result without the lace locking device equipped (B) result with the lace locking device equipped. X and Y axis of the plots were scaled to the same values. There is a larger change in displacement measurements pre versus post skate with the lace locking device equipped meaning there is more change to lace tension with the device equipped at eyelet 6.

Phase 2 – Results of lace tension with lace locking device installed on a skate boot in a real-world environment while skating on the ice

The change in displacement of the lace tension measurements were plotted and statistically compared pre and post skate without and with lace locking device using the same analysis performed in phase 1. Pre and post skate measurement differences were used as a comparative lace tension metric between the right (without lace locking device) and left (with lace locking device) skate.

Results revealed significant differences in lace tension on the right skate (without lace locking device installed) pre and post a bout of simulated game play skating on ice. However, no significant difference in lace tension was seen on the left skate (with the lace locking device) (see Table 2). Force-displacement plots provide a graphical representation of the collected data (Figure 9) where the slope of the line represents the stiffness property of the lace materials and the translation value represents the shift in displacement (mm) for pre and post measurements. The shift in displacement (mm) was then used as a quantitative metric for lace tension.

Table 2

Paired comparison t-test results for on-ice data collection.

Compared Pair	Mean ± SD	p-value
Without Pre – Post	2.76 ± 4.30	0.016*
With Pre – Post	1.43 ± 4.29	0.116

*p<0.05 for indication of statistical significance.

Figure 9

Note. Pre and post skate comparison of force – displacement plots for on-ice skating session. (a) result without the lace locking device equipped (b) result with the lace locking device equipped. X and Y axis of the plots were scaled to the same values. There is a larger change in displacement measurements pre versus post skate without the lace locking device equipped meaning there is more change to lace tension without the device equipped during on-ice skating session.

DISCUSSION

The study provided a comparative analysis of lace tension parameters without the lace locking device versus with the lace locking device installed in the controlled environment of a laboratory on a skating treadmill and in a sport-specific real-world environment on the ice. The behaviour of the laces is governed by their material properties, and as such, the lace locking device is an add-on device with the specific purpose of adding value to the role of laces. By maintaining tension established by the athlete, the lace locking device could potentially help reduce friction induced injuries, increase athlete confidence, and footwear comfort.

Results revealed more change in pre and post lace tension measurements without the lace locking device versus with the lace locking device installed. These results were consistent across both environments, on skating treadmill and on ice. This potentially implies that the lace locking device consistently maintains tension at the location where it is installed in both a controlled lab and a real-sport environment.  

Through improving the fit of the footwear and foot containment, maintaining lace tension at preferred tension or higher lace tension can provide athletes with confidence in their equipment while performing dynamic movements. The lack of shifting of the lace tension with the lace locking device throughout the skate has multiple implications for a user. By holding lace tension, the lace locking device made it easier for athletes to customize their skate lacing setup knowing that the setup would maintain tension, providing athletes with the confidence to perform dynamic movements required throughout their performance.

Limiting the movement of the foot inside the footwear can also further reduce the potential for friction related injuries, such as lace bite, heel spurs and bunions. Treatment for these friction related injuries involve reducing contact force and friction between the footwear and the foot or ankle¹⁰. The lace locking device achieved that objective by holding lace tension at its optimal tension to prevent further irritation due to overly tight skate. The lace locking device may potentially be used as preventative measures for friction induced injuries as well.

FUTURE RESEARCH

It is a logical progression to extrapolate and implement assessment methodologies across athletic footwear in different sports requiring footwear models consisting of 2-15 eyelets. Funding has been secured to investigate the generalization of the lace locking device across different footwear models consisting of 2-15 eyelets. Further investigation is needed to provide insight on how different configurations and quantity of the lace locking device on the laces could potentially impact the tension results. Further investigation is also needed to help eliminate friction-based injury in footwear with the lace locking device. This will build upon the current academic-industrial relationship established and generalize the effectiveness of the device across a variety of athletic footwear.

REFERENCES

George Minoso2025-05-20T15:27:15-05:00

Transitioning from High School Athletic Director to School Level Administrator: Leadership Considerations

George Minoso2025-05-20T15:27:15-05:00May 16th, 2025|Contemporary Sports Issues, Leadership, Sports Coaching, Sports Management|

Transitioning from High School Athletic Director to School Level Administrator:
Leadership Considerations

Athletic or Activities Directors are prominent in high schools and middle schools across the U.S. This position often carries similar responsibilities to those of school administrators, and it is common for athletic directors to transition into other school leadership roles. This study provides insight into the transition from Athletic/Activities Director (AD) to building-level administrator by examining leadership characteristics and traits consistent with individuals who have made the transition. This mixed methods study gathered perceptions from six currently seated high school principals (former ADs) through two-stage interviews and compared the interview data with responses from a corresponding survey that gathered data from a state-wide sample of principals who had also transitioned from AD. The results suggest multiple connections in responsibilities between the AD and principal position that could aid in preparing ADs who aspire to become building administrators. Likewise, results point to various factors that influence an AD to exit the position, whether or not they are seeking an administrative role. Data are broken into external factors that contribute to the decision, as well as internal characteristics that are consistent with those who transition into administrative positions. Words of advice are shared for those considering this transition.

Keywords: Principal, Assistant Principal, High School, Coach

Transitioning from Athletic Director to School Principal:

Leadership Considerations

            Multiple pathways exist to the role of principal; however, a frequently observed career trajectory starts with a teacher, often with experience as an athletic coach, and transitions into the position of athletic director (AD). This is followed by a move to assistant principal, and, for many, culminates in the position of head principal or even superintendent. For those in an AD role, responsibilities vary but often include leading fundraising efforts, managing large budgets, overseeing disbursement and payment of coaches and officials, coordinating multiple schedules, managing human resources, and communicating with various stakeholders. Indeed, the AD position is often associated with high levels of stress as these leaders juggle the often-competing interests of school administrators, coaches, parents, and athletes, all while working with finite resources of money, time, and facility availability. Many of these duties and stresses are not unlike those expected of school administrators themselves. As athletic directors gain experience and confidence in managing these varied responsibilities, they often realize that a transition to a school-wide leadership role, such as assistant principal, is a logical progression in their professional careers.

            This study more closely examines the leadership characteristics, managerial expectations, and career decisions of secondary school administrators (assistant principals and principals) who were once secondary school athletic or activities directors. Consideration is given to leadership expectations and managerial responsibilities inherent to the athletic director position, and how this position and its corresponding experiences can assist in preparing future school administrators.

For this study, no distinction is used between athletic directors who are solely responsible for athletic programs, and “activities directors” who also are responsible for managing and scheduling other extra-curricular activities in the school or district beyond athletics (such as musical and/or theatrical events). Rather, the term “athletic director” (abbreviated AD) will be used to encompass either or both roles, always inclusive of athletics. Additionally, for the purposes of this study, “building-level administrators” include assistant principals and head principals. Individuals in a “dean of students” role are not considered building-level leaders within the context of this study. In fact, oftentimes, the dean of students role is found combined with AD positions, especially in smaller districts. Yet, this position will not be considered parallel to that of assistant principal or principal, because it often lacks administrative responsibilities consistent with the principal role, such as faculty supervision, instructional leadership, and resource management.

The purpose of this study was to provide insight into the transition from athletic director to building-level administrator by examining leadership characteristics and traits consistent with individuals who have made the transition from AD to building-level administrator. Three research questions guided the study: (a) What leadership skills or traits required of the AD position assist in transitioning to a building-level leadership position? (b) What similarities and differences exist between the characteristics of the athletic directors and building-level leaders? and (c) What internal and external factors influence athletic directors to transition from AD to building-level administrator? Before addressing these questions, attention is given to literature that informs the study.

Literature Review

This study is meant to provide further insight into potential upward mobility for those in an AD role. To better understand the complexities associated with transitioning from AD to a building-level administrative position, a brief review of relevant literature provides important foundational knowledge and context related to a) the evolution of the athletic director position, b) responsibilities of the athletic director, and c) transitioning to building-level leadership.

The Evolution of the Athletic Director Position

A paucity of research exists on potential upward mobility for high school athletic directors (AD) seeking advancement in their career. Although some studies exist that emphasize the teacher/coach balance (Conner, 2020; Konukman et al., 2010; Richards et al., 2018) or the gender inequities in AD positions (Ray, 2010; Sisley & Steigelman, 1994; Whisenant et al., 2015), fewer highlight the leadership characteristics that both positions have in common or engage with stakeholders who have made this transition. To better understand the complexities associated with transitioning from AD to building-level administrator, this research study aims to contribute to an area of educational scholarship that is currently underexplored. The following literature review provides context for the role of AD and its evolution, as well as highlighting research studies that have attempted to begin filling this void in the field.

The role of the athletic director (AD) in U.S. high schools has undergone significant transformation, becoming a more organized and demanding position than in the past (DeCesare, 2017; Furr, 2015; TSSAA, 2024) In the early 1900s, the informality of school sports meant that physical education teachers and coaches handled most athletic duties. At that time, the position of a dedicated athletic administrator had not yet developed. Coaches, who often held additional teaching responsibilities, organized athletic teams, coordinated schedules, and managed logistics in addition to teaching. Modern conceptualizations of the athletic director did not emerge until the mid-20th century, as school sports programs grew both in size and complexity (DeCesare, 2017).

The expansion of high school athletics during the 20th century necessitated specialized administrative oversight. By the 1950s and 1960s, many schools hired full-time ADs to maintain pace with the popularity and competition of new sports programs. To show this decades-long change, Nixon (1974) referred to “modern” sports as “large-scale social units with highly specialized divisions of labor, elaborate hierarchies of authority, and highly rationalized, formalized goal pursuits and normative controls” (p. 108). The establishment of organizations like the National Interscholastic Athletic Administrators Association (NIAAA) in 1977, which provided athletic directors with training, certification, and professional development, continued to professionalize the role of ADs (Blackburn et al., 2013). Early ADs typically still served as coaches or teachers, but their roles shifted to include the more strategic management of budgets, scheduling, and compliance with expanding state and national regulations.

The complexity of ADs’ responsibilities grew in the 1980s and 1990s. This era saw a shift toward greater attention on student-athlete well-being, including academic eligibility, mental health, and injury prevention. Thus, beyond logistical tasks like scheduling and equipment management, principals expected ADs to manage public relations, fundraising, legal compliance, and conflict resolution, with a particular focus on Title IX and student-athlete safety (Blackburn et al., 2013). Furthermore, ADs started assuming greater leadership responsibilities, overseeing coaches and athletes while navigating the intersection of education, sports, and community involvement (Hoch, 2014).

The position of athletic director had evolved into a highly specialized, full-time profession by the 2000s, especially in larger school districts. Part of this shift occurred in response to high-stakes testing; districts demanded principals with educational backgrounds rather than athletic ones, the latter a more commonplace reality before No Child Left Behind. Before this change, principals could support ADs more actively because they shared an athletic background (Furr, 2015). As high school sports programs became more integral to school communities, the responsibilities of ADs expanded to include advanced skills in management, communication, and financial oversight (Croskrey et al., 2018; Green & Reese, 2006). Fowler and colleagues (2017) found that in their study examining multi-level perspectives, the majority of principals desired ADs they hired to have content knowledge in law, budget, finance, and ethics. As the position continues to change, the ongoing professionalization of the role remains a priority, ensuring that athletic directors are equipped to meet the challenges of managing high school sports in the modern era (DeCesare, 2017).

Responsibilities of the Athletic Director

Expectations and responsibilities within the AD position vary widely and sometimes remain elusive until the candidate has been hired and arrives on the job (Lindsay et al., 2024). For example, Smith and colleagues’ (2023) study revealed that ADs tend to be under-prepared for legal issues that may arise. They explain that “social media issues, sexual harassment, and hazing are incidents that put schools under the microscope, and a well thought out and detailed education and awareness program as well as a response plan are similarly imperative” (p. 173). Furthermore, ADs frequently serve solely as an athletic or activities director; however, in some instances, ADs split their duties by combining the role of AD and another assignment within the school. In his dissertation research, DeCesare (2017) reported 43% of ADs worked full-time, while the other 57% served in some sort of dual role where they worked in another position in addition to being a high school athletic director, including school administrator, teacher, dean of students, or counselor. Athletic directors frequently report their coaching responsibilities when discussing their job description (Baghurst et al., 2014). DeCesare (2017) found that 32% of ADs had coaching responsibilities in addition to serving as AD. A larger percentage (67%) of these AD/coach combinations existed in small schools (DeCesare, 2017). This dynamic proves particularly challenging due to the resource scarcity often present at small schools where ADs are more likely expected to serve dual roles (Smith et al., 2023).

According to Baghurst and colleagues (2014), an AD’s ability to balance the myriad of new expectations and provide strong leadership determine the success of athletic programs. Stier and Schneider (2000) provide an exhaustive list of the “successful” AD: creating positive relationships with parents, community members, and other staff in school settings; maintaining high visibility at athletic events; preventing and solving problems; establishing networks with the media, booster clubs, and support groups; using and creating department handbooks; and possessing fundraising skills. Moreover, ADs must guarantee that coaches develop the necessary skills and have a comprehensive understanding of their legal obligations, including compliance with safety protocols and athlete protection standards (Armstrong & Stevenson, 2023). DeCesare (2017) explains that “although a dilution of duties occurs between collegiate and high school athletics, the essential elements and competencies of the position remain the same” (p. 18). Thus, the leadership quality expected of college athletic directors holds for secondary schools as well.

Athletic directors at middle and high schools across the United States play a multifaceted role that extends well beyond organizing sports events. According to Fegeley (2023):

I think AD stands for “All Day” and “All Duties.” I just hope that people outside of our

profession realize all the responsibilities that a high school athletic director has. It is more

than just two or three 14-hour days a week. We are responsible for the safety and

well-being of hundreds of student-athletes on a daily basis, and this includes eligibility

checks, facility maintenance, scheduling officials, planning special events, hiring

coaches, event management, community outreach, and countless other tasks. I could add

dozens of more items to this list. (as cited in Hoch, 2023, para. 11)

ADs develop comprehensive safety plans that must be meticulously documented and regularly updated to reflect changes in sports regulations and school policies (Armstrong & Stevenson, 2023). Subsequently, they oversee the implementation of these plans, verifying that all coaching staff are fully trained on the specifics of supervision, risk management, and student safety (Fowler et al., 2017; Armstrong & Stevenson, 2023).

In regard to staff management, ADs help select and train coaches and other athletic personnel (Croskey et al., 2018; Fowler et al., 2017). This responsibility encompasses more than just hiring qualified individuals; it involves continuous professional development and ascertaining that all staff meet the state and district requirements for certifications, such as CPR (Fowler et al., 2017; Armstrong & Stevenson, 2023). Athletic directors must also enforce policies around technique instruction and injury prevention, which are vital in minimizing risks associated with sports participation (Emery et al., 2006). They facilitate training sessions and meetings to discuss and reinforce these topics to coaches, emphasizing the importance of proper technique and the legal implications of negligence (Doleschal, 2006). School leaders also expect ADs to proactively communicate important information not only to coaches and staff but also to student-athletes and their parents. In Kerr and colleagues’ (2023) study about sports communication within middle schools, researchers noted that the parents had “concerns about how well policies were implemented, particularly when there was pressure to win. This included athletes feeling pressured to continue playing and thus not disclosing their injuries, officials missing illegal/foul play, and prioritizing winning over safety” (para. 30). By managing these responsibilities appropriately, ADs uphold that school sports programs are not only compliant with legal standards but also aligned with educational goals that prioritize student safety, well-being, and development.

Transition to Building-Level Leadership

As ADs assume a variety of administrative roles, they develop a highly transferable skill set to the principalship, which often leads them to explore the transition from managing sports programs to managing entire schools. ADs must work closely with principals and other administrators to match their program’s alignment with the school’s overall goals; this collaboration allows ADs to gain insight into the day-to-day operations of school management, including budgeting, staff supervision, and policy implementation (O’Brien, 2017). In many cases, ADs develop strong communication skills, ethical leadership, and business management abilities, all of which are critical for success as a school principal (DeCesare, 2017). As their experience grows, many ADs find that their administrative expertise and leadership capabilities make them well-suited for the principalship, where these same skills are in high demand (Elam, 2022).

However, ADs who make the transition to building-level administrator cite other motivation than similarity of skill set or confidence in their administrative expertise. According to Elam’s (2022) qualitative study, some districts are consolidating the role of assistant principal with athletic director to cut costs. Unfortunately, participants in Elam’s (2022) study who served in the dual AP/AD role lambasted the time lost with family, mental exhaustion, and their “extensive managerial responsibilities” (para. 36). Several external factors also factor into this particular career transition. According to Joy and Radhakrishnan (2012), career growth opportunities, increased job security, and the desire for greater influence within the school system could motivate ADs to consider building-level administrative positions. In particular, the principalship can offer more power and influence compared to the AD role, which may appeal to individuals seeking to expand their leadership footprint. Additionally, the principal position arguably comes with less physical and logistical pressure compared to the demanding nature of overseeing sports programs, especially in larger schools with competitive athletic teams. In his dissertation research, Epps (1991) explored differences between the AD role and other building-level administrators in Detroit Public Schools. He found that principals and assistant principals had high respect for their ADs compared to head coaches, and they valued business and management skills in their ADs because they understood the rigorous demands of the position.

All ADs and principals bring unique perspectives on leadership, understanding that it is integral to their roles. The studies that examine leadership style of successful ADs and principals more broadly highlight the positive effect of transformational leadership (Hobbs, 2018; Pharion, 2014); however, those that address diversity explain that “experiencing shifts in student demographics becomes increasingly complex, requiring strategy, reflection, distributed leadership, and vision” (Monogue, 2015, p. 213). Conversely, Rodin (2014) identified instructional leadership and collaborative team building as the two most important leadership skills for working with diverse populations. In Macdonald’s (2012) dissertation work, he explored the relationship between public high school athletic directors’ leadership style and the outcome on head coach behavior. He found that contrary to previous research, ADs needed to use a comprehensive leadership approach, incorporating transformational, transactional, and passive/avoidant styles, to create a significantly positive impact. These studies suggest that the leadership competencies required for both roles are complex and scarcely examined, especially regarding the transition between them.

Current Context

The purpose of this study is to provide insight into the transition from AD to building-level administrator by examining leadership characteristics and traits consistent with individuals who have made this transition. While ample research exists on the roles and responsibilities of both athletic directors and school principals (DeCesare, 2017; Judge & Judge, 2009; Mathis et al., 2014; Stier & Schneider, 2000; Young et al., 2010; Zayas, 2018) and subsequently their effect on student achievement (Karadağ et al., 2017; Waters, 2003), there is a notable gap in the literature regarding the experience, motivation, knowledge base, skills transfer, and leadership style from AD to principal. By studying these transitional components, valuable insights into the pathways and challenges involved in moving from the athletic director role to the school level administrator role can be gained.

Methods

Both qualitative and quantitative data were collected as a part of this multiple case study. Interviews were conducted with six principals from a midwestern state who have transitioned from AD to principal. The interviews were analyzed and thematically coded. All interviews were confidential. Individuals who participated were assigned pseudonyms, and all identifying characteristics were removed that could connect participants to their districts. Initial contact with participants was via email. Through email, the study was explained and consent was given.

Surveys were administered to individuals from the state who were not selected for the interview but who have made the transition from AD to school-level administrator. Interview data triangulated survey data to ensure trustworthiness.

Participant Selection

Participants were selected by first emailing the assistant director of the state’s athletic director association, requesting information for school-level administrators in that state who were previously athletic directors. The state was divided into six geographic areas by grouping state-established cooperative educational service regions. After grouping the regions of the state into six geographic areas, one individual (principal) was purposely selected from each of the six areas. This approach ensured broad representation across the state and reduced the potential for location bias or region-specific responses. All six participants were White males over the age of 40.

 Additionally, electronic surveys were emailed to all school-level administrators who were potential participants but were not selected for interviews (65 potential). A total of 43 individuals responded to the survey. Of the 43, 38 (88%) were male, and 5 (12%) were female. Most were over 40 (88%). Many (42%) were over 50. All participants were White.

Data Collection

Data were collected from two sources. Qualitative data were collected through telephone interviews with six participants. Quantitative data were collected through online surveys using Google Forms.

 Interview participants (n=6) were contacted via a telephone call and asked a set of 14 questions. Interview questions were grounded in literature and developed to gain insight into the research questions for the study. Among other questions (such as demographic information, experience, background, etc.), participants were asked, through open-ended questions, to describe their lived experiences in the AD position and compare that to their current school-level leadership role. Participants were also asked to describe and prioritize (rank order) factors that had contributed to their decision to leave the AD position and discuss internal and external factors and stressors that contributed to their decision to exit the AD position. Additionally, all participants were asked to share advice for other individuals who could be considering a transition from AD to school level administrator. All interviews were recorded, transcribed, and thematically coded using HyperResearch Software.

            Survey research was used to gather quantitative data from former athletic directors who were currently serving as school-level administrators (n=43). Survey data were used to triangulate and provide trustworthiness to qualitative data. Individuals were asked to complete an online survey consisting of 40 questions. Multi-level, ranking questions, and some open-ended questions were asked that were closely aligned to the interview questions and that informed the research questions for the study. No statistical measures were used to ensure the validity or reliability of the survey because the primary reason for the survey was to triangulate interview questions, gather demographic information, and give participants a chance to answer open-ended questions anonymously and in private. Questions began with demographic information (gender, age, and race), and continued with professional questions (job responsibilities, stressors of the positions, time management, etc.). The final question of the survey was open-ended and asked respondents to provide additional comments and/or advice for anyone considering the transition from AD to school-level leader. 

Data Analysis and Findings

            This section includes analysis of data from both the interviews and the surveys. By including both qualitative and quantitative data sources, an effort was made to provide a broad understanding of the motivations for moving from the athletic-director position to a building administrator position, while addressing the identified research questions. Rather than separate data analysis from findings, a decision was made to embed findings within the analysis section and then follow up with a brief discussion. This allows the reader to consider findings within the context of the data.

Interviews

Interview participants shared insights into their lived experiences during their time as athletic directors and as school-level administrators. Participants identified factors impacting their choice to transition from athletic director to building administrator. Based on recurring comments made throughout all interviews, a total of 15 thematic codes emerged in this study.  These codes were then divided into three main categories: a) external factors, b) internal factors, and c) stressors. Thematic codes aligned to external and internal factors as shown in Table 1. Thematic codes aligning to items causing stress in the AD role are shared and discussed later.

External Themes

            External themes are related to factors associated with the organization itself. Only the top three most-coded responses in this category are discussed.

Parental Pressure.

The external factor coded most often was that parental issues play a major factor in the decision to leave the position and pursue a building-level administrator role. Although only four of the six interview participants mentioned parental factors playing a role in their decision, the four who mentioned this did so multiple times (10 total).

When referring to parental issues causing stress and creating problems, the participants mentioned that parents get too involved in conduct violations and playing time for their children. One participant stated that most of their issues arose from “. . . conflict with parent and [athletic] code enforcement. Usually, it didn’t matter if the athlete or the parent knew they were guilty [of the infraction]. They would still fight it!”

            Another participant noted that parental problems helped make the decision to change jobs much easier: “As an AD, I was getting tired of the same parents complaining about playing time or other issues. In the school I came from, this was the main problem. We had great kids at the school. But the parents gave me troubles when it came to athletics. They made the switch that much easier to make.”

            Parental pressures and issues often came about unwarranted and unexpected. While some of the athletic directors acknowledged that they expected issues with parents to a certain extent, they also said that parents often caused problem after problem. One participant commented, “. . .parents always seemed to exceed expectations for the number of problems they can create. I swear they have nothing better to do!”

Student / Staff Issues.

The only external factor that contributed to the job change that was mentioned by all participants was that student and staff issues contributed to their decision to exit the position. All six interviews contained this response, and all participants mentioned it only once. Constantly managing students and staff is the primary role of a building administrator, which might make that higher-paying position more appealing. One participant stated, “I would spend the majority of my day dealing with student or staff issues, and I decided, I don’t get paid enough for this!” Another commented that, “There is no end to the stupid things some student athletes do. And we often involve the principal in our discussions, so I thought, I might as well [be in that position].”

Staff issues primarily involved hiring more so than staff conflict. Getting the best coaches and assistant coaches, officials, and event workers all took sufficient time. Some ADs had support in these hiring decisions but not always. One AD commented, “I can spend countless hours getting everyone to work a track meet. And that same week, I might have other events too, baseball, softball, and usually I have people hired well in advance, but some people cancel and there is a last-minute sprint to get everything covered.”

Coach Conflict.

            Lastly, one participant mentioned twice that “coaches creating problems” was a reason they transitioned away from the athletic director position. Coaches can cause stress on athletic directors by being demanding and requesting too many things, as well as being allowed to have too much control. The participant said the following about where the issues originated: “Coaches in our own school . . . the situation I came into was about coaches being able to do more than what they probably should have been allowed to. I would have coaches knocking on my door, complaining about the schedule. It wasn’t the parents for me; it was my own coaches. They were unrealistically demanding.”

Internal Themes

Internal themes are tied to the characteristics or attributes directly associated with the participants. Three prominent themes of internal factors contributed to the decision of an athletic director leaving his/her position for a building administrator position. Only the three most-coded internal themes are discussed below.

Better Hours for Family.

            The most frequently applied code (15 times) for internal themes was “better hours for family,” which was mentioned in all six interviews. Every participant mentioned that a reason they stepped away from the athletic director position was because it would benefit their family. This meant different things for each participant. One shared:

As an AD, I was [at work] 7am to 10pm some days. Long, tough hours away from family, dealing with unexpected issues or parent issues. That wasn’t fun. Day to day as a principal, I am much more sure about what I’ll be dealing with. It helps too because I have assistants [principals] who share some of the load.

Some participants wanted to spend more time with their kids and be able to see them grow:

It’s better hours for me. I had two sons that played Division III college sports. They were both in college playing; I was an AD and coach. This [job change] provided an opportunity to do something different but also better for me. I could see my kids more.

Another participant added, “It was strictly the family needs. That’s really all it was for me. I needed more time with my family. I had a seven-year-old and a two-year-old. I didn’t want to see my kids raised without a father.”

One participant mentioned that the stress of the athletic director position caused a marriage to be ruined, so a change of positions was needed to fix the relationship. “Without getting into too much personal detail, it kind of crossed into my marriage. It wasn’t doable. It tore my family and my wife at that time apart. After that I just realized it wasn’t workable.”

Upward Mobility / Increase in Pay.

Others wanted to be able to make more money. In one case, it was specifically so their spouse could stay home to raise their children:

I was a teacher, then became a stay-at-home dad. I got my masters so that I could have the ability to increase my income. I needed to make this change in my career so my wife could help raise our own kids. Making more money allowed our family to do this. Status wasn’t really a reason for me. I loved being an AD. That was my dream job. But I knew my family needs, and that led me to becoming a principal. Increase in pay and a feeling that I could contribute more ultimately led me to change roles.”

One participant commented,

The increase in pay was important. The way the [state] retirement system works came into consideration as well. It is based on your top few years of compensation. That impacts the rest of your life in retirement. So, I wanted to get those last three years’ salary as high as I could.

Increase in Power / Influence.

Only one participant made the decision to move to a school administrator position because of the desire to have a broader influence over students’ lives. Although others did mention that they felt an increased sense of impact on student learning and the educational process, one contributed the decision to having broader influence, stating. “For me, it was a desire to make change in kids’ lives. I felt as a coach I impacted my players, but as an AD, I didn’t have that direct impact. As a principal, I indirectly influence their educational experience every day.”

Stressors

            As codes were applied to participant interviews, stress became a recurring theme. So much so, that the decision was made to address it separately. This decision was in part because many of the stressors present in participant perceptions are also present in the literature regarding school level administrative leadership. Therefore, when making a decision regarding transition to school level administration, a current AD could reflect on these individual stressors to compare their experience to that of the participants of this study.

            Worthy of notice is that two of the six participants shared that they had multiple responsibilities while they served as an AD. One was an assistant principal, and another was a teacher. Undoubtedly, wearing multiple hats results in multiple sources of stress. The participant who was also a teacher shared that there was more structure in the day as an AD than in the current role of building administrator:

My day as a school administrator is a little different because I’m no longer teaching. I taught as an AD, and now I don’t. My days are less structured. More contact with staff and parents now. I work with the community now. When I was an AD, it was more structured and I was dealing with scheduling and things like that. Now there are very few days that are structured. Even this [interview] could’ve been interrupted. Things came up as an AD, but not in the same sense as it is now.

Another participant, however, was in a unique situation in that the individual was in a dual role of AD and assistant principal. Although their comments were coded, it is worth noting the unique circumstance. This participant shared:

The district in which I was AD I was also an AP [assistant principal]. It was an experiment of the district. I was able to do both and kind of see what a principal position would be like, but it was like having two jobs and it was a nightmare! It was for personal and family health need that I had to make a change. That struck me the most because I was AP for a year before becoming principal. I needed to do one job instead of two jobs. It was just too much.

The position of AD is one that is rich with stressful situations daily. As participants shared their perceptions, several thematic codes developed and were applied. The codes applied most often can be found in Table 2.

Information gathered from the six interviews provided insight about the many stressors of both the athletic director and, to a degree, principal positions because several participants discussed similarities and differences between the two positions. While the stressors of the athletic director position are said by some to lead to them transitioning out of the position, the stressors are not necessarily exclusive to that position. Nonetheless, the five stressors shared most during interviews are reviewed in order of the number of participants sharing the stressor.

Teacher and Student Issues.

Not surprisingly, stress associated with teacher and student issues was most common among participant responses. As shown earlier, all participants commented that this concern contributed to their decision to transition to the school level administrator position. Likewise, all participants listed these issues as one of the most stressful aspects of their jobs. One participant shared, “It gets old fast. Day in and day out. If it were just the students, it wouldn’t be as bad.” Another commented, “Teachers often question eligibility. When grades come out, stress goes up!”

Current administrators often shared that these concerns were consistent across positions. One shared, “It’s the usual suspects: Parents, teachers, students [where stress originates].” Another participant summed it up this way: “It’s people that are unwilling to pull along with, and are trying to even pull against [you]. Students and my family cause stress. I cause my own stress. But the adults who don’t want to change are what cause the most stress.”

Unexpected Problems.

It appears that, despite many hours of planning, unexpected problems are a regular occurrence in the lives of contemporary ADs. Five participants commented on unexpected problems contributing to their stress. The nature of the unexpected problems varied but often involved weather. One participant shared a personal story: “You’re sitting there dealing with a situation about kids smoking pot, and you have to deal with that and investigate the situation, but then all of a sudden it starts to rain. You’ve got baseball and soccer and softball games or practices going on. Now what happens?” Another shared, “You want to be able to manage the conflict in a way that best represents the school. And there’s a lot of problem solving. You’re troubleshooting with whatever comes up. Always putting out fires. And you never know where they will start on a given day, or a given hour.”

Managing Conflict.

Managing conflict appears to be a regular stressor for participants. Four participants commented many times (10 total) on the importance of managing conflict in their role. One participant shared, “I’m just always dealing with conflict. That’s just the nature of the job. I can’t lie to you, but that’s a lot of what it is.” Comparing the AD to the principalship, another shared,

In this job [school administrator], you can never make everyone happy. It’s the nature of the position sometimes. It was the same as AD. If a student athlete gets suspended, the parent, and sometimes the coach, are unhappy. When grades come out, I swear that some parents think I assign their kids’ grades!

A few of the participants shared concern that they did the AD job with very little assistance, making the stress something that isn’t shared. One participant stated, “At my school the AD has such a wide variety of responsibilities because you’re the only one working to get this stuff done. Everyone wants it done, and many complain when it isn’t.”

Parent Pressure.

Another source of stress externalized as parental pressure. Parent issues was the most-coded response regarding the decision to transition to a building level administrator role, and likewise, it was coded ten times in the responses of four participants as a source of stress. One participant shared only this, “Definitely the parents [causing the most stress]. Not a question about that one. Not much more to say than that.”

One participant added that code enforcement (ensuring students follow the athletic code) and parent pressure often go hand in hand, “Conflict with parents and code enforcement. That I think were the most stressful situations. They often happen at the same time.”

Another participant felt that the experience of being an AD helped in preparation for dealing with similar situations as a building administrator, sharing, “As an AD, I dealt with a lot of parent issues. This helped me get ready for problems that I would get as a principal. … I got comfortable with most things that would come my way as an AD, and this helped me when I took the principal job.”

Scheduling Conflicts.

Though hand-in-hand with unexpected problems, the sheer volume of scheduling conflicts and issues appears to cause stress for ADs. One participant shared, “Scheduling and transportation are sources of stress. Just trying to get everyone everywhere they need to be.” Another commented on the difference between the current building level administrator position and the previous AD position, sharing, “You really spend more time with people as a principal. As an AD, I spent a lot more time scheduling officials and those types of things.”

Similar to comments regarding unexpected problems, weather seems to impact stress regarding scheduling conflicts for some ADs. One stated, “Scheduling causes a lot of stress. Spring sports are tough. The weather controls everything in [state]. That always is the hardest and causes stress.”

Survey Data

            Survey data were gathered through online surveys of individuals who met criteria for inclusion in the qualitative section of the study but who were not selected as participants (n=43). Survey questions were designed to triangulate and provide validity to qualitative data, often providing opportunity for ranking of criteria related to different aspects of the positions.

Demographics

            To gain better understanding of the survey respondents, demographic data were first gathered. Of the 43 respondents, 38 identified as male (88.4%) with the remaining 5 (11.6%) identifying as female. The same percentage (88.4%) were over 40 years of age, with 17 (39.5%) over the age of 50. All but one of the respondents (97.6%) worked as an AD in schools with enrollment of fewer than 2,000 pupils, and 26 respondents (60.5%) worked in schools with enrollment of 1,000 or fewer students. As building level administrators, 38 (88.4%) had worked in schools with 2,000 or fewer students, and 28 (65.1%) worked in schools of 1,000 or fewer students. Not surprisingly, based on student enrollment, most respondents categorized their schools (while ADs) as rural (48.8%) or rural and remote (32.6%). While serving as building-level administrators, 39.5% categorized their schools as rural and 37.2% as rural and remote. The distinction between rural and rural and remote was based on location and distance to an urban center. A location could still be characterized as rural and be within 25 miles of an urban center, while rural and remote required it to be both small and outside of a 25 radius of an urban center. As mentioned previously, all survey respondents identified as White / Caucasian.

Career Path

To provide a better view of the survey respondents, several questions asked about the career path the building level administrator had taken. Nearly all the survey respondents were head principals (97.7%), with one respondent (2.3%) also holding a combined position of building principal and district administrator / superintendent. Most had been in their current position for 1-5 years (37.2%) or 6-10 years (32.6%).

With regard to which position survey respondents held directly before accepting the building level administrator position, a surprising number of respondents (17 or 39.5%) held a combined position of assistant principal and athletic director, while 34.9% were solely athletic directors (23.3%) or activities directors (11.6%). Five individuals (11.6%) were in a combined role of dean of students and athletic/ activities director. Thus, roughly half of the respondents (22 of 43) were in a combined role of assistant principal / dean of students and athletic / activities director before transitioning to the head principal role.

            When respondents selected (from a list) all the positions they had held in their careers, not surprisingly 100% had been teachers. Nearly all respondents also had coaching experience, with 88.4% having served as a varsity head coach. A complete list of responses is shown in Figure 1.

Perceptions

Survey questions (non-demographic or career path) were asked in two distinct parts so the respondents could answer both for their time as an athletic director, as well as their time as a building level administrator. These questions were designed to gather information in four main areas: a) management perceptions, b) job stress, c) leadership preparation, and d) job responsibilities. Each area is examined further below.

Management Perceptions.

            Regarding perceptions of managerial responsibilities of both the AD and school level administrator positions, respondents were asked four questions regarding a predetermined list of managerial aspects of the positions. The list of seven managerial responsibilities was developed from contemporary literature. The seven responsibilities included were:

1. Managing students (including athletic eligibility)
2. Managing parents / community (including booster clubs)
3. Managing staff (including coaches, referees, event workers, etc.)
4. Managing finances (budgets)
5. Managing facilities (scheduling gymnasiums / facilities, locking up after events, etc.)
6. Managing events (including scheduling, execution, and transportation to/from)
7. Managing safety (including planning and drills)

Respondents were also provided an “other” category, in which they could write in responsibilities that were not available in the provided list.

            When asked to select which responsibility took up most of their time in the AD role, the top responses were managing events (58.1%), managing staff (26.5%), and a three-way tie for third between managing parents / community, managing students, and managing facilities (all at 4.7%). This contrasted with the responses regarding the question about what should take up most of their time. When answering that question, managing staff was first at 53.5%, followed by managing events (23.3%) and managing students (20.9%). It appeared that ADs spent more of their time than they would like on managing events, but for the most part, the top categories were consistent.

            Regarding the building level administrator position, respondents ranked their responsibilities differently than they did in their AD role. School level administrators ranked managing students as the number one responsibility that consumed their time (46.5%), with managing staff a close second (44.2%). No other category received more than one selection. As administrators though, respondents overwhelmingly felt that most of their time should be spent managing staff (69.8%), while managing students also received several selections (20.9%). Interestingly, more than one school level administrator took the opportunity to write in other options, which included teaching and learning, educational leadership of staff, and professional development (each written in one time).

Job Stress.

Regarding job stress in each position, respondents were provided a list of potential areas of stress for each position. They were then asked to rank (1-8) each item in relation to the amount of perceived stress it caused in each position (AD and building level administrator). The list of job stress items was developed from contemporary literature regarding stress in the AD and school administrator positions. The list stayed consistent between both positions so that a better comparison could be made; however, respondents did have an option to add an “other” and assign it a rank order.

The potential areas of job stress used for this study:

1. Having too heavy of a workload to finish during a normal workday
2. Being interrupted frequently
3. Imposing excessively high expectations on self
4. Feeling that meetings take up too much time
5. Trying to resolve parent/school conflicts
6. Supervising and coordinating tasks of many people
7. Administering student discipline

For athletic directors, the stressor selected as number one most often (18 times) was #1 Having too heavy of a workload to finish during a normal workday. Subsequently, thirty-four respondents ranked that stressor in their top three. With regards to other stressors ranked most often in the top three, #3 Imposing excessively high expectations on self, was ranked 27 times in the top three, while #2 Being interrupted frequently, saw 22 responses in the top three.

As school level administrators, respondents also selected #1 (too heavy of a workload) as their top stressor most often (12 times), with 25 ranking that stressor in their top three. The stressor that was ranked in the top three most often was #3 (imposing high expectations on self), which was selected 25 times and was ranked first by nine respondents. Unlike their responses regarding the AD position, the third most ranked (in top three) stressors showed a tie between #8 Administering student discipline and #6 Having to make decisions that affect the lives of people you know, each being ranked in the top three 23 times. Thus, there appears to be similarities with regards to stressors across both the AD and school administrator role, but it appears that handling student discipline and making important life-impacting decisions more heavily weighs on school administrators.

Leadership Preparation.

The first research question for this study asked what leadership skills or traits required of the AD position assist in transitioning to the building-level administrator position. Although interviews provided an opportunity for participants to discuss this transition and the salient leadership skills and traits, the survey more clearly asked respondents to provide weight to different skills, thus ranking them.

One question on the survey provided a list of eight skill / traits and asked respondents to rank them in order one through eight in the order of importance that the skill / trail helped them prepare to be a school-level administrator. The list was generated from contemporary literature with regard to important skills that are relevant to both athletic administrators and school-level administrators:

1. Overseeing school events
2. Working with students
3. Working with faculty and school staff (including supervising coaches)
4. Working with parents and/ or community members
5. Working with booster clubs or support organizations
6. Managing conflict
7. Solving problems
8. Managing operational / organizational tasks (handbooks, budgets, scheduling, officials, etc.)

Athletic directors overwhelmingly selected #7 Solving problems as the skill / trait that helped them best prepare for the role of school-level administrator. This skill / trait was ranked number one by 18 respondents and ranked number two by another eight. Overall, this skill /trait was ranked in the top three by 30 individuals (70%). This supports data from the interviews where four participants commented 10 times on the stress caused by managing conflict in the role of AD. Survey data support that by being subjected to the stress associated with managing conflict, athletic administrators build the necessary skills to handle stressful situations, which applies directly to their success as building-level administrators.

The skill / trait that was next in importance was #3 Working with faculty and school staff (including supervising coaches). This skill was ranked number one by 13 respondents, and it was ranked in the top three by 29 individuals (67%). From interview data, “student and staff issues” was a highly ranked external factor in the decision to change from an AD role to building-level administrator. Further, “teacher and student” issues was listed as a source of stress by all participants in the interviews.

Also ranked in the top three skills / traits by many respondents was #6 Managing conflict. This was only selected as number one by eight individuals, but it was ranked in the top three by 30 respondents (70%). Consistent with interview data, managing conflict was mentioned as a source of stress by four participants a total of 10 times. Furthermore, an external factor that contributed to the decision to transition to building-level administrator was often conflict related, specifically including conflict with coaches.        

Job Responsibilities.

Regarding perceptions of job responsibilities of both the AD and school-level administrator positions, respondents were given a list of 12 common job responsibilities consistent with the literature on athletic director and/or building-level administrator positions. Respondents were then asked to rate each responsibility on a scale of 1 to 5, with 5 being “very important” and 1 being “unimportant.” A rank-ordered list for both roles, based on the mean score for each responsibility, is shown in Table 3.

For the athletic director position, the two job responsibilities that scored the highest are “setting ethical standards in the school” (mean of 4.53) and “working with the community.” For reference, these two job responsibilities had mean scores of 4.84 and 4.7 respectively for the building-level administrator position. According to these data, as building-level administrators, those job responsibilities are deemed to be more important than they are for athletic directors.

The highest mean scores for the building-level administrator responsibilities are “hiring staff” (mean of 4.91) and “managing or supervising staff” (mean of 4.88), suggesting that these two job responsibilities are deemed to be the most important according to the building-level administrators. The largest difference of scores between the two positions is for the job responsibility of policy development. Building-level administrators deemed this to be a more important job responsibility for their current role than when they were athletic directors. The difference in mean scores is .7, as it averaged to be a 3.65 for athletic directors and a 4.35 for principals.

The job responsibility that was scored lowest for both job positions is “Fundraising.” This job responsibility had a mean score of 3.26 for athletic directors and a mean score of 2.58 for building-level administrators. These data suggest that both positions did not find importance in focus on fundraising. This is somewhat inconsistent with literature related to athletic administrators, as often, the job entails raising funds for the athletic programs. However, at the high school level, this is often handled by booster clubs, and although the AD oversees the booster club, he or she is often not directly involved in the fund-raising efforts.

Discussion

Building-level administrators and athletic directors have similar job responsibilities in terms of working with students, staff, and parents. Both have similar stressors, which include dealing with parental complaints, violations of the school code of conduct, and handling issues that arise from the staff, as well as issues that pop up randomly on a day-to-day basis. There is little doubt that work done as an AD helps prepare individuals for the building-level administrator role. In fact, over half of the survey respondents (51%) had been in a combined role of either assistant principal or dean of students and athletic director before transitioning to the building-level administrator role. Undoubtedly, crossover existed between these two roles, and the distinction between which hat one was wearing during a given situation became blurry. However, important to this study is that these two roles assume similar levels of stress and handle similar situations, thus honing leadership skills that will translate.

Similarities and consistencies across both the interview and survey data suggest that the AD role helps prepare building-level leaders. The three areas highlighted in the study included: a) comparable stressors, b) comparable leadership skills or tasks, and c) comparable job responsibilities.

Data suggest that ADs and building-level administrators experience similar stressors and respond to similar origins of stress. For example, school personnel (e.g., coaches, teachers) with unique issues demand time of both the AD and building-level administrator. Handling the stressors requires ADs to build necessary skills, which transfer to other leadership roles. These included problem solving, working with faculty and school staff, and managing conflict. Likewise, although ADs scored some job responsibilities differently than did building-level administrators, consistencies still arose within the top five responsibilities, including the importance of hiring staff, setting ethical standards, and working with the community.

Interview data shared primary sources of stress for athletic directors, and these data supported reasons cited for transitioning out of the AD position. For example, some participants felt strongly that they were undergoing substantial pressure from parents, student and staff issues, and conflicts with coaches, and these stressors contributed to their decision to leave the position. In some instances, participants made it clear that they felt they “might as well” move into a building-level administrator position because they were already undergoing parallel levels of stress or pressure. Even others noted that their level of stress and pressure went down after they transitioned to a building-level administrator position, and the time spent with their families went up. In fact, better hours for their family created a clear internal pressure that contributed to the decision to transition. Athletic directors felt that they put in many hours at school away from their family and that they would actually be able to spend more time with their families after becoming building-level administrators.

One final discussion point worthy of mention is that all six of the interview participants became building-level administrators in the same school district in which they were an athletic directors. The decision to transition was influenced by many factors, including the desire to spend more time with family, make more money for similar levels of stress, and be able to influence educational change on a larger scale.

Advice from Interview Participants

            Though not a specific research question, it seems fitting to conclude with advice from the six individual interview participants. Each of these individuals agreed to participate in a telephone interview to share their perceptions of both roles, and the interviews concluded with a final open-ended question asking these current building-level administrators what advice they would offer an individual who is considering transitioning from an AD position to school administrator. These bits of advice can further assist someone who might be contemplating the same transition.

Each participant’s response is shown below.

 Participant 1:

Take an opportunity to sit down with someone who has gone through it. The more people you can get input from will help you. I don’t think I came into this job knowing exactly what I was getting into, but I thought I could easily take it on, and I probably undermined some of the challenges that came with switching roles. Be open minded and be willing to listen to others. You’re going to make mistakes, but you’ve just got to be ready to respond to them.

Participant 2:

I think you have to be prepared to make the call. Now the buck is at your desk. You have to make decisions that you didn’t have to before. You’re going to be involved in more political types of situations. More meetings. Some of those meetings are about things you aren’t passionate about, but you still have to go.

Participant 3:

I would say to make sure you understand the demands of the positions are different. The principal position will take you away from the love of athletics. If that’s why you were an AD, you won’t appreciate it. You can still go watch events, but you aren’t going to be just focusing on that. Eyes are on you in a different way. Your responsibilities are different now. A lot of people love athletics, and that’s why they do the AD spot. The principal and administrative role is just different now.

Participant 4:

The buck stops with you. Be ready to deal with larger problems that mean much more than athletics. You’ve got to be able to work under pressure, and you’ve got to be a good leader to get people to do what you want. I also think it’s important to be open to change yourself and take advice from others. Listen to others.

Participant 5:

I think one thing that a lot of people don’t understand is that an AD has a lot of nights, but a principal has them as well, plus more. Principals have music concerts, plays, everything that goes on. ADs don’t have all that. You have to have an understanding family. My wife always tells me that I picked this. I’m sick and tired of a play on the fourth night in a row. The sacrifices your family has to make are huge. I don’t live near school, so it’s tough on my family. You’ve got to be willing to get out of bed and be present. It’s a lot, but I would do it all over again.

Participant 6:

I think you have to ask yourself what is it that you exactly want to do,  and why would you leave the AD role for a principal position. Is it that you are motivated and really want to be associated with leading a building? Or is it about getting out of the AD role? If you think it’s going to be easier as principal, I don’t know if that was correct or should be your reason. If you don’t have interest in dealing with data or student academic achievement, [don’t make the transition]. You know, my spouse is an AD and she doesn’t always like it, but she has no desire to be a principal. She loves her job, but there’s just moments when she doesn’t like it. It’s great when it’s tournament time. On a day like today when there’s no events, it’s not very glamorous. Being a building principal, you know, there’s not a whole lot of glamorous stuff. You get to prep for graduation and scholarship night, but it’s not the same. You’ve got to be certain about why you’re doing it. If it’s for the wrong reasons, you’re not going to be happy.

George Minoso2025-03-26T15:53:42-05:00

Bridging Practice and Pedagogy: The Role of Practitioners as Professors in Higher Education

George Minoso2025-03-26T15:53:42-05:00May 2nd, 2025|Contemporary Sports Issues, Research, Sport Education, Sports Management|

Bridging Practice and Pedagogy: The Role of Practitioners as Professors in Higher Education

Abstract

Many smaller institutions are seeing an uptick in students interested in obtaining degrees in the more applied fields.  For the applied fields, there is a distinct benefit to having practitioners move into the role of professor as a “second career.”  The value of this approach has been recognized in fields such as public policy, education, and nursing for quite some time (He, et. al 2022; LaRocco & Bruns, 2006; Ritter, 2007).  These practitioners bring not only the content expertise, but also the depth and credibility to draw the connection between theory and practice for the students. The concept of practitioners as professors has yet to be studied in business and sport management programs.  The use of practitioners in the field of accounting and finance has been examined as a means to combat the shortage of doctorates in the field, but not to a great extent (Boyle, et. Al, 2013).  The research on “second career academics” (LaRocco & Bruns, 2006) has predominately been centered on the challenges the individuals face when moving from the corporate to academic environment.  This study is aimed at understanding the trends in business and sport management programs with respect to hiring practitioners into full time professorship positions. 

Keywords: Second career academics, practitioner professors, business, sport management, professional experience, faculty hiring trends, accreditation impact, student learning outcomes, networking opportunities, faculty recruitment

Introduction

The study will examine the number of professors with professional experience as practitioners in their fields prior to entering the academic world.  While textbooks are great tools, they cannot replace the experiences that professional practitioners encounter. The research will use survey methodologies to get an understanding of the status of practitioners in sport management and business programs in the United States.  As a second phase to the research, we hope to uncover the motivations for moving into academic and to better understand the challenges and rewards for making such a transition.  Future research will also be centered on potential tensions that might exist between the first career academics and the second career academics (practitioners) (Clinebell & Clinebell, 2008). We hypothesize that there is an increasing number of business and sport management programs employing practitioners into professorship positions. 

Literature Review

While there is some previous research that focuses on aspects of this study, there is a lack of focus in previous studies on institution size and accreditation status in relation to second career academics. However, there were many studies that focused on the move towards hiring second career academics, doctoral shortages in certain business fields, and overall collaboration in the business world.  It seems that the pendulum in business schools is moving towards hiring more professors that have been practitioners previously (Clinebell & Clinebell, 2008).

In our careers as professors, we have seen many changes in students, and in what the business world expects of them.  Interpersonal and intrapersonal skills which are components of Emotional Intelligence (EI) are something that many employers now want (Manna, et. Al, 2017).   This may largely be because business in our world today is incredibly complex and constantly in flux.  Communication skills are a key to reaching high levels of career success (Manna, et. Al, 2017).   For example, preparing reports and various financial statements may have been enough for an individual to be successful in the past in the accounting field, but that is not necessarily the case today (Manna, et. Al, 2017).   Due to the overall nature of business, it is increasingly important that the hybrid and clinical aspects of business be incorporated into business education (Clinebell & Clinebell, 2008). 

Another issue facing sport management and business schools is a shortage of faculty that have their doctoral degree (Clinebell & Clinebell, 2008).  With that being said, for institutions to survive and thrive in the educational environment important steps need to be made. Effective and impactful learning requires collaboration between professors and students.  Even further collaboration such as between managers, educators, and researchers could be beneficial as well (Sohrabi & Zarghi, 2015).   Networking with the local community and creating employee culture that fosters collaboration between first and second career academics are both important (Clinebell & Clinebell, 2008).  In collaborating with the community good relationships can also be built with various business entities.  According to a study conducted by Henningsson and Geschwind (2017), both local and top management agree that adjunct professors who are industry practitioners can help increase collaboration as well as quality of education.

Accreditation is a peer-to-peer arrangement relying on volunteerism by higher education professionals to pledge to students that the education offered by universities is of great excellence and value.  The accreditation process leans on the openness of universities to assess themselves against a set of policies, procedures, and standards to recognize strengths, weaknesses, opportunities, and threats while using the accreditation process for improvement in these areas (Brittingham, 2009).  For sport management programs the Commission of Sport Management Accreditation (COSMA) uses myriad of criteria, such as outcome assessments, strategic planning, curriculum, faculty qualifications, scholarly and professional activities, educational resources, and internal and external relationships (stakeholders such as alumni) to gauge the rigor of quality (COSMA, 2024). Accreditation has evolved over time to link federal financial aid to accreditation status and monitor both qualitative and quantitative outcomes.  The achievement of accredited status is frequently used to assess the quality of an academic program (Hobbs, McMahan, Stawski, 2018).  The utilization of second career academics can be of great value during the accreditation process as these individuals are versed in the professional needs of the industry.  In addition, second career academics may have the external relationships needed to facilitate a successful accreditation review.

The changes in student demographics also raise questions about equitable access to high-quality education, which presents more challenges for university professors and accrediting agencies. Utilizing practitioners in higher education classrooms can be instrumental in mitigating some of these challenges.  By providing students with more direct and consistent access to those that have been in the field can prove beneficial in preparing students for careers in those industries.  Second career academics again are likely to have access to networks that can assist students as they prepare to enter the workforce in that field.

The lack of previous research and the need to address current challenges facing sport management and business programs have provided the foundation for the research questions examined in this exploratory study.  The research questions were devised to better understand the current state of utilizing second career academics in sport management and business programs.

RQ1: To what extent does the size of the institution impact the hiring of second career academics?

• H1: Smaller institutions will hire second career academics more frequently.
• H2: Smaller institutions will actively recruit second career faculty.

RQ2: To what extent does program accreditation impact the recruitment of second career academics?

• H1: Programs that are not accredited will actively recruit second career faculty to a higher degree

RQ3: To what extent does accreditation status impact the hiring of second career academics?

• H1: Institutions that are accredited will have fewer second career academics.

RQ4: To what extent does the institution have barriers to second career academics for career advancement

• H1: Institutions that are accredited will not have advancement opportunities for faculty without a terminal degree.
• H2: Smaller institutions will have advancement opportunities for faculty without a terminal degree.

Method

The study was designed as an exploratory study to a larger research effort centered on uncovering the benefits to students of having second career academics as faculty in their programs. Currently, little research exists on the hiring practices of second career academics in the sport management and business fields. A survey was developed to investigate the prevalence (or lack of) of faculty in programs that are considered second career academics. The 12 questions on the survey were used to gather information on the size of the institution and their practices around recruiting and hiring second career academics. The survey also included questions about advancement opportunities for second career academics in higher education institutions. Initially, a convenience sample was selected of 62 institutions. As the initial response rate was low, the survey was then distributed through the Commission on Sport Management Accreditation (COSMA) membership list. A total of 22 responses were collected. Although a high response rate was not achieved, this data does provide insight into current hiring trends and provides a solid foundation for future research.

Results and Discussion

The data from the survey were analyzed using Microsoft Excel. As the dataset was small, hypotheses were evaluated using Pearson’s correlation coefficient. The institutions that responded were from a variety of locations in the United States and were of varying size and type (private, public). For the purposes of this study, it was assumed that the Sport Management and Business programs were in the same department, which was a limitation that is discussed later in the paper.

RQ1 examined the impact of size of the institution on the hiring of second career academics. Many smaller institutions are seeing an uptick in students interested in obtaining degrees in the more applied fields. For the applied fields, there is a distinct benefit to having practitioners move into the role of professor as a “second career.”  The value of this approach has been recognized in fields such as public policy, education, and nursing for quite some time (He, et. Al 2022; LaRocco & Bruns, 2006; Ritter, 2007). The use of practitioners in the field of accounting and finance has been examined as a means to combat the shortage of doctorates in the field, but not (Boyle, et. Al, 2013). A general size of the institution can be inferred from the number of faculty and students in the programs. Table 1 summarizes the data related to the relative size of the program based on the number of faculty and number of students.

Table 1: Size of programs in terms of faculty and number of students
	M	SD
1. Total number of FT faculty in business and sport management	7.64	9.08
2. Total number of PT faculty in business and sport management	6.50	0.71
3. Total number of students in business and sport management – undergrad	185.00	87.61
4. Total number of students in business and sport management – graduate	56.55	40.81
5. Total number of faculty (FT and PT) that are “second career academics”	9.50	12.03

The number of FT faculty varied considerably. The median was 4, but the responses ranged from a high of 42 to a low of 1. There was more consistency with PT faculty. 23% of the institutions reported having more PT faculty than FT time faculty. These programs were the smaller programs in the data set in terms of the number of students. The size of the programs in terms of students was quite varied as well. The median number of undergraduate students was 180 with the highest being 750 and the lowest being 55. The median number of graduate students was 40, with the highest at 130 students and the lowest at 7 students. Eleven (50%) of the institutions did not have graduate programs. Finally, the average number of second career academics (FT and PT) across the sample was 9.5 with a high of 30 and a low of 1. Of particular note was that none of the institutions reported having no faculty that were second career academics.

H1 postulated that smaller institutions would hire second career faculty members more frequently. To assess this, a correlation between the number of students in the program (combined undergraduate and graduate) and the number of second career academics reported in the department was determined. The Pearson’s correlation coefficient was 0.739 (p <.0001) and thus supported H1.

H2 predicted that smaller institutions would also specifically recruit second career academics for their programs. The correlation between the total number of students and whether the institution actively seeks out second career academics was 0.522 (p = 0.013). H2 was supported showing that smaller institutions actively recruit second career academics to a higher degree than larger institutions.

RQ2 centered on the impact of accreditation and the relationship to actively recruiting second career academics in sport management and business programs. Of the responses, there was a near equal split between institutions that were not accredited by an outside accrediting body such as AASCB or COSMA (9) and those that were currently accredited (10). Three institutions were in the accreditation process at the time of the survey.

H1 stated programs that are not accredited are more likely to actively recruit second career academics. The correlation coefficient between accreditation status (n=19 because 3 were in process) and whether or not the institution reported that they actively recruited second career academics was 0.056 and therefore H1 was not supported. From this data set, there is no evidence that accreditation status impacts the recruiting of second career academics.

RQ3 examined the impact of accreditation status on the hiring of second career academics. Institutions that do not actively recruit second career academics may still find that through their search process they tend to hire more frequently second career academics.

H1 stated that institutions that area accredited will have fewer second career faculty in their programs. This contention was not supported by the data collected (r = 0.253; p = 0.298).

Although not the focus of this paper, size of the institution does appear to have an impact on accreditation status although not statistically significant (r = 0.441; p = 0.06). Larger institutions are more likely to be accredited than smaller programs.

RQ4 investigated the barriers to career advancement that second career academics sometimes face. The research on “second career academics” has been centered on the challenges the individuals face when moving from the corporate to academic environment (Clinebell & Clinebell, 2008; LaRocco & Bruns, 2006).

H1 postulates that institutions that are accredited will not have career advancement opportunities for faculty without terminal degrees. In this study, it was assumed that second career academics do not have terminal degrees. This presents a distinct limitation that will be discuss later it the paper. Of the total sample, 41% of the institutions did not offer promotion opportunities for faculty without terminal degrees. When comparing accreditation status to promotion opportunities for those without terminal degrees, there was no relationship between the two (r = 0.045; p = 0.855). H1 was not supported. Of the sample only 2 of the institutions (9%) offered tenure opportunities for faculty without terminal degrees. Finally, participants were asked if their institution allowed faculty on term contracts to move to tenure track if a terminal degree was obtained. When comparing accreditation status to the offering of moving to a tenure track position, there was some evidence that those that were accredited offered this option, although not statistically significant (r = 0.367; p = 0.123).

H2 examined the impact of the size of the institution on the promotion opportunities for faculty without terminal degrees. Smaller programs might be less likely to be accredited and therefore better able to hire faculty without terminal degrees. Offering career advancement opportunities could be easier to implement in smaller institutions. The correlation between size of the institutions as measured by the total number of students and promotion opportunities for faculty without terminal degrees was 0.140 (p = 0.535). No statistically significant relationships were found between the size of the institution and tenure opportunity or the ability to switch to tenure track. Therefore, H2 was not supported. This small sample did not offer any evidence that the size of the institution impacted the career advancement opportunities for faculty without terminal degrees.

Study Limitations

Although some interesting findings were obtained, the study had several limitations. The most obvious limitation was the sample size. With only 22 responses, in-depth analysis was limited. However, as an exploratory study, this research does offer some key insights to build a more robust research agenda on the subject of second career academics. Next, the size of the institution was estimated using the number of students and the number of FT and PT faculty in the programs. This does not necessarily offer a good measure of the size of the institution as a whole. A better measure might be size of the institutional endowment, total student population across campus(es), or total number of faculty at the institution. Another measure to add for additional analysis would be the type of institution (private versus public). In this study, it was assumed that the business and sport management programs were in the same department or school. This is not always the case and many anecdotal comments on the survey mentioned that the departments are completely separate. Finally, this research assumed that second career academics did not have terminal degrees. This may not be the case and therefore provides an additional avenue for future research to explore.

Future Research

This topic presents many areas for additional study. Firstly, a more comprehensive study with a larger sample size could be conducted to provide more in-depth analysis of the trends in higher education with respect to second career academics. This could extend beyond the sport management and business fields into additional applied fields. Future research could be centered on determining whether second career academics have terminal degrees or plan to obtain terminal degrees and their motivation for doing so (i.e., can switch to tenure track). As a second phase to this research, we hope to uncover the motivations for moving into academia and to better understand the challenges and rewards for making such a transition. Additionally, we will research potential tensions that might exist between the first career academics and the second career academics (practitioners) (Clinebell & Clinebell, 2008). Finally, future research could be centered on the benefits to students and the value these practitioners bring into the classroom.

Conclusion

In conclusion, in order for practical application purposes, institutions could influence and tailor curriculum to the practitioners’ level of expertise and/or vice versa.  Institutions can link second career academics with students, to promote professional networking and experiential learning.  Universities could collaborate with practitioners to create internship/project opportunities, networking or informational interviews that would add more hands-on experiences and opportunities.  Senior faculty could implement student feedback loops to evaluate the effectiveness of practitioner led courses thus refining teaching strategies and enriching student learning outcomes.  These approaches could uplift educational quality, opportunities and better prepare students for their careers.

References

George Minoso2025-07-08T10:57:33-05:00

The Association Between Pitch Accuracy and Batter Outcomes in Major League Baseball

George Minoso2025-07-08T10:57:33-05:00April 19th, 2025|General, Research, Sports Coaching, Sports Health & Fitness|

Authors: Benjamin E. Napoli,¹ Daniel A. Napoli,¹ Anthony M. Napoli MD MHL¹, Timmy R Lin PhD¹, Macall S. Robertson JD, Jason Machan PhD,¹ Janette Baird PhD¹

¹Department of Emergency Medicine, Alpert Medical School of Brown University

Editor’s Note: This article’s formatting was corrected on July 8, 2025. All tables, figures, and appendices are now present in the article.

Abstract

Purpose: Pitch speed is considered synonymous with pitching success. In years past, the accuracy of a pitch was paramount but in recent years this has been deprioritized as compared with pitch speed. Over the years, batters have adapted to higher pitching speeds so pitch strategy and placement may be more important.  Our hypothesis was that pitching accuracy associated with intended pitch location would be a significant factor in pitching success in Major League Baseball (MLB).  

Methods: To study this, we studied multiple facets of a pitch, including pitch accuracy, to assess the association between pitch accuracy and batter outcome in over 1000 at bats in over 20 randomly selected games during the 2022 MLB season.  Our primary goal was to determine if there was an association between pitch accuracy, after controlling for confounders, and batting average against (BAA) and slugging percentage (SLG). 

Results: We found that at bats in which the last pitch hit the intended spot reduced batter success by greater than 50%.  Higher pitching speed was not associated with success partly because it was associated with lower accuracy. If a pitch was accurate, after adjusting for other variables, it was 3.28 times more likely to be successful. 

Conclusions: This study demonstrates that pitching accuracy is a highly important skill in determining the success of pitchers, beyond that of other traditional pitching features.  Future studies to automate this work and integrate machine learning and predictive modeling could be used to optimize pitcher success or identify the most accurate pitchers.

Applications in Sport: This study demonstrates that pitching accuracy is a critical determinant of success in Major League Baseball (MLB), with accurate pitches reducing batter success rates, including batting average and slugging percentage, by over 50%. While pitch speed has traditionally been prioritized, this research highlights that accuracy has a far greater impact on outcomes, as accurate pitches are more than three times as likely to succeed. These findings challenge the current emphasis on velocity, suggesting that focusing on accuracy could optimize performance and reduce injury risks for pitchers.

Introduction:

In recent years, hitting and pitching analytics have been used extensively to study talent and study what makes a successful pitcher or hitter. For pitchers, these features can include pitch movement, pitcher mechanics,(Manzi et al., 2022) pitch selection, pitch types, and spin rate.(Whiteside et al., 2016)  While pitch speed has continued to increase(Cooper, 2020) and batters have adjusted, these other features become increasingly important.  An ideal pitcher would have peak velocity and accuracy but having both is not easy to obtain.(Venkadesan & Mahadevan, 2017) 

Pitch accuracy in common baseball parlance is often referred to as whether the pitcher “hits his spot.”  Hitting or missing the spot refers to the intended pitch location indicated by the catcher.  Before each pitch, catchers set their glove for the intended pitch location.  While there are some observational studies of pitch accuracy over different levels of baseball,(Kawamura et al., 2017) in the modern era there is no published study to indicate just how much of an effect accurate pitching has on batter performance.

Our hypothesis was that a significant difference in hitter outcomes will exist between pitchers who “hit his spot” (are within 6 inches of the intended pitch location) and those who do not.  A secondary outcome of our study was to examine whether pitch accuracy had a greater association with batter outcome than pitch selection, location, or speed.

Methods:

This was a retrospective study of 17 randomly selected games during the 2022 Major League Baseball (MLB) Season.  Investigators used a pre-specified Google Form (Mountain View, CA) with all key pitcher, pitch, and batter outcomes identified.  The investigators documented the outcome of the last pitch of each at bat.  This is consistent with current MLB methods of determining measures like batting average against (BAA) as it is the only pitch in the at bat in which the batter is forced to make a decision that will result in an at bat outcome. BAA and Slugging Percentage (SLG) were calculated using standardly accepted methodology (BAA = H/AB; SLG = (1B + 2Bx2 + 3Bx3 + HRx4)/AB).

An a priori sample size estimate based upon an estimated effect size of a 20-point decrease in BAA indicated a need to study approximately 1700 at bats.  An interim analysis of effect was planned at 500 and 1000 at bats. One game from each MLB team’s 2022 season was randomly selected until the study was complete.  Data abstractors were blind to the study results.  Data collection included pitcher handedness, pitch speed, intended pitch location, pitch type, and outcome.  As is customary for measuring BAA, only the last pitch of an at bat was used in this study.  

Our primary outcome was to demonstrate that a significant difference will exist between accurate pitches, “hit his spot” (estimated to be within 6 inches of the intended pitch location) and inaccurate pitches (“missed his spot”).  The secondary outcomes were to study if pitch speed, pitch selection, or pitch location had an impact on pitcher success.  Statistical analysis was done using SAS 9.4 (Cary, N.C.). We report on median with interquartile range (IQR) and proportions with 95% confidence intervals (95% CI).  Direct comparisons of unadjusted data were completed using a Chi-square test for proportions while a logistic regression was used to adjust for all variables associated with pitching accuracy.  A definitions table can be found in the Appendix due to the number of abbreviated outcomes.   

Results:

Primary Outcome:

The results of the interim analysis at 1000 at bats demonstrated a significant effect of pitch accuracy on BAA and SLG.   Of these 1000 at bats, represented by 17 randomly selected MLB games, the pitching accuracy was 45.3% (95% CI: 42.2% – 48.4%) and median pitch speed was 91 (IQR: 85 – 94) miles per hour.  The top three pitch types thrown were the fastball (31.8%), sinker (21.4%), and slider (19.5%) [Table 1].  Nearly 66% of pitches targeted low in the zone [Table 2]. Overall outcome (BAA and SLG) as it relates to pitch accuracy can be found in Table 3. 

Unadjusted analysis for the primary outcome of BAA and SLG demonstrated pitch accuracy was a significant predictor of pitcher success.  At bats ending in a pitch that hit the intended spot reduced batter success by greater than 50%; accurate pitches resulted in a BAA and SLG of 0.166 and 0.343 vs. 0.262 and 0.558 for inaccurate pitches, p<0.01.   If a pitcher hits his spot, after adjusting for other variables, he was 3.28 times more likely to be successful. No other variable was significantly associated with the pitcher success in the adjusted model (Table 4).

Table 3: Pitch Accuracy and Batter Outcome

*See Appendix for index of abbreviations

Secondary Outcome

There was no relationship between pitch speed and accuracy though pitchers who threw in the 80’s miles per hour (mph) appeared more likely to be accurate than those in the 90’s mph [Figure 1]. There was no statistically significant difference in pitch accuracy and BAA or SLG between left-handed pitchers and right BAA and SLG as they relate to pitch type and pitch location can be found in Tables 5 and 6, respectively.

Unadjusted analysis demonstrated that sliders had the lowest BAA (0.190), while changeups had the highest (0.293) [Table 5]. Higher pitching speed was not significantly associated with preventing a hit (χ2 (5) = 9.9, p=.08) but was significantly associated with lower accuracy (χ2 (5) = 13.2, p=.02).  No other pitcher variable other than accuracy of intended location was significantly associated with pitcher success. 

Assuming an accurate pitch, then high-in (2.02) and high-middle (2.96) pitches were significantly more likely to be successful at hitting the spot than baseline comparison (middle-middle). All off-speed pitches except for curveballs were significantly more likely to be successful than their baseline comparator (the fastball) with odds ratios ranging from 1.6 (sinker) to 2.8 (slider) [Table 8].

Discussion:  

Success in pitching is a combination of many different features, including game situation, the batter, the pitcher, and the pitch.  However, pitch accuracy has always been considered one of the most important features of a pitcher until recently when much of the attention has turned to pitch speed.  This study sought to investigate the impact of pitching accuracy, specifically the ability to hit the location of an intended pitch, on the success of pitchers in MLB games.  While many features of a pitch can affect the outcome, few of them (if not none in our study) have the impact that accuracy has on batter outcome.  In fact, our study indicates an unadjusted reduction of nearly 50% in batting average and slugging percentage when pitchers hit their intended location with an adjusted odds ratio of 3.28.          

Sports analytics is a $2.7B industry that is expected to have a compound annual growth rate of more than 20% over the next ten years.(Research, 2022)  The sport of baseball has been one of the earliest adopters and the clearest example of successful use of baseball analytics. Though baseball is considered a team sport, it is in fact a series of sequential events and therefore lends itself to more precise statistical analyzation.(Bechtold, 2023)  This has led to the rise of slow-motion video, new metrics for pitch movement and success, and the development of whole analytics departments in all MLB teams.  This information has given rise to pitch location analysis, enhanced studying of the influence of framing of pitches by catchers, profiles of pitch spin and movement, comparative pitching analytics, and even the possibility of machine learning and predictive analytics for pitching. 

The success of a pitcher is affected by so many features of the pitcher and the batter.  In the pitcher alone, features like pitch speed, spin rate, and selection are important.(Manzi et al., 2022)  Even pitcher mechanics has an effect on pitch accuracy.(Venkadesan & Mahadevan, 2017)  Pitching mechanics gets refined over time and studies indicate pitchers have greater accuracy at higher performance levels.(Kawamura et al., 2017)  However, it is hard to quantify the importance of each of these features.   The common belief is that pitch speed dominates all other features of pitcher success.  However, one study that incorporated pitch speed was only able to show that the combination of pitch speed, refined special pitch release location, and variation in pitch selection accounted for only 22% of the variance in pitcher performance.(Whiteside et al., 2016)  Our study demonstrated a significant portion of variance in accuracy is explained by pitch speed (21.5%); the higher the pitch speed the less accurate the pitch.  Overall, the adjusted model demonstrated pitch speed did not affect pitch success as opposed to an accurate pitch being greater than three times more likely to be successful. 

This study relied upon retrospective review of a random sample of regular season games to determine the effect of pitch accuracy while simultaneously accounting for some of the most important, traditional features of pitching success – pitch speed, location, and selection.  Unadjusted analysis reinforced some of the common opinions about current pitching – sliders and splitters if placed correctly are some of the most difficult pitches to hit (Table 5) and pitches thrown over the middle of the plate are more likely to lead to batter success (Table 6).  However, it also demonstrated some unexpected outcomes – that fastballs were some of the least accurate pitches and that pitch speed had limited, if any, effect on batter outcome.  However, after adjustment some interesting associations were revealed.  Pitching high in the zone had an odds ratio of success consistently two times greater than the middle of the zone, and that pitching low in the zone was not associated with improved pitcher outcome.  While interesting, this is not entirely surprising as it follows the trend of pitchers throwing higher much more commonly than they once did as batters have adopted an upward sloping swing to maximize launch angle.(Gutwein, 2021; Lu Chen, 2022)   Additionally, almost all pitches were more successful than a fastball at getting a batter out; this too reflects the trend toward increasing use of off-speed pitches in the MLB.(Norris, 2023)  After adjustment, all these other features of commonly accepted pitching importance – speed, pitch selection, and pitch location had limited effect on the outcome of the pitch in comparison with pitch accuracy.  An accurate pitch was more than three times (OR 3.28, 95% CI 2.45-4.4) more likely to result in a favorable outcome and resulted in 50% reduction in batting average and slugging percentage. 

Any study that attempts to study one feature (pitch accuracy) amongst a number of complex other potential confounding factors is bound to have some limitations.  Complexity itself is a limitation. Though many of the variables (pitcher, game, time in the season, etc.) were randomized, the complexity offered by any one pitcher limits interpretation.  For example, certain pitchers may not offer certain pitches or the quality of their pitches varies as compared to another when pitching in one location.  Any study incorporating such features would be much more complex and would also limit real world real-time application.  The retrospective nature of this study would normally be a limitation, but it allowed specificity of pitch location due to the ability to review the video and record features of each pitch.  However, certain features of each pitch were not available to the investigators – like spin rate, lateral and vertical movement.  These features may have a role in determining pitcher accuracy as well as batter outcome independent of pitcher accuracy.  Lastly, any study involving individual review with a general goal of subjectively identifying whether a pitch was accurate runs the risk of consistent over-estimation or under-estimation and/or variation.  Only computerized video review algorithms would be able to reduce imprecision further.  Computer algorithms and machine learning may eventually be able to be incorporated to refine this work. 

Conclusion:

In conclusion, this study suggests that pitching accuracy, particularly hitting the intended location of a pitch, is a crucial factor in determining pitcher success in MLB.  The findings provide valuable insights into the relative importance of various pitching variables, emphasizing the significance of strategic accuracy over sheer pitching speed.  This study contributes to the ongoing discourse about the multifaceted nature of successful pitching in professional baseball and emphasizes the importance of one of the most important features of a successful pitcher – accuracy. 

Applications in Sport:

This study highlights the critical importance of pitching accuracy in Major League Baseball (MLB), demonstrating that accurate pitches significantly reduce batter success rates, with a 50% decrease in batting average and slugging percentage when pitchers “hit their spot.” While pitch speed has traditionally been emphasized, this research shows that higher speeds often reduce accuracy and have limited impact on outcomes compared to precise pitch placement. The findings suggest that prioritizing accuracy over speed could improve pitcher performance and reduce injury risks associated with the current focus on velocity. These insights could inform training strategies, analytics, and even machine learning applications to optimize pitching success, offering a shift in how pitching effectiveness is evaluated and developed in modern baseball.