Authors: Mark Mitchell, Yoav Wachsman, and Monica Fine
Mark Mitchell, DBA
Professor of Marketing
Associate Dean, Wall College of Business
NCAA Faculty Athletics Representative (FAR)
Coastal Carolina University
P. O. Box 261954
Conway, SC 29528
Mark Mitchell, DBA is Professor of Marketing at Coastal Carolina University in Conway, SC.
Yoav Wachsman, PhD is Professor of Economics at Coastal Carolina University in Conway, SC.
Monica Fine, PhD is Professor of Marketing at Coastal Carolina University in Conway, SC.
Evaluating the Impact of Concentrated Match Scheduling in College Volleyball during the COVID-19 Pandemic
Athletic conferences worked to lower the cost of delivering athletic programs while operating during the COVID-19 global pandemic. One strategy was the use of concentrated schedules for competitions. For example, the Sun Belt Conference focused on divisional play and a concentrated schedule for women’s volleyball for the 2020 season. Schools played three matches in a two-day period against the same team. This practice lowered travel costs and isolated player contact in the event of needed contact tracing as part of player safety protocols. This study evaluates the impact of this scheduling format on player performance and the overall quality of competition. Gathering data from the box scores from all Sun Belt Conference volleyball matches, the impact of player fatigue (daily and cumulative) is not present in the team statistics. Player performance and the overall quality of team play did not decline, even when playing three matches in a two-day period. Conference personnel and university athletic administrators may take comfort that their efforts to lower costs and ensure player safety during a global pandemic did not affect player performance and the overall quality of competition. It remains to be seen if this new scheduling approach will be used in the post-COVID period in women’s volleyball and possibly expanded to other sports. The need to control/lower operating costs will not disappear. This constraint may lead to the adoption of new conference scheduling formats in the future.
Key Words: player fatigue in college volleyball, team sport scheduling models
In March 2020, collegiate athletics in the United States was beginning a transformation that few could have anticipated as a result of the then-expanding COVID-19 pandemic. During that month, the National Association of Collegiate Athletics (NCAA) abruptly cancelled the Men’s Division I Basketball Tournament. This cancellation is noteworthy as the tournament contributes substantially to the NCAA’s annual revenues of $1.1 billion, with more than half of this amount directly distributed to member schools and conferences. With the cancellation of that one tournament, the NCAA would be forced to reduce its distribution of funds by approximately $375 million to its members. Many institutions had already factored these revenues into their spending plans for the year (1).
In the months that followed, athletic departments experienced the downsizing, cancellation or delay of many fall and winter sports. Clemson University, for example, expected a reduction in revenue of over $40 million from its athletic department (15). This adjusted amount included reduced ticket sales, reduced fan attendance, lowered annual giving, and other financial impacts. These actions have put additional financial pressures on institutions. With reductions in revenues, schools had to look at ways to lower operating expenses. Many schools mandated employee furlough days, eliminated positions, delayed new hires, reduced non-conference schedules, focused on regional competition, and reduced overall travel.
Some institutions elected to eliminate entire teams, mostly in rowing, swimming, tennis, track and field, and volleyball. Over 2,000 student-athletes saw their teams eliminated at the varsity level. Hundreds of coaches and support staff had their jobs eliminated (11). NCAA Division I schools at the Football Bowl Subdivision (FBS) level must sponsor at least 16 sports, a number the NCAA has steadfastly maintained during the COVID-10 pandemic (20). As a result, athletic departments have been challenged to institute cost-cutting measures to support their broad-based athletic programs.
During the pandemic, athletic conferences worked to lower the cost of delivering athletics by adjusting conference schedules, focusing on regional competition, bringing fewer teams to championship events, using centralized locations for championship events, lowering the number of officials per contest, and implementing other creative strategies. For example, the Sun Belt Conference focused on exclusive regional competition (i.e., only play East or West division opponents) for conference play for women’s volleyball for the 2020 season. Programs played three matches (which consists of best-of-5-games) in a two-day period against the same team. This scheduling strategy lowered travel costs and isolated player contact in the event of needed contact tracing as part of COVID-19 safety protocols. This 2020 scheduling pattern was dramatically different than prior years when teams typically only played one match per day and did not play the same team for consecutive matches.
The purpose of this manuscript is to examine the results of the Sun Belt Conference using a concentrated schedule format for fall 2020 season for women’s volleyball. The focus of this study is player performance and overall team quality of play. Specifically, two teams were scheduled to play two best-of-five-game matches on one day and then play a single match the very next day. Intuitively, this change in format could potentially lead to a change in player or team performance as players experience the fatigue of a two-match day followed up with the cumulative fatigue of a three-matches in two-days approach. A higher level of fatigue during the second and third matches may be concerning to the NCAA because additional fatigue could lead to more injuries and reduce the quality of play. It is hypothesized that fatigued volleyball players are more likely to commit errors and less likely to execute athletic plays including kills, blocks, and saves. If players are more fatigued during the second and third matches, the game may be less entertaining for fans and participants.
It must be noted that other conferences and other NCAA sports also adjusted their schedules during the COVID-19 pandemic. Out of the thirty-two Division I volleyball conferences, only two maintained a typical schedule during the 2020-21 school year. Six conferences elected to play in the fall with teams playing back-to-back games (e.g., similar to the Sun Belt Conference). Seventeen conferences opted to reschedule their games to Spring 2021 with teams playing each other in consecutive games. The remaining seven conferences chose to play in the spring with no back-to-back games. While each conference and each sport responded differently to the pandemic, most NCAA conferences adjusted to the pandemic by making teams play fewer contests and with less time between contests, in addition to having teams play each other multiple times in a row.
WOMEN’S VOLLEYBALL PARTICIPATION
According to NCAA data, women’s volleyball was a sponsored sport at 97% of NCAA institutions in 2019 (DI = 95%, DII = 99%, and DIII = 97%). That statistic makes it the third most-frequently sponsored women’s NCAA sport behind basketball and cross country (14). According to data compiled by the National Federation of State High School Associations over the last decade, the number of high school volleyball players has increased by more than 40,000 while the number of basketball players has declined by over 23,000 (6). NCAA data indicates approximately 3.9% of high school volleyball players advance to playing college volleyball (13).
Most state high school athletic organizing bodies place limits on the number of allowable matches per team per day. For example, the Texas University Intercollegiate Scholastic League (UIL) constitution contains the following player restrictions for volleyball (21):
- Number of Matches Per Day in Tournaments. No team or student shall compete in more than three matches per day in tournament play. Exception: Contestants or teams may play in four matches per calendar day in a one-day tournament scheduled on a Saturday, and contestants or teams may play in four matches per day during a two-day tournament.
As high school athletes, many volleyball players are active on a club team as well. During this phase of their development, volleyball players often play 2-3 matches per day, and 4-6 matches over the typical two-day tournament. When these high school student-athletes transition to college volleyball, they typically are playing one match per day.
Volleyball Player Fatigue
One of the most important aspects in sporting competitions is to maintain a high level of mental and physical performance, hence not suffering from what is called fatigue. Fatigue has been defined as the decline of muscles to generate force (power and speed) after prolonged sustained output (17). Gawron, French and Funke (8) discuss two types of fatigue: central (mental) fatigue and peripheral (physical) fatigue. Peripheral fatigue is the reduction in ability to perform as compared to previous physical effort prior. Alternatively, central fatigue effects the central nervous system which can cause decreased mental alertness and attention (8). Fatigue could impact tasks including simple and choice reaction time, movement time, visual search and also motor skills (12).
Studies have investigated the relationship of fatigue on aspects like training (4) and recovery (7). Volleyball like many sports is made up of a mix of high and low intensity periods. During one training or practice, hundreds of jumps and hits occur. Volleyball has balanced fatigue with substitutions, nutrition, strategic warmups/cooldowns, and other approaches.
Competitive volleyball is a physically demanding sport as players combine short sprints, jumps, dives, and crouching over digs of the opponent’s shots. The physical demands of competitive volleyball have been described as follows:
“Volleyball is a high-intensity intermittent sport with frequent explosive movements, where trained players, for example, have been shown to perform approximately 115 jumps and 85 hits in a game (10). Volleyball players have been demonstrated to cover approximately 1,200 and 1,750 meters in total in a 3-set and 4-set game, respectively (12), which is markedly less than football (i.e., soccer), team handball, hockey, basketball, and ultimate frisbee where total distances covered ranges from 5,000 to 12,000 meters Moreover, the exercise periods in volleyball are relatively short, lasting approximately 9 seconds on average interspersed by approximately 12 second recovery intervals and display work-to-rest ratios of 1:1.6 and 2.2, (18; 22) which differs markedly from other team sports (16).”
Given the demands on the bodies of players, neuromuscular fatigue (e.g. your voluntary muscle control communicating with your brain) is likely to occur during a volleyball match due to the high number of high-intensity and explosive movements demanded of players (16).
Sun Belt Conference Women’s Volleyball
The Sun Belt Conference has 12 schools that compete in women’s volleyball. For the fall 2019 competition season, volleyball teams played each team in the conference two times: one home match and one away match. The schedule was built using a two-match road trip approach. For example, Coastal Carolina would play at Georgia State (Atlanta, GA) on Friday and then travelled to Georgia Southern (Statesboro, GA) for a Saturday or Sunday match (3).
In order to reduce their operating costs and to limit player exposure for COVID-19, the league’s coaches voted to implement a revised conference scheduling format for the Fall 2020 season. The league has two geographic divisions:
- East Division: Appalachian State University, Coastal Carolina University, Georgia State University, Georgia Southern University, University of South Alabama, and Troy University.
- West Division: University of Arkansas Little Rock, Arkansas State University, University of Louisiana Lafayette, University of Louisiana Monroe, University of Texas Arlington, and Texas State University.
In each division, each institution has a designed travel partner for ease of scheduling in select sports. For example, Georgia State and Georgia Southern are paired together as are Arkansas State and Arkansas Little Rock. For the Sun Belt Conference schedule, competition would be exclusively between divisional opponents. Teams would play three best-of-five-game matches against each other over a two-day period. The time table was typically an 11:00 AM and a 6:00 PM match on day one, followed by a 1:00 PM match on day two. Each team played one three-match series with each non-travel partner school in their division (a total of 12 best-of-five-game-matches). Then, each school played two 2-match series with their travel partner (one home and one away) to arrive at a schedule of 16 conference matches per team.
Historically in college volleyball, teams play one best-of-five-game match per day. Due to the 2020 schedule alteration, players were asked to compete in two matches per day four times that season. Intuitively, researchers hypothesized that players may be fatigued during the second match played in one day (an uncommon event in college volleyball). Further, researchers believed players may be fatigued after playing two matches on one day and single match the next day. Table 1 includes a description of six statistics that are reported for each game and match on the official box score along with a description of the potential player fatigue that may be experienced due to the compressed schedule.
Table 1 – Defining Performance Data Collected
|Item||Description||Anticipated Impact of Fatigue|
|Kills (K)||An attack is a player sending the ball over the net in an attempt to score. A kill is recorded whenever an attack is unreturnable. A kill always results in point.||Teams with more fatigue will have a LOWER # of Kills in a match. Teams with less fatigue will have a HIGHER # of Kills in a match.|
|Errors (E)||An error is recorded when an attack attempt gives the opposition a point. Examples of an attack error include an opponent blocking the attack for a point or a player hitting the ball out of bounds. An attack error always results in a point for the other team.||Teams with more fatigue will have a HIGHER # of Errors in a match. Teams with less fatigue will have a LOWER # of Errors in a match.|
|Hitting Percentage (PCT)||Hitting percentage is derived from this formula: (KILLS – ERRORS) / TOTAL ATTEMPTS = HITTING PERCENTAGE.||Teams with more fatigue will have a LOWER Hitting Percentage in a match. Teams with less fatigue will have a HIGHER Hitting Percentage in a match.|
|Service Error (SE)||A service error is charged when the serve is unsuccessful. Examples include: when the serve does not clear the net or lands out of bounds, or when the server commits an error, such as foot faulting.||Teams with more fatigue will have a HIGHER # of Service Errors in a match. Teams with less fatigue will have a LOWER # of Service Errors in a match.|
|Block Assists (BA)||If more than one player goes up to block, even if only one of the players touches the ball, and the block results in a point then each player receives a block assist.||Teams with more fatigue will have a LOWER # of Block Assists in a match. Teams with less fatigue will have a HIGHER # of Block Assists in a match.|
|DIGS (dig)||A statistical dig is given anytime an attack is successfully defended. The dig can be passed to another player or sent back over the net. A dig that is not kept in play is not awarded in the stats.||Teams with more fatigue will have a LOWER # of Digs in a match. Teams with less fatigue will have a HIGHER # of Digs in a match.|
If fatigue affected player performance while playing two matches per day, it is hypothesized that the following statistical outcomes should be present in the box score:
- H1: Kills (K) should be LOWER in MATCH 2 compared to MATCH 1.
- H2: Errors (E) should be HIGHER in MATCH 2 compared to MATCH 1.
- H3: Hitting Percentage (%) should be LOWER in MATCH 2 compared to MATCH 1.
- H4: Service Errors (SE) should be HIGHER in MATCH 2 compared to MATCH 1.
- H5: Block Assists (BA) should be LOWER in MATCH 2 compared to MATCH 1.
- H6: Number of Digs (dig) should be LOWER in MATCH 2 compared to MATCH 1.
If there are cumulative or residual effects of playing three matches in a two-day period, intuitively the same anticipated fatigue effects should be evident. The following indicates the researcher’s hypotheses for player and team performance in match three (the last match played) compared with match one (the first match played the prior day).
- H7: Kills (K) should be LOWER in MATCH 3 compared to MATCH 1.
- H8: Errors (E) should be HIGHER in MATCH 3 compared to MATCH 1.
- H9: Hitting Percentage (%) should be LOWER in MATCH 3 compared to MATCH 1.
- H10: Service Errors (SE) should be HIGHER in MATCH 3 compared to MATCH 1.
- H11: Block Assists (BA) should be LOWER in MATCH 3 compared to MATCH 1.
- H12: Number of Digs (dig) should be LOWER in MATCH 3 compared to MATCH 1.
For each volleyball match played, a box score results sheet is posted to the Sun Belt Conference website (19). Most schools also post this box score to their athletic website. The box scores for all conference matches were collected. Data tied to player fatigue were identified and recorded. Volleyball matches are best-of-five-game competitions. So, data must be computed on a ‘per game’ basis to allow comparisons of 3-, 4- and 5-game matches. This data was entered into Microsoft Excel to allow for statistical evaluation.
It must be noted that volleyball games 1-4 are played to a 25-point total (win by 2 points) whereas the fifth game is only played to a 15-point total (win by 2 points). This dynamic of the possible shorter game five (should a game five be required) does mean average (or mean) scores per game are not always linear. A volleyball match has a beginning and an end. There are a certain number of kills, blocks, digs, etc. to determine the outcome of the match. So, while the conversion to average game figures is not perfectly linear, it does show the presence of larger or smaller numbers of each variable between matches evaluated. That variance, ultimately, is the focus of this analysis.
The need to evaluate the influence of player fatigue by comparing team performance in two matches in one day (H1-H6) presented a paired-comparison in the data. The Student’s T-Test is used to assess the statistical significance of any difference between two independent sample means (9). In this case, the Student’s T-Test compared the mean values of each variable for match and match two. The overall results for all 12 teams are presented in Table 2.
Table 2 – Evaluating Daily Fatigue (Impact of 2 Matches in 1 Day)
|Average Match 1||Average Match 2||Student’s T-Test Results|
|H1: Kills (K) should be LOWER in MATCH 2 compared to MATCH 1.||12.08||12.02||p = 0.753 No Statistical Difference|
|H2: Errors (E) should be HIGHER in MATCH 2 compared to MATCH 1.||5.63||5.68||p = 0.988 No Statistical Difference|
|H3: Hitting Percentage (%) should be LOWER in MATCH 2 compared to MATCH 1.||0.16||0.17||p = 0.387 No Statistical Difference|
|H4: Service Errors (SE) should be HIGHER in MATCH 2 compared to MATCH 1.||1.90||1.87||p = 0.856 No Statistical Difference|
|H5: Block Assists (BA) should be LOWER in MATCH 2 compared to MATCH 1.||3.03||3.00||p = 0.778 No Statistical Difference|
|H6: Number of Digs (dig) should be LOWER in MATCH 2 compared to MATCH 1.||15.64||16.81||p = 0.092 No Statistical Difference|
As shown in Table 2, all related research hypotheses related to daily fatigue (H1-H6) are rejected. While players may have experienced normal in-match fatigue, it did not affect their on-court performance. There was no statistical difference between match one and match two for the following variables: # of kills per game (H1); # of errors per game (H2); hitting percentage (H3); # of service errors per game (H4); # of block assists per game (H5) and # of digs per game (H6). There was no drop-off in the quality of play between match one and match two played on the same day.
Similarly, the need to evaluate the influence of cumulative player fatigue by comparing team performance in the first match played versus the final match played (H7-H12) presented a paired-comparison on the data. As noted above, the Student’s T-Test is used to assess the statistical significance of any difference between two independent sample means (9). In this case, the Student’s T-Test compared the mean values of each variable for match one and match three. The overall results for all 12 teams are presented in Table 3.
Table 3 – Evaluating Cumulative Fatigue (Impact of 3 Matches in 2 Days)
|Average Match 1||Average Match 3||Student’s T-Test Results|
|H7: Kills (K) should be LOWER in MATCH 3 compared to MATCH 1.||12.08||12.21||p = 0.843 No Statistical Difference|
|H8: Errors (E) should be HIGHER in MATCH 3 compared to MATCH 1.||5.63||5.53||p = 0.483 No Statistical Difference|
|H9: Hitting Percentage (%) should be LOWER in MATCH 3 compared to MATCH 1.||0.16||0.17||p = 0.873 No Statistical Difference|
|H10: Service Errors (SE) should be HIGHER in MATCH 3 compared to MATCH 1.||1.90||1.71||p = 0.653 No Statistical Difference|
|H11: Block Assists (BA) should be LOWER in MATCH 3 compared to MATCH 1.||3.03||3.03||p = 0.434 No Statistical Difference|
|H12: Number of Digs (dig) should be LOWER in MATCH 3 compared to MATCH 1.||15.64||16.84||***p = 0.033 Statistically Different|
In the analysis of cumulative player fatigue for playing three matches in two days (Table 3), five of six related research hypotheses (H7-H11) are rejected while one hypothesis (H12) related to digs per game was supported. Again, while players may have experienced the normal physical and emotional stress of athletic competition, that fatigue largely did not affect their on-court performance. There was no statistical difference between match one and match three for the following variables: # of kills per game (H7); # of errors per game (H8); hitting percentage (H9); # of service errors per game (H10); # of block assists per game (H11). Further, the differences identified in the number of digs per game (H12) were positive; that is, teams tended to complete more digs in match three (last match of series) than they did in match one (first match of the series).
Given that college volleyball players would play two matches in one day (potentially 10 games that day), one wondered if fatigue would affect player performance, particularly in the second match played in one day. Increased fatigue represented by a significant decrease in team statistics, however, did not occur in the second match of the day. There were not statistically significant differences in player performance between match one and match two. Further, given that college volleyball players would play three matches in two days, one wondered if cumulative fatigue would affect player performance, particularly in match three (the final match of the three-match series). Similar to playing two matches in one day, increased fatigue represented by a significant drop in a team’s statistical performance did not occur. With one exception, there were not statistically significant differences in player performance between match one and match three (or, the first match and last match of a match-match series).
Entering the season, coaches and athletic administrators were cognizant that this new schedule format was created to strike a balance in team performance, player safety, and the cost of delivering the volleyball season. Coaches adjusted their practices to avoid creating player fatigue. Teams reduced gameday activities to conserve energy. Coaches adjusted their game plans for match two as a result of what they learned during match one played earlier that day. In the end, this new format (and the associated player fatigue) did not impact on-court player performance. The effects of player fatigue (daily and cumulative) did not seem to manifest themselves as changes in on-court player performance as recorded on the box scores for each match.
One Team Experience – Coastal Carolina University
Volleyball teams in the Sunbelt Conference are not required to collect or publish data on fatigue, such as the number of injuries, training routine, sleeping patterns, or diet. Nonetheless, the authors obtained anecdotal information from the Head Coach of the Indoor Volleyball team at Coastal Carolina University. According to Coach Jozsef Forman, the players experienced more injuries during the 2020-21 season. The team’s All-American player severely injured her knee during the conference finals. Her injury was difficult to explain since she was fit and landed well when she experienced the injury. After the season, a couple more players got injured without an apparent reason. While it is not possible to conclude what caused these injuries, fatigue may have been a contributing factor (5).
Coach Forman reduced the amount of practice during the season. He did so partly due to combat fatigue but also because, at one point, the team was down to seven active players due to COVID protocol. The coaching staff does not manage the players’ sleep or diet. However, the head coach noted that the players slept more during the season. It may have been due to fatigue or because they were streaming their classes and were spending more time in their rooms. The season was mentally tough for the team. The players had to remain disciplined and follow COVID protocol. They attempted to stay isolated from other individuals to minimize exposure to the virus (5).
The Sun Belt Conference volleyball schedule was modified to focus exclusively on regional competition in a shortened season for conference play for the 2020 season. Teams played three matches in a two-day period against the same opponent. This altered schedule did lower travel costs and did isolate player contact in the event of needed contact tracing as part of COVID-19 safety protocols. Entering the season, there was uncertainty if teams would even compete, yet alone complete their season. In the end, the season was successfully delivered and a conference champion was crowned. The new compressed schedule format helped to reduce operating costs, protect player health and safety, and do so with minimal impact on the overall quality of team competition and individual player performance.
APPLICATIONS IN SPORT
As society continues to combat the COVID-19 virus and its impact, it is reasonable to consider that some new behaviors learned during the COVID pandemic will remain in use as the effects of the virus are reduced or eradicated. This study suggests that a more concentrated schedule for women’s college volleyball (i.e., playing two matches in one day and playing a total of three matches in two days) did not affect player performance or the overall quality of competition as noted by box score statistics. Coaches and athletic administrators may consider the continued use of this compressed scheduling format in volleyball in future years. Benefits to the schools include lowering travel costs by reducing number of hotel night stays, reducing the number of classes missed due to competition, and reducing meal costs on the road.
Many athletes are accustomed to playing multiple matches/games per day in high school and club sports. In college, however, these athletes are exposed to the one game/match per day model similar to those playing soccer, lacrosse, tennis, and other sports. Coaches and athletic administrators may consider adopting this compressed schedule format in these sports as well. The same cost savings referenced above would be realized. Replicating this study in other sports would provide the data to aid their decision-making. There was no attempt to evaluate the incidents of player injury with this altered format. That data must be another input for coaches and athletic administrators as they plan for future competitions.
Before COVID-19, teams did not play against each other in back-to-back games. Furthermore, teams would typically play other teams in conference once on their home court and once on the other team’s court each regular season. A losing team would have time to train and make necessary adjustments before facing the same rival again. During the 2020 season, when teams played each other twice on the same day, the losing team had little time to make any adjustments. This difference in preparation may mean that the team that loses the first match is more likely to lose the second or third match under the current, adjusted schedule. It may also mean that the winning team could play with more confidence, commit fewer errors, and use substitutes more frequently. Therefore, the winning teams may exhibit less fatigue during the second match while the losing team exhibits more fatigue.
As noted in the beginning of this manuscript, this is a new era for college sports. The pressure for cost reductions in 2020-2021 will not likely disappear overnight. As conferences and member schools look for creative ways to reduce costs and deliver athletic programs, new schedule formats (such as the one outlined here) may provide one avenue to save money while not significantly impacting the opportunity for success for the student-athletes.
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