Volleyball in the United States continues to grow in popularity with young females. In 2015, high school volleyball surpassed basketball as having the greatest participation numbers among female team sports and Amateur Athletic Union (AAU) volleyball has more participants than any other youth girls' team sport (21). According to the American Volleyball Coaches Association, 96% of National Collegiate Athletic Association (NCAA) schools sponsor a women's volleyball team, translating into approximately 17,026 collegiate players (28). With such meaningful participation numbers and the NCAA's emphasis on student-athlete well-being, the need for accurate quantification of in-game workload demands is undeniable in helping to maintain the physical health of volleyball participants.
Volleyball is a power-based sport involving short, high-intensity rallies with brief periods of rest (24,29). Several studies have attempted to quantify in-game demands (32,37,40) and positional demands (10,32,34). These studies typically use video time-motion analysis and are rare because the changes to rally scoring and substitution rules, including the addition of the libero (16,36). The increase in availability of wearable microsensor technology has fundamentally changed the way sport demands are quantified, particularly in team sports over the past 6 years (5,9). Several studies using this technology exist in sports such as soccer (2,25), rugby (1,35), and Australian rules football (15,39). Very few studies exist using this technology to quantify the demands of indoor, court-based sports (6,19). The expensive nature of these sensors and the lack of global positioning system information available indoors may contribute to the limited number of research studies. However, accelerometer data alone have been shown to accurately quantify external load demands of team sports (6). To date, no studies have attempted to use this technology to quantify in-game demands of collegiate female volleyball players.
Workload has been defined as “the cumulative amount of stress placed on an individual from multiple training sessions or games over a period of time” (12). This load can be viewed as external work completed and internal physiological and psychological stress (17). It is the balance between these 2 measures of workload that determine the “cost” or state of fatigue experienced by the athlete. Measures of external load can include distance, weight lifted, and player load (PL) (33). Session rating of perceived exertion (S-RPE, defined as RPE × duration of activity and measured in arbitrary units [AUs]) was developed by Foster to measure internal training load (11). This method has shown to be effective across multiple sports including volleyball (30). Other methods of internal load measurement include psychological inventories, recovery-stress questionnaires, sleep quality, and heart rate monitoring (33).
Although high absolute training load has been identified as a risk factor for injury in volleyball (38), recent studies have shifted focus to rapid increases in load, specifically large weekly fluctuations (13,26). This paradigm is reflected in the small amount of time in which multiple volleyball practices and competitions occur. The increase in density of practices and competitions known as “calendar congestion” has been investigated in multiple studies across several sports (4,22,26). Although some conflicting results exist, it is generally accepted that the greater number of competitions in a smaller time frame leads to greater risk of injury (33). This risk is believed to be the result of “spikes” in workload (13). In the collegiate setting, short training camps (17 days as outlined by NCAA Bylaws 17.25.2 and 17.25.3) are followed by tournaments of 3 competitions in 2 days (27). Overall, collegiate teams play at least 30 matches in a period of 13 weeks. Recent research has determined that the number of match sets played per week was a strong predictor of jumper's knee (38). This research has been corroborated by several previous studies, indicating that the prevalence of jumper's knee is affected by the high number of jumps in training and competition, increasing with frequency of both (23).
Given the potential connection between workload, physical health, and injury, the primary aim of this study was to quantify the external and internal load experienced in game during women's collegiate volleyball matches. In practical terms, this study sought to better understand and quantify the physical challenges an athlete's body encounters as a result of participating in a volleyball match. To date, no other research has attempted to quantify volleyball in-game demands using microsensors. Establishing load demands based on position allows coaches, trainers, and strength and conditioning professionals to implement training programs for position-specific demands. Training specificity creates consistent, peak performance while reducing the risk of injury. In addition, this study seeks to investigate the relationship between internal and external load demands in a competition setting.
Experimental Approach to the Problem
Establishing game demands is crucial to developing safe and effective training protocols. Identifying positional demands will further aid in the recruitment and training of athletes that are best suited for each position. The aim of this study was to investigate internal and external load match demands of NCAA Division I collegiate volleyball players, including positional demands, and to determine the relationship between these 2 parameters. Microsensor technology was used to quantify external load, whereas S-RPE was used to quantify internal load. Subjects were monitored using microsensor technology during matches, and S-RPE was taken 20-minute postmatch. Quantification of demands experienced during a match is the vital information that can be used in training athletes for maximum performance and reducing the risk of injury.
Eleven collegiate volleyball players (age = 19.99 [18.0–21.9] years; body mass = 69.43 [55.79–77.11] kg; and height = 178.95 [165.1–187.96] cm) wore microsensor technology (Optimeye S5; Catapult Sports, Chicago, IL, USA) during 15 NCAA Division I matches played throughout the 2016 season. All athletes had played volleyball for a minimum of 7 years and completed off-season strength training and conditioning for a minimum of 4 weeks before the start of the season. Training included conditioning sessions designed to increase anaerobic capacity and weight training for strength and power. In addition, all athletes completed preseason training, including 17 days of volleyball and strength training. Representation across positions included the following: 1 setter (S), 2 defensive specialists (DSs), 2 middle blockers (MBs), and 6 outside hitters (OHs). All athletes were in good physical condition and cleared for participation through physical screening. All subjects were 18 years or older. Participants received clear explanation of the study, and written informed consent was obtained from each subject. All procedures were approved by the University of Wyoming Institutional Review Board.
Catapult Optimeye S5 sensors were placed in a padded pouch sewn into the athlete's sports bra at the base of the neck, between the scapulae. Placement of the sensor minimized risk to the athlete and reduced opportunities to damage the equipment. Athletes wore the same sensor during each match to control interunit variations. Sensors were activated 45 minutes before the start of each match and removed at the conclusion of play. Variation in the start time of each match resulted from changing time zones and day matches were played on. Nutrition and hydration status at the onset of the match were not verified; however, the structured schedule of meals and water breaks during match preparation provided consistency in those factors at the onset of each match. Precise time markers were established through radio frequency live tracking, marking the beginning, and end of the warm-up and each set. Movements in all 3 axes were tracked as accumulated instantaneous accelerations sampled at a frequency of 100 Hz; previous research has validated this method as a reliable way to measure magnitude and frequency of accelerations (18).
Specific metrics were analyzed using Openfield 1.13.1 software provided by Catapult Sports. Player load is the sum of instantaneous accelerations in all 3 axes divided by a scaling factor of 100. The PL metric has been shown to be highly reliable in previous research (3). High impact player load (HI PL) is the quantity of PL derived from accelerations greater than 2 m·s−2, whereas percentage of high impact player load (% HI PL) is HI PL divided by PL. Explosive efforts (EEs) are the count of accelerations greater than 3.5 m·s−2 recorded in the mediolateral (x) and anteroposterior (y) axes (31). Jumps are the count of accelerations occurring in the craniocaudal (z) axis (7). Automatic jump detection as a method for athlete monitoring has been validated for reliability in previous research (7,14).
Session rating of perceived exertion is a method commonly used to determine internal training load across a wide variety of sports including volleyball (30). Session rating of perceived exertion was collected 20-minute postmatch using a modified Borg scale in which athletes were prompted with the question, “How physically difficult was today's competition?” (11). Athletes were encouraged to consider the entire match with a score of 0 being rest or nonparticipation and 10 being hardest competition ever experienced. All athletes were familiar with the RPE scale from practice data collected during the preseason. Session rating of perceived exertion data are calculated using the formula RPE × duration (no. of game minutes) and is presented in AUs.
The mean and SD of all external workload parameters and S-RPE, collected over 15 matches, were determined for the entire team and each position group. Match data were then separated by sets (i.e., set 1, set 2, etc.) to achieve greater precision of workload demands (Table 1). Data were only collected while players were active on the playing surface during the match. Analysis was completed using Microsoft Excel (2013) spreadsheets (Microsoft Corporation, Redmond, WA, USA).
Subsequently, match data were divided by match duration (i.e., 3-, 4-, and 5-set matches) with mean and SD for external workload and S-RPE calculated for each. These data were also separated by sets for greater precision and comparison of demands (Tables 2–4). Differences in 3-, 4-, and 5-set matches were calculated for each external load metric and S-RPE to determine the impact of playing 1 or 2 additional sets (Table 5).
The relationship between internal and external load was explored by comparing S-RPE data with the metrics collected using the microsensor devices (PL, HI PL, % HI PL, EEs, and jumps). A Pearson product-moment correlation with 95% confidence intervals was used to examine the relationship between microsensor data and S-RPE (Figures 1–4). Correlations were evaluated using the following criteria: trivial: 0–0.10; small: 0.11–0.3; moderate: 0.31–0.50; large: 0.51–0.70; very large: 0.71–0.90; and almost perfect: 0.91–1.00 (20). All positions were included in the analysis apart from DS in the S-RPE and jumps correlation (Figure 3). The DSs were omitted from this calculation because of the lack of jumping required at that position. Data were analyzed using a p-value of 0.001 to determine the significance of the results; the number of observations allowed for statistical power greater than 0.90.
Finally, a multivariate analysis of variance (MANOVA) was performed, comparing external and internal load demands from all matches across position groups using IBM SPSS Statistics (version 24.0; IBM Corporation, Armonk, NY, USA). Cohen's d was also calculated to determine the effect size of the differences in position groups for each parameter (Table 6). Effect size was evaluated using the following interpretation: small: 0.0–0.4; medium: 0.5–0.7; and large: ≥ 0.8 (8).
External Load Demands
Data were collected from 15 contests during the 2016 season; 9 matches lasted 3 sets, 3 lasted 4 sets, and 3 lasted 5 sets. Table 1 contains a summary of all match data specified by position. The S had the greatest mean PL and highest number of jumps of all positions in a 5-1 system, playing all 6 rotations. Defensive specialist had the second highest mean PL and the lowest number of mean jumps. Middle blocker had the highest mean HI PL, % HI PL, and EEs, while recording the second highest mean jump total. Finally, OH had the second highest mean % HI PL and the lowest mean PL.
Differences in external load between 3-, 4-, and 5-set matches (Table 5) revealed changes in the way the team and each position group accumulated external load. Playing 4 sets yielded a mean PL increase of 25.1% compared with 3 sets, with OH had the largest positional change at 25.4%. Middle blocker experienced the greatest increase in % HI PL and HI PL rising 11.8 and 34.4%, respectively, whereas the team increased 4.3 and 28.6%. Outside hitter had the largest gain in EEs, increasing 31.6% with the team overall rising 20.0%. Finally, the team experienced 24.1% more jumps from 3- set matches to 4-set matches.
Further increases from 3-set matches to 5-set matches were also observed across position groups (Table 5). Team PL rose 31.0% compared with 3-set matches with DS seeing the largest increase, 31.6%. Middle blocker saw the largest increase in % HI PL and jumps with 6.3 and 31.6%, outpacing team increases of 0.13 and 27.4%. High impact load for DS rose to 42.9% while the team experienced a 32.4% increase. Five-set matches yielded a 44% increase in EEs for DS while the team rose 30.4%.
The MANOVA analysis revealed significant differences in several of the parameters across position groups (Table 6). Notably, significant differences were found across all position groups when examining % HI PL and jumps. The Cohen's d analysis noted large effect sizes (d ≥ 0.8) when comparing the same parameters across all positions. In addition, HI PL saw significant differences (p ≥ 0.001) across all positions apart from S and OH (p = 0.190).
Internal Load Demands
As shown in Table 1, DS recorded the greatest mean S-RPE values over all 15 matches (886 ± 384.6), whereas S recorded the second highest (685 ± 250.5). Middle blocker and OH recorded the lowest mean S-RPE (661 ± 224.0 and 541 ± 307.6). Four-set matches resulted in a 41.3% increase in S-RPE compared with 3-set matches with DS having the largest positional increase of 45.5% (Table 5). Setter showed the smallest change with a 9.0% increase in S-RPE from 3- to 4-set matches. Team internal load rose 49.2% from 3 compared with 5-set matches with DS experiencing the greatest increase (52.0%). Setter again showed the lowest positional increase in S-RPE at 27.7%. Session rating of perceived exertion had the fewest significant differences across positions; only OH compared with DS yielded a significant difference (p = 0.002).
The aim of this study was to quantify the external and internal loads experienced in game during women's collegiate volleyball matches. Data from 15 NCAA Division I matches during the 2016 season revealed unique demands for each position. Player load and jumps are both considered measures of volume, indicating physical workload of the athlete. The S experienced the greatest mean PL and jumps for 3-, 4-, and 5-set matches. For all matches, the S had statistically higher mean differences in PL and jumps than other position groups (p ≤ 0.01). Cohen's d analyses revealed large effect sizes (≥0.8) for these differences. However, the S did not report the greatest mean S-RPE or show a significantly larger S-RPE than any other position group. In addition, mean S-RPE increased only 9.0% from 3-set matches to 4-set matches when PL (21.9%) and jumps (20.6%) increased by greater margins. It seems that fatigue does not take hold until the fifth set where increases in S-RPE (27.7%) are similar to those of PL (27.9%) and jumps (21.6%). This is surprising considering the large correlation (r = 0.54; p ≤ 0.001) between S-RPE and jumps and the very large correlation (r = 0.73; p ≤ 0.001) between S-RPE and PL. A possible explanation is that only 1 S who played all 6 rotations during matches participated in the study. As a result, S-RPE data were entirely dependent on this athlete's responses, a senior with 13 years of playing experience. It is conceivable that this athlete's level of physical conditioning and accumulated match history kept S-RPE responses in a certain range regardless of the external load experienced. It is worth noting that large changes in these demands would occur if multiple S split time or the team used a 6-2 system in which 2 S are on the floor concurrently. Despite these potential limitations, these findings are critical from a performance and injury risk standpoint. In a 5-1 system, the S must have cardiovascular endurance and be well conditioned for jumping to withstand the workload without injury.
Although the largest workload during matches was experienced by the S, another critical element in determining match demands is the intensity at which the workload is performed. Percentage of HI PL, HI PL, and EEs are all measures of workload intensity as quantified by wearable microsensors. Middle blocker recorded the highest values in all 3 of these parameters. The mean differences between these parameters and all other positions were statistically significant (p ≤ 0.01) and showed large effect sizes (d ≥ 0.8). In the 5-1 system, there are typically 2 MBs on the floor; however, teams typically use substitutions, so athletes who possess greater passing ability (i.e., the libero) play the back-row rotations for the second middle. This results in demands that reflect a large amount of high-intensity movements at the net including blocking and attacking. Although S may experience the greatest number of jumps, the intensity at which MB jump is greater. Despite having the greatest intensity, MB had the second lowest mean S-RPE (661 ± 224.0). Explosive efforts had a moderate correlation to S-RPE (r = 0.43; p ≤ 0.001), whereas % HI PL showed a small, inverse correlation (r = 0.11; p = 0.167). This may be attributed to the shorter amount of time the MB spent on the court, playing 3 or 4 rotations, and resting the remaining rotations. These data indicate that MB must be trained for short bouts of maximum intensity to perform optimally and reduce injury risk. As noted in the literature on jumper's knee, high performers (i.e., athletes who exhibit large vertical jumping ability) may be at an increased risk to develop this condition (22). The current study indicates that MB would be at an increased risk because of the high-intensity demands of the position. Furthermore, the altitude at which matches were played (7,200 feet above sea level) may have limited intensity numbers because of increased fatigue. All athletes (including MBs) playing near sea level may show even higher intensity numbers, further increasing injury risk. Combine this information with a congested playing schedule, it becomes increasingly important to monitor these athletes and understand the specific demands associated with playing MB.
The findings in this study on workload volume and intensity, specifically jumping, agree with the findings of Sheppard et al. (32) who noted that MBs show the greatest stress from maximal jumping, but S perform large amounts of submaximal jumps. The study also indicated that this stress does not correlate with greatest amount of physiological stress among position groups because of the substitution of the DS in the back-row rotations. This study demonstrated similar findings, despite subjects in the previous study being elite male volleyball players. Middle blocker accumulated 28.13% of PL in the mediolateral (x) axis throughout the 15 matches which suggests that jumping (45.60%) and lateral movement at the net are of primary importance at that position. The comparison between these 2 studies highlights the novelty and importance of quantifying these positional observations. These data also suggest that the importance of these qualities among MBs is high regardless of sex.
Similar to the S position, the DS involves a large volume of workload with very little high-intensity demands. Defensive specialist has the second largest mean PL (415 ± 104.9) and the largest mean S-RPE (886 ± 384.6). An analysis of intensity reveals significantly lower (p ≤ 0.01) % HI PL, HI PL, and jumps compared with all other position groups. The Cohen's d analysis showed a large effect size (≥0.8) for these differences in intensity and jumps. These findings present a conflict of information between internal and external load parameters. Several possible explanations exist as to why the S-RPE was the highest in this position group, whereas external load parameters would suggest that it should be less. As previously stated, the S-RPE of the S position group may be underreported because of the experience of the sole athlete playing that position. This theory is further supported when examining the 2 athletes who comprise the DS position group; 1 was a freshman with the least experience of all 11 who participated, whereas the other was a junior in her third year of collegiate experience. The lack of college playing experience for 1 DS may have inflated the S-RPE numbers because of unfamiliarity with the pace of the college game. A comparison of PL and S-RPE for this individual reveals the discrepancy between the 2 parameters increased throughout the season, suggesting that it may take more than 1 season to adjust to the demands of collegiate volleyball or that other factors (i.e., academic stress, sleep, and nutrition) affect internal load. Another explanation is that a parameter which was not measured may have led to the increased feeling of difficulty in individuals playing the DS position. Diving is an athletic skill required by this position to dig hard-driven balls in the backcourt. Repetitive impacts against a hard gym surface have the potential to take a physical and mental toll over the course of a match. Further analysis of this skill or the creation of a dive parameter may be beneficial in understanding this relationship. In addition, the demands of a low squat position where the hips are parallel to the knees are crucial for creating a stable passing platform. Defensive specialists spend a large amount of time in this position, creating the potential for lower extremity fatigue as a match progresses.
The OH position requires a dynamic skill set that incorporates blocking, attacking, and passing skills in the backcourt rotations. One OH played all 6 rotations, whereas the remaining 5 in the study had substitutions for the back-row rotations. It is worth noting that throughout the season, several lineups and substitution patterns were used, which had the greatest impact on the OH position. As a result, 6 of the 11 athletes monitored in match play were OHs. Spreading out the workload across more athletes resulted in the lowest mean PL (330 ± 104.5) and S-RPE (541 ± 307.6). The difference in PL was statistically significant (p ≤ 0.01) and showed a large effect size (≥0.8) when compared with all positions except MB (p = 0.050). Outside hitter had the second highest mean % HI PL (8.17 ± 2.1) with a large effect size (≥0.8) and was third in mean jumps (67 ± 26.8). These results indicate that OHs need to be capable of high-intensity efforts and have the skill and work capacity to play all 6 rotations, similar to an S.
Using microsensor technology and S-RPE to quantify match demands of collegiate volleyball players revealed a great deal about external and internal workloads at both the team and positional levels. Traditional workload measurements such as jump count may be applicable to the S and MB positions more than the OH and DS based on jump demands. Furthermore, an analysis of PL broken down by axis revealed that 44.59% of workload came from the vertical (z) axis, 29.64% from side-to-side movement (x), and 25.77% from forward and back (y). Less than half of the total workload on average comes from jumping. Even the S, who had the most jumps by a statistically significant margin, still only experienced 46.91% of PL from the z axis. This information is unique to this study and has huge training and monitoring implications for both coaches and athletic trainers seeking to control workload on a return to play progression. Using jump count only in return to play may result in larger intended loads because of the lack of load monitoring in other axis, particularly at the OH and DS positions.
The number of sets played in a match is another area with huge performance and injury risk ramifications. A team that plays 4 sets instead of 3 sets will experience 25.1% increase in PL and a 31.0% increase if a match goes to 5 sets. The internal load is even greater with 4-set matches yielding 41.3% increase in S-RPE over 3 sets and 49.2% increase in 5 sets over 3 sets. Coaches and trainers can use this information to make decisions about which positions and athletes experience the greatest load demand. Using the available information that S have the largest volume of work (PL and jumps) or MBs experience the greatest intensity (% HI PL, HI PL, and EEs) and DSs have the largest internal load (S-RPE) can have huge impacts if properly applied to game strategy.
Training and substitution decisions based on which athletes and positions experience the greatest demand have the potential to increase performance and decrease injury risk. It is important to note that this study was completed using NCAA Division I athletes; thus, data from lower divisions may vary in intensity. The small sample size of this study limits the ability of these results to be applied generally across all volleyball athletes and offensive systems; however, these data may be helpful to other practitioners in the early stages of implementing wearable microsensors. In addition, expanding methods of internal load monitoring (i.e., recovery questionnaires and heart rate variability) completed prepractice and postpractice/competition may yield stronger relationships with existing external workload parameters. In a sport where participation numbers continue to increase and “calendar congestion” is experienced at all levels, this research has the potential to be very influential on training and coaching methods in the sports of volleyball.
The authors thank the University of Wyoming, Wyoming Athletics, and the University of Wyoming women's volleyball team for their collaboration and support on this project. The results of this study do constitute endorsement of the product by authors or the NSCA. The authors have no conflicts of interest to disclose.
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Keywords:Copyright © 2017 by the National Strength & Conditioning Association.
NCAA; inertial measurement unit; S-RPE; workload