The purpose of this study was to examine the effects of an overreach and taper on measures of muscle architecture, jumping, and throwing performance in Division I collegiate throwers preparing for conference championships. Six collegiate track and field throwers (3 hammer, 2 discus, 1 javelin) trained for 12 weeks using a block-periodization model culminating with a 1-week overreach followed by a 3-week taper (ORT). Session rating of perceived exertion training load (RPETL) and strength training volume-load times bar displacement (VLd) were recorded weekly. Athletes were tested pre-ORT and post-ORT on measures of vastus lateralis architecture, unloaded and loaded squat and countermovement jump performance, underhand and overhead throwing performance, and competition throwing performance. There was a statistical reduction in weight training VLd/session (d = 1.21, p ≤ 0.05) and RPETL/session (d = 0.9, p ≤ 0.05) between the in-season and ORT training phases. Five of 6 athletes improved overhead throw and competition throwing performance after the ORT (d = 0.50, p ≤ 0.05). Vastus lateralis muscle thickness statistically increased after the in-season training phase (d = 0.28, p ≤ 0.05) but did not change after the ORT. Unloaded countermovement jump peak force and relative peak power improved significantly after the ORT (d = 0.59, p ≤ 0.05, d = 0.31, p ≤ 0.05, respectively). These findings demonstrate that an overreaching week followed by a 3-week taper is an effective means of improving explosive ability and throwing performance in collegiate track and field throwers despite the absence of detectable changes in muscle architecture.

This study investigated physiological and performance changes of a national level 69kg female weightlifter following three competition phases over a 28-week training period. The athlete first trained for a regional championship (weeks 1-12), followed by a local competition (weeks 13-23) and the national championship (weeks 24-28). Body mass, vastus lateralis cross-sectional area, and unloaded and loaded squat jump performance were assessed weekly during each 4-week competition phase. Serum biomarkers, and dynamic mid-thigh pulls were assessed prior to and after each competition phase. Weightlifting performance goals were met for the regional championship (total=200kg) and the local competition (total=193kg), but not the national championship (total=196kg). She lost more body mass in preparation for nationals (-6.0kg) compared to regionals (-2.5kg) and the local competition (+2.2kg). Vastus lateralis cross-sectional area very likely decreased following nationals (precision=99%, effect size=2.08). Her testosterone:cortisol ratio likely increased (88%, 2.64), while interleukin-6 (79%, 2.47), and tumor necrosis factor-alpha (81%, 3.59) likely decreased following nationals. Serum myostatin (99%, 1.95) and decorin (99%, 1.96) very likely decreased following the local competition. Unloaded squat jump height likely increased the week of regionals (89%, 0.95) and the local competition (99%, 1.83), whereas unloaded and loaded squat jump height possibly (69%, 0.99) and likely (82%, 1.52) decreased the week of nationals. Dynamic mid-thigh pull vertical displacement likely increased following regionals (93%, 0.84), and likely decreased following nationals (94%, 0.87). These findings indicate that biomarkers of stress, inflammation, and hypertrophy are related to changes in training volume-load; however, performance measures are needed to assess competition preparedness. Considering the reductions in muscle cross-sectional area corresponding with the large reductions in body mass and underperformance at the national championship, sport scientists and coaches should instruct weightlifters to not attempt large losses in body mass (e.g. >3 kg) close to competition (e.g. <1 week).

Athlete preparation and performance continue to increase in complexity and costs. Modern coaches are shifting from reliance on personal memory, experience, and opinion to evidence from collected training-load data. Training-load monitoring may hold vital information for developing systems of monitoring that follow the training process with such precision that both performance prediction and day-to-day management of training become adjuncts to preparation and performance. Time-series data collection and analyses in sport are still in their infancy, with considerable efforts being applied in "big data" analytics, models of the appropriate variables to monitor, and methods for doing so. Training monitoring has already garnered important applications but lacks a theoretical framework from which to develop further. As such, we propose a framework involving the following: analyses of individuals, trend analyses, rules-based analysis, and statistical process control.Keywords: time-series analysis, dose-response, statistical process control Training is a process and a historical sequence of events. As with all processes, whether biochemical, manufacturing, raising children, or others, these processes require some type of regulation and oversight. 1 The level of oversight is related to the complexity of the process and the potential cost of losing process control. 2 Complex processes tend to have more opportunities for error resulting in a deviation from the most desired path. 3 For example, game sports are unique events that involve dynamic interactions between players, and as a result the observed behavior of an athlete or team is influenced by a situation or opponent. 4 The "unstable" nature of game sports creates a challenge to quantify performance indicators. Statistical process control is one method used to identify stable performance traits. 4 Statistical process control uses normative profiles or the averages of variables from several games, as well as tolerance limits expressed in confidence intervals based on a mean and variance estimates, to determine "reliable" or typical indicators of performance. 5 Mathematical modeling, such as probability analysis, also has been used for estimating the impact of a single player on team performance, predicting future behavior and identifying optimal decision-making strategies. 6 Processes can operate along a continuum from deterministically controlled to utterly chaotic 7-9 with both sharing a potential for unexpected problems and considerable expense in terms of money and threats to life and health. 2,10 Historically, more effort has been expended in assessing the athlete as a system. However, the preparation and performance of an athlete can also be addressed as a system. When viewed from the perspective of dynamic systems, athletes are described as "non-ergodic, out of equilibrium systems, exploring larger and larger regions of the state space but eventually getting trapped within some relatively small set of the whole state space by the constraints of their sport disci...

A four-year retrospective analysis of injury data was conducted on a collegiate (NCAA Division I) women’s volleyball team. Twenty athletes (Year 1: age = 19.4 ± 0.9 y, height = 175.2 ± 5.1 cm, body mass = 70.5 ± 10.2 kg; Year 2: age = 20.1 ± 1.0 y, height = 175.7 ± 4.7 cm, body mass = 69.5 ± 10.1 kg; Year 3: age = 20.1 ± 1.4 y, height = 173.8 ± 6.3 cm, body mass = 69.9 ± 10.8 kg; Year 4: age = 19.5 ± 1.4 y, height = 174.4 ± 8.6 cm, body mass = 72.7 ± 10.8 kg) participated in this study, accounting for 1483 total training exposures. Injury was defined as any damage to a body part, incurred during volleyball or strength and conditioning-related activities, which interfered with training and/or competition. Injury rate was normalized to the number of athletes and exposure and expressed as injuries per 1000 exposures. A total of 133 injuries were recorded. The most common injury was to the knee (left = 7.5%, right = 12.0%). Injuries occurred most often in volleyball practice (75.2%), followed by competition (20.3%), and strength and conditioning-related activities (4.5%). Non-contact injuries (upper body = 26.3%, lower body = 53.4%) were more common than contact injuries (upper-body = 13.5%, lower-body = 6.8%). An examination of injury rates relative to the training year revealed patterns in injury occurrence. Specifically, spikes in injury rate were consistently observed during periods of increased training volume that were preceded by breaks in organized training, such as the early pre-season and off-season training periods.

Bazyler, CD, Mizuguchi, S, Sole, CJ, Suchomel, TJ, Sato, K, Kavanaugh, AA, DeWeese, BH, and Stone, MH. Jumping performance is preserved but not muscle thickness in collegiate volleyball players after a taper. J Strength Cond Res 32(4): 1020-1028, 2018-The purpose of this study was to examine changes in muscle architecture and jumping performance in NCAA division I women's volleyball players throughout a competitive season and in preparation for conference championships. Ten women volleyball players were tested at preseason (T1), pretaper (T2), and post-taper (T3) on measures of vastus lateralis muscle thickness (MT), pennation angle (PA) and fascicle length (FL) using ultrasonography, and unloaded and loaded squat jump height (SJH) and peak power allometrically scaled to body mass (SJPPa) on a force platform. Rating of perceived exertion training load and strength training volume load were monitored weekly. Player's MT (p < 0.001, Glass's Δ = 2.8) and PA increased (p = 0.02, Δ = 3.9) after in-season training. However, MT decreased after the taper (p = 0.01, Δ = 0.6) but remained elevated above preseason values (p < 0.001, Δ = 1.7). There were no statistical changes in FL, SJH, or SJPPa. Large-to-very large negative relationships (r = -0.51 to -0.81) were observed between preseason relative maximal strength and changes in SJH and SJPPa with various loads over the season. These findings demonstrate that relatively low volumes of strength training and concurrent sport training during a tapering period are capable of preserving jumping performance, but not MT in women's volleyball players; however, jumping performance changes seem to be related to the player's strength level. Stronger players may benefit from an overreaching microcycle before the taper to preserve previously accrued muscular adaptations and jumping performance.

Objectives:The purpose of this study was to determine the best predictor of training and/or match load using session RPE. Design and Methods: 20 NCAA DI male soccer players participated in the study during the 2014 and 2015 competitive seasons.Players completed 15.20 ± 1.05 matches for a total of 304 individual data points and 29.90 ± 1.89. training sessions for a total of 598 individual data points. GPS variables (total distance, High-intensity running distance, and Player load) were analyzed with session RPE using Pearson product-moment correlations. To evaluate various methods of session RPE, "match duration" was recorded using eight different definitions: total match duration including warm-up and half-time, total match duration and warm-up, total match duration and half-time, total match duration only, minutes played including warm-up and half-time, minutes played and warm-up, minutes played and half-time, and minutes played only. A one-way ANOVA with repeated measures was used to determine if differences existed between the eight session RPE calculations. Results:Results from the ANOVA showed that all session RPE measures were significantly different from one another (P < 0.05).Very large correlations were reported between session RPE calculated using minutes played and total distance (0.81), while session RPE calculated using match duration showed less magnitude (0.57). Conclusions:Minutes played should be used to calculate session RPE as it was found to most closely reflect the actual workloads incurred during competitive matches.(Journal of Trainology 2017;6:26-30)

Kavanaugh, AA, Mizuguchi, S, Sands, WA, Ramsey, MW, and Stone, MH. Long-term changes in jump performance and maximum strength in a cohort of NCAA division I women's volleyball Athletes. J Strength Cond Res 32(1): 66-75, 2018-The purpose of this investigation was to quantify the magnitude of change in maximal strength and jumping abilities over approximately 1, 2, and 3 years of supervised sport and resistance training in National Collegiate Athletic Association (NCAA) division I women's volleyball athletes. This was an exploratory study on a cohort of women's volleyball athletes (n = 29) split into 3 groups based on the length of the time spent in a supervised resistance training program: group 1 (n = 11): 0.7 ± 0.3 years, group 2 (n = 9): 1.6 ± 0.2 years, and group 3 (n = 9): 2.4 ± 0.6 years. Monitoring tests consisted of standing height (cm), body mass (kg), body fat (%), static jump height (SJH) and countermovement JH (CMJH) with 0-, 11-, and 20-kg loads (cm), and midthigh clean pull isometric peak force (IPF) and allometrically scaled IPF (IPFa) (N·kg). Increasing trends were observed for all variables from groups 1 to 2 to 3. Statistically greater improvements (p ≤ 0.05) with moderate to large effect sizes were found between groups 1 and 3 for SJH 0 (19.7%, d = 1.35), SJH 11 (23.8%, d = 1.23), SJH 20 (30.6%, d = 1.20), CMJH 11 (22.6%, d = 1.18), IPF (44.4%, d = 1.22), and IPFa (41.2%, d = 1.32). A combination of traditional resistance training exercises and weightlifting variations at various loads, in addition to volleyball practice, seem to be effective at increasing maximal strength by 44% and vertical JH by 20-30% in NCAA division I women's volleyball athletes after about 2.5 years of training. Furthermore, these characteristics can be improved in the absence of additional plyometric training outside normal volleyball-specific practice.

Purpose: To determine if jumping performance changes during a peaking phase differed between returners and new players in a female collegiate volleyball team and to determine which variables best explained the variation in performance changes. Methods: Fourteen volleyball players were divided into two groups: returners (n=7) and new players (n=7) who completed a 5-week peaking phase prior to conference championships. Players were tested at baseline prior to the pre-season on measures of vastus lateralis cross-sectional area using ultrasonography, estimated back squat one repetition maximum (1-RM), and countermovement jump height (JH) and relative peak power (PPa) on a force platform. Jumping performance, rating of perceived exertion training load, and sets played were recorded weekly during the peaking phase. Results: There were moderate to very large (p<0.01, Glass's Δ=1.74), and trivial to very large (p=0.07, Δ=1.09) differences in JH and PPa changes in favor of returners over new players during the peaking phase, respectively.Irrespective of group, 7 of 14 players achieved peak JH two weeks after the initial overreach. The number of sets played (r=0.78, p<0.01) and athlete's pre-season relative 1-RM (r=0. 54, p=0.05) were the strongest correlates of JH changes during the peaking phase. Conclusions: Returners achieved greater improvements in jumping performance during the peaking phase compared to new players, which may be explained by the returners' greater relative maximal strength, time spent competing, and training experience. Thus, volleyball and strength coaches should consider these factors when prescribing training during a peaking phase to ensure their players are prepared for important competitions.