Golf swing performance has been found to be influenced by health status, physical fitness, and technical abilities. However, as the sport becomes more competitive, athletes and coaches regularly seek novel approaches to optimizing athletic performance. This study sought to identify key performance indicators that are significantly related to golf club head speed within an elite collegiate golf population. Height, weight, vertical and lateral jump, upper-body rotational velocity, club head speed, and swing kinematics for 21 NCAA Division I female (n = 11) and male (n = 10) golf athletes were collected for analysis. Significant differences between female and male athletes were observed for measures of anthropometry, jumping ability, upper-extremity rotational velocity as well as clubhead speed (p < 0.05). Of particular interest were findings that suggest load-velocity profiling as a novel (for the sport of golf) but useful tool to support individualized golf swing profiling and technical improvement approaches. However, no significant differences between groups were observed for any rotational kinematic measure (p > 0.05). Interestingly, a novel assessment of swing kinematics revealed that peak rotational velocities at specific loads relative to an athlete’s body mass were discovered to have a significantly moderate positive correlation with fast club head speed (p < 0.05). Altogether, these findings can be utilized by sports performance professionals to improve the assessment, development, and performance capabilities of golfers interested in enhancing their clubhead speed.

The purpose of this study was to investigate the acute responses of accentuated eccentric loaded (AEL) back squats to induce a post activation performance enhancement (PAPE) effect in youth athletes across three jump conditions: countermovement jump (CMJ), squat jump (SJ), and novel propulsive-only jump (POJ). Fifteen participants (age: 15.6 ± 1.1 years; RT experience: 1.3 ± 0.9 years; relative strength (back squat 1RM: body mass; 1.32 ± 0.3) completed three sessions (one familiarization, two experimental). AEL interventions were performed on each experimental session (3 sets x 3 repetitions, with only the initial repetition of each set was overloaded during the eccentric phase followed by 2 full repetitions of nonvarying loads) (ECC: 95%, 105%, 115%; CON: 60% 1RM) with pre- and post-testing (3 min, 6 min, 9 min, 12 min). Random assignment to either (a) CMJ, (b1) SJ+POJ, or (b2) POJ+SJ, where jump height (JH), net propulsive impulse (NPI) and peak relative propulsive power (PrPP) were assessed for each jump. Three 3x5 repeated measures ANOVAs were used to analyze each dependent variable across jump conditions and time with a level of significance of ρ ≤ 0.05. Results revealed a significant increase in POJ JH performance at 9 min (+12.26% ± 13.65%, p < 0.05), while CMJ and SJ performance did not show statistically differences from pre-testing. JH performances peaked at 12 min for CMJ (+2.22% ± 7.71%) and SJ (+5.03% ± 12.77%) but did not reach statistical significance. These findings suggest that male high school basketball players may realize superior or unaffected jump performances at 9-12 min post-supramaximal AEL back squats. In addition, no significant deficits in performance outcomes were found for any condition from pre- to post-testing.

Purpose: This study examined the emerging countermovement rebound jump (CMJ-RE) test as an alternative to the drop jump (DJ) to grade reactive strength abilities in elite basketball players. Normative percentile data, rebound jump filter criteria, novel effort metrics, and reliability statistics were analyzed in order to better define the standards for a high-quality CMJ-RE test and to democratize the measurement of reactive jump testing. Methods: 96 NCAA Division-I men’s college basketball players from seven teams performed the CMJ-RE (bounce-method) on bilateral force plates with hands on hips during the 2023-2024 pre-season. Only 87 players met the quality filter criteria for further analysis (three centers, 31 forwards, 53 guards); the center position was not compared in depth due to the limited sample size. Results: Between forwards and guards, positional differences were found for 37% of the 38 reported jump metrics, with guards showing higher rebound jump heights (JH), all other metrics (p < 0.05) favored forwards. Mean ground contact times (GCT) for both positions were >250 ms. On average for both positions, the first jump in the CMJ-RE was completed at 94% JH of a previously collected standalone CMJ JH; and the second jump was completed at 98% JH of the first CMJ-RE JH. Furthermore, percentile ranges (3rd to 97th) accompanied by qualitative descriptors and a traffic light classification system were reported. Conclusion: Overall, the CMJ-RE (bounce-method) is a reliable alternative to the DJ for assessing reactive strength qualities in elite basketball players and may be more sport-specific, due to players frequently performing subsequent jumps rather than one single effort jump. The normative data prepared in this study should serve as a reference point throughout the evaluation of reactive strength for basketball performance coaches and in the return to play process for sports medicine professionals. Those administering the CMJ-RE should use the filter criteria measures outlined in this paper to ensure quality and effort are being met.

This study assessed the anthropometry and physical conditioning attributes of young male inter-county (national level) Gaelic football players, comparing possible differences according to age categories (under-14 [U14] to under-21 [U21]) and position. In total, 3547 assessments were undertaken in 2588 players across a 10-year period (2013 to 2023). Assessments included anthropometry (stature, body mass, sum of 3 skinfolds), flexibility (sit and reach test), muscular power (countermovement jump and standing long jump), running speed (5-m and 20-m), muscular endurance (maximum push ups) and aerobic capacity (yo-yo intermittent recovery test level 1 [YYIRT1]). Increases were demonstrated when comparing age categories from U14 to U21 for anthropometry (e.g., height [cm] +5.7%), muscular power (e.g., Standing long jump [cm] +16.1%). running speed (e.g., 5 m speed [s] -4.8% from U14 to U20), running momentum (e.g., 20 m momentum [kg.ms-1] +41.5% from U14 to U20), muscular endurance (push-ups [n] +107.7%), flexibility (sit and reach [cm] +36.0%) and aerobic capacity (YYIRT1 [m] +42.3%). When comparing positions, goalkeepers and midfielders were taller and heavier than backs and forwards, respectively. Additionally, midfielders displayed superior aerobic conditioning when compared with other positions, while goalkeepers displayed significantly lower aerobic conditioning. These findings offer comparative data of young Gaelic football players from varying age categories (U14 to U21) which are further classified according to positions. Practitioners may utilize this data to inform player identification processes, characterize position-specific benchmarks and monitor physiological adaptations in young players.

This case study aimed to design a 4-week training intervention and monitor the physical capabilities of three amateur boxers as they prepared for and competed in a distinguished international boxing tournament. Three male senior elite amateur boxers (19.3 ± 1.5 years; height 178 ± 3 cm; mass 77 ± 4.6 kg) visited the laboratory on three separate occasions across an 8-week period around competition. Specifically, participants performed baseline physical tests comprising one repetition maximum of bench press and hex bar deadlift, counter-movement jumps, maximal punches against a vertically mounted force plate, and the Yo-Yo Level 1. This testing battery was repeated following a 4-week contrast training intervention prior to competition, and again following competition. Generally, meaningful or worthwhile improvements in all physical performance tests from baseline to pre-competition was observed. An increase of between 1.4% to 8.1% in punch force was also observed. Likewise, improvements in jump height of between 0.1% and 3.1% were found. Performance gains were maintained at post-competition, albeit with a downward trend towards baseline values. In conclusion, the 4-week contrast training intervention implemented by amateur boxers preparing for competition, served to improve general physical performance qualities, and perhaps more importantly, punch force. In a time-limited scenario, a 4-week contrast training programme may be an effective method of improving punch-specific and physical performance, thus perhaps transferring to greater performance in the ring. Future research may wish to explore the effectiveness of longer-term training methods.

Strengthening muscle is a critical aspect of injury resistance and training for performance gain, and as such, a wide variety of resistance training methods are available to the coach. Many of these training methods have been shown to enhance running performance, however, the influence of flywheel resistance training (FRT) is relatively unknown. FRT is known for a concentric force-time profile similar to ballistic movement and also for the eccentric overload the technology provides. Both these features could have positive adaptive effects for running performance. Initially some of the benefits of FRT as compared to traditional resistance training are discussed. With limited research in the area, what little is known is discussed and integrated into loading parameters and training guidelines for using this technology to improve running performance. Finally, some of the limitations and future research directions are detailed.

Strength and conditioning coaches (SCCs) are in a unique position to coach psychological skills and strategies (PSS) alongside physical training to improve athlete performance (Moore et al., 2019; Radcliffe et al., 2013, 2016). The primary purpose of this study was to examine the frequency of use of PSS within collegiate strength and conditioning. A modified version of the Strength and Conditioning Sport Psychology Questionnaire (Radcliffe et al., 2013) and the Mental Skills Questionnaire (Bull et al., 1996) was completed by collegiate SCCs (N = 364). The three PSS used most frequently by college SCCs were building self-confidence, motivation, and goal setting. No significant differences (p > .01) were found between male strength and conditioning coaches (n = 290) and female strength and conditioning coaches (n = 74) in the frequency of use of PSS. SCCs who felt content knowledge was a barrier toward the use of psychological skills reported significantly lower (p < .01) use of all PSS except for self-confidence. No significant difference in PSS use (p >. 01) was found between coaches who listed lack of time as a barrier to coaching PSS with their athletes and those who did not. Advanced coaches with 10 or more years of coaching experience utilized self-talk skills more with their athletes (p < .01) than novice coaches with less than four years of coaching experience. SCCs would benefit from sport psychology professional development opportunities to improve their knowledge in coaching PSS with their athletes. Additionally, future researchers may want to analyze the differences in how and why PSS are used by SCCs across all divisions of colleges and universities.

Jump height (JH) achieved in a countermovement jump (CMJ) has been suggested to allow for the monitoring of neuromuscular fatigue (NMF) and assessment of lower body power. Although force platforms (FP) are considered the gold standard for measuring CMJ height, they are expensive compared to mobile apps such as My Jump Lab (MJL). Therefore, this study aimed to assess the concurrent validity and agreement of the MJL app compared to a FP (ForceDecks [FD]) system and to determine its test-rest reliability. A convenience sample of 26 (n = 11 females and n = 15 males) recreationally active university sport students and staff (mean ± SD; age: 23.08 ± 6.33 years; mass: 72.85 ± 9.93 kg; stature: 176.63 ± 10.18 cm) participated in the study. Participants attended the laboratory for testing on two separate occasions, separated by one week. After a standardised warm-up, they completed three CMJs on each occasion, with CMJ height simultaneously assessed by the FD and MJL app. The MJL Artificial Intelligence mode showed a mean bias of 4.32 cm [95% CI: 3.4, 5.26] overestimation with 95% limits of agreement ranging from -3.33 cm [95% CI: -4.96, -0.85] to 11.98 cm [95% CI: 10.13, 13.41]. Both methods demonstrated minimal mean bias (FD = 0.61 cm [95% CI: -0.31, 1.37] and MJL = 0.25 cm [95% CI = -0.48, 0.98]) between sessions, and both showed a similar width to their limits of agreement, ranging ~7 cm about the mean bias. In summary, the MLJ overestimated CMJ height in this sample compared to the FD system, but both methods were reliable. Given the significant differences in cost for these two methods, teams on a budget may interested in trialling the MJL app.

The countermovement jump (CMJ) is a widely used test for monitoring neuromuscular (NM) function. However, its suitability for longitudinal NM function monitoring in sprinters remains uncertain. This study examines the relationship between quantified training load and sprinters’ CMJ performance over a 10-week training block. Five high-level male university sprinters participated in this longitudinal study and underwent CMJ testing once a week. Their training load was quantified using the weekly training impulse (wTRIMP) calculated from training duration and session rate of perceived exertion (RPE). Subjective wellness ratings (Wellness) were assessed using a 7-point scale questionnaire based on the Hooper-Mackinnon Questionnaire. Average weekly Wellness and CMJ variables were used for further analysis, examining their relationship with wTRIMP. During the monitoring period, 80-85% of participants’ wTRIMP was attributed to sprint training. Significant relationships were observed between wTRIMP and CMJ variables in four out of five participants (e.g. Sub A: Eccentric Impulse; r = -0.814, p = 0.014, Sub B: Concentric Impulse; r = -0.775, p = 0.041, Sub C: Jump height; r = -0.704, p = 0.034, Concentric mean power; r = -0.825, p = 0.006, Sub E: Jump height; r = -0.723, p = 0.028, Concentric Impulse; r = -0.737, p = 0.024), with force-time related variables being more sensitive to wTRIMP than jump height. Additionally, a significant relationship between Wellness and wTRIMP was observed for only one sprinter (Sub A: Wellness; r = -0.620, p = 0.056), while no significant relationship was found for the remaining four sprinters. In conclusion, our results demonstrated that the CMJ is suitable for longitudinal NM function monitoring of sprinters.

Sprint acceleration is a fundamental component of team sports and is typically measured from a static start, despite athletes frequently initiating sprints from a walking/jogging/running start on the field. The focus of this study was how the entry velocity varies within and between sessions and whether this affects subsequent sprint performance (i.e., pickup acceleration). Sixteen male athletes (age 21.9 ± 4.8 years) performed three sessions, each consisting of two sprints at each of the four pre-determined entry velocities (static start (0%), 20%, 40%, and 60% of maximum velocity), guided via an LED pacing system. Data were measured via a linear position encoder (1080 Sprint), from which maximum acceleration (amax), maximum velocity (vmax), and split times (2, 5, 10, and 20 m) were determined following the moment of pickup. Linear mixed-effects models were used, with entry velocity, trial, and session (and their interaction) as fixed effects and participant as a random effect. Entry velocity had a large effect on all variables (ηp2=0.22 to 0.97; p <0.001). The only variable to differ significantly across trial and session was vmax. However, the effect sizes were small and within trial (6.0-7.0%), and between-session (0.15 to 0.67%) coefficients of variation were less than 10%. In summary, entry velocity had minimal effect on the stability of amax, vmax, and split times across trials and sessions, and the practitioner can be confident that these measures are reasonably stable across repeated testing occasions and, therefore, can be used to measure and monitor pickup acceleration.