Assess the influence of a 20% reduction in game time on the volume and intensity of elite AF players’ match activity profiles. GPS technology was used to analyse the movement demands of 45 AF players from the same AFL club during 43 matches across the 2019 and 2020 seasons. GPS data were categorised into measures of volume (total match time [TMT] and total distance [TD]) and intensity (metres per minute [m.min-1], high-intensity running [HIR] distance and m.min-1 [>17 km·h-1], and very-high intensity running [VHIR] distance and m.min-1 [>23 km·h-1]). Volume decreased in 2020 with reductions in TMT (effect size [ES] ± 95% confidence interval [CI] = -1.8 ± 0.2; p < .001) and TD (ES = -1.8 ± 0.2; p < .001) overall, across all positional groups, and quarters. Intensity increased, evidenced by increases in HIR m.min-1 (ES = 0.3 ± 0.1; p < .001), and VHIR m.min-1 (ES = 0.3 ± 0.2; p = .006). HIR m.min-1 increased for midfielders (ES = 0.6 ± 0.3; p = .017). Defenders exhibited increases in HIR m.min-1 (ES = 0.2 ± 0.2; p = .007), and VHIR m.min-1 (ES = 0.4 ± 0.2; p = .010). Intensity of third quarters decreased at a greater rate in 2020 with reductions in m.min-1 (ES = -0.2 ± 0.1; p = .004) and HIR m.min-1 (ES = -0.2 ± 0.1; p = .037) compared to Q1. Systematic reductions in volume were found overall, across positional groups, and quarters. Average movement speed remained relatively stable overall, across quarters and positional groups. Increases in intensity were defined predominately by increases at high and very-high intensity speeds per minute, with defenders exhibiting the greatest increase in intensity and change to their match activity profiles. Longer quarter and three-quarter time breaks, and time between goals preserved intensity.

This systematic review and meta-analysis evaluated the validity of tests / markers of athletic readiness to predict physical performance in elite team and individual sport athletes. Ovid MEDLINE, Embase, Emcare, Scopus and SPORT Discus databases were searched from inception until 15 March 2023. Included articles examined physiological and psychological tests / markers of athletic readiness prior to a physical performance measure. 165 studies were included in the systematic review and 27 studies included in the meta-analysis. 20 markers / tests of athletic readiness were identified, of which five were meta-analysed. Countermovement jump (CMJ) jump height had a large correlation with improved 10m sprint speed / time (r = 0.69; p = .00), but not maximal velocity (r = 0.46; p = .57). Non-significant correlations were observed for peak power (r = 0.13; p = .87) and jump height (r = 0.70; p = .17) from squat jump, and 10m sprint speed / time. CMJ jump height (r = 0.38; p = .41) and salivary cortisol (r = -0.01; p = .99) did not correlate with total distance. Sub-maximal exercise heart rate (r = -0.65; p = .47) and heart rate variability (r = 0.66; p = .31) did not correlate with Yo-Yo Intermittent Recovery Test 1 performance. No correlation was observed between blood C-reactive protein and competition load (r = 0.33; p = .89). CMJ jump height can predict sprint and acceleration qualities in elite athletes. The validity of the other readiness tests / markers meta-analysed warrants further investigation.

PURPOSE To investigate the effects of a training camp with heat and/or hypoxia sessions on hematological and thermoregulatory adaptations. METHODS Fifty-six elite male rugby players completed a 2-week training camp with 5 endurance and 5 repeated-sprint sessions, rugby practice, and resistance training. Players were separated into 4 groups: CAMP trained in temperate conditions at sea level, HEAT performed the endurance sessions in the heat, ALTI slept and performed the repeated sprints at altitude, and H + A was a combination of the heat and altitude groups. RESULTS Blood volume across all groups increased by 140 mL (95%CI, 42-237; P = .006) and plasma volume by 97 mL (95%CI 28-167; P = .007) following the training camp. Plasma volume was 6.3% (0.3% to 12.4%) higher in HEAT than ALTI (P = .034) and slightly higher in HEAT than H + A (5.6% [-0.3% to 11.7%]; P = .076). Changes in hemoglobin mass were not significant (P = .176), despite a ∼1.2% increase in ALTI and H + A and a ∼0.7% decrease in CAMP and HEAT. Peak rectal temperature was lower during a postcamp heat-response test in HEAT (0.3 °C [0.1-0.5]; P = .010) and H + A (0.3 °C [0.1-0.6]; P = .005). Oxygen saturation upon waking was lower in ALTI (3% [2% to 5%]; P < .001) and H + A (4% [3% to 6%]; P < .001) than CAMP and HEAT. CONCLUSION Although blood and plasma volume increased following the camp, sleeping at altitude impeded the increase when training in the heat and only marginally increased hemoglobin mass. Heat training induced adaptations commensurate with partial heat acclimation; however, combining heat training and altitude training and confinement during a training camp did not confer concomitant hematological adaptations.

The growth of sport science technology is enabling more sporting teams to implement athlete monitoring practices related to performance testing and load monitoring. Despite the increased emphasis on youth athlete development, the lack of longitudinal athlete monitoring literature in youth athletes is concerning, especially for indoor sports such as basketball. The aim of this study was to evaluate the effectiveness of six different athlete monitoring methods over 10 weeks of youth basketball training. Fourteen state-level youth basketball players (5 males and 9 females; 15.1 ± 1.0 years) completed this study during their pre-competition phase prior to their national basketball tournament. Daily wellness and activity surveys were completed using the OwnUrGoal mobile application, along with heart rate (HR) and inertial measurement unit (IMU) recordings at each state training session, and weekly performance testing (3x countermovement jumps [CMJs], and 3x isometric mid-thigh pulls [IMTPs]). All of the athlete monitoring methods demonstrated the coaching staff’s training intent to maintain performance and avoid spikes in workload. Monitoring IMU data combined with PlayerLoad™ data analysis demonstrated more effectiveness for monitoring accumulated load (AL) compared to HR analysis. All six methods of athlete monitoring detected similar trends for all sessions despite small-trivial correlations between each method (Pearson’s correlation: −0.24 < r < 0.28). The use of subjective monitoring questionnaire applications, such as OwnUrGoal, is recommended for youth sporting clubs, given its practicability and low-cost. Regular athlete education from coaches and support staff regarding the use of these questionnaires is required to gain the best data.

Background There is extensive research investigating the match demands of players in the Australian Football League (AFL). Objective This systematic literature review and meta-regression sought to analyse the evolution of in-game demands in AFL matches from 2005 to 2017, focusing on the relationship between volume and intensity. Methods A systematic search of Ovid MEDLINE, Embase, Emcare, Scopus, SPORTDiscus, and Cochrane Library databases was conducted. Included studies examined the physical demands of AFL matches utilising global positioning system (GPS) technology. Meta-regression analysed the shift in reported volume (total distance and total match time) and intensity (metres per minute [m.min −1 ], sprint duration and acceleration) metrics for overall changes, across quarters and positional groups (forwards, nomadics and defenders) from 2005 to 2017 inclusive and for each year between 2005 and 2007, 2007 and 2010, 2010 and 2012, and 2012 and 2015/2017 breakpoints. Results Distance ( p = 0.094), m.min −1 ( p = 0.494), match time ( p = 0.591), time over 18 km·h −1 ( p = 0.271), and number of accelerations greater than 4 km·h −1 ( p = 0.498) and 10 km·h −1 ( p = 0.335) in 1 s did not change from 2005 to 2017. From 2005 to 2007 volume decreased (− 6.10 min of match time; p = 0.010) and intensity increased (6.8 m.min −1 increase; p = 0.023). Volume and intensity increased from 2007 to 2010, evidenced by increases in total distance (302 m; p = 0.039), time over 18 km·h −1 (0.31 min; p = 0.005), and number of accelerations greater than 4 km·h −1 (41.1; p = 0.004) and 10 km·h −1 (3.6; p = 0.005) in 1 s. From 2010 to 2012, intensity decreased, evidenced by reductions in metres per minute (− 4.3; p = 0.022), time over 18 km·h −1 (− 0.93 min; p < 0.001), and number of accelerations greater than 4 km·h −1 (− 104.4; p < 0.001) and 10 km·h −1 (− 8.3; p < 0.001) in 1 s, whilst volume stabilised with no changes in distance ( p = 0.068) and match time ( p = 0.443). From 2012 to 2015/2017 volume remained stable and intensity increased with time over 18 km·h −1 (0.27 min; p = 0.008) and number of accelerations greater than 4 km·h −1 (31.6; p = 0.016) in 1 s increasing. Conclusions Changes in volume and intensity of AFL match demands are defined by discrete periods from 2007 to 2010 and 2010 to 2012. The interaction of rule and interpretation changes and coaching strategies play a major role in these evolutionary changes. In turn, modified game styles impact player game demands, training, and selection priorities. Standardisation and uniformity of GPS data reporting is recommended due to inconsistencies in the literature.

Purpose: To investigate whether including heat and altitude exposures during an elite team-sport training camp induces similar or greater performance benefits. Methods: The study assessed 56 elite male rugby players for maximal oxygen uptake, repeated-sprint cycling, and Yo-Yo intermittent recovery level 2 (Yo-Yo) before and after a 2-week training camp, which included 5 endurance and 5 repeated-sprint cycling sessions in addition to daily rugby training. Playerswere separated into 4 groups: (1) control (all sessions in temperate conditions at sea level), (2) heat training (endurance sessions in the heat), (3) altitude (repeated-sprint sessions and sleeping in hypoxia), and (4) combined heat and altitude (endurance in the heat, repeated sprints, and sleeping in hypoxia). Results: Training increased maximal oxygen uptake (4%[10%], P =.017), maximal aerobic power (9%[8%], P <.001), and repeated-sprint peak (5%[10%], P =.004) and average power (12%[14%], P <.001) independent of training conditions. Yo-Yo distance increased (16% [17%], P <.001) but not in the altitude group (P =.562). Training in heat lowered core temperature and increased sweat rate during a heat-response test (P <.05). Conclusion: A 2-week intensified training camp improved maximal oxygen uptake, repeatedsprint ability, and aerobic performance in elite rugby players. Adding heat and/or altitude did not further enhance physical performance, and altitude appears to have been detrimental to improving Yo-Yo.

Magnetic-inertial measurement units (MIMUs) are becoming more prevalent in sports biomechanics and may be a viable tool to evaluate kinematic parameters. This study examined the accuracy of a MIMU to estimate orientation angles under static conditions and dynamically from a squash racket during a forehand drive shot. A MIMU was mounted onto a goniometer and moved through 0-90 degrees, with static data collected at 10 degrees increments during 10 repetitions of all three axes. Typical error analyses showed the MIMU to be very reliable (TE <= 0.03 degrees). MIMU accuracy was determined via intraclass correlation coefficients (ICC) (r > 0.999, p < 0.001). An ordinary least products regression showed no proportional bias and minimal fixed bias for all axes. Dynamic accuracy was assessed by comparing MIMU and optical motion capture data of squash racket swing kinematics. A MIMU was fixed onto a racket and 10 participants each hit 10 forehand shots. Mean orientation angle error at ball impact was <0.50 degrees and ICC showed very high correlations (r >= 0.988, p < 0.001) for all orientations. Swing phase root mean squared errors were <= 2.20 degrees. These results indicate that a MIMU could be used to accurately and reliably estimate selected racket swing kinematics.

PURPOSE: This study examined changes in anthropometric and cardiorespiratory fitness (CRF) characteristics of 26,325 Grade 6 (G6) schoolboys (11.0 -12.99 y) living in the State of Qatar between 2003-2016. METHODS: Anthropometric measures included standing height (cm), body mass (kg) and body mass index (BMI, kg/m2). A multistage shuttle run test (MSRT, laps) was used to assesses CRF. Comparisons between Qatari and non-Qatari boys were also conducted. RESULTS: The results showed a trend for decreasing CRF (less MSRT laps) and increasing fatness (higher BMI) across the study period, irrespective of nationality. Qatari students generally performed worse on the MSRT test and were fatter than their non-Qatari peers. Also, the Qatari students displayed bigger decreases in MSRT (10 vs 4 laps) and their body mass (2.5 vs 0.7 kg) and BMI (1.3 vs 0.6 kg/m2) increased more over the study period than their non-Qatari peers. Furthermore, the percentage of G6 schoolboys classified as overweight or obese increased over the study period for all nationalities, with Qatari boys showing a greater prevalence of overweight or obesity than their non-Qatari peers. For example, the percentage of Qatari boys classified as overweight or obese by Centers for Disease Control and Prevention (CDC) standards increased from an average of 40.1% between 2003-2006 to 49.3% between 2013-2016 while the average for non-Qatari boys increased from 32.6% to 39.9% for the same periods. CONCLUSIONS: These data support the need to establish a mechanism for the prevention and treatment of obesity and the development of physical activity strategies in the State of Qatar.

This study aimed to identify the minimum increment duration required to accurately assess 2 distinct lactate thresholds. A total of 21 elite rowers (12 women and 9 men) participated in this study, and each performed 8 or 9 rowing tests comprising 5 progressive incremental tests (3-, 4-, 5-, 7-, or 10-min steps) and at least three 30-min constant-intensity maximal lactate steady-state assessments. Power output (PO) at lactate threshold 1 was higher in the 3- and 4-min incremental tests. No other measures were different for lactate threshold 1. The PO at the second lactate threshold was different between most tests and was higher than the PO at maximal lactate steady state, except for the 10-min incremental test. Lactate threshold 2 oxygen consumption was higher in the 3-, 4-, and 5-min tests, but heart rate (HR) and rating of perceived exertion were not different between tests. Peak PO in the incremental tests was inversely related to the step durations (r2 = .86, P ≤ .02). Peak oxygen consumption was higher in the shorter (≤5 min) than the longer (≥7 min) incremental tests, whereas peak HR was not different between tests. These data suggest that for the methods used in this study, incremental exercise tests with step durations ≤7 min overestimate maximal lactate steady-state exercise intensity, peak physiological values are best determined using incremental tests with step durations ≤4 min, and HR measures are not affected by step duration, and therefore, prescription of training HRs can be made using any of these tests.

BACKGROUND: The aim of this study was to identify the exercise intensity that elicited the highest rate of fat utilization (FAT(max)) and to assess its relationship with the aerobic threshold (AeT) in male athletes. We hypothesized existence of high correlation of these two parameters when a short-staged graded treadmill test with AeT identified through breath-by-breath gas exchange analysis was used. METHODS: Fifty-six trained male athletes (age 25.6 +/- 3.4 y, height 197.81 +/- 5.6 cm, body mass 98.5 +/- 6.6 kg) participated in the study. Pearson correlation coefficient (r) and effect size (R-2) were used to evaluate the existence of connection between VO2 at AeT and at FAT(max) Maximal oxygen consumption (VO2max) and substrate oxidation were determined using breath-by-breath indirect calorimetry during a short-staged graded treadmill test to exhaustion. RESULTS: Mean VO2max was 52.12 +/- 9.02 mL.kg(-1).min(-1). FAT(max) and AeT occurred at 47.47 +/- 10.59% of VO2max. and 45.95 +/- 10.21% of VO2max, respectively. Fat utilization at FAT(max) was 0.59 +/- 0.24 gmin(-1). A high correlation was found between VO2 at FAT(max) and at AeT (r =0.88, P<0.01, 95% CI: 0.80 to 0.93). The effect size was 77.44%. CONCLUSIONS: Our results confirm the hypothesis of an existence of a high correlation between AeT and FAT(max) allowing implementation of more accurate training approach.

The haematological module of the Athlete's Biological Passport (ABP) has significantly impacted the prevalence of blood manipulations in elite sports. However, the ABP relies on a number of concentration-based markers of erythropoiesis, such as haemoglobin concentration ([Hb]), which are influenced by shifts in plasma volume (PV). Fluctuations in PV contribute to the majority of biological variance associated with volumetric ABP markers. Our laboratory recently identified a panel of common chemistry markers (from a simple blood test) capable of describing ca 67% of PV variance, presenting an applicable method to account for volume shifts within anti-doping practices. Here, this novel PV marker was included into the ABP adaptive model. Over a six-month period (one test per month), 33 healthy, active males provided blood samples and performed the CO-rebreathing method to record PV (control). In the final month participants performed a single maximal exercise effort to promote a PV shift (mean PV decrease -17%, 95% CI -9.75 to -18.13%). Applying the ABP adaptive model, individualized reference limits for [Hb] and the OFF-score were created, with and without the PV correction. With the PV correction, an average of 66% of [Hb] within-subject variance is explained, narrowing the predicted reference limits, and reducing the number of atypical ABP findings post-exercise. Despite an increase in sensitivity there was no observed loss of specificity with the addition of the PV correction. The novel PV marker presented here has the potential to improve the ABP's rate of correct doping detection by removing the confounding effects of PV variance.

Background: The increasing focus on international sporting success has led to many countries introducing sport schools and academies. Limited empirical evidence exists that directly compares student-athletes from different continents. This study investigated whether male Australian and Qatari student-athletes differ in anthropometry, physical fitness and biological maturity. Methods: 150 male student-athletes (72 Qatari, 78 Australian; age = 11.8 - 18.6 y) completed a fitness testing session involving anthropometric (standing height, sitting height, leg length, body mass, peak height velocity (PHV) measures) and physical capacity (40 m sprint, countermovement jump (CMJ), predicted maximal oxygen uptake (VO2 max) tests. Differences were assessed using a one-way multivariate analysis of variance (MANOVA), effect size (Cohen’s d) and regression coefficients. Results: The Australian student-athletes possessed a greater standing height and body mass (P < 0.01) at their age at PHV (APHV) and had an increased rate of leg length development (P < 0.05) in contrast to the sitting height of the Qataris (P < 0.01). The Qatari student-athletes had significantly (P < 0.01) faster 40 m sprint times (mean ± SD: 5.88 ± 0.53 vs 6.19 ± 0.44 s) and greater CMJ heights (36.9 ± 7.2 vs 34.0 ± 6.0 cm) than their Australian counterparts. Although not statistically different, the Qatari student-athletes also matured earlier (APHV: d = 0.35) and had greater aerobic power results (predicted VO2 max: d = 0.22). Conclusions: Despite lower stature and body mass values, Qatari student-athletes exhibited physical fitness ascendancy over their Australian counterparts.

Plasma volume and red cell mass are key health markers used to monitor numerous disease states, such as heart failure, kidney disease, or sepsis. Nevertheless, there is currently no practically applicable method to easily measure absolute plasma or red cell volumes in a clinical setting. Here, a novel marker for plasma volume and red cell mass was developed through analysis of the observed variability caused by plasma volume shifts in common biochemical measures, selected based on their propensity to present with low variations over time. Once a month for 6 months, serum and whole blood samples were collected from 33 active males. Concurrently, the CO-rebreathing method was applied to determine target levels of hemoglobin mass (HbM) and blood volumes. The variability of 18 common chemistry markers and 27 Full Blood Count variables was investigated and matched to the observed plasma volume variation. After the removal of between-subject variations using a Bayesian model, multivariate analysis identified two sets of 8 and 15 biomarkers explaining 68% and 69% of plasma volume variance, respectively. The final multiparametric model contains a weighting function to allow for isolated abnormalities in single biomarkers. This proof-of-concept investigation describes a novel approach to estimate absolute vascular volumes, with a simple blood test. Despite the physiological instability of critically ill patients, it is hypothesized the model, with its multiparametric approach and weighting function, maintains the capacity to describe vascular volumes. This model has potential to transform volume management in clinical settings. Am. J. Hematol. 92:62-67, 2017. © 2016 Wiley Periodicals, Inc.

Monitoring the load placed on athletes in both training and competition has become a very hot topic in sport science. Both scientists and coaches routinely monitor training loads using multidisciplinary approaches, and the pursuit of the best methodologies to capture and interpret data has produced an exponential increase in empirical and applied research. Indeed, the field has developed with such speed in recent years that it has given rise to industries aimed at developing new and novel paradigms to allow us to precisely quantify the internal and external loads placed on athletes and to help protect them from injury and ill health. In February 2016, a conference on "Monitoring Athlete Training Loads-The Hows and the Whys" was convened in Doha, Qatar, which brought together experts from around the world to share their applied research and contemporary practices in this rapidly growing field and also to investigate where it may branch to in the future. This consensus statement brings together the key findings and recommendations from this conference in a shared conceptual framework for use by coaches, sport-science and -medicine staff, and other related professionals who have an interest in monitoring athlete training loads and serves to provide an outline on what athlete-load monitoring is and how it is being applied in research and practice, why load monitoring is important and what the underlying rationale and prospective goals of monitoring are, and where athlete-load monitoring is heading in the future.

This study examined the validity of the Hunt Squash Accuracy Test (HSAT) for predicting within-game shot performance and tournament rank. Shots from eight male junior squash players performing the HSAT and tournament match-play were analysed. A typical-error analysis from repeated trials showed the HSAT to be very reliable (1.82%). HSAT rank had significant correlations (p < 0.05) to tournament rank (r = 0.98) and tournament shot success (r = 0.95). HSAT score showed significant correlations to the percentage of winning shots during match-play (r = 0.88). HSAT shots with significant correlations to successful match-play shots were backhand-drive (r = 0.92) and backhand-volley (r = 0.97). These results suggest the HSAT is a valid method of assessing the accuracy and performance of junior squash players. It could potentially be used to track shot improvements and predict match-play performance.

Primary affiliations: Bourdon, Cardinale, Murray, and Cable, Sport Science, and Varley and Gregson, Football Performance and Science, Aspire Academy, Doha, Qatar. Gastin, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, Australia. Kellmann, Faculty of Sport Science, Ruhr-University Bochum, Germany. Gabbett, Inst for Resilient Regions, University of Southern Queensland, Ipswich, Australia. Coutts, Sport and Exercise Discipline Group, University of Technology Sydney, Australia. Burgess, Port Adelaide Football Club, Adelaide, Australia. Secondary affiliations: Bourdon and Burgess, School of Health Sciences, University of South Australia, Adelaide. Cardinale, University of St Mark & St John, Plymouth, UK. Murray, Athletics Dept, University of Oregon, Eugene. Kellmann, School of Human Movement and Nutrition Sciences, University of Queensland, Brisbane, Australia. Varley, Inst of Sport, Exercise and Active Living, Victoria University, Melbourne, Australia. Gabbett, Gabbett Performance Solutions, Brisbane, Australia. Gregson and Cable, Research Inst for Sport and Exercise Sciences, Liverpool Johns Moore University, UK Address author correspondence to Pitre C. Bourdon at pitre.bourdon@aspire.qa. https://doi.org/10.1123/IJSPP.2017-0208

Leptin and adiponectin play an essential role in energy metabolism. Leptin has also been proposed as a marker for monitoring training load. So far, no studies have investigated the variability of these hormones in athletes and how they are regulated during cumulative exercise. This study monitored leptin and adiponectin in 15 endurance athletes twice daily in the days before, during and after a 9-day simulated cycling stage race. Adiponectin significantly increased during the race (p = 0.001) and recovery periods (p = 0.002) when compared to the baseline, while leptin decreased significantly during the race (p < 0.0001) and returned to baseline levels during the recovery period. Intra-individual variability was substantially lower than inter-individual variability for both hormones (leptin 34.1 vs. 53.5%, adiponectin 19% vs. 37.2%). With regards to exercise, this study demonstrated that with sufficient, sustained energy expenditure, leptin concentrations can decrease within the first 24 hours. Under the investigated conditions there also appears to be an optimal leptin concentration which ensures stable energy homeostasis, as there was no significant decrease over the subsequent race days. In healthy endurance athletes the recovery of leptin takes 48-72 hours and may even show a supercompensation-like effect. For adiponectin, significant increases were observed within 5 days of commencing racing, with these elevated values failing to return to baseline levels after 3 days of recovery. Additionally, when using leptin and adiponectin to monitor training loads, establishing individual threshold values improves their sensitivity.

Recently, 3D body scanning has emerged as a promising alternative to traditional anthropometry. However, various steps in the 3D data collection process and analysis can potentially introduce error. PURPOSE: To assess the reliability and validity of the 3D body scanning (3D) technology for use in the assessment of anthropometric variables. METHODS: Physical measurements (PM) were collected using both traditional ISAK protocols and 3D body scans on 30 highly trained young male athletes (age 15.8 ± 1.7 y, body mass 62.9 ± 15.1 kg, height 174.0 ± 7.9 cm). Digital data analyses of the 3D scans were carried out in duplicate by 3 trained technicians. Interclass correlation coefficients (ICC) and relative technical error of measurements (%TEM) were calculated to assess the reliability of the 3D analyses between technicians, and also between the 3D and PM data. Pearson correlation and a paired sample t test (p ≤ 0.05) were also conducted. RESULTS: There was no significant difference for intra-retest reliability (ICC: 037 - 1.00) and inter-rater reliability for the 3D analyses (ICC: 0.63 - 1.00) or between each technicians 3D data analyses and the PM (ICC: 0.47 - 1.00). 3D intra-tester %TEMs (≤ 1%) showed agreement for height and 4 body lengths. Inter-tester %TEMs (≤ 1.5%) showed agreement for height and 5 body lengths between 3D data analyses and PM. The correlation analyses revealed moderate to strong but statistically significant Pearson correlation coefficients (range: 0.67 - 0.99) for all variables. Results of the paired t tests were significant for several variables, indicating a significant difference between the 3D and PM. CONCLUSIONS: This study shows that there is evidence for the repeatability of 3D measurements when assessing height, breadths, and several limb lengths and girths. However, 3D collection cannot exactly replicate the techniques used for PM (i.e. ISAK protocols). Therefore, a combination of both digital and physical methods should be considered.

The purpose of this study was to examine the validity of the Hunt Squash Accuracy Test (HSAT) for predicting within-game performance on specific shot types. A correlation analysis compared the accuracy of specific shots during tournament match-play to the accuracy of the corresponding shot type in the HSAT. The correlation of the overall HSAT score against tournament rank was significantly large (0.95), as were the total % shot accuracy (0.90), total % backhand (BH) accuracy (0.94) and total % forehand (FH) accuracy (0.77) correlations. The only specific HSAT shot types with significantly large correlations to the corresponding match-play shots were; BH straight drive (0.92) and BH straight volley (0.97). The remaining shot types; boast, volley-drop and drops all showed non-significant correlations on both the BH and FH sides.