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This study aimed to optimise performance prediction in short-course swimming through Principal Component Analyses (PCA) and multiple regression. All women’s freestyle races at the European Short-Course Swimming Championships were analysed. Established performance metrics were obtained including start, free-swimming, and turn performance metrics. PCA were conducted to reduce redundant variables, and a multiple linear regression was performed where the criterion was swimming time. A practical tool, the Potential Predictor, was developed from regression equations to facilitate performance prediction. Bland and Altman analyses with 95% limits of agreement (95% LOA) were used to assess agreement between predicted and actual swimming performance. There was a very strong agreement between predicted and actual swimming performance. The mean bias for all race distances was less than 0.1s with wider LOAs for the 800 m (95% LOA −7.6 to + 7.7s) but tighter LOAs for the other races (95% LOAs −0.6 to + 0.6s). Free-Swimming Speed (FSS) and turn performance were identified as Key Performance Indicators (KPIs) in the longer distance races (200 m, 400 m, 800 m). Start performance emerged as a KPI in sprint races (50 m and 100 m). The successful implementation of PCA and multiple regression provides coaches with a valuable tool to uncover individual potential and empowers data-driven decision-making in athlete training.

Objectives To evaluate the new age groups of the World Junior Championships in swimming from a scientific perspective, establish benchmarks and performance corridors that predict success at peak performance age and compare performance corridors between men and women and short-, middle-, and long-distance freestyle races. Design Longitudinal big data analysis. Methods In total, 347,186 annual best times of male (n = 3360, 561 ± 177 Swimming Points) and female freestyle swimmers (n = 2570, 553 ± 183 Swimming Points) were collected across all race distances at peak performance age and retrospectively analyzed throughout adolescence. Cumulative Poisson distribution was used to calculate probabilities of becoming world-class finalist, international-class, or national-class swimmer for each age group. Performance corridors were expressed relative to the World Record and compared between performance levels, sex, race distances, and age groups with a 2-way analysis of variance. Results Females are required to swim faster relative to the World Record at a younger age and show earlier performance plateaus than males at national and international levels. Additionally, world-class long-distance finalists show higher Swimming Points earlier in their career compared to short-distance swimmers. This effect is more distinctive in females than males. Conclusions Based on the sex-specific performance corridors and developments, the newly aligned age groups for the World Junior Championships are questionable regarding long-term athlete development. Based on race times from 131 nations, the present benchmarks provide valid international normative values to predict success chances at peak performance age and guide young swimmers along their talent pathway. Abbreviations FINA, Fédération Internationale de Natation; 850 swimmers, world-class finalists; 750 swimmers, international-class swimmers; 650 swimmers, national-class swimmers

Introduction: This study (i) examined Norwegian and Swedish sports coaches’ employment, practices, and beliefs during the first wave of the COVID-19 pandemic, (ii) compared these aspects between coaches in Norway and Sweden, two countries with clearly different movement restrictions strategies in this period. Methods: An online survey was distributed to coaches via email and social media.The survey was open between June and August 2020. In total, 348 coaches responded, 141 from Norway, and 207 from Sweden. Results: Among responders, 2% had lost their job due to the pandemic, 17% had been furloughed, 28% worked from home office, and 39% worked as usual. Norwegian coaches were more likely to work from home (48% vs. 15%, p < .001), while Swedish coaches were more likely to work as usual (60% vs. 9%, p < .001). Coaches in both countries communicated less frequently with their athletes (p < .001) and had less in-person communication (p < .001) compared to pre-Covid levels. Larger declines existed among Norwegian coaches regarding communication frequency (p < .001) and in-person communication (p < .001). Video calls and phone calls usage increased (p < .001 and p = .009 respectively). We recorded low levels of concern among coaches about the effects of the pandemic on their relationship with their athletes. There were considerable levels of concern about athletes’ maintaining their motivation to train (Norway: 43.3%, Sweden: 50.7%), and low levels of concern about the coaches’ relationships with their athletes (Norway: 14.1%, Sweden: 17.8%). Discussion: Overall, this study showed the imposed movement restrictions had several negative consequences for the employment and work practices of sports coaches in Norway and Sweden. However, it also highlighted that coaches were able to adapt their work practices to the constraints and were able to maintain relationships with their athletes. The consequences raised in this paper can act as a guide during possib

INTRODUCTION: A greater usage of gear 3 is associated with improved performance in cross-country skiing. However, it remains unclear whether rifle carriage in biathlon affects sub-technique choice, and therefore biathlon skiing performance. Therefore,this study aimed to investigate whether rifle carriage affects gear distribution during on-snow skiing in biathletes, and whether there are any associated sex differences. METHODS: Twenty-eight tier 3 biathletes (17 men, 11 women) skied a 2230-m lap at competition speed twice, once with the rifle (WR) and once without the rifle (NR) in a counter-balanced and randomized order. The course was divided into seven uphill sections, three flat sections and seven downhill sections. The biathletes wore aportable 3D-motion analysis system while skiing, which together with GNSS data enabled the characterization of distance covered and time spent in gears 2–4 and skiing without poles (NP). A two-way mixed model ANOVA was used to investigate whether rifle carriage and/or sex influenced gear distribution or performance. RESULTS: Skiing WR increased the lap time compared to NR (412 (90) vs.395 (91) s, p < 0.001). The biathletes used gear 2 to a greater extent during WR compared to NR (distance: 413 ± 139 vs. 365 ± 142 m; time: 133 (95) vs. 113 (86) s;both p < 0.001) and gear 3 to a lesser extent (distance: 713 ± 166 vs. 769 ± 182 m,p < 0.001; time: 141 ± 33 vs. 149 ± 37 s, p < 0.01) without differences in gear 4 or NP.The women demonstrated a greater proportional use of gear 2 compared to the men (p < 0.05). For the two longest uphill sections, the uphill with a more moderate incline (4.5°) revealed more differences between WR and NR in the use of gears 2 and 3 compared to the uphill with a steeper incline (6.7°). Increased usage of gear 2 (distance and time) was also negatively related to performance during WR and NR (r = 0.516–0.901, p < 0.01). DISCUSSION/CONCLUSION: Rifle carriage decreases the distance covered an

Purpose To examine the micro-pacing (within-lap) strategies during biathlon skiing with and without the biathlon rifle. Methods Twenty biathletes (7 women, 13 men) performed two crosscountry skiing time-trials on a ≈2300 m course, once with and once without the biathlon rifle. During time-trials, biathletes wore a sensor that recorded position, distance and skiing-speed. A trajectory correction and statistical parametric mapping procedure determined the course positions (clusters) where instantaneous skiing-speed was significantly related to time-trial performance. The time differences between the fastest and slowest skier in these clusters were calculated. Results The fastest biathletes skied with greater instantaneous speeds in specific clusters, which included both uphill and downhill sections. The clusters represented time gains for the fastest skier over the slowest skier of between 16.1 and 25.8 s for the women and between 18.9 and 21.9 s for the men. The largest time gains between the fastest and slowest biathletes were observed in a downhill section that was preceded by a 180° turn, where time gains were between 2.9 and 4.1 s in clusters of between just 12 to 62 m. In biathlon skiing with-rifle, there were more clusters that were not present during without-rifle. When skiing with-rifle, there were additional clusters in the uphill sections that represented time gains of 5.2 s and 2.3 s for the women and men, respectively. Conclusions Statistical parametric mapping can be used in biathlon to provide pacing and performance feedback to athletes and coaches.

INTRODUCTION: At upper secondary school (USS) with a sport profile, late adolescent athletes choose to specialize in one sport and balance an academic career while they aim to become a top-level athlete. Hence, the aim for this study was to examine the distribution in endurance training volume and intensity in two different performance groups of biathletes during their student-athlete pathway at USS. METHODS: Retrospective training data of thirty adolescent biathletes (15 men and 15 women) was collected. The training data included training volume (h·y-1) and intensity distribution, using a five-zone model (Seiler, 2010). Inclusion criteria was completion of four years of USS (16 to 19 years old) and with a continued competing in biathlon post USS. The biathletes were grouped based on biathlon performance: that if they during or after USS were selected as a nationalteam biathlete (NTB, n = 9) by the Swedish Biathlon Federation or stayed a national level biathlete (NLB, n = 21). A linear mixed model was fitted for assessing the relationship between performance group and age-group on the training characteristics. RESULTS: NTB and NLB had equal training volume and intensity distribution at year one, two and three. At year four NTB had a ~30% greater training volume than NLB (594 ± 71 h·y-1 vs 461 ± 127 h·y-1, p = .003) while performing the same number of training sessions (NTB, 359 ± 40 n, NLB, 320 ± 83 n, p = 0.878). There was no difference in training distribution at year one, two and three. At year four the NTB accumulated greater training volume compared to NLB in zone 1 (495 ± 54 vs 387 ± 107, p = .008) and zone 3 (21 ± 12 vs 13 ± 6, p = .019) respectively. DISCUSSION/CONCLUSION: This study showed that a greater overall training volume during the last year of USS is of importance to become a NTB compared to NLB. The increased training volume for NTB was distributed in zone 1 and 3. The equal amount of training sessions indicates that NTB trained longer sessions.

The purpose of this study was to retrospectively describe the longitudinal changes of training variables in adolescent biathletes based on performance level. Thirty biathletes (15 men and 15 women) were included in the study and categorized as either national level biathletes (NLB, n = 21) or national team biathletes (NTB, n = 9). Retrospective training data was collected from training diary covering the biathletes’ four years (Y1-Y4) as student-athletes at upper secondary school. Training data was divided into physical and shooting training variables. A linear mixed-effect model was used for comparing the difference of the performance group and year of upper secondary school on training characteristics. The NTB group achieved a greater annual training volume than the NLB group, especially during Y4 (594±71 h·y-1 vs 461±127 h·y-1, p < 0.001), through an increase in duration of each session and by completing more weekly training volume during the general phase (13.7±4.6 vs 10.0±4.9 h·w-1, p = 0.004). No difference was observed in relative training intensity distribution between the groups. The total number of shots fired was also greater for the NTB (9971±4716 vs 7355±2812 shots·y-1, p = 0.003). There was an equal frequency in illness and injury for both the NLB and NTB. Accordingly, the results of the present study describe longitudinal changes of biathlon training in adolescent biathletes that also may affect performance development.

The aim of this study was to determine the impact of runs 1 and 2 on overall rank in Giant Slalom. Data from 15 seasons (2005/2006–2019/2020) including and unique starts for women (n = 2,294) and men (n = 2,328) were analyzed. Skiers were grouped based on final ranks 1–3 (G3), 4–10 (G10), and 11–20 (G20) and separately analyzed for women and men. A Wilcoxon-signed rank test was used for comparisons between runs 1 and 2, while a multi-nominal logistic regression was used to identify odds ratios (OR) associated with group rank. Women had similar run times for runs 1 and 2 (p = 0.734), while men had faster times on run 2 (p < 0.001). The strongest association to G3 was during run 1 for run time (men: OR 1.06–1.12; women: OR 1.06–1.11, all p < 0.01) and gate-to-gate times (men: OR 33–475; women: OR 81–2,301, all p < 0.001). Overall, this study demonstrates the importance of a fast first run for improving the final ranking group and the need to increase the tempo going from the first to the second run for men. Copyright © 2022 Björklund and Swarén.

This study aimed to investigate how skiing speed (SS), number of missed targets (MT) and range time (RT) were associated with final rank in biathlon pursuit and mass start competitions. Data were collected from the International Biathlon Union’s database over 17 seasons. Biathletes were categorised into three groups (rank 1–3, G3; rank 4–10, G10; rank 11–20, G20). Multinomial regression was used to identify odds ratios associated with group rank in both sexes. The only variable found to be consistently related to G3 in both pursuit and mass start was MT (men OR 1.206–1.729 and women OR 1.340–3.124, all p < 0.01). SS during lap four of pursuit and mass start was most strongly related to G3 for both sexes (men OR 0.231–0.094 and women OR 0.339–0.126, all p < 0.001). RT during shooting four in pursuit was most strongly related to G3 compared to G10 and G20 (men OR 1.067; 95% CI, 1.030–1.105 and women OR 1.076; 95% CI, 1.020–1.134, all p < 0.001, respectively). Accordingly, MT was most strongly related to final rank in both sexes, while SS during lap four and last RT was also significant.

The aim was to investigate whether rifle carriage affects gear distribution during on-snow skiing in highly-trained biathletes, and whether there were any associated sex differences. Twenty-eight biathletes (11 women, 17 men) skied a 2230-m lap at competition speed twice, one lap with the rifle (WR) and the other lap without the rifle (NR). The biathletes wore a portable 3D-motion analysis system while skiing, which enabled characterisation of distance and time in different gears. Skiing WR increased lap time compared to NR (412 (90) vs. 395 (91) s, p < 0.001). The biathletes used gear 2 to a greater extent WR compared to NR (distance: 413 ± 139 vs. 365 ± 142 m; time: 133 (95) vs. 113 (86) s; both p < 0.001) and gear 3 less (distance: 713 ± 166 vs. 769 ± 182 m, p < 0.001; time: 141 ± 33 vs. 149 ± 37 s, p = 0.008), with similar patterns for women and men. Differences between WR and NR in the use of gears 3 and 2 were more extensive for moderate compared to steeper uphill terrain. Rifle carriage increased the use of gear 2, which was negatively associated with performance. Therefore, preparing biathletes to be able to cover more distance in gear 3 WR, especially in moderate uphill terrain, may improve biathlon skiing performance.

Aim To assess the effect of rifle carriage on skiing performance and angular kinematics in biathletes,and whether the rifle’s effect on angular kinematics could explain its effect on skiing performance, under ecologically valid conditions. Methods Twenty-nine biathletes (11 females, 18 males) performed two maximum effort skiing time-trials on snow, once with and once without rifle. During time-trials, biathletes wore a three-dimensional motion analysis system to measure maximum angle, minimum angle and range of motion of knee, hip and elbow flexion. A two-way mixed model analysis of variance and multilevel linear model were used to assess the effect of rifle carriage on skiing performance and angular kinematics respectively. In case of a significant effect, a Pearson correlation coefficient was calculated to assess the relation between the rifle’s effect on the respective angular measure and the rifle’s effect on skiing speed. Results Skiing performance was impaired with rifle compared to without rifle (P<.001; ƞ2p=0.04). No effect of rifle carriage was observed on any angular measure, except for maximum elbow flexion(P=.03; Cohen f 2<0.01). Maximum elbow flexion was reduced with rifle compared to without rifle (mean difference 11.9±37.6°). Pearson correlation coefficient revealed a positive correlation between the rifle’s effect on maximum elbow flexion and its effect on skiing speed (r=0.19; P=.04). Practical application The results demonstrate the negative impact of rifle carriage on biathlon skiing performance and suggest that maximum elbow flexion plays a role. Therefore, upper body contributions, elbow flexion angles in particular, might be of interest for biathlon coaches.

The purpose of this study was to develop a method which uses positioning data to analyse player-specific skating characteristics and to investigate the possibility to use a cost function to generate new individual and game-specific training drills. Positioning data and video from a game for professional ice hockey team in the Swedish Hockey League were synchronized, and nine different skating characteristics were manually identified and tagged for two forward players. A cost function was developed to generate individual, continuous skating sequences and to create new individual and game-specific skating drills. Skating forward was the most commonly used skating characteristic for both player but the numbers of times the players used the different skating characteristics varied, (H(8) = 23.2, p = 0.003). The number of skating characteristics between the two players differed, (chi(2) (8, N = 688) = 3 4.0, p < 0.001). While the total average time spent in a skating characteristic was 2.01 +/- 1.13 s, time spent within each skating characteristic varied (p < 0.001). The presented method could generate individual skating drills with different attributes and shows promising results for creating individual and game-specific tests and training drills for ice hockey players, based on individual and game-specific skating characteristics.

Purpose: Investigate the effect of biathlon rifle carriage on physiological and accelerometer-derived responses during biathlon skiing. Methods: Twenty-eight biathletes (11F, 17M) completed two XC skiing time-trials (~2,300 m), once with and once without the biathlon rifle, with concurrent measurements of HR, skiing speed and accelerations recorded from three triaxial accelerometers attached at the Upper-spine, Lower-spine and Pelvis. Exercise intensity was quantified from HR, skiing speed as well from accelerometry-derived PlayerLoad™ per minute (PL·min−1) and average net force (AvFNet). All metrics were analyzed during Uphill, Flat and Downhill sections of the course. Relationships between accelerometry-derived metrics and skiing speed were examined. Results: Time-trials were faster for males compared with females (mean difference: 97 ± 73 s) and No-Rifle compared to With-Rifle (mean difference: 16 ± 9 s). HR was greatest during Downhill (183 ± 5 bpm), followed by Uphill (181 ± 5 bpm) and was lowest in the Flat sections (177 ± 6 bpm, p <0.05). For PL·min−1 and AvFNet there were 3-way Rifle x Gradient x Sensor-Position interactions. Typically, these metrics were greatest during Uphill and Flat sections and were lowest during Downhill sections. Rifle carriage had no impact on the AvFNet at the Lower-Spine or Pelvis. Significant positive linear relationships were identified between skiing speed and accelerometer-derived metrics during Uphill, Flat and Downhill skiing (r = 0.12–0.61, p < 0.05). Conclusions: The accelerometry-derived approach used in this study provides the potential of a novel method of monitoring the external demands during skiing. In particular, AvFNet with sensors located close to the center of mass displayed greatest utility because it followed the expected response of external intensity where responses were greatest during uphill sections, followed by flats and lowest during downhills. In addition, there were significant positive relationsh

Purpose: The present study aimed to determine the association of skiing speed (SS), range time (RT), and the number of missed targets (MT) with rank in sprint and individual biathlon competitions. Methods: Data were collected from the International Biathlon Union's database for 17 seasons (2002/2003–2018/2019). Furthermore, the biathletes were divided into three rank groups (G3, rank 1–3; G10, rank 4–10; and G20, rank 11–20). Multinominal regression was used to detect odds ratios associated with group rank for both sexes, separately. Results: MT was the only variable that was constantly related to G3 (OR 1.90–6.35, all p < 0.001) for both women and men. SS was associated with G3 in the last lap in the sprint for both sexes (OR 0.46–0.66, all p < 0.001) and RT for standing shooting (OR 1.04–1.14, all p < 0.05). Conclusion: These results show that shooting is the fundamental factor for performance in both competitions, but that SS is increasingly important for the last lap in the sprint for both sexes. Further, a fast RT in the standing shooting for women in individual and men in the sprint seems important for improving final rank.

BACKGROUND: The effects of flywheel resistance training (FRT) on youth are relatively unknown. The aim of this study was to compare the effects of autoregulated FRT with traditional strength training (TST) on jumping, running performance and resistance training load progression in youth athletes. Thirty youth athletes (11.8 ± 0.9 yr) were matched for peak height velocity (PHV) status and block-randomised into two groups: FRT (n = 15, PHV -0.8 ± 1.6) and TST (n = 15, PHV -0.8 ± 1.5). Twelve resistance training sessions over a six-week intervention with flywheel or barbell squats were performed using autoregulated load prescription. Squat jump (SJ); countermovement jump (CMJ); and 10 m, 20 m and 30 m sprints were assessed pre- and post-intervention. The external load increased similarly for FRT and TST (z = 3.8, p = 0.06). SJ increased for both groups (p < 0.05) but running performance was unaffected (p > 0.05). CONCLUSIONS: FRT resulted in similar load progression and motor skill development in youth athletes as TST, but the perceived exertion was less. Autoregulation is a practical method for adjusting training load during FRT and should be considered as an alternative to autoregulated TST.

Introduction: To date, no study has compared anaerobic capacity (AnC) estimates computed with the maximal accumulated oxygen deficit (MAOD) method and the gross energy cost (GEC) method applied to treadmill running exercise. Purpose: Four different models for estimating anaerobic energy supply during treadmill running exercise were compared. Methods: Fifteen endurance-trained recreational athletes performed, after a 10-min warm-up, five 4-min stages at ∼55–80% of peak oxygen uptake, and a 4-min time trial (TT). Two linear speed-metabolic rate (MR) regression models were used to estimate the instantaneous required MR during the TT (MRTT_req), either including (5+YLIN) or excluding (5-YLIN) a measured Y-intercept. Also, the average GEC (GECAVG) based on all five submaximal stages, or the GEC based on the last submaximal stage (GECLAST), were used as models to estimate the instantaneous MRTT_req. The AnC was computed as the difference between the MRTT_req and the aerobic MR integrated over time. Results: The GEC remained constant at ∼4.39 ± 0.29 J⋅kg–1⋅m–1 across the five submaximal stages and the TT was performed at a speed of 4.7 ± 0.4 m⋅s–1. Compared with the 5-YLIN, GECAVG, and GECLAST models, the 5+YLIN model generated a MRTT_req that was ∼3.9% lower, with corresponding anaerobic capacities from the four models of 0.72 ± 0.20, 0.74 ± 0.16, 0.74 ± 0.15, and 0.54 ± 0.14 kJ⋅kg–1, respectively (F1.07,42 = 13.9, P = 0.002). The GEC values associated with the TT were 4.22 ± 0.27 and 4.37 ± 0.30 J⋅kg–1⋅m–1 for 5+YLIN and 5-YLIN, respectively (calculated from the regression equation), and 4.39 ± 0.28 and 4.38 ± 0.27 J⋅kg–1⋅m–1 for GECAVG and GECLAST, respectively (F1.08,42 = 14.6, P < 0.001). The absolute typical errors in AnC ranged between 0.03 and 0.16 kJ⋅kg–1 for the six pair-wise comparisons and the overall standard error of measurement (SEM) was 0.16 kJ⋅kg–1. Conclusion: These findings demonstrate a generally high disagreement in estimated anaerobic capacities be

The aim of this study was to investigate the influence of slope and speed on lower-limb kinematics and energy cost of running. Six well-trained runners (VO2max 72 ± 6 mL·kg−1·min−1) were recruited for the study and performed (1) VO2max and energy cost tests and (2) an experimental running protocol at two speeds, 12 km·h−1 and a speed corresponding to 80% of VO2max (V80, 15.8 ± 1.3 km·h−1) on three different slopes (0°, −5°, and −10°), totaling six 5-min workload conditions. The workload conditions were randomly ordered and performed continuously. The tests lasted 30 min in total. All testing was performed on a large treadmill (3 × 5 m) that offered control over both speed and slope. Three-dimensional kinematic data of the right lower limb were captured during the experimental running protocol using eight infrared cameras with a sampling frequency of 150 Hz. Running kinematics were calculated using a lower body model and inverse kinematics approach. The generic model contained three, one, and two degrees of freedom at the hip, knee, and ankle joints, respectively. Oxygen uptake was measured throughout the experimental protocol. Maximum hip extension and flexion during the stance phase increased due to higher speed (p < 0.01 and p < 0.01, respectively). Knee extension at the touchdown and maximal knee flexion in the stance phase both increased on steeper downhill slopes (both p < 0.05). Ground contact time (GCT) decreased as the speed increased (p < 0.01) but was unaffected by slope (p = 0.73). Runners modified their hip movement pattern in the sagittal plane in response to changes in speed, whereas they altered their knee movement pattern during the touchdown and stance phases in response to changes in slope. While energy cost of running was unaffected by speed alone (p = 0.379), a shift in energy cost was observed for different speeds as the downhill gradient increased (p < 0.001). Energy cost was lower at V80 than 12 km·h−1 on a −5° slope but worse o