The relationship countermovement jump braking-phase metrics have with jump height and strategy: A cross-sectional study

The relationship countermovement jump braking-phase metrics have with jump height and strategy: A cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The relationship countermovement jump braking-phase metrics have with jump height and strategy: A cross-sectional study Moses K. Bygate-Smith, C. Martyn Beaven, Mark Drury, Weilun Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9194025/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background The countermovement jump (CMJ) is the most common jumping variation seen in jumping-based sports and physical assessment batteries. Current evidence indicates large heterogeneity in the influence of braking-phase metrics on CMJ performance, with recent literature suggesting there may be unique braking-phase movement strategies that exist. This investigation aimed to determine how braking-phase measures affect CMJ height and identify whether distinct braking-phase strategies are adopted in trained team-sport athletes. Methods Fifty-one team-sport athletes (30 male, 21 female) performed three maximal CMJ trials using motion capture and force deck analysis. Associations with CMJ height were assessed using correlation coefficients and linear regression, while principal component analysis (PCA) and k -means clustering were adopted to identify different movement strategies. Results Twenty-three significant correlations were observed for the group ( p < 0.05), with weak to very strong relationships, twelve (52%) of which were braking-phase measures. Knee flexion peak velocity, braking peak power relative to body mass, hip flexion acceleration, and time to peak ankle dorsiflexion were the braking-phase measures retained for the final regression model. Two clusters were identified and could primarily be distinguished by PC1 (hip flex accel, knee flex accel, time to peak knee flex, time to peak hip flex, and relative braking PP), accounting for 41.61% of the group variance. Conclusions Consideration should be made towards individualising training interventions, match play tactics, return-to-play criteria, and biomechanical frameworks based on movement strategy to enhance individual athlete outcomes. Trial registration : Not applicable. Stretch-shortening cycle rate of force development force-time characteristics movement variability team-sport athletes. Figures Figure 1 Figure 2 Figure 3 Background Vertical jumping is a key skill that has been shown to not only differentiate between starting and bench players in certain team-sports ( 6 , 34 ) but also underpins many match determining actions like spiking and blocking attempts in volleyball ( 12 , 25 , 39 ), as well as lay-ups and blocking attempts in basketball ( 41 ). There are a variety of ways in which a vertical jump can be performed, with the countermovement jump (CMJ) being the most common jumping variation seen in jumping-based sports and physical assessment batteries ( 17 , 30 , 41 ). The CMJ is a stationary vertical jump, typically performed off two feet, which is comprised of multiple phases that interact to maximize jump performance, such as unweighting, braking, and propulsion ( 28 ). Collectively lasting anywhere between 530–1186 ms ( 19 ), each phase has been said to play a role in maximizing CMJ performance. In particular, the propulsive-phase and many of its associated metrics have been shown to be one of the most important aspects for achieving greater CMJ performances ( 3 , 16 , 29 , 40 ). For example, net vertical impulse, which is the integration of force and time, has been shown to be directly proportional to velocity at take-off and therefore jump height ( 22 ). Recent literature has also suggested that rate of force development (RFD) and power have significant positive correlations with CMJ performance ( 3 , 5 , 21 , 44 ). While it is well understood that jumps with a countermovement, and therefore a braking-phase, elicit greater jump heights compared to jumps without a countermovement ( 37 , 43 ), current evidence indicates large heterogeneity in the influence of braking-phase metrics on CMJ performance ( 3 , 5 , 16 , 20 , 35 , 40 ). For example, a recent systematic review found that the link between braking-phase metrics and CMJ height was underreported compared to propulsive-phase metrics ( 5 ). Of the braking-phase metrics that were reported, such as braking impulse and peak knee flexion angle, there were trivial to very strong correlations with CMJ height found, as well as negative and positive relationships. Failing to understand the influence certain braking-phase metrics have on CMJ performance may have consequences for practitioners across a wide range of disciplines, as this can create uncertainty when making decisions to enhance athlete outcomes. Recent literature has also emerged suggesting there may be unique braking-phase movement strategies that exist ( 6 , 7 , 12 , 16 , 21 , 27 , 29 , 35 , 40 ), which may explain the large variance reported on the influence braking-phase metrics have on CMJ height. For example, Kipp and colleagues ( 21 ) assessed kinetic and kinematic differences between 11 male National Collegiate Athletic Association (NCAA) basketball players using single-subject analysis and revealed four different CMJ strategies based on relative joint work of the hip, knee, and ankle. Interestingly, some studies have reported little to no differences in CMJ heights between groups with different braking-phase strategies ( 12 , 16 , 21 , 35 ). These data suggest there is more than one viable technique to maximize jump height, which may have implications on the current practices of practitioners when it comes to individualising jumping performance prescription. However, current research is unclear which strategies exist and how they are distinguished. Available studies have analyzed individuals using force platforms or motion capture systems alone, rather than in conjunction, which limits the depth of understanding what braking-phase strategies may exist and how they are accurately characterized based on all their biomechanical features. To the author’s knowledge, only two studies have employed both force platform and motion capture analysis concurrently ( 21 , 35 ), but have only examined male basketball players, thereby limiting understanding to homogenous groups. Therefore, the purpose of this investigation was to firstly understand how kinetic, kinematic, and temporal braking-phase metrics affect CMJ height and assess their relative importance compared to propulsive-phase metrics. Secondarily, the aim of this research was to determine which CMJ braking-phase strategies exist in elite male and female team-sport athletes, identify the biomechanical metrics that distinguish jumping strategies, and elucidate whether one strategy is the most effective for maximizing jump height. Based on the available literature, it was hypothesised that ( 1 ) braking-phase metrics associated with generating a faster braking-phase and a larger countermovement depth would lead to greater CMJ heights, reflecting a well-developed stretch-shortening cycle (SSC) and neuromuscular system, ( 2 ) braking-phase metrics would have a less significant and direct contribution to CMJ height compared to their equivalent propulsive-phase metrics due to the sequencing of phases, ( 3 ) braking-phase strategies would be distinguished by their rate and excursion-based metrics, and ( 4 ) jump heights were unlikely to differ significantly between strategies, provided take-off velocity and propulsive-phase impulse relative to body mass were adequate. Methods Design A cross-sectional research design was used to assess the CMJ performances of volunteer athletes from different team-sport backgrounds. Due to some form of vertical jumping being present in the athlete’s regime, as well as the previously acknowledged differences in braking-phase strategies observed between sexes, sports, and playing positions, a mixed cohort was selected. Due to the mixed team-sport sample, testing took place during variable time points of the athlete’s season. CMJ height was selected as the dependent performance variable, while multiple kinetic, kinematic, and temporal performance measures were selected as independent variables to capture a range of metrics to explain maximal jump height and differences in braking-phase strategies. Sample size estimation and justification A priori sample size of 38 was determined on August 29 th , 2024, using G*Power (G*Power Version 3.1.9.6, Düsseldorf, Germany) to ensure adequate statistical power (0.80) when assessing correlations with an r-value of at least 0.50 (moderate) between CMJ performance metrics and CMJ height. The correlation coefficient threshold was selected based on similar studies that have previously been conducted on biomechanical relationships with CMJ performance (3, 21). However, considering the secondary aim of the research is to identify different CMJ braking-phase strategies within the sample, necessitating the use of exploratory methods such as cluster analysis, adjustments to the sample size may be warranted. Therefore, a sample size between 40-60 was selected based on the assumption of two clusters being identified using a minimum effect size (Cohen’s d ) of 1.00 (large) following dimensionality reduction techniques (10, 11). Participants Fifty-one team-sport athletes (age = 20.4 ± 2.5 y, height = 184.6 ± 10 cm, body mass = 83.5 ± 13.9 kg), consisting of 30 males and 21 females, volunteered for the study. Ten different team-sport backgrounds were represented within the sample, including basketball ( n = 26), field-hockey ( n = 7), rugby-union ( n = 6), netball ( n = 4), volleyball ( n = 3), cricket ( n = 1), rowing ( n = 1), lacrosse ( n = 1), handball ( n = 1), and sprint relay ( n = 1). All participants were actively participating in a team-sport, either on a regional or national representative team. The participants were also required to have at least one year of previous experience with jumping or plyometric activity with no present self-reported injuries. Testing procedures Prior to data collection, participants were taken through the testing procedure for familiarization and then individual descriptive information was gathered, including age, height (SECA, Hamburg, Germany), body mass (VALD, Brisbane, Australia), team-sport background, and injury history. Everyone performed a standardized warm-up on a 20-metre runway, involving jogging, side shuffling, sprint drills, squats, hip hinges, lunges, leg swings, and three CMJ attempts completed at the end, building up through 50%, 75%, and 90% of each individual’s perceived maximum jump height. Additional practice jump attempts were prescribed if individuals were unable to execute the CMJ correctly, such as failing to land back on the force plates. After the warm-up, individuals were fitted with ten reflective markers (BTS Bioengineering, Milan, Italy) spread across five different landmarks on the body. Markers were placed on the first metatarsal joint, the lateral malleolus, the lateral condyle, the greater trochanter, and the acromion (35). An image of the marker placements can be seen in Figure 1. Participants were given three maximal CMJ trials with three minutes rest between attempts. Since the objective of the research was to assess maximal CMJ height, participants were instructed to “jump as high as possible” while using an arm-swing. The average of the three trials were taken for group analysis. A seven-camera SMART-DX EVO motion capture system (BTS Bioengineering, Milan, Italy) and dual VALD force decks (VALD, Brisbane, Australia) were utilized to collect the CMJ data. All data collection took place at the same Sport Science Laboratory. Data analysis Force plate data was sampled at 1,000 Hz and the data from the individual’s three CMJ attempts were extracted manually to an Excel spreadsheet (Microsoft, Redmond, WA) where the means were calculated. Motion capture data were sampled at 120 Hz where raw marker signals were digitally filtered using a fourth-order zero-lag low-pass Butterworth filter with a 10 Hz cutoff frequency (9) to maximize precision and reduce noise during data collection. Markers were manually labeled using SMART software (BTS Bioengineering, Milan, Italy) according to their joint location. Joint kinematics were configured based on three-dimensional cosine angles between the marker points. The ankle-joint was established at the lateral malleolus between the first metatarsal joint and the lateral condyle, the knee-joint at the lateral condyle between the lateral malleolus and the greater trochanter, and the hip-joint at the greater trochanter between the lateral condyle and the acromion. Following SMART software data analysis, the data was transferred to the same spreadsheet as the force plate data. The CMJ phases and force plate variables were defined based on the work from McMahon and colleagues (28), while the motion capture variables were defined based on the work from McErlain-Naylor and colleagues (26). Although there are multiple phases of the CMJ involved, this research focused primarily on the braking and propulsive phases (Figure 2). The braking-phase was defined as the completion of unweighting to when zero velocity of the center of mass is achieved (12, 28). The propulsive-phase was defined as the completion of the braking-phase to when a positive center of mass velocity is achieved (12, 28). A summary of the metrics and their definitions can be seen in Table 1. Take-off velocity and propulsive impulse relative to body mass were excluded from the analysis due to being mathematically equivalent to jump height through the impulse-momentum calculation (22). Modified reactive strength index (RSImod) was also excluded due to being half comprised of jump height. [ Figure 2 near here] Statistical analysis Reliability of the CMJ metrics were assessed using intraclass correlation coefficients (ICC) with acceptable reliability set at >0.70 (4). Pearson’s product-moment correlation coefficient was used to assess the metrics where statistically significant relationships with CMJ height were observed. Pearson correlation coefficients (r-values) were interpreted as negligible (0.00-0.10), weak (0.10-0.39), moderate (0.40-0.69), strong (0.70-0.89), and very strong (0.90-1.00)(38). Stepwise linear regression was conducted on the variables that reported statistically significant correlations with jump height to reveal the best model for prediction. The Akaike Information Criterion (AIC) was applied at each step of the linear regression where the lowest AIC score determined the best final model for CMJ height. Following group analysis, data was normalized into Fisher’s z-scores and k -means clustering was applied to identify distinct movement strategy sub-groups within the sample based on the braking-phase metrics gathered. K -means cluster analysis is a common method of clustering (23) and has been used to identify different CMJ strategies in the past (16, 35). The elbow method was adopted to determine the optimal number of clusters before conducting k -means (23). Due to the large number of braking-phase metrics assessed, principal component analysis (PCA) was used to reduce the dimensionality of the data by converting the braking-phase metrics into principal components (PCs) and revealing which variables captured the maximum group variance (15, 16, 29). Since not all PCs are typically required to capture maximum group variance, Horn’s parallel analysis logic was applied where eigenvalues >1.00 and exceeding their 95 th percentile threshold determined which PCs to retain (16, 18, 32). To further understand the braking-phase metrics that meaningfully contribute to each PC that was retained, squared cosine values (cos 2 ) and v-statistics were calculated (1, 29). If both a high squared cosine value and a significant v-statistic (>1.96) were met, the braking-phase metric was retained. A negative v-statistic for a given loading was interpreted as being an under expression of the braking-phase metric, while a positive v-statistic was interpreted as being an over-expression. Independent samples T-tests with a Bonferroni correction for multiple comparisons were performed to identify statistically significant differences between clusters in terms of the retained PCs and mean jump heights. Additionally, Cohen’s d effect sizes were used to assess the magnitude of the differences and were interpreted as negligible (0.00-0.20), small (0.20-0.49), moderate (0.50-0.79), large (0.80-1.29), and very large (1.30+)(8). All statistical analyses were performed in RStudio (RStudio Version 4.4.2, Boston, MA), with alpha levels set at p ≤ 0.05 for statistical significance and data was presented as mean and standard deviation ( SD ). Results Reliability The mean, SD and reliabilityfor each CMJ variable can be found in Table 2, 3, and 4. All metrics met acceptable reliability, except for braking duration (ICC = 0.61 ± 0.10), propulsive RFD at 50 ms (ICC = 0.47 ± 0.12), propulsive RFD at 100 ms (ICC = 0.57 ± 0.11), and propulsive RFD at 200 ms (ICC = 0.66 ± 0.09). [ Table 2 near here] [ Table 3 near here] [ Table 4 near here] k-means cluster analysis The elbow method determined that two groups was the optimal number of clusters. Parallel analysis revealed that clusters could primarily be distinguished based on three principal components which accounted for 71.0% of the total group variance (PC1 = 41.6%, PC2 = 20.7%, PC3 = 8.7%). Eigenvalues for PC1, PC2 and PC3 were 9.15, 4.56, and 1.91 respectively. The PCs, braking-phase metrics, loadings, cos 2 values, v-statistics, and significance levels can be seen in Table 5. There were 22 individuals in Cluster One (19 males and three females), while Cluster Two included 29 individuals (11 males and 18 females). Cluster One had a positive loading for PC1 and negative loadings for PC2 and PC3. Conversely, Cluster Two had a negative loading for PC1 and positive loadings for PC2 and PC3. However, T-tests revealed only PC1 was significantly different between clusters with very large effects ( p < 0.001, d = 2.65 ± 0.74). Although PC2 and PC3 failed to meet statistical significance, the effect size for PC2 was small-moderate ( d = 0.45 ± 0.58). There was a significant difference in mean jump height between the groups, in favor of Cluster One ( p = 0.006, d = 0.82 ± 0.59). However, when controlled for sex there were no significant differences between males from each cluster ( p = 0.217, Cluster One = 50.5 ± 10.7 cm, Cluster Two = 46.5 ± 7.1 cm; d = 0.45 ±0.73), but there was a significant difference for females in favor of Cluster Two ( p = 0.003, Cluster One = 23.9 ± 21.3 cm, Cluster Two = 32.1 ± 5.9 cm; d = 0.53 ± 0.86). [Table 5 near here] [Table 6 near here] Pearson’s correlation coefficients Correlations between jump height and each CMJ variable can be seen for the group and individual clusters in Figure 3. Twenty-three significant correlations were observed for the group (all p < 0.05), ranging between weak and very strong ( r = -0.29-0.94). Twelve of the total correlations were braking-phase metrics (52%) with r-values ranging between weak and moderate ( r = -0.29-0.43). At the cluster-specific level, Cluster One had 17 significant correlations ranging between moderate and very strong ( r = -0.43-0.94), consisting of braking (five, 29%) and propulsive-phase metrics (12, 71%). Cluster Two had three significant correlations that ranged between weak and very strong ( r = 0.39-0.92) and consisted entirely of propulsive-phase metrics. [Figure 3 near here] Stepwise linear regression Results of the stepwise linear regression analyses are provided in Tables 8, 9, and 10. At the group level, relative propulsive peak power, relative propulsive mean power, hip extension peak velocity, hip extension acceleration, relative propulsive RPD, knee flexion peak velocity, relative propulsive RPD at 100 ms, relative braking peak power, hip flexion acceleration, ankle plantarflexion peak velocity, and time to peak ankle dorsiflexion were found to contribute to the best final model for jump height ( R 2 1.00, adjusted R 2 0.99), producing the lowest AIC (-0.34). At the cluster level, Cluster One shared the same group metrics of relative propulsive mean power, hip extension peak velocity, hip extension acceleration, knee flexion peak velocity, relative propulsive RPD at 100 ms, and ankle plantarflexion peak velocity, in addition to knee extension peak velocity, propulsive impulse at 100 ms, relative propulsive peak force, relative braking peak force, braking duration, relative propulsive RPD at 50 ms, and relative braking RFD ( R 2 1.00, adjusted R 2 1.00). For Cluster Two, relative propulsive peak and mean power, as well as propulsive RFD at 100ms were found to contribute to the best final model for jump height ( R 2 0.89, adjusted R 2 0.87). AICs for Cluster One and Cluster Two were -7.90 and 74.31, respectively. [ Table 8 near here] [ Table 9 near here] [Table 10 near here] Discussion The aim of this investigation was to firstly determine how braking-phase metrics affect CMJ height and understand their relative importance compared to propulsive-phase metrics. The secondary aim of this research was to determine if there were different CMJ braking-phase strategies, distinguishing metrics, and the relative effectiveness of the strategies for increasing CMJ height. It was hypothesised that braking-phase metrics associated with generating a faster braking-phase and a larger countermovement depth would lead to greater CMJ heights but would have a less significant and direct contribution to CMJ height compared to their equivalent propulsive-phase metrics. It was also hypothesised that braking-phase strategies would be distinguished by their rate and excursion-based metrics and jump heights would not differ significantly between strategies. Regression modelling revealed four out of the 11 variables included in the final model for CMJ height were braking-phase measures, consisting of knee flexion peak velocity, relative braking peak power, hip flexion acceleration, and time to ankle dorsiflexion. These findings highlight the significant contribution that different kinetic, kinematic, and temporal braking-phase factors have on jump height. However, these results also perhaps highlight the greater importance of propulsive-phase measures for maximizing jump height by comparison, as there was a larger representation of these types of metrics in the final model (seven versus four). The dominance of propulsive measures is also supported by the results of the Pearson correlation coefficients where propulsive-phase metrics had larger r-values presented overall (0.34–0.94 versus − 0.29–0.43). Some of the findings from the current study partially align with of Nishiumi and colleagues ( 31 ), where they reported a moderate relationship between relative braking peak power and CMJ height. While the current study assessed 51 team-sport participants, Nishiumi and colleagues derived their findings from a total of 148 individuals of varying training backgrounds. Based on these factors, this would suggest there is a high probability that braking peak power contributes to CMJ height in non-homogenous groups. Interestingly, a systematic review demonstrated that relative braking peak power had a slightly stronger correlation with CMJ height compared to relative propulsive peak power ( 5 ), which contrasts with the findings of the present study. In contrast, some research has indicated that power is not causally related to jump height and shows artificially inflated associations ( 24 , 36 , 45 ). More research is perhaps warranted to ascertain the relative importance of these metrics, particularly using prediction-based analysis. The weak relationship between braking peak velocity and CMJ height observed in the current work conflicts with other research findings that have found stronger relationships ( 14 ). González-Badillo and Marques found a moderate correlation in 48 male track and field athletes during a CMJ on a Smith machine. However, given the obvious differences in the participants and the execution of the CMJ, this could primarily account for the discrepancies. Floría et al., ( 13 ), although not looking specifically at correlations with jump height, found significant differences in braking peak velocity between high and low performers during an unresisted CMJ in a sample of young elite rugby players. Specifically, higher jumpers appeared to achieve greater braking velocities. While these findings conflict with the current study, it is worth noting that Floría and colleagues assessed an entirely male cohort. Therefore, it is possible that the differences between studies are sex-related, highlighting the need to assess the relative importance of braking peak velocity for males and females. For example, the best female jumper in the present study had one of the lowest braking peak velocities (-0.51m/s) while the best male jumper in the present study had the second highest braking peak velocity (-1.25m/s). If the authors were to speculate possible reasons for these differences, it would perhaps be related to the unique morphology and physiology between sexes ( 42 ). Future research should explore sex-specific performance relationships and determinants of CMJ height. Of note, the current study shows that a variety of joint-related measures at the hip, knee and ankle were significantly correlated with improvements in CMJ height, albeit a weak to moderate relationship. Although not looking at braking-phase joint accelerations, times or peak velocities specifically, Kipp et al., ( 21 ) found weak to moderate correlations between braking-phase lower-limb joint work and CMJ height. Joint work was defined in their study as joint power, which was the product of net joint moments and angular velocities, and this partially corroborates the current study’s findings by demonstrating that braking-phase joint-related factors influence jump height. Compared to jumps without a countermovement or braking-phase, jump height differences of around 12–18% are typically expected due to the contribution of the stretch-shortening-cycle ( 37 ). Therefore, without the contribution of the lower-limb joints flexing rapidly during the braking-phase, an effective stretch-shortening-cycle would not be possible. This mechanism could begin to explain the statistically significant but minor contribution to CMJ height in the current study. Kipp and colleagues did find that braking-phase hip and knee joint work had stronger correlations with CMJ height compared to propulsive-phase hip and knee joint work ( r = 0.52 and 0.57 versus 0.37 and 0.52), which opposes the findings of the current study on hip and knee flexion versus hip and knee extension ( r = -0.43 and − 0.39 versus 0.52 and 0.54). Given the Kipp study primarily assessed college basketball players who rely heavily on braking-phase variables for success in their sport ( 6 , 34 ), this may explain the differences in outcomes. It is possible that the general cohort of team-sport athletes in the study have not maximized their stretch-shortening-cycle ability and therefore rely more on propulsive ability. Two distinct clusters emerged from this research which could primarily be distinguished by their hip and knee flexion accelerations, time to peak hip and knee flexion angles, and the magnitude of peak power production relative to body mass. Interestingly, Cluster One, that displayed greater joint flexion rates and relative braking peak power production, achieved greater jump heights compared to Cluster Two, which displayed the opposite traits. However, when controlled for sex there were no significant differences between males of Cluster One and Cluster Two. However, there was a significant difference between females of each cluster, in favor of Cluster Two. Some of these findings partially align with those of Rauch and colleagues ( 35 ) who examined CMJ braking-phase strategies in 178 National Basketball Association (NBA) players. After performing cluster analysis, they discovered three different braking-phase strategies which they termed as “stiff-flexors”, “hyper-flexors”, and “hip-flexors”. Based on the characteristics described for each cluster within their study, stiff-flexors mimic those of Cluster One by flexing rapidly, while hyper-flexors mimic those of Cluster Two by flexing slower. However, these American authors also found that there were significant differences between the clusters in terms of their lower-limb joint range of motion and relative joint contribution. For example, stiff-flexors traveled through less range of motion during flexion at the knee-joint while hyper-flexors traveled through more range of motion at the knee-joint, with their third hip-flexor group achieving greater hip flexion and lower knee flexion compared to the other strategies. In the current study, one of the PCs initially found to distinguish between the two clusters was PC3 which consisted of peak knee flexion angle, peak ankle dorsiflexion angle, and peak hip flexion angle. However, total lower-limb joint flexion angles were not found to significantly differ between clusters ( p = 0.622, d = 0.14 ± 0.57). PC2, although not being significantly different between clusters, included the metrics of countermovement depth and CMJ stiffness and were reported with small-moderate effects. Therefore, it is possible these outcomes may have aligned with Rauch and colleagues with a greater sample size. Jump heights did not differ significantly between the three clusters identified in their cohort of male court-based, team sport athletes. While these findings match those of the current study when looking at the males from each cluster, this was not the case at the female and group-level. Donahue and colleagues ( 12 ) found that there were no significant differences in jump height between female college athletes with different movement strategies. It is worth noting that although females in Cluster Two jumped higher on average than the females in Cluster One in the present study, there was a clear imbalance in the number of females between clusters (Cluster One = 3, Cluster Two = 18). Due to the clear imbalance, these findings may not necessarily suggest that females in Cluster One use an ineffective strategy to maximize jump height. This data reinforces a further need for research to be conducted on females and their CMJ braking-phase strategies. It is noteworthy that similar jump height outcomes with different movement strategies across several studies, and we suggest that this similarity may relate to take-off velocity. With velocity being calculated as displacement divided by time, individuals can produce the same take-off velocity while having different displacement and time values. As take-off velocity is directly proportional to jump height, theoretically, this means that individuals can either increase their movement displacement or minimize their movement time during the propulsive-phase to jump higher, which can be manipulated by the execution of the braking-phase. Further analysis of the characteristics of the two jump strategies showed noticeable differences in their final regression models and correlated metrics with jump height. For example, Cluster One had an additional 10 metrics in their regression model compared to Cluster Two, and only relative propulsive mean power was shared between the clusters. Cluster One had a combination of braking and propulsive-phase metrics retained, including knee flexion peak velocity, relative braking peak force, braking duration, and relative braking RFD, while Cluster Two only had propulsive-phase metrics. Therefore, Cluster Two predominantly relied upon propulsive-phase metrics to maximize CMJ height, whereas Cluster One had a reliance upon braking and propulsive-phase metrics. These findings would suggest that individuals in Cluster One that are seeking improvements in CMJ height could benefit from combined eccentric concentric interventions focusing on the storage and utilisation of elastic energy, such as plyometric training ( 43 ). By contrast, individuals in Cluster Two seeking similar improvements in CMJ height could benefit from concentric interventions focused on reducing muscle slack, such as non-countermovement jump training ( 43 ). However, relating to the discussions made previously about the sex composition of the groups, it is possible that the differences in regression models between clusters may be accounted for by Cluster One having significantly less females than Cluster Two. McMahon et al., ( 27 ) discovered sex-specific differences in CMJ strategies where males displayed significantly greater countermovement depths, braking peak velocities and braking impulses. While these authors did not assess joint kinematics, their findings potentially support the general concept that male athletes rely upon braking-phase performance factors to maximize their CMJ height; therefore, explaining the strategy-specific differences in their measures which related with CMJ height in the present study. However, McMahon and his colleagues compared regional female netball players and professional male rugby league players, so it is unclear whether the differences they identified were influenced primarily by the playing level, sporting background, or participant factors other than biological sex. This study is not without limitations. Firstly, since the study design was cross-sectional athletes were only assessed in one session, as well as at variable time points of their competition calendar. Therefore, this may have failed to accurately capture the strategy of the individual with performance being influenced by varying levels of physical readiness upon assessment, a factor which can affect jump height and movement strategy ( 2 , 33 ). Furthermore, braking-phase strategies were identified using principal components of discrete force-time data. While discrete metrics provide simplified interpretation of movement strategies, this potentially limits the depth of understanding movement strategies when compared to assessing the entire force-time curve ( 16 ). Also, there was an imbalance of team-sport representation with basketball players making up most of the sample. Therefore, the findings may be more applicable to basketball players as opposed to team-sport athletes. Conclusions The findings of this research highlight the relative importance of different braking-phase measures, compared to propulsive-phase measures, for enhancing CMJ height, as well as aid in the identification of specific CMJ strategies. However, what was perhaps most significant was that each strategy presented with differences in their CMJ height performance indicators, with one cluster having a reliance upon braking and propulsive-phase metrics while another cluster relied primarily on propulsive-phase metrics. Therefore, each strategy is likely to require a different approach to maximize their jump height. Coaches and practitioners should consider individualising their training interventions, match play tactics, return-to-play criteria, and biomechanical frameworks to enhance individual athlete outcomes. This may include incorporating eccentric and concentric methods such as plyometric training with faster jump strategies and concentric methods such as non-countermovement jump training with slower jump strategies to maximise results. However, prescribing a combination of these methods to each strategy at different times of the year using strategic periodisation may also provide value. This study also identified sex-specific differences during the analysis with a clear gap being revealed in the literature. Females potentially present with their own unique set of CMJ height performance indicators compared to males, perhaps with less of a requirement for braking-phase metrics. Also, different strategies are likely to exist in female team-sport athletes with unclear indications on which strategy is best for maximizing CMJ height. For these reasons, male data cannot be extrapolated to female team-sport athletes regarding factors related to jumping performance. Abbreviations CMJ Countermovement jump SSC Stretch-shortening cycle NCAA National Collegiate Athletic Association PCA Principal component analysis RFD Rate of force development RPD Rate of power development RSImod Modified reactive strength index ICC Intraclass correlation coefficient AIC Akaike Information Criterion SD Standard deviation PC Principal component Declarations Ethics approval and consent to participate The study protocol adhered to the tenets of the Declaration of Helsinki and was ethically approved by the University of Waikato Human Research Ethics Committee (HREC2024#24). Before testing, each participant was informed of the benefits and risks of the investigation prior to providing written informed consent. Consent for publication Each participant has provided their written consent for their data to be published if the manuscript is to be accepted into a journal. Competing interests The authors declare that they have no competing interests. Funding No funding was received for this research. Author Contribution MBS conceived the study. MBS, MB, MD, and WW designed the study. MBS collected the data. MBS and WW performed statistical analyses. MBS drafted the manuscript. MB and MB critically revised the manuscript. All authors read and approved the final manuscript. Acknowledgement The authors would like to thank the participants for volunteering their time to take part in the study. The authors would also like to thank Mr. Gavin Blackwell for his support. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Abdi H, Williams LJ. Principal component analysis. WIREs Comput Stat. 2010;2:433–59. 10.1002/wics.101 . Alba-Jiménez C, Moreno-Doutres D, Peña J. Trends assessing neuromuscular fatigue in team sports: a narrative review. Sports (Basel). 2022;10:33. 10.3390/sports10030033 . Amasay T, Suprak DN. Predicting time to take-off in a countermovement jump for maximal quickness from upright and squat starting positions. J Hum Kinet. 2022;84:53–63. 10.2478/hukin-2022-0091 . Baumgartner TA, Chung H. Confidence limits for intraclass reliability coefficients. Meas Phys Educ Exerc Sci. 2001;5:179–88. 10.1207/S15327841MPEE0503_4 . Bygate-Smith MK, Beaven CM, Drury M. Physical and biomechanical relationships with countermovement jump performance in team sports: implications for athletic development and injury risk. 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Nat Rev Methods Primers. 2022;2:100. 10.1038/s43586-022-00184-w . Guess TM, Gray AD, Willis BW, Guess MM, Sherman SL, Chapman DW, et al. Force-time waveform shape reveals countermovement jump strategies of collegiate athletes. Sports (Basel). 2020;8:159. 10.3390/sports8120159 . Holmberg P. Preseason preparatory training for a division III women's college basketball team. Strength Cond J. 2010;32:42–54. 10.1519/SSC.0b013e3181fc259d . Horn JL. A rationale and test for the number of factors in factor analysis. Psychometrika. 1965;30:179–85. 10.1007/BF02289447 . Hughes S, Warmenhoven J, Haff GG, Chapman DW, Nimphius S. Countermovement jump and squat jump force-time curve analysis in control and fatigue conditions. J Strength Cond Res. 2022;36:2752–61. 10.1519/JSC.0000000000003955 . Johnston LA, Butler RJ, Sparling TL, Queen RM. A single set of biomechanical variables cannot predict jump performance across various jumping tasks. J Strength Cond Res. 2015;29:396–407. 10.1519/JSC.0000000000000779 . Kipp K, Kiely M, Giordanelli M, Malloy P, Geiser C. Joint- and subject-specific strategies in male basketball players across a range of countermovement jump heights. J Sports Sci. 2020;38:652–7. 10.1080/02640414.2020.1723374 . Kirby TJ, McBride JM, Haines TL, Dayne AM. Relative net vertical impulse determines jumping performance. J Appl Biomech. 2011;27:207–14. 10.1123/jab.27.3.207 . Kodinariya T, Makwana P. Review on determining of cluster in k-means clustering. Int J Adv Res Comput Sci Manag Stud. 2013;1:90–5. Linthorne NP. The correlation between jump height and mechanical power in a countermovement jump is artificially inflated. Sports Biomech. 2021;20:3–21. 10.1080/14763141.2020.1721737 . Marques MC, Tillaar R, Vescovi JD, González-Badillo JJ. Changes in strength and power performance in elite senior female professional volleyball players during the in-season: a case study. J Strength Cond Res. 2008;22:1147–55. 10.1519/JSC.0b013e31816a42d0 . McErlain-Naylor S, King M, Pain MT. Determinants of countermovement jump performance: a kinetic and kinematic analysis. J Sports Sci. 2014;32:1805–12. 10.1080/02640414.2014.924055 . McMahon JJ, Rej SJE, Comfort P. Sex differences in countermovement jump phase characteristics. Sports (Basel). 2017;5:8. 10.3390/sports5010008 . McMahon J, Suchomel T, Lake J, Comfort P. Understanding the key phases of the countermovement jump force-time curve. Strength Cond J. 2018;40. 10.1519/SSC.0000000000000375 . Merrigan JJ, Rentz LE, Hornsby WG, Wagle JP, Stone JD, Smith HT, et al. Comparisons of countermovement jump force-time characteristics among NCAA division I American football athletes: use of principal component analysis. J Strength Cond Res. 2022;36:411–9. 10.1519/JSC.0000000000004173 . Morrison M, Martin DT, Talpey S, Scanlan AT, Delaney J, Halson SL, et al. A systematic review on fitness testing in adult male basketball players. Sports Med. 2022;52:1491–532. 10.1007/s40279-021-01626-3 . Nishiumi D, Nishioka T, Saito H, Kurokawa T, Hirose N. Associations of eccentric force variables during jumping and eccentric lower-limb strength with vertical jump performance: a systematic review. PLoS ONE. 2023;18:e0289631. 10.1371/journal.pone.0289631 . O’Connor KM, Bottum MC. Differences in cutting knee mechanics based on principal components analysis. Med Sci Sports Exerc. 2009;41:867–78. 10.1249/MSS.0b013e31818f8743 . Petway AJ, Freitas TT, Calleja-González J, Torres-Ronda L, Alcaraz PE. Seasonal variations in game activity profiles and players' neuromuscular performance in collegiate division I basketball. Front Sports Act Living. 2020;2:592705. 10.3389/fspor.2020.592705 . Philipp NM, Cabarkapa D, Nijem RM, Blackburn SD, Fry AC. Vertical jump neuromuscular performance characteristics determining on-court contribution in male and female NCAA division I basketball players. Sports (Basel). 2023;11:239. 10.3390/sports11120239 . Rauch J, Leidersdorf E, Reeves T, Borkan L, Elliott M, Ugrinowitsch C. Different movement strategies in the countermovement jump amongst a large cohort of NBA players. Int J Environ Res Public Health. 2020;17:6394. 10.3390/ijerph17176394 . Ruddock AD, Winter EM. Jumping depends on impulse not power. J Sports Sci. 2016;34:584–5. 10.1080/02640414.2015.1064157 . Santos SCR, Oliveira AR, Costa RA, Nascimento KSB, Alvares PD, Medeiros FB, et al. Stretch-shortening cycle utilization in female and male soccer players: a systematic review. J Strength Cond Res. 2024;38:e600–25. 10.1519/JSC.0000000000004904 . Schober P, Boer C, Schwarte LA. Correlation coefficients: appropriate use and interpretation. Anesth Analg. 2018;126:1763–8. 10.1213/ANE.0000000000002864 . Sheppard JM, Cronin JB, Gabbett TJ, McGuigan MR, Etxebarria N, Newton RU. Relative importance of strength, power, and anthropometric measures to jump performance of elite volleyball players. J Strength Cond Res. 2008;22:758–65. 10.1519/JSC.0b013e31816a8440 . Sole CJ, Mizuguchi S, Sato K, Moir GL, Stone MH. Phase characteristics of the countermovement jump force-time curve. J Strength Cond Res. 2018;32:1155–65. 10.1519/JSC.0000000000001945 . Talpey S, Smyth A, O’Grady M, Morrison M, Young W. The occurrence of different vertical jump types in basketball competition and their relationship with lower-body speed-strength qualities. Int J Strength Cond. 2021;1. 10.47206/ijsc.v1i1.52 . Taş S, Yüzbaşıoğlu Ü, Ekici E, Katmerlikaya A. Sex-related differences in human tendon stiffness: a systematic review and meta-analysis. Indian J Orthop. 2025;59:876–87. 10.1007/s43465-025-01398-2 . Van Hooren B, Zolotarjova J. The difference between countermovement and squat jump performances. J Strength Cond Res. 2017;31:2011–20. 10.1519/JSC.0000000000001913 . Vanezis A, Lees A. A biomechanical analysis of good and poor performers of the vertical jump. Ergonomics. 2005;48:1594–603. 10.1080/00140130500101262 . Winter EM. Jumping: power or impulse? Med Sci Sports Exerc. 2005;37:523. 10.1249/01.MSS.0000155703.50713.26 . Tables Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables.docx Appendices.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 28 Apr, 2026 Reviewers agreed at journal 04 Apr, 2026 Reviewers invited by journal 29 Mar, 2026 Editor assigned by journal 29 Mar, 2026 Editor invited by journal 27 Mar, 2026 Submission checks completed at journal 26 Mar, 2026 First submitted to journal 26 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9194025","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614825031,"identity":"7a2c0b7a-e4d8-47dd-9023-7eab418da3ba","order_by":0,"name":"Moses K. Bygate-Smith","email":"data:image/png;base64,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","orcid":"","institution":"University of Waikato","correspondingAuthor":true,"prefix":"","firstName":"Moses","middleName":"K.","lastName":"Bygate-Smith","suffix":""},{"id":614825032,"identity":"2e140442-9552-4265-b335-c34cb7c1d508","order_by":1,"name":"C. Martyn Beaven","email":"","orcid":"","institution":"University of Waikato","correspondingAuthor":false,"prefix":"","firstName":"C.","middleName":"Martyn","lastName":"Beaven","suffix":""},{"id":614825033,"identity":"223625af-efca-45b2-a00d-e5be2d741d1f","order_by":2,"name":"Mark Drury","email":"","orcid":"","institution":"University of Canterbury","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Drury","suffix":""},{"id":614825034,"identity":"076268ac-592b-42af-add6-edf2d9889d87","order_by":3,"name":"Weilun Wu","email":"","orcid":"","institution":"University of Canterbury","correspondingAuthor":false,"prefix":"","firstName":"Weilun","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2026-03-22 23:23:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9194025/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9194025/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106048411,"identity":"70b19ea8-95c1-42f8-84b8-3381ddc92fbb","added_by":"auto","created_at":"2026-04-02 20:32:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":127905,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant retroreflective marker placement.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9194025/v1/a80ca671f9e3995a19a65a8a.jpg"},{"id":106094229,"identity":"f9c4db82-1aa3-46b6-a416-cca654f3f132","added_by":"auto","created_at":"2026-04-03 11:41:50","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":75502,"visible":true,"origin":"","legend":"\u003cp\u003eCountermovement jump (CMJ) phases. (A) Unweighing phase, (B) braking phase, (C) propulsive phase, (D) take-off phase.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9194025/v1/66c5780334437090248a2e62.jpg"},{"id":106094204,"identity":"bd7adb24-16e4-4a43-a632-6ad8562a0331","added_by":"auto","created_at":"2026-04-03 11:41:43","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":747868,"visible":true,"origin":"","legend":"\u003cp\u003eGroup and cluster-specific Pearson’s correlation coefficients between jump height and CMJ metrics.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9194025/v1/b41da95725b62c13527781cd.jpg"},{"id":106096448,"identity":"4c35ae5e-0698-4ed4-93a1-bb3706d57ace","added_by":"auto","created_at":"2026-04-03 11:54:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1599683,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9194025/v1/3267b8e0-c9ec-4262-8f27-ec01cdc1bb1f.pdf"},{"id":106048412,"identity":"799e1136-c72d-432e-88af-fbf3e08bbabe","added_by":"auto","created_at":"2026-04-02 20:32:55","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":51985,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-9194025/v1/2f05cd37b00707fb57e88773.docx"},{"id":106048413,"identity":"9a36e80f-353a-4eb0-9941-f4306450b948","added_by":"auto","created_at":"2026-04-02 20:32:55","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17536,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-9194025/v1/830459c382b4bf6bfa06c8fc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The relationship countermovement jump braking-phase metrics have with jump height and strategy: A cross-sectional study","fulltext":[{"header":"Background","content":"\u003cp\u003eVertical jumping is a key skill that has been shown to not only differentiate between starting and bench players in certain team-sports (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) but also underpins many match determining actions like spiking and blocking attempts in volleyball (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), as well as lay-ups and blocking attempts in basketball (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). There are a variety of ways in which a vertical jump can be performed, with the countermovement jump (CMJ) being the most common jumping variation seen in jumping-based sports and physical assessment batteries (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe CMJ is a stationary vertical jump, typically performed off two feet, which is comprised of multiple phases that interact to maximize jump performance, such as unweighting, braking, and propulsion (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Collectively lasting anywhere between 530\u0026ndash;1186 ms (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), each phase has been said to play a role in maximizing CMJ performance. In particular, the propulsive-phase and many of its associated metrics have been shown to be one of the most important aspects for achieving greater CMJ performances (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). For example, net vertical impulse, which is the integration of force and time, has been shown to be directly proportional to velocity at take-off and therefore jump height (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Recent literature has also suggested that rate of force development (RFD) and power have significant positive correlations with CMJ performance (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile it is well understood that jumps with a countermovement, and therefore a braking-phase, elicit greater jump heights compared to jumps without a countermovement (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), current evidence indicates large heterogeneity in the influence of braking-phase metrics on CMJ performance (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). For example, a recent systematic review found that the link between braking-phase metrics and CMJ height was underreported compared to propulsive-phase metrics (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Of the braking-phase metrics that were reported, such as braking impulse and peak knee flexion angle, there were trivial to very strong correlations with CMJ height found, as well as negative and positive relationships. Failing to understand the influence certain braking-phase metrics have on CMJ performance may have consequences for practitioners across a wide range of disciplines, as this can create uncertainty when making decisions to enhance athlete outcomes.\u003c/p\u003e \u003cp\u003eRecent literature has also emerged suggesting there may be unique braking-phase movement strategies that exist (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), which may explain the large variance reported on the influence braking-phase metrics have on CMJ height. For example, Kipp and colleagues (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) assessed kinetic and kinematic differences between 11 male National Collegiate Athletic Association (NCAA) basketball players using single-subject analysis and revealed four different CMJ strategies based on relative joint work of the hip, knee, and ankle. Interestingly, some studies have reported little to no differences in CMJ heights between groups with different braking-phase strategies (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). These data suggest there is more than one viable technique to maximize jump height, which may have implications on the current practices of practitioners when it comes to individualising jumping performance prescription. However, current research is unclear which strategies exist and how they are distinguished. Available studies have analyzed individuals using force platforms or motion capture systems alone, rather than in conjunction, which limits the depth of understanding what braking-phase strategies may exist and how they are accurately characterized based on all their biomechanical features. To the author\u0026rsquo;s knowledge, only two studies have employed both force platform and motion capture analysis concurrently (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), but have only examined male basketball players, thereby limiting understanding to homogenous groups.\u003c/p\u003e \u003cp\u003eTherefore, the purpose of this investigation was to firstly understand how kinetic, kinematic, and temporal braking-phase metrics affect CMJ height and assess their relative importance compared to propulsive-phase metrics. Secondarily, the aim of this research was to determine which CMJ braking-phase strategies exist in elite male and female team-sport athletes, identify the biomechanical metrics that distinguish jumping strategies, and elucidate whether one strategy is the most effective for maximizing jump height. Based on the available literature, it was hypothesised that (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) braking-phase metrics associated with generating a faster braking-phase and a larger countermovement depth would lead to greater CMJ heights, reflecting a well-developed stretch-shortening cycle (SSC) and neuromuscular system, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) braking-phase metrics would have a less significant and direct contribution to CMJ height compared to their equivalent propulsive-phase metrics due to the sequencing of phases, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) braking-phase strategies would be distinguished by their rate and excursion-based metrics, and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) jump heights were unlikely to differ significantly between strategies, provided take-off velocity and propulsive-phase impulse relative to body mass were adequate.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDesign\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional research design was used to assess the CMJ performances of volunteer athletes from different team-sport backgrounds. Due to some form of vertical jumping being present in the athlete’s regime, as well as the previously acknowledged differences in braking-phase strategies observed between sexes, sports, and playing positions, a mixed cohort was selected. Due to the mixed team-sport sample, testing took place during variable time points of the athlete’s season. CMJ height was selected as the dependent performance variable, while multiple kinetic, kinematic, and temporal performance measures were selected as independent variables to capture a range of metrics to explain maximal jump height and differences in braking-phase strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSample size estimation and justification\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA priori sample size of 38 was determined on August 29\u003csup\u003eth\u003c/sup\u003e, 2024, using G*Power (G*Power Version 3.1.9.6, Düsseldorf, Germany) to ensure adequate statistical power (0.80) when assessing correlations with an r-value of at least 0.50 (moderate) between CMJ performance metrics and CMJ height. The correlation coefficient threshold was selected based on similar studies that have previously been conducted on biomechanical relationships with CMJ performance (3, 21). However, considering the secondary aim of the research is to identify different CMJ braking-phase strategies within the sample, necessitating the use of exploratory methods such as cluster analysis, adjustments to the sample size may be warranted. Therefore, a sample size between 40-60 was selected based on the assumption of two clusters being identified using a minimum effect size (Cohen’s \u003cem\u003ed\u003c/em\u003e) of 1.00 (large) following dimensionality reduction techniques (10, 11).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFifty-one team-sport athletes (age = 20.4 ± 2.5 y, height = 184.6 ± 10 cm, body mass = 83.5 ± 13.9 kg), consisting of 30 males and 21 females, volunteered for the study. Ten different team-sport backgrounds were represented within the sample, including basketball (\u003cem\u003en\u003c/em\u003e = 26), field-hockey (\u003cem\u003en\u003c/em\u003e = 7), rugby-union (\u003cem\u003en\u003c/em\u003e = 6), netball (\u003cem\u003en\u003c/em\u003e = 4), volleyball (\u003cem\u003en\u003c/em\u003e = 3), cricket (\u003cem\u003en\u003c/em\u003e = 1), rowing (\u003cem\u003en\u003c/em\u003e = 1), lacrosse (\u003cem\u003en\u003c/em\u003e = 1), handball (\u003cem\u003en\u003c/em\u003e = 1), and sprint relay (\u003cem\u003en\u003c/em\u003e = 1). All participants were actively participating in a team-sport, either on a regional or national representative team. The participants were also required to have at least one year of previous experience with jumping or plyometric activity with no present self-reported injuries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTesting procedures\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to data collection, participants were taken through the testing procedure for familiarization and then individual descriptive information was gathered, including age, height (SECA, Hamburg, Germany), body mass (VALD, Brisbane, Australia), team-sport background, and injury history. Everyone performed a standardized warm-up on a 20-metre runway, involving jogging, side shuffling, sprint drills, squats, hip hinges, lunges, leg swings, and three CMJ attempts completed at the end, building up through 50%, 75%, and 90% of each individual’s perceived maximum jump height. Additional practice jump attempts were prescribed if individuals were unable to execute the CMJ correctly, such as failing to land back on the force plates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter the warm-up, individuals were fitted with ten reflective markers (BTS Bioengineering, Milan, Italy) spread across five different landmarks on the body. Markers were placed on the first metatarsal joint, the lateral malleolus, the lateral condyle, the greater trochanter, and the acromion (35). An image of the marker placements can be seen in Figure 1. Participants were given three maximal CMJ trials with three minutes rest between attempts. Since the objective of the research was to assess maximal CMJ height, participants were instructed to “jump as high as possible” while using an arm-swing. The average of the three trials were taken for group analysis. A seven-camera SMART-DX EVO motion capture system (BTS Bioengineering, Milan, Italy) and dual VALD force decks (VALD, Brisbane, Australia) were utilized to collect the CMJ data. All data collection took place at the same Sport Science Laboratory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eForce plate data was sampled at 1,000 Hz and the data from the individual’s three CMJ attempts were extracted manually to an Excel spreadsheet (Microsoft, Redmond, WA) where the means were calculated. Motion capture data were sampled at 120 Hz where raw marker signals were digitally filtered using a fourth-order zero-lag low-pass Butterworth filter with a 10 Hz cutoff frequency (9) to maximize precision and reduce noise during data collection. Markers were manually labeled using SMART software (BTS Bioengineering, Milan, Italy) according to their joint location.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJoint kinematics were configured based on three-dimensional cosine angles between the marker points. The ankle-joint was established at the lateral malleolus between the first metatarsal joint and the lateral condyle, the knee-joint at the lateral condyle between the lateral malleolus and the greater trochanter, and the hip-joint at the greater trochanter between the lateral condyle and the acromion. Following SMART software data analysis, the data was transferred to the same spreadsheet as the force plate data. The CMJ phases and force plate variables were defined based on the work from McMahon and colleagues (28), while the motion capture variables were defined based on the work from McErlain-Naylor and colleagues (26).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough there are multiple phases of the CMJ involved, this research focused primarily on the braking and propulsive phases (Figure 2). The braking-phase was defined as the completion of unweighting to when zero velocity of the center of mass is achieved (12, 28). The propulsive-phase was defined as the completion of the braking-phase to when a positive center of mass velocity is achieved (12, 28). A summary of the metrics and their definitions can be seen in Table 1. Take-off velocity and propulsive impulse relative to body mass were excluded from the analysis due to being mathematically equivalent to jump height through the impulse-momentum calculation (22). Modified reactive strength index (RSImod) was also excluded due to being half comprised of jump height.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[\u003cem\u003eFigure 2 near here]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReliability of the CMJ metrics were assessed using intraclass correlation coefficients (ICC) with acceptable reliability set at \u0026gt;0.70 (4).\u0026nbsp;Pearson’s product-moment correlation coefficient was used to assess the metrics where statistically significant relationships with CMJ height were observed. Pearson correlation coefficients (r-values) were interpreted as negligible (0.00-0.10), weak (0.10-0.39), moderate (0.40-0.69), strong (0.70-0.89), and very strong (0.90-1.00)(38). Stepwise linear regression was conducted on the variables that reported statistically significant correlations with jump height to reveal the best model for prediction. The Akaike Information Criterion (AIC) was applied at each step of the linear regression where the lowest AIC score determined the best final model for CMJ height.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFollowing group analysis, data was normalized into Fisher’s z-scores and \u003cem\u003ek\u003c/em\u003e-means clustering was applied to identify distinct movement strategy sub-groups within the sample based on the braking-phase metrics gathered. \u003cem\u003eK\u003c/em\u003e-means cluster analysis is a common method of clustering (23) and has been used to identify different CMJ strategies in the past (16, 35). The elbow method was adopted to determine the optimal number of clusters before conducting \u003cem\u003ek\u003c/em\u003e-means (23).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDue to the large number of braking-phase metrics assessed, principal component analysis (PCA) was used to reduce the dimensionality of the data by converting the braking-phase metrics into principal components (PCs) and revealing which variables captured the maximum group variance (15, 16,\u0026nbsp;29). Since not all PCs are typically required to capture maximum group variance, Horn’s parallel analysis logic was applied where eigenvalues \u0026gt;1.00 and exceeding their 95\u003csup\u003eth\u003c/sup\u003e percentile threshold determined which PCs to retain (16, 18, 32). To further understand the braking-phase metrics that meaningfully contribute to each PC that was retained, squared cosine values (cos\u003csup\u003e2\u003c/sup\u003e) and v-statistics were calculated (1, 29). If both a high squared cosine value and a significant v-statistic (\u0026gt;1.96) were met, the braking-phase metric was retained. A negative v-statistic for a given loading was interpreted as being an under expression of the braking-phase metric, while a positive v-statistic was interpreted as being an over-expression. Independent samples T-tests with a Bonferroni correction for multiple comparisons were performed to identify statistically significant differences between clusters in terms of the retained PCs and mean jump heights. Additionally, Cohen’s \u003cem\u003ed\u0026nbsp;\u003c/em\u003eeffect sizes were used to assess the magnitude of the differences and were interpreted as negligible (0.00-0.20), small (0.20-0.49), moderate (0.50-0.79), large (0.80-1.29), and very large (1.30+)(8).\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed in RStudio (RStudio Version 4.4.2, Boston, MA), with alpha levels set at\u0026nbsp;\u003cem\u003ep\u003c/em\u003e ≤ 0.05 for statistical significance and data was presented as mean and standard deviation (\u003cem\u003eSD\u003c/em\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eReliability\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean, \u003cem\u003eSD\u0026nbsp;\u003c/em\u003eand reliabilityfor each CMJ variable can be found in Table 2, 3, and 4. All metrics met acceptable reliability, except for braking duration (ICC = 0.61 ± 0.10), propulsive RFD at 50 ms (ICC = 0.47 ± 0.12), propulsive RFD at 100 ms (ICC = 0.57 ± 0.11), and propulsive RFD at 200 ms (ICC = 0.66 ± 0.09).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[\u003cem\u003eTable 2 near here]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e[\u003cem\u003eTable 3 near here]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e[\u003cem\u003eTable 4 near here]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ek-means cluster analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe elbow method determined that two groups was the optimal number of clusters. Parallel analysis revealed that clusters could primarily be distinguished based on three principal components which accounted for 71.0% of the total group variance (PC1 = 41.6%, PC2 = 20.7%, PC3 = 8.7%). Eigenvalues for PC1, PC2 and PC3 were 9.15, 4.56, and 1.91 respectively. The PCs, braking-phase metrics, loadings, cos\u003csup\u003e2\u0026nbsp;\u003c/sup\u003evalues, v-statistics, and significance levels can be seen in Table 5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere were 22 individuals in Cluster One (19 males and three females), while Cluster Two included 29 individuals (11 males and 18 females). Cluster One had a positive loading for PC1 and negative loadings for PC2 and PC3. Conversely, Cluster Two had a negative loading for PC1 and positive loadings for PC2 and PC3. However, T-tests revealed only PC1 was significantly different between clusters with very large effects (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003ed\u0026nbsp;\u003c/em\u003e= 2.65 ± 0.74). Although PC2 and PC3 failed to meet statistical significance, the effect size for PC2 was small-moderate (\u003cem\u003ed\u0026nbsp;\u003c/em\u003e= 0.45 ± 0.58). There was a significant difference in mean jump height between the groups, in favor of Cluster One (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.006, \u003cem\u003ed\u0026nbsp;\u003c/em\u003e= 0.82 ± 0.59). However, when controlled for sex there were no significant differences between males from each cluster (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.217, Cluster One = 50.5 ± 10.7 cm, Cluster Two = 46.5 ± 7.1 cm; \u003cem\u003ed\u003c/em\u003e = 0.45 ±0.73), but there was a significant difference for females in favor of Cluster Two (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.003, Cluster One = 23.9 ± 21.3 cm, Cluster Two = 32.1 ± 5.9 cm; \u003cem\u003ed\u003c/em\u003e = 0.53 ± 0.86).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e[Table 5 near here]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e[Table 6 near here]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePearson’s correlation coefficients\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelations between jump height and each CMJ variable can be seen for the group and individual clusters in Figure 3. Twenty-three significant correlations were observed for the group (all \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05), ranging between weak and very strong (\u003cem\u003er\u003c/em\u003e = -0.29-0.94). Twelve of the total correlations were braking-phase metrics (52%) with r-values ranging between weak and moderate (\u003cem\u003er\u003c/em\u003e = -0.29-0.43). At the cluster-specific level, Cluster One had 17 significant correlations ranging between moderate and very strong (\u003cem\u003er\u003c/em\u003e = -0.43-0.94), consisting of braking (five, 29%) and propulsive-phase metrics (12, 71%). Cluster Two had three significant correlations that ranged between weak and very strong (\u003cem\u003er\u003c/em\u003e = 0.39-0.92) and consisted entirely of propulsive-phase metrics.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e[Figure 3 near here]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStepwise linear regression\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults of the stepwise linear regression analyses are provided in Tables 8, 9, and 10. \u0026nbsp;At the group level, relative propulsive peak power, relative propulsive mean power, hip extension peak velocity, hip extension acceleration, relative propulsive RPD, knee flexion peak velocity, relative propulsive RPD at 100 ms, relative braking peak power, hip flexion acceleration, ankle plantarflexion peak velocity, and time to peak ankle dorsiflexion were found to contribute to the best final model for jump height (\u003cem\u003eR\u003c/em\u003e2\u0026nbsp;1.00, adjusted\u0026nbsp;\u003cem\u003eR\u003c/em\u003e2\u0026nbsp;0.99), producing the lowest AIC (-0.34). At the cluster level, Cluster One shared the same group metrics of relative propulsive mean power, hip extension peak velocity, hip extension acceleration, knee flexion peak velocity, relative propulsive RPD at 100 ms, and ankle plantarflexion peak velocity, in addition to knee extension peak velocity, propulsive impulse at 100 ms, relative propulsive peak force, relative braking peak force, braking duration, relative propulsive RPD at 50 ms, and relative braking RFD (\u003cem\u003eR\u003c/em\u003e2\u0026nbsp;1.00, adjusted\u0026nbsp;\u003cem\u003eR\u003c/em\u003e2\u0026nbsp;1.00). For Cluster Two, relative propulsive peak and mean power, as well as propulsive RFD at 100ms were found to contribute to the best final model for jump height (\u003cem\u003eR\u003c/em\u003e2\u0026nbsp;0.89, adjusted\u0026nbsp;\u003cem\u003eR\u003c/em\u003e2\u0026nbsp;0.87). AICs for Cluster One and Cluster Two were -7.90 and 74.31, respectively.\u003c/p\u003e\n\u003cp\u003e[\u003cem\u003eTable 8 near here]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e[\u003cem\u003eTable 9 near here]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e[Table 10 near here]\u003c/em\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aim of this investigation was to firstly determine how braking-phase metrics affect CMJ height and understand their relative importance compared to propulsive-phase metrics. The secondary aim of this research was to determine if there were different CMJ braking-phase strategies, distinguishing metrics, and the relative effectiveness of the strategies for increasing CMJ height. It was hypothesised that braking-phase metrics associated with generating a faster braking-phase and a larger countermovement depth would lead to greater CMJ heights but would have a less significant and direct contribution to CMJ height compared to their equivalent propulsive-phase metrics. It was also hypothesised that braking-phase strategies would be distinguished by their rate and excursion-based metrics and jump heights would not differ significantly between strategies.\u003c/p\u003e \u003cp\u003eRegression modelling revealed four out of the 11 variables included in the final model for CMJ height were braking-phase measures, consisting of knee flexion peak velocity, relative braking peak power, hip flexion acceleration, and time to ankle dorsiflexion. These findings highlight the significant contribution that different kinetic, kinematic, and temporal braking-phase factors have on jump height. However, these results also perhaps highlight the greater importance of propulsive-phase measures for maximizing jump height by comparison, as there was a larger representation of these types of metrics in the final model (seven versus four). The dominance of propulsive measures is also supported by the results of the Pearson correlation coefficients where propulsive-phase metrics had larger r-values presented overall (0.34\u0026ndash;0.94 versus \u0026minus;\u0026thinsp;0.29\u0026ndash;0.43).\u003c/p\u003e \u003cp\u003eSome of the findings from the current study partially align with of Nishiumi and colleagues (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), where they reported a moderate relationship between relative braking peak power and CMJ height. While the current study assessed 51 team-sport participants, Nishiumi and colleagues derived their findings from a total of 148 individuals of varying training backgrounds. Based on these factors, this would suggest there is a high probability that braking peak power contributes to CMJ height in non-homogenous groups. Interestingly, a systematic review demonstrated that relative braking peak power had a slightly stronger correlation with CMJ height compared to relative propulsive peak power (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), which contrasts with the findings of the present study. In contrast, some research has indicated that power is not causally related to jump height and shows artificially inflated associations (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). More research is perhaps warranted to ascertain the relative importance of these metrics, particularly using prediction-based analysis.\u003c/p\u003e \u003cp\u003eThe weak relationship between braking peak velocity and CMJ height observed in the current work conflicts with other research findings that have found stronger relationships (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Gonz\u0026aacute;lez-Badillo and Marques found a moderate correlation in 48 male track and field athletes during a CMJ on a Smith machine. However, given the obvious differences in the participants and the execution of the CMJ, this could primarily account for the discrepancies. Flor\u0026iacute;a et al., (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), although not looking specifically at correlations with jump height, found significant differences in braking peak velocity between high and low performers during an unresisted CMJ in a sample of young elite rugby players. Specifically, higher jumpers appeared to achieve greater braking velocities.\u003c/p\u003e \u003cp\u003eWhile these findings conflict with the current study, it is worth noting that Flor\u0026iacute;a and colleagues assessed an entirely male cohort. Therefore, it is possible that the differences between studies are sex-related, highlighting the need to assess the relative importance of braking peak velocity for males and females. For example, the best female jumper in the present study had one of the lowest braking peak velocities (-0.51m/s) while the best male jumper in the present study had the second highest braking peak velocity (-1.25m/s). If the authors were to speculate possible reasons for these differences, it would perhaps be related to the unique morphology and physiology between sexes (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Future research should explore sex-specific performance relationships and determinants of CMJ height.\u003c/p\u003e \u003cp\u003eOf note, the current study shows that a variety of joint-related measures at the hip, knee and ankle were significantly correlated with improvements in CMJ height, albeit a weak to moderate relationship. Although not looking at braking-phase joint accelerations, times or peak velocities specifically, Kipp et al., (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) found weak to moderate correlations between braking-phase lower-limb joint work and CMJ height. Joint work was defined in their study as joint power, which was the product of net joint moments and angular velocities, and this partially corroborates the current study\u0026rsquo;s findings by demonstrating that braking-phase joint-related factors influence jump height. Compared to jumps without a countermovement or braking-phase, jump height differences of around 12\u0026ndash;18% are typically expected due to the contribution of the stretch-shortening-cycle (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Therefore, without the contribution of the lower-limb joints flexing rapidly during the braking-phase, an effective stretch-shortening-cycle would not be possible.\u003c/p\u003e \u003cp\u003eThis mechanism could begin to explain the statistically significant but minor contribution to CMJ height in the current study. Kipp and colleagues did find that braking-phase hip and knee joint work had stronger correlations with CMJ height compared to propulsive-phase hip and knee joint work (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.52 and 0.57 versus 0.37 and 0.52), which opposes the findings of the current study on hip and knee flexion versus hip and knee extension (\u003cem\u003er\u003c/em\u003e = -0.43 and \u0026minus;\u0026thinsp;0.39 versus 0.52 and 0.54). Given the Kipp study primarily assessed college basketball players who rely heavily on braking-phase variables for success in their sport (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), this may explain the differences in outcomes. It is possible that the general cohort of team-sport athletes in the study have not maximized their stretch-shortening-cycle ability and therefore rely more on propulsive ability.\u003c/p\u003e \u003cp\u003eTwo distinct clusters emerged from this research which could primarily be distinguished by their hip and knee flexion accelerations, time to peak hip and knee flexion angles, and the magnitude of peak power production relative to body mass. Interestingly, Cluster One, that displayed greater joint flexion rates and relative braking peak power production, achieved greater jump heights compared to Cluster Two, which displayed the opposite traits. However, when controlled for sex there were no significant differences between males of Cluster One and Cluster Two. However, there was a significant difference between females of each cluster, in favor of Cluster Two.\u003c/p\u003e \u003cp\u003eSome of these findings partially align with those of Rauch and colleagues (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) who examined CMJ braking-phase strategies in 178 National Basketball Association (NBA) players. After performing cluster analysis, they discovered three different braking-phase strategies which they termed as \u0026ldquo;stiff-flexors\u0026rdquo;, \u0026ldquo;hyper-flexors\u0026rdquo;, and \u0026ldquo;hip-flexors\u0026rdquo;. Based on the characteristics described for each cluster within their study, stiff-flexors mimic those of Cluster One by flexing rapidly, while hyper-flexors mimic those of Cluster Two by flexing slower. However, these American authors also found that there were significant differences between the clusters in terms of their lower-limb joint range of motion and relative joint contribution. For example, stiff-flexors traveled through less range of motion during flexion at the knee-joint while hyper-flexors traveled through more range of motion at the knee-joint, with their third hip-flexor group achieving greater hip flexion and lower knee flexion compared to the other strategies.\u003c/p\u003e \u003cp\u003eIn the current study, one of the PCs initially found to distinguish between the two clusters was PC3 which consisted of peak knee flexion angle, peak ankle dorsiflexion angle, and peak hip flexion angle. However, total lower-limb joint flexion angles were not found to significantly differ between clusters (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.622, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57). PC2, although not being significantly different between clusters, included the metrics of countermovement depth and CMJ stiffness and were reported with small-moderate effects. Therefore, it is possible these outcomes may have aligned with Rauch and colleagues with a greater sample size. Jump heights did not differ significantly between the three clusters identified in their cohort of male court-based, team sport athletes.\u003c/p\u003e \u003cp\u003eWhile these findings match those of the current study when looking at the males from each cluster, this was not the case at the female and group-level. Donahue and colleagues (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) found that there were no significant differences in jump height between female college athletes with different movement strategies. It is worth noting that although females in Cluster Two jumped higher on average than the females in Cluster One in the present study, there was a clear imbalance in the number of females between clusters (Cluster One\u0026thinsp;=\u0026thinsp;3, Cluster Two\u0026thinsp;=\u0026thinsp;18). Due to the clear imbalance, these findings may not necessarily suggest that females in Cluster One use an ineffective strategy to maximize jump height. This data reinforces a further need for research to be conducted on females and their CMJ braking-phase strategies.\u003c/p\u003e \u003cp\u003eIt is noteworthy that similar jump height outcomes with different movement strategies across several studies, and we suggest that this similarity may relate to take-off velocity. With velocity being calculated as displacement divided by time, individuals can produce the same take-off velocity while having different displacement and time values. As take-off velocity is directly proportional to jump height, theoretically, this means that individuals can either increase their movement displacement or minimize their movement time during the propulsive-phase to jump higher, which can be manipulated by the execution of the braking-phase.\u003c/p\u003e \u003cp\u003eFurther analysis of the characteristics of the two jump strategies showed noticeable differences in their final regression models and correlated metrics with jump height. For example, Cluster One had an additional 10 metrics in their regression model compared to Cluster Two, and only relative propulsive mean power was shared between the clusters. Cluster One had a combination of braking and propulsive-phase metrics retained, including knee flexion peak velocity, relative braking peak force, braking duration, and relative braking RFD, while Cluster Two only had propulsive-phase metrics. Therefore, Cluster Two predominantly relied upon propulsive-phase metrics to maximize CMJ height, whereas Cluster One had a reliance upon braking and propulsive-phase metrics. These findings would suggest that individuals in Cluster One that are seeking improvements in CMJ height could benefit from combined eccentric concentric interventions focusing on the storage and utilisation of elastic energy, such as plyometric training (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). By contrast, individuals in Cluster Two seeking similar improvements in CMJ height could benefit from concentric interventions focused on reducing muscle slack, such as non-countermovement jump training (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). However, relating to the discussions made previously about the sex composition of the groups, it is possible that the differences in regression models between clusters may be accounted for by Cluster One having significantly less females than Cluster Two. McMahon et al., (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) discovered sex-specific differences in CMJ strategies where males displayed significantly greater countermovement depths, braking peak velocities and braking impulses. While these authors did not assess joint kinematics, their findings potentially support the general concept that male athletes rely upon braking-phase performance factors to maximize their CMJ height; therefore, explaining the strategy-specific differences in their measures which related with CMJ height in the present study. However, McMahon and his colleagues compared regional female netball players and professional male rugby league players, so it is unclear whether the differences they identified were influenced primarily by the playing level, sporting background, or participant factors other than biological sex.\u003c/p\u003e \u003cp\u003eThis study is not without limitations. Firstly, since the study design was cross-sectional athletes were only assessed in one session, as well as at variable time points of their competition calendar. Therefore, this may have failed to accurately capture the strategy of the individual with performance being influenced by varying levels of physical readiness upon assessment, a factor which can affect jump height and movement strategy (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Furthermore, braking-phase strategies were identified using principal components of discrete force-time data. While discrete metrics provide simplified interpretation of movement strategies, this potentially limits the depth of understanding movement strategies when compared to assessing the entire force-time curve (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Also, there was an imbalance of team-sport representation with basketball players making up most of the sample. Therefore, the findings may be more applicable to basketball players as opposed to team-sport athletes.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe findings of this research highlight the relative importance of different braking-phase measures, compared to propulsive-phase measures, for enhancing CMJ height, as well as aid in the identification of specific CMJ strategies. However, what was perhaps most significant was that each strategy presented with differences in their CMJ height performance indicators, with one cluster having a reliance upon braking and propulsive-phase metrics while another cluster relied primarily on propulsive-phase metrics. Therefore, each strategy is likely to require a different approach to maximize their jump height. Coaches and practitioners should consider individualising their training interventions, match play tactics, return-to-play criteria, and biomechanical frameworks to enhance individual athlete outcomes. This may include incorporating eccentric and concentric methods such as plyometric training with faster jump strategies and concentric methods such as non-countermovement jump training with slower jump strategies to maximise results. However, prescribing a combination of these methods to each strategy at different times of the year using strategic periodisation may also provide value. This study also identified sex-specific differences during the analysis with a clear gap being revealed in the literature. Females potentially present with their own unique set of CMJ height performance indicators compared to males, perhaps with less of a requirement for braking-phase metrics. Also, different strategies are likely to exist in female team-sport athletes with unclear indications on which strategy is best for maximizing CMJ height. For these reasons, male data cannot be extrapolated to female team-sport athletes regarding factors related to jumping performance.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCMJ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCountermovement jump\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStretch-shortening cycle\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCAA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Collegiate Athletic Association\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal component analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRFD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRate of force development\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRate of power development\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRSImod\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eModified reactive strength index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntraclass correlation coefficient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAkaike Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal component\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The study protocol adhered to the tenets of the Declaration of Helsinki and was ethically approved by the University of Waikato Human Research Ethics Committee (HREC2024#24). Before testing, each participant was informed of the benefits and risks of the investigation prior to providing written informed consent.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eEach participant has provided their written consent for their data to be published if the manuscript is to be accepted into a journal.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding was received for this research.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMBS conceived the study. MBS, MB, MD, and WW designed the study. MBS collected the data. MBS and WW performed statistical analyses. MBS drafted the manuscript. MB and MB critically revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank the participants for volunteering their time to take part in the study. The authors would also like to thank Mr. Gavin Blackwell for his support.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdi H, Williams LJ. Principal component analysis. 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Med Sci Sports Exerc. 2005;37:523. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1249/01.MSS.0000155703.50713.26\u003c/span\u003e\u003cspan address=\"10.1249/01.MSS.0000155703.50713.26\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Stretch-shortening cycle, rate of force development, force-time characteristics, movement variability, team-sport athletes.","lastPublishedDoi":"10.21203/rs.3.rs-9194025/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9194025/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe countermovement jump (CMJ) is the most common jumping variation seen in jumping-based sports and physical assessment batteries. Current evidence indicates large heterogeneity in the influence of braking-phase metrics on CMJ performance, with recent literature suggesting there may be unique braking-phase movement strategies that exist. This investigation aimed to determine how braking-phase measures affect CMJ height and identify whether distinct braking-phase strategies are adopted in trained team-sport athletes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFifty-one team-sport athletes (30 male, 21 female) performed three maximal CMJ trials using motion capture and force deck analysis. Associations with CMJ height were assessed using correlation coefficients and linear regression, while principal component analysis (PCA) and \u003cem\u003ek\u003c/em\u003e-means clustering were adopted to identify different movement strategies.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTwenty-three significant correlations were observed for the group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with weak to very strong relationships, twelve (52%) of which were braking-phase measures. Knee flexion peak velocity, braking peak power relative to body mass, hip flexion acceleration, and time to peak ankle dorsiflexion were the braking-phase measures retained for the final regression model. Two clusters were identified and could primarily be distinguished by PC1 (hip flex accel, knee flex accel, time to peak knee flex, time to peak hip flex, and relative braking PP), accounting for 41.61% of the group variance.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eConsideration should be made towards individualising training interventions, match play tactics, return-to-play criteria, and biomechanical frameworks based on movement strategy to enhance individual athlete outcomes. \u003cb\u003eTrial registration\u003c/b\u003e: Not applicable.\u003c/p\u003e","manuscriptTitle":"The relationship countermovement jump braking-phase metrics have with jump height and strategy: A cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 20:32:45","doi":"10.21203/rs.3.rs-9194025/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"98152960485471612919518812855795809325","date":"2026-04-28T14:16:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34279026083115973621795970885565597769","date":"2026-04-04T14:14:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-29T05:25:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-29T05:24:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-27T04:56:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-26T22:28:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Sports Science, Medicine and Rehabilitation","date":"2026-03-26T22:24:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e8f7640b-9ed1-4661-84a2-2346efc50a33","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-02T20:32:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 20:32:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9194025","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9194025","identity":"rs-9194025","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}