Is curve sprint performance in soccer related to other speed and power abilities across age categories?

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Tércio Araújo do Rego Barros, Fábio Yuzo Nakamura, Rostand Souza Lira Filho, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4649173/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Oct, 2025 Read the published version in Sport Sciences for Health → Version 1 posted 11 You are reading this latest preprint version Abstract This study investigated whether curve sprint (CS) performance in soccer was related to linear sprint (LS), change of direction (COD), and countermovement jump (CMJ) in highly trained soccer players across different age categories. One hundred and twenty-one soccer players (U-13, U-15, U-17, and Senior) from the same professional club were recruited and performed all tests. One-way ANOVA and effect sizes were used to compare CS across age categories, while Pearson’s r correlation coefficient measured the relationships between all physical test performance. CS performance improved from the U-13 to the Senior category, exhibiting very large differences across all age categories (Cohen’s d > 2.0), except between the U-15 and U-17 categories. Moderate-large correlations (r = from 0.38 to 0.77) were found in most relationships between the CS and LS (5 and 20 meters), COD, and CMJ performance. Based on the findings CS improved gradually from the U-13 to the Senior category, with the smallest improvement occurring from the U-15 to the U-17. Regarding the association between CS and the speed-power abilities assessed, we suggest that specific training and assessments for young and professional soccer players should be utilized to develop such capacities (i.e., CS, LS, COD, and CMJ). Multidirectional speed Performance Soccer Youth. Figures Figure 1 Figure 2 INTRODUCTION High-intensity locomotor actions in soccer have received considerable attention due to their increased frequency over consecutive seasons in important leagues such as the English Premier League (EPL) [ 1 ], their occurrence at decisive moments of the match [ 2 , 3 ], and the importance they have in differentiating high-, medium- and low-ranked teams [ 4 ]. In the Bundesliga, high-ranked teams covered ~ 2.17 km/match while sprinting with ball possession and performed an average of 105 sprinting actions per game [ 4 ]. Meanwhile, medium- and lower-ranked teams roughly covered ˜1.9 km/match and performed approximately 93 and 91 sprints, respectively, while sprinting with ball possession. However, it is important to point out that these sprint actions are not always linear in their trajectories. Video analyses have shown that most of match sprints occur in a curvilinear trajectory [ 5 , 6 ]. An analysis of EPL matches found that 86% of sprinting maneuvers consisted of CS, while only 14% were in a linear trajectory [ 7 , 8 ]. It is also reported that CS is often performed at angles/arcs between 5° to 30° degrees (0.09 to 0.52 radians), with differences depending on the playing position [ 9 ]. The CS has unique biomechanical properties and can reveal asymmetries between the dominant and non-dominant leg (Smith et al., 1997), which are not evident in linear sprint (LS). In this sense, the inside leg tends to spend more time on the ground, and forces are greater in the outside limb that provides a major contribution to a curvilinear motion [ 11 ]. However, it is worth noting that only recently a specific standardized CS test has been developed for soccer [ 11 ], and few is known about how performance improves across age categories and the CS determinants compared to other physical abilities that composes the multidirectional spectrum, such as LS and change of direction (COD) [ 12 ]. Nonetheless, some few studies have investigated which field-based physical abilities correlate with CS performance [ 13 , 14 ]. In semi-professional senior players, LS performance was not highly correlated with CS speed, and LS explained just ~ 35% of the variance in CS [ 15 ]. On the other hand, younger soccer players (under-20 years old) displayed higher correlations between LS and CS performances, with values of 0.82 and 0.74 for CS being documented regarding the good and weak side, respectively (Loturco et al., 2020). In another study, it was recently shown that as player age increases from under-15 to under-20. The relationship between LS and CS declines from r = 0.76 to r = 0.27, respectively [ 16 ], suggesting that these abilities may become increasingly more specialized and differentiated with age. In addition to the CS-LS relationship, other soccer abilities such as change of direction (COD) and vertical jump have been explored as abilities share common physical attributes with CS in young soccer players [ 12 , 17 ]. Loturco et al. (2020) and Freitas et al. (2021) have shown that CS exhibited moderate correlations with measures of COD ( r varying between 0.39 and 0.42) and vertical jump ( r varying between 0.50 and 0.61), in both good and weak legs. Currently, the interrelationships between CS and different speed-power attributes (LS, COD and CMJ) have not yet been investigated while considering the possible variations related to age categories. These assessments could contribute to better understanding whether these associations weaken or strengthen due to age, maturation and soccer-specific training experience. As previous studies have differed in the sample characteristics, examining players from the same club that are submitted to similar training processes could provide insights into the performance attributes that are more or less related to CS. Thus, the aim of study was to investigate the differences of CS performance from the U-13 to Senior category in a professional soccer club, as well as the relationship between CS and different physical abilities (LS, COD and vertical jump) in highly trained soccer players across different age categories. METHODS Subjects One hundred and twenty-one players from the same professional club were recruited. Players were categorized according to their chronological age in three categories: U-13 (n=25; 11.0-12.9 years old), U-15 (n=35; 13.0-14.9 years old), and U-17 (n=31; 15.0–16.9 years old). In addition, senior players (n=30; aged 25.59 ± 3.83) from the professional squad were also evaluated. All young soccer players were training in a high-performance soccer academy and completed, at least, five training sessions and one competitive match per week. All senior players competed in the first State league and the fourth Brazilian league at the time of conduction of the study. Compliance with Ethical Standards This study is in accordance with the Declaration of Helsinki and was ethically approved in accordance with the norms of the Universidade Federal de Pernambuco Ethics Committee (Process: 4.085.259). All participants were informed about the procedures and the aim of the study and participants and legal guardians provided signed informed consent to voluntary participation in this investigation. Procedures Firstly, all participants completed a warm-up session involving 5 minutes of jogging at a self-selected pace, followed by a set of dynamic stretching and drills involving multidirectional short accelerations and movement preparation exercises to enable maximal performance in the tests. The tests were performed in the following order: countermovement jumps, 17-m CS, 20-m LS, and COD tests. A 1-3 min passive rest between the trials of each test and 10-15 min rest intervals between tests were provided to explain the procedures, allow recovery, and set the equipment used for the tests. Although the athletes are familiar with the assessments, all tests were preceded by familiarization before the formal measurements. All tests were conducted by two trained assessors and took place under similar environmental conditions (25-28ºC and 55-60% relative humidity) on natural grass. Measures Curve sprint test This test followed the guidelines put forth by Filter et al. (2020). This test uses the penalty arc as a reference line to measure the ability to accelerate and sprint on a curved trajectory. Three timing gates (CEFISE, São Paulo, Brazil) at 0-m, 8.5-m, and 17-m were used. Prior to the CS test, two submaximal trials were performed on the same test route for both sides. Each player performed three trials of the CS for the right and left sides, with ~3-min recovery between each sprint. The front foot was placed 1-m before the first timing gate in a split standing start. The best trial was considered for analysis. CS was classified as “weak” (the slowest side) (CSWS) or “good” (the fastest side) (CSGS). Reliability data was previously reported (0.89 for the weak side and 0.93 for the good side) [11]. Linear sprinttest The players' acceleration and maximum running speed was determined using a 20-m linear sprint with three timing gates (0-m, 5-m, and 20-m) (CEFISE, São Paulo, Brazil) [18]. Three trials were performed and the fastest time of the three 20-m trials was used for analysis. Each trial was separated by a recovery period of 3 minutes. Zigzag COD test The Zigzag COD test consisted of four 5-m sections (20-m total linear distance) marked with cones. In each cone, players performed a 100º change of direction [18]. Two maximal attempts were performed, with a 5-min rest interval between attempts. Players started from a standing position 0.5-m behind the first pair of timing gates (CEFISE, São Paulo, Brazil) and were instructed to complete the test as quickly as possible until crossing the second pair of timing gates, which were placed at the 20-m mark of the non-linear route from the start line. The fastest time from the two attempts was recorded for analysis. Countermovement jump test In the countermovement jump (CMJ) test, players started from a standing position and performed a downward movement followed by a powerful triple extension (hips, knees and ankles). Players were free to determine the countermovement depth to avoid disturbing their natural movement coordination. All jumps were performed with the hands on the hips, and the players were instructed to jump as high as possible. The jumps were performed on a contact platform (CEFISE, São Paulo, Brazil), and jump heights were calculated based on flight time. The players were instructed to land in the same place where they had taken off. Three attempts were allowed, interspersed by 15-s intervals. The best attempt was used for the subsequent analysis. Statistical analysis Statistical analysis was performed using SPSS 21.0 (IBM Corp., Armonk, NY). Descriptive statistics (means ± standard deviations) are reported. One-way analysis of variance (ANOVA) was used to compare CS performance between age categories (U-13, U-15, U-17, and Senior). When ANOVA showed a significant group effect, differences between groups were identified using Bonferroni post hoc tests. The magnitude of differences was assessed (for pairwise comparisons) using standardized mean differences [19]. The criteria used to interpret the magnitude of the effect sizes were: ≤ 0.2 trivial, 0.2–0.6 small, 0.6–1.2 moderate, 1.2–2.0 large, and 2.0–4.0 very large [20]. The relationships among the physical performances were determined by Pearson correlation coefficients. The significance level was set at p<0.05. The following criteria were used to interpret the magnitude of the correlations ( r ) between the test measures: ≤0.2 trivial, 0.2–0.4 small, 0.4–0.6 moderate, 0.6–0.8 large, 0.8–1.0 very large [21]. RESULTS Differences in performance parameters across age categories Table 1 shows the data of the physical tests performed according to each age category. Figure 1 (A and B) shows the median, interquartile range, maximum and minimum values of performance in the CSGS and CSWS from players according to each category, all age categories were statistically different (CSGS: F = 137.268; df = 3; p 0.01). Figure 2 (A and B) displays the effect size of the difference in performance between pairs of age categories for the curve sprint test in the good and weak side. Differences between all pairs of age categories are very large, with Cohen’s d varying from 2.09 to 5.72 for both legs, except of the difference between U-15 and U-17 group that reached only a moderate effect size, with Cohen’s d values of 1.05 and 0.90, in the good and weak legs, respectively. *** Please Insert Table 1*** *** Please Insert Figure 1*** *** Please Insert Figure 2*** Correlations between performance parameters across age categories Table 2 shows the correlation between CS (both sides) and LS, COD and CMJ performances. Statistically significant moderate to large correlations were found between LS-5 m and CSGS and CSWS in the U-15 group, as well as in the Senior group for CSWS and LS-5 m, with r -values equal to 0.53, 0.56, and 0.46, respectively. Except for the U-17 group in CSGS, large correlations were found for CSGS, CSWS and LS-20m in all groups, with r- values varying from 0.54 to 0.77. Only the U-15 and U-17 groups exhibited statistically significant correlations between CS and COD. For both age categories the r-values were lower in the CSGS (r = 0.46 and 0.48) than CSWS (r = 0.59 and 0.64). Correlations between CS and CMJ were statistically significant in all age categories, excepting for the U-17 group. In the U-13 group correlations were large (r = 0.61 and 0.67, respectively) for CMJ and CSGS, and CMJ and CSWS. In the U-15 group, CSGS had a moderate correlation with CMJ (r = 0.46) while CSWS showed a large relationship with CMJ (r = 0.66). Finally, the Senior group demonstrated a large correlation between CSGS and CMJ (r = 0.57), and between CSWS and CMJ (r = 0.58) performance. ***Insert Table 2*** DISCUSSION This study examined the relationships of CS performance in soccer with LS, COD and CMJ from the U-13 to Senior category soccer players in both the good and weak sides, as well as the difference in performance across age categories. Moderate to large correlations were found in most relationships between the CS test and LS (5 and 20 meters), COD and CMJ performance, irrespective of age category. Moreover the findings showed very large differences in the CS performance (>2.0 Cohen’s d ), on both good and weak sides, between all pairs of age categories, with exception of U-15 and U-17 categories, that presented a moderated difference. There were considerable differences in CS performance between the age categories, both on the good and the weak side. From a developmental perspective, it is plausible to assume that advancing age, accompanied by the improvement of neuromuscular function and mechanical properties as well as the accumulation of experience, might explain the observed improvements in CS performance [22]. We cannot clearly justify the smaller magnitude of differences between the U-15 and U-17 categories, but it is well known that physical performance in these categories can be influenced by maturational changes. It would be expected large differences in the timing and tempo of maturation between players in these categories. However, the selection of early maturing players particularly in the U-15 category might have caused them to differ little in their physical and physiological characteristics from the U-17 categories, which are no longer strongly influenced by maturation, as most players in this category have already passed peak height velocity. There is evidence that the magnitude of the effect of relative age among young soccer players in the U-15 category may be greater than in the U-17 [23]. Nonetheless, U-17 were still moderately better than U-15 players in CS. Future research should investigate how maturational aspects can influence CS performance, especially in players surrounding the peak height velocity age. As found in a study with the U-20 category, the correlation between CS and LS performances tended to be higher when distance travelled was longer (i.e. from 5- to 20-m) [14]. For the CS and 5-m LS relationship, it could be seen that the correlation from the U-13 to Senior category for the dominant leg remained stable and non-significant ( r from 0.30 to 0.34), with the exception of U-15, where a significant relationship of 0.53 was observed. For the non-dominant leg, the relationship was significant in the U-15 and Senior categories, but both with moderate magnitude ( r from 0.46 to 0.56). This suggests that CS performance in these categories depends to a small extent on the player's ability to linearly accelerate (<30% of shared variance). When we examined the relationship between CS and 20-m LS, a consistent correlation of moderate to high magnitude was observed in CSWS, from U-13 to senior, with a slightly lower magnitude for the U-17 category. Furthermore, the relationship between CSGS and 20-m LS was greater in Seniors than in the other age categories. As our participants in the Senior category are of a higher competitive level, unlike other samples involving semi-professional athletes, it is apparent that more qualified players are more efficient at stabilizing the joints in the frontal plane, allowing them to generate the adequate centripetal force to achieve high speeds in both linear and curvilinear sprinting [14,24,25]. On the other hand, previous studies with U-20, U-17, and U-15 soccer players have shown that these relationships did show a consistent trend in any of the categories [13,14,16]. For example, Filter-Ruger et al. (2022) showed that the CS-LS relationship decreased with age from the U-15 group ( r = 0.75 and 0.76) to the U-20 group ( r = 0.27 and 0.41) for the weak and good sides, respectively. This is consistent with the idea that as sport specialization increases, abilities that share similar determining factors become more independent and tend to become unrelated. Future studies are necessary to determine whether older and more highly trained players have LS and CS performances more or less related to each other than younger and less specialized players. The observed correlations between CS and COD performance suggest an increase in the strength of the relationships from U-13 to U-17 ( r from 0.20 to 0.65), but they cease to be significant in Senior. However, these correlations are generally lower than those observed between CS and LS [26], with shared variance of ~24% and ~42% for the good and weak sides, respectively. These results are similar to those observed in other studies showing that while LS is more strongly related to CS [13,26], linearly faster athletes are not necessarily faster in curves or in COD trajectories due to differences in kinematic and neuromuscular demands [15]. Our results add to the literature showing different patterns in the magnitude of associations between CS and COD, depending on age category. Future research should seek to understand the mechanisms underlying CS performance in soccer players from a biomechanical and developmental perspective. The relationship between CS and CMJ has received little attention in the literature. Loturco et al. (2020a) observed relationships of 0.57 (CSGS) and 0.61 (CSWS) in U-20 athletes, while Kobal et al. (2021) found correlations of 0.56 and 0.53 for CSGS and CSWS, respectively, in an elite women's soccer group. Despite the similar correlations found in our study compared to the others (with the exception of U-17, in which the correlation was not significant), it is possible to observe a reduction in the magnitude of the associations from U-13 to U-17, although the relationship between CS and CMJ is still moderate in seniors. The relationship between jumping and sprinting performance has been justified from a mechanical perspective [27]. To achieve high performance in both capacities, high demands on vertical force production are required (i.e., from the beginning of the jumping movement to the take-off and during the acceleration phase of the sprint) to project the body in space and achieve high sprint speed [27]. Interestingly, improvements in jumping ability might be at least partially “transferred” to CS performance at the youngest age, but this hypothesis needs to be tested in future intervention studies. Overall, the strength of the correlations tended to be higher in the CSWS with the physical attributes assessed (i.e., LS, COD, and CMJ). Running in curvature brings some biomechanical differences compared to straight running, for example, the shortening of the stride length as a biomechanical strategy to maintain the body in a more upright posture [10]. The curvilinear motion prolongs the neuromuscular activity in the outside leg [10], which is probably explained by the necessity to generate ground reaction forces to accelerate the body towards the curvature [28]. By scrutinizing the assessments of our players, it was possible to notice that the fastest CS performance was for most of the athletes (65 out of 121 athletes, 54%) in the non-dominant side, which was confirmed for almost all categories (U-13: 64%, U-17: 61%, and Senior: 53%), except for the U-15 (40%). This implies that the dominant side was the outside limb and therefore the major contributor to the curvilinear performance, as it has been previously demonstrated [10,28]. Despite the important results of our study, some limitations can be raised. This study was conducted in only one club, which limits the generalization of the results for athletes that compete in higher or lower levels. Although it is important to emphasize that the same training “philosophies” are followed (i.e., from U-13 to senior level) in athletes from the same club. Nonetheless, this is the first study to examine the relationship between CS performance and other physical abilities from the younger ages to the Senior category, which can serve as a basis for monitoring the related variables at different age groups. Future studies are desirable to investigate the determinants of CS performance in each category, involving longitudinal design and other relevant measurements (e.g., maturation and biomechanical analysis), as well as to test different training strategies to develop CS performance. CONCLUSIONS In conclusion, our results show expressive differences in CS performance between all age categories. More importantly, moderate to strong relationships were observed between CS, both on the good and weak sides, and LS (5- m and 20- m), 20-m COD, and CMJ performance, although the pattern did not always increase or decrease as a function of advancing age. The relationships between CS and LS performance tended to be stronger than for the other pairs of tests. However, when looking at the coefficient of determination in the younger categories, the most strongly correlated tests only share a maximum of 46% of the shared variance. In other words, it is plausible to assume that in soccer players under the age of 17, maximum sprint speed in curved and linear trajectories, acceleration and deceleration, and CMJ are abilities that can be considered and trained independently of each other. Declarations ACKNOWLEDGEMENTS The authors are grateful for the participation of athletes and coaches that motivated the athletes to participate in this study. STATEMENTS AND DECLARATIONS The authors declare that they have no competing interests. INFORMED CONSENT This study is in accordance with the Declaration of Helsinki and was ethically approved in accordance with the norms of the Universidade Federal de Pernambuco Ethics Committee (Process: 4.085.259). All participants were informed about the procedures and the aim of the study and participants and legal guardians provided signed informed consent to voluntary participation in this investigation. References Barnes C, Archer D, Hogg B, et al. 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U-13 (n=25) U-15 (n=35) U-17 (n=31) SENIOR (n=30) Mean±SD Min-Max Mean±SD Min-Max Mean±SD Min-Max Mean±SD Min-Max CSGS (s) 3.10±0.11 2.86-3.37 2.85±0.11 2.65-3.10 2.74±0.10 2.53-2.98 2.55±0.08 2.44-2.74 CSGS (m∙s -1 ) 5.48±0.20 5.03-5.93 5.95±0.24 5.49-6.40 6.20±0.22 5.71-6.72 6.67±0.21 6.20-6.97 CSWS (s) 3.20±0.12 2.96-3.44 2.95±0.13 2.88-3.18 2.85±0.11 2.56-3.08 2.61±0.09 2.49-2.80 CSWS (m∙s -1 ) 5.32±0.19 4.95-5.74 5.78±0.25 5.35-6.27 5.98±0.23 5.51-6.64 6.51±0.22 6.07-6.83 Linear 5m (s) 1.14±0.05 1.03-1.25 1.03±0.07 0.92-1.17 0.95±0.07 0.84-1.09 0.95±0.07 0.83-1.06 Linear 5m (m∙s -1 ) 4.38±0.21 4.01-4.88 4.86±0.30 4.29-5.43 5.27±0.38 4.58-5.98 5.27±0.37 4.72-6.02 Linear 20m (s) 3.54±0.11 3.36-3.76 3.15±0.17 2.83-3.61 2.99±0.15 2.66-3.54 2.90±0.12 2.73-3.22 Linear 20m (m∙s -1 ) 5.65±0.18 5.32-5.96 6.37±0.34 5.54-7.07 6.70±0.32 5.65-7.52 6.90±0.28 6.21-7.33 COD (s) 5.64±0.27 5.17-6.20 5.34±0.33 4.62-6.00 5.07±0.19 4.75-5.51 4.71±0.22 4.30-5.20 COD (m∙s -1 ) 3.56±0.16 3.22-3.87 3.76±0.24 3.33-4.33 3.95±0.15 3.63-4.21 4.26±0.20 3.86-4.61 CMJ (cm) 26.21±4.06 20.20-34.70 32.61±4.77 25.60-43.90 38.00±3.96 31.00-45.80 41.65±4.73 31.80-52.00 Note : CSGS, Curve sprint ‘good side’; CSWS, Curve sprint ‘weak side’;COD, change-of-direction; CMJ, Countermovement jump; Table 2 . Correlation coefficients ( r ) and 95% confidence intervals for the coefficients (CI95%) between performance variables. Category Curve Sprint Linear 5m (m∙s -1 ) Linear 20-m (m∙s -1 ) COD 20-m (m∙s -1 ) CMJ (cm) U-13 (n=25) Good side (m∙s -1 ) 0.32 ns (-0.08 to 0.63) 0.57** (0.23 to 0.79) 0.23 ns (-0.18 to 0.58) 0.62** (0.20 to 0.85) Weak side (m∙s -1 ) 0.22 ns (-0.19 to 0.57) 0.70** (0.43 to 0.86) 0.20 ns (-0.21 to 0.55) 0.67** (0.29 to 0.87) U-15 (n=35) Good side (m∙s -1 ) 0.53** (0.24 to 0.73) 0.55** (0.26 to 0.74) 0.46** (0.15 to 0.69) 0.47** (0.15 to 0.70) Weak side (m∙s -1 ) 0.56** (0.28 to 0.75) 0.74** (0.52 to 0.85) 0.59 ** (0.32 to 0.77) 0.66** (0.42 to 0.84) U-17 (n=31) Good side (m∙s -1 ) 0.30 ns (-0.06 to 0.59) 0.38* (0.03 to 0.65) 0.49** (0.16 to 0.72) 0.10 ns (-0.44 to 0.26) Weak side (m∙s -1 ) 0.23 ns (-0.13 to 0.54) 0.55** (0.24 to 0.75) 0.65** (0.39 to 0.81) 0.30 ns (-0.59 to 0.07) Senior (n=30) Good side (m∙s -1 ) 0.34 ns (-0.02 to 0.63) 0.76** (0.55 to 0.88) 0.22 ns (-0.20 to 0.57) 0.57** (0.26 to 0.77) Weak side (m∙s -1 ) 0.46** (0.12 to 0.80) 0.77** (0.58 to 0.89) 0.11 ns (-0.30 to 0.50) 0.58** (0.28 to 0.78) Note : COD, change of direction; CMJ, Countermovement jump. *p<0.05; **p<0.01 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Oct, 2025 Read the published version in Sport Sciences for Health → Version 1 posted Editorial decision: Revision requested 24 Mar, 2025 Reviews received at journal 22 Mar, 2025 Reviews received at journal 08 Mar, 2025 Reviews received at journal 03 Mar, 2025 Reviewers agreed at journal 02 Mar, 2025 Reviewers agreed at journal 27 Feb, 2025 Reviewers agreed at journal 25 Feb, 2025 Reviewers invited by journal 25 Feb, 2025 Editor assigned by journal 28 Jun, 2024 Submission checks completed at journal 28 Jun, 2024 First submitted to journal 27 Jun, 2024 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. 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Maia","correspondingAuthor":false,"prefix":"","firstName":"Fábio","middleName":"Yuzo","lastName":"Nakamura","suffix":""},{"id":325752426,"identity":"6a7803fe-521f-4ec3-9ee6-16508b44e3f2","order_by":2,"name":"Rostand Souza Lira Filho","email":"","orcid":"","institution":"Federal University of Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Rostand","middleName":"Souza Lira","lastName":"Filho","suffix":""},{"id":325752427,"identity":"6484a134-74f4-4f2a-ace6-ab424fd1cd9b","order_by":3,"name":"Noadia Maria Guimarães Silva","email":"","orcid":"","institution":"Federal University of Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Noadia","middleName":"Maria Guimarães","lastName":"Silva","suffix":""},{"id":325752428,"identity":"4376c747-8cf8-48e2-9c60-83c3e1933507","order_by":4,"name":"Victor Ferreira Lima","email":"","orcid":"","institution":"Federal University of Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"Ferreira","lastName":"Lima","suffix":""},{"id":325752429,"identity":"2f83a7e0-5b2b-43d6-b332-9fdfbf6d2004","order_by":5,"name":"Fabiano Souza Fonseca","email":"","orcid":"","institution":"Federal Rural University of Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Fabiano","middleName":"Souza","lastName":"Fonseca","suffix":""},{"id":325752430,"identity":"400ad654-7464-46f6-802f-74921590fc1e","order_by":6,"name":"Bartosz Bibrowicz","email":"","orcid":"","institution":"Legia Warszawa Laziekowska Warsaw","correspondingAuthor":false,"prefix":"","firstName":"Bartosz","middleName":"","lastName":"Bibrowicz","suffix":""},{"id":325752431,"identity":"1a1166dd-4ceb-4f83-81ad-11662f693549","order_by":7,"name":"Rafael Santos Henrique","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYFACxgcMDAUMDAYgdgIQ84NFGA7g0cJsAFYP1yLZABIhWgsIGBwgoMW8/TDjhw8GDPbm7L3HPjwoOyxnfCOZ7cEHhjv5uLTInElmlpxhwJC4s+dc8oyEc4eNzW4ksxvOYHhm2YBDiwRD/gFpHgOGBIMbOcYMiW2HE7fdyD8mzcNw2ACHDgYJ/sfMv/8AHWZw/w1YS/3mGcls0n/waZEAKgD6nXHDDR6wlgQDsAheLY/ZLHsMJBI3nAE6LOFcuuGMM4/ZDXsMnuFxWDLzjR8VNvYGx88YM/4os5bnbweG2I+KOzi1wEMBAthgJCENCMCGRI6CUTAKRsEogAIAvNNTTuGhFlEAAAAASUVORK5CYII=","orcid":"","institution":"Federal University of Pernambuco","correspondingAuthor":true,"prefix":"","firstName":"Rafael","middleName":"Santos","lastName":"Henrique","suffix":""}],"badges":[],"createdAt":"2024-06-27 14:23:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4649173/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4649173/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11332-025-01570-z","type":"published","date":"2025-10-17T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60785655,"identity":"24d830db-48fb-49f3-8866-514e8a64c43e","added_by":"auto","created_at":"2024-07-22 05:18:37","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":210040,"visible":true,"origin":"","legend":"\u003cp\u003eBox and whisker of curve sprint in the good side (CSGS) and weak side (CSWS) across age categories.\u003c/p\u003e","description":"","filename":"Figure1CSboxandwhiskerES.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4649173/v1/bd07c2d7e2b93561b0965a13.jpg"},{"id":60785325,"identity":"2beceb38-94b4-49ae-8955-cc11f019befa","added_by":"auto","created_at":"2024-07-22 05:02:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":130139,"visible":true,"origin":"","legend":"\u003cp\u003eEffect sizes of differences across age categories in curve sprint in the good (CSGS) and weak side (CSWS).\u003c/p\u003e","description":"","filename":"Figure2CSeffectsizeacrossage.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4649173/v1/8576f14eb8226daef61e1f23.jpg"},{"id":93956007,"identity":"ac94f1b9-6b4d-45d0-badb-5d2e7eb27e26","added_by":"auto","created_at":"2025-10-20 16:09:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1066952,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4649173/v1/9cc11e91-d38c-4b69-a471-ed1f4f12887c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Is curve sprint performance in soccer related to other speed and power abilities across age categories?","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eHigh-intensity locomotor actions in soccer have received considerable attention due to their increased frequency over consecutive seasons in important leagues such as the English Premier League (EPL) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], their occurrence at decisive moments of the match [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and the importance they have in differentiating high-, medium- and low-ranked teams [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In the Bundesliga, high-ranked teams covered\u0026thinsp;~\u0026thinsp;2.17 km/match while sprinting with ball possession and performed an average of 105 sprinting actions per game [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Meanwhile, medium- and lower-ranked teams roughly covered ˜1.9 km/match and performed approximately 93 and 91 sprints, respectively, while sprinting with ball possession. However, it is important to point out that these sprint actions are not always linear in their trajectories.\u003c/p\u003e \u003cp\u003eVideo analyses have shown that most of match sprints occur in a curvilinear trajectory [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. An analysis of EPL matches found that 86% of sprinting maneuvers consisted of CS, while only 14% were in a linear trajectory [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It is also reported that CS is often performed at angles/arcs between 5\u0026deg; to 30\u0026deg; degrees (0.09 to 0.52 radians), with differences depending on the playing position [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe CS has unique biomechanical properties and can reveal asymmetries between the dominant and non-dominant leg (Smith et al., 1997), which are not evident in linear sprint (LS). In this sense, the inside leg tends to spend more time on the ground, and forces are greater in the outside limb that provides a major contribution to a curvilinear motion [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, it is worth noting that only recently a specific standardized CS test has been developed for soccer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and few is known about how performance improves across age categories and the CS determinants compared to other physical abilities that composes the multidirectional spectrum, such as LS and change of direction (COD) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNonetheless, some few studies have investigated which field-based physical abilities correlate with CS performance [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In semi-professional senior players, LS performance was not highly correlated with CS speed, and LS explained just\u0026thinsp;~\u0026thinsp;35% of the variance in CS [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. On the other hand, younger soccer players (under-20 years old) displayed higher correlations between LS and CS performances, with values of 0.82 and 0.74 for CS being documented regarding the good and weak side, respectively (Loturco et al., 2020). In another study, it was recently shown that as player age increases from under-15 to under-20. The relationship between LS and CS declines from \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.76 to \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.27, respectively [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], suggesting that these abilities may become increasingly more specialized and differentiated with age.\u003c/p\u003e \u003cp\u003eIn addition to the CS-LS relationship, other soccer abilities such as change of direction (COD) and vertical jump have been explored as abilities share common physical attributes with CS in young soccer players [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Loturco et al. (2020) and Freitas et al. (2021) have shown that CS exhibited moderate correlations with measures of COD (\u003cem\u003er\u003c/em\u003e varying between 0.39 and 0.42) and vertical jump (\u003cem\u003er\u003c/em\u003e varying between 0.50 and 0.61), in both good and weak legs. Currently, the interrelationships between CS and different speed-power attributes (LS, COD and CMJ) have not yet been investigated while considering the possible variations related to age categories. These assessments could contribute to better understanding whether these associations weaken or strengthen due to age, maturation and soccer-specific training experience. As previous studies have differed in the sample characteristics, examining players from the same club that are submitted to similar training processes could provide insights into the performance attributes that are more or less related to CS.\u003c/p\u003e \u003cp\u003eThus, the aim of study was to investigate the differences of CS performance from the U-13 to Senior category in a professional soccer club, as well as the relationship between CS and different physical abilities (LS, COD and vertical jump) in highly trained soccer players across different age categories.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSubjects\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne hundred and twenty-one players from the same professional club were recruited. Players were categorized according to their chronological age in three categories: U-13 (n=25; 11.0-12.9 years old), U-15 (n=35; 13.0-14.9 years old), and U-17 (n=31; 15.0–16.9 years old). In addition, senior players (n=30; aged 25.59 ± 3.83) from the professional squad were also evaluated. All young soccer players were training in a high-performance soccer academy and completed, at least, five training sessions and one competitive match per week. All senior players competed in the first State league and the fourth Brazilian league at the time of conduction of the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompliance with Ethical Standards\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is in accordance with the Declaration of Helsinki and was ethically approved in accordance with the norms of the Universidade Federal de Pernambuco Ethics Committee\u0026nbsp;(Process: 4.085.259).\u0026nbsp;All participants were informed about the procedures and the aim of the study and participants and legal guardians provided signed informed consent to voluntary participation in this investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eProcedures\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirstly, all participants completed a warm-up session involving 5 minutes of jogging at a self-selected pace, followed by a set of dynamic stretching and drills involving multidirectional short accelerations and movement preparation exercises to enable maximal performance in the tests. The tests were performed in the following order: countermovement jumps, 17-m CS, 20-m LS, and COD tests. A 1-3 min passive rest between the trials of each test and 10-15 min rest intervals between tests were provided to explain the procedures, allow recovery, and set the equipment used for the tests. Although the athletes are familiar with the assessments, all tests were preceded by familiarization before the formal measurements. All tests were conducted by two trained assessors and took place under similar environmental conditions (25-28ºC and 55-60% relative humidity) on natural grass.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMeasures\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eCurve sprint test\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis test followed the guidelines put forth by Filter et al. (2020). This test uses the penalty arc as a reference line to measure the ability to accelerate and sprint on a curved trajectory. Three timing gates (CEFISE, São Paulo, Brazil) at 0-m, 8.5-m, and 17-m were used. Prior to the CS test, two submaximal trials were performed on the same test route for both sides. Each player performed three trials of the CS for the right and left sides, with ~3-min recovery between each sprint. The front foot was placed 1-m before the first timing gate in a split standing start. The best trial was considered for analysis. CS was classified as “weak” (the slowest side) (CSWS) or “good” (the fastest side) (CSGS). Reliability data was previously reported (0.89 for the weak side and 0.93 for the good side)\u0026nbsp;[11]. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eLinear sprinttest\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe players' acceleration and maximum running speed was determined using a 20-m linear sprint with three timing gates (0-m, 5-m, and 20-m) (CEFISE, São Paulo, Brazil)\u0026nbsp;[18]. Three trials were performed and the fastest time of the three 20-m trials was used for analysis. Each trial was separated by a recovery period of 3 minutes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eZigzag COD test\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003e\u003cu\u003eZigzag\u0026nbsp;\u003c/u\u003e\u003c/em\u003eCOD test consisted of four 5-m sections (20-m total linear distance) marked with cones. In each cone, players performed a 100º change of direction\u0026nbsp;[18]. Two maximal attempts were performed, with a 5-min rest interval between attempts. Players started from a standing position 0.5-m behind the first pair of timing gates (CEFISE, São Paulo, Brazil) and were instructed to complete the test as quickly as possible until crossing the second pair of timing gates, which were placed at the 20-m mark of the non-linear route from the start line. The fastest time from the two attempts was recorded for analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eCountermovement jump test\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the countermovement\u0026nbsp;jump (CMJ) test, players started from a standing position and performed a downward movement followed by a powerful triple extension (hips, knees and ankles). Players were free to determine the countermovement depth to avoid disturbing their natural movement coordination. All jumps were performed with the hands on the hips, and the players were instructed to jump as high as possible. The jumps were performed on a contact platform (CEFISE, São Paulo, Brazil), and jump heights were calculated based on flight time. The players were instructed to land in the same place where they had taken off. Three attempts were allowed, interspersed by 15-s intervals. The best attempt was used for the subsequent analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was performed using SPSS 21.0 (IBM Corp., Armonk, NY). Descriptive statistics (means ± standard deviations) are reported. One-way analysis of variance (ANOVA) was used to compare CS performance between age categories (U-13, U-15, U-17, and Senior). When ANOVA showed a significant group effect, differences between\u0026nbsp;groups\u0026nbsp;were identified using Bonferroni post hoc tests.\u003c/p\u003e\n\u003cp\u003eThe magnitude of differences was assessed (for pairwise comparisons) using standardized mean differences\u0026nbsp;[19]. The criteria used to interpret the magnitude of the effect sizes were: ≤ 0.2 trivial, 0.2–0.6 small, 0.6–1.2 moderate, 1.2–2.0 large, and 2.0–4.0 very large\u0026nbsp;[20]. The relationships among the physical performances were determined by Pearson correlation coefficients. The significance level was set at p\u0026lt;0.05. The following criteria were used to interpret the magnitude of the correlations (\u003cem\u003er\u003c/em\u003e) between the test measures: ≤0.2 trivial, 0.2–0.4 small, 0.4–0.6 moderate, 0.6–0.8 large, 0.8–1.0 very large [21].\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDifferences in performance parameters across age categories\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 shows the data of the physical tests performed according to each age category. Figure 1 (A and B) shows the median, interquartile range, maximum and minimum values of performance in the CSGS and CSWS from players according to each category, all age categories were statistically different (CSGS: F = 137.268; df \u0026nbsp;= 3; p \u0026lt; 0.01; CSWS: F = 126.400; df = 3; p \u0026gt; 0.01). Figure 2 (A and B) displays the effect size of the difference in performance between pairs of age categories for the curve sprint test in the good and weak side. Differences between all pairs of age categories are very large, with Cohen’s \u003cem\u003ed\u003c/em\u003e varying from 2.09 to 5.72 for both legs, except of the difference between U-15 and U-17 group that reached only a moderate effect size, with Cohen’s d values of 1.05 and 0.90, in the good and weak legs, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*** Please Insert Table 1***\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*** Please Insert Figure 1***\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*** Please Insert Figure 2***\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCorrelations between performance parameters across age categories\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 shows the correlation between CS (both sides) and LS, COD and CMJ performances. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Statistically significant moderate to large correlations were found between LS-5 m and CSGS and CSWS in the U-15 group, as well as in the Senior group for CSWS and LS-5 m, with \u003cem\u003er\u003c/em\u003e-values equal to 0.53, 0.56, and 0.46, respectively. Except for the U-17 group in CSGS, large correlations were found for CSGS, CSWS and LS-20m in all groups, with \u003cem\u003er-\u003c/em\u003evalues varying from 0.54 to 0.77. Only the U-15 and U-17 groups exhibited statistically significant correlations between CS and COD. For both age categories the \u003cem\u003er-values\u003c/em\u003e were lower in the CSGS (r = 0.46 and 0.48) than CSWS (r = 0.59 and 0.64).\u003c/p\u003e\n\u003cp\u003eCorrelations between CS and CMJ were statistically significant in all age categories, excepting for the U-17 group. In the U-13 group correlations were large (r = 0.61 and 0.67, respectively) for CMJ and CSGS, and CMJ and CSWS. In the U-15 group, CSGS had a moderate correlation with CMJ (r = 0.46) while CSWS showed a large relationship with CMJ (r = 0.66). Finally, the Senior group demonstrated a large correlation between CSGS and CMJ (r = 0.57), and between CSWS and CMJ (r = 0.58) performance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e***Insert Table 2***\u003c/strong\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study examined the relationships of CS performance in soccer with LS, COD and CMJ from the U-13 to Senior category soccer players in both the good and weak sides, as well as the difference in performance across age categories. Moderate to large correlations were found in most relationships between the CS test and LS (5 and 20 meters), COD and CMJ performance, irrespective of\u0026nbsp;age category. Moreover the findings showed very large differences in the CS performance (\u0026gt;2.0 Cohen’s \u003cem\u003ed\u003c/em\u003e), on both good and weak sides, between all pairs of age categories, with exception of U-15 and U-17 categories, that presented a moderated difference.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere were considerable differences in CS performance between the age categories, both on the good and the weak side. From a developmental perspective, it is plausible to assume that advancing age, accompanied by the improvement of neuromuscular function and mechanical properties as well as the accumulation of experience, might explain the observed improvements in CS performance\u0026nbsp;[22]. We cannot clearly justify the smaller magnitude of differences between the U-15 and U-17 categories, but it is well known that physical performance in these categories can be influenced by maturational changes. It would be expected large differences in the timing and tempo of maturation between players in these categories. However, the selection of early maturing players particularly in the U-15 category might have caused them to differ little in their physical and physiological characteristics from the U-17 categories, which are no longer strongly influenced by maturation, as most players in this category have already passed peak height velocity. There is evidence that the magnitude of the effect of relative age among young soccer players in the U-15 category may be greater than in the U-17\u0026nbsp;[23]. Nonetheless, U-17 were still moderately better than U-15 players in CS. Future research should investigate how maturational aspects can influence CS performance, especially in players surrounding the peak height velocity age.\u003c/p\u003e\n\u003cp\u003eAs found in a study with the U-20 category, the correlation between CS and LS performances tended to be higher when distance travelled was longer (i.e. from 5- to 20-m)\u0026nbsp;[14]. For the CS and 5-m LS relationship, it could be seen that the correlation from the U-13 to Senior category for the dominant leg remained stable and non-significant (\u003cem\u003er\u003c/em\u003e from 0.30 to 0.34), with the exception of U-15, where a significant relationship of 0.53 was observed. For the non-dominant leg, the relationship was significant in the U-15 and Senior categories, but both with moderate magnitude (\u003cem\u003er\u003c/em\u003e from 0.46 to 0.56). This suggests that CS performance in these categories depends to a small extent on the player's\u0026nbsp;ability to linearly accelerate (\u0026lt;30% of shared variance).\u003c/p\u003e\n\u003cp\u003eWhen we examined the relationship between CS and 20-m LS, a consistent correlation of moderate to high magnitude was observed in CSWS, from U-13 to senior, with a slightly lower magnitude for the U-17 category. Furthermore, the relationship between CSGS and 20-m LS was greater in Seniors than in the other age categories. As our participants in the Senior category are of a higher competitive level, unlike other samples involving semi-professional athletes, it is apparent that more qualified players are more efficient at stabilizing the joints in the frontal plane, allowing them to generate the adequate centripetal force to achieve high speeds in both linear and curvilinear sprinting\u0026nbsp;[14,24,25]. On the other hand, previous studies with U-20, U-17, and U-15 soccer players have shown that these relationships did show a consistent trend in any of the categories\u0026nbsp;[13,14,16]. For example, Filter-Ruger et al. (2022) showed that the CS-LS relationship decreased with age from the U-15 group (\u003cem\u003er\u003c/em\u003e = 0.75 and 0.76) to the U-20 group (\u003cem\u003er\u003c/em\u003e = 0.27 and 0.41) for the weak and good sides, respectively. This is consistent with the idea that as sport specialization increases, abilities that share similar determining factors become more independent and tend to become unrelated. Future studies are necessary to determine whether older and more highly trained players have LS and CS performances more or less related to each other than younger and less specialized players.\u003c/p\u003e\n\u003cp\u003eThe observed correlations between CS and COD performance suggest an increase in the strength of the relationships from U-13 to U-17 (\u003cem\u003er\u003c/em\u003e from 0.20 to 0.65), but they cease to be significant in Senior. However, these correlations are generally lower than those observed between CS and LS\u0026nbsp;[26], with shared variance of ~24% and ~42% for the good and weak sides, respectively. These results are similar to those observed in other studies showing that while LS is more strongly related to CS\u0026nbsp;[13,26], linearly faster athletes are not necessarily faster in curves or in COD trajectories due to differences in kinematic and neuromuscular demands\u0026nbsp;[15]. Our results add to the literature showing different patterns in the magnitude of associations between CS and COD, depending on age category. Future research should seek to understand the mechanisms underlying CS performance in soccer players from a biomechanical and developmental perspective.\u003c/p\u003e\n\u003cp\u003eThe relationship between CS and CMJ has received little attention in the literature. Loturco et al. (2020a) observed relationships of 0.57 (CSGS) and 0.61 (CSWS) in U-20 athletes, while Kobal et al. (2021) found correlations of 0.56 and 0.53 for CSGS and CSWS, respectively, in an elite women's soccer group. Despite the similar correlations found in our study compared to the others (with the exception of U-17, in which the correlation was not significant), it is possible to observe a reduction in the magnitude of the associations from U-13 to U-17, although the relationship between CS and CMJ is still moderate in seniors. The relationship between jumping and sprinting performance has been justified from a mechanical perspective\u0026nbsp;[27]. To achieve high performance in both capacities, high demands on vertical force production are required (i.e., from the beginning of the jumping movement to the take-off and during the acceleration phase of the sprint) to project the body in space and achieve high sprint speed\u0026nbsp;[27]. Interestingly, improvements in jumping ability might be at least partially “transferred” to CS performance at the youngest age, but this hypothesis needs to be tested in future intervention studies.\u003c/p\u003e\n\u003cp\u003eOverall, the strength of the correlations tended to be higher in the CSWS with the physical attributes assessed (i.e., LS, COD, and CMJ). Running in curvature brings some biomechanical differences compared to straight running, for example, the shortening of the stride length as a biomechanical strategy to maintain the body in a more upright posture\u0026nbsp;[10]. The curvilinear motion prolongs the neuromuscular activity in the outside leg\u0026nbsp;[10], \u0026nbsp;which is probably explained by the necessity to generate ground reaction forces to accelerate the body towards the curvature\u0026nbsp;[28].\u0026nbsp;By scrutinizing the assessments of our players, it was possible to notice that the fastest CS performance was for most of the athletes (65 out of 121 athletes, 54%) in the non-dominant side, which was confirmed for almost all categories (U-13: 64%, U-17: 61%, and Senior: 53%), except for the U-15 (40%). This implies that the dominant side was the outside limb and therefore the major contributor to the curvilinear performance, as it has been previously demonstrated\u0026nbsp;[10,28].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite the important results of our study, some limitations can be raised. This study was conducted in only one club, which limits the generalization of the results for athletes that compete in higher or lower levels. Although it is important to emphasize that the same training “philosophies” are followed (i.e., from U-13 to senior level) in athletes from the same club. Nonetheless, this is the first study to examine the relationship between CS performance and other physical abilities from the younger ages to the Senior category, which can serve as a basis for monitoring the related variables at different age groups. Future studies are desirable to investigate the determinants of CS performance in each category, involving longitudinal design and other relevant measurements (e.g., maturation and biomechanical analysis), as well as to test different training strategies to develop CS performance.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn conclusion, our results show expressive differences in CS performance between all age categories. More importantly, moderate to strong relationships were observed between CS, both on the good and weak sides, and LS (5- m and 20- m), 20-m COD, and CMJ performance, although the pattern did not always increase or decrease as a function of advancing age. The relationships between CS and LS performance tended to be stronger than for the other pairs of tests. However, when looking at the coefficient of determination in the younger categories, the most strongly correlated tests only share a maximum of 46% of the shared variance. In other words, it is plausible to assume that in soccer players under the age of 17, maximum sprint speed in curved and linear trajectories, acceleration and deceleration, and CMJ are abilities that can be considered and trained independently of each other.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful for the participation of athletes and coaches that motivated the athletes to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSTATEMENTS AND DECLARATIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eINFORMED CONSENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is in accordance with the Declaration of Helsinki and was ethically approved in accordance with the norms of the Universidade Federal de Pernambuco Ethics Committee (Process: 4.085.259). All participants were informed about the procedures and the aim of the study and participants and legal guardians provided signed informed consent to voluntary participation in this investigation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBarnes C, Archer D, Hogg B, et al. The Evolution of Physical and Technical Performance Parameters in the English Premier League. Int J Sports Med 2014; 35: 1095\u0026ndash;1100. doi:10.1055/s-0034-1375695\u003c/li\u003e\n \u003cli\u003eFaude O, Koch T, Meyer T. Straight sprinting is the most frequent action in goal situations in professional football. J Sports Sci 2012; 30: 625\u0026ndash;631. doi:10.1080/02640414.2012.665940\u003c/li\u003e\n \u003cli\u003eGualtieri A, Rampinini E, Dello Iacono A, et al. High-speed running and sprinting in professional adult soccer: Current thresholds definition, match demands and training strategies. A systematic review. 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The effect of the bend on technique and performance during maximal effort sprinting. Sport Biomech 2015; 14: 106\u0026ndash;121. doi:10.1080/14763141.2015.1024717\u003c/li\u003e\n \u003cli\u003eLoturco I, Jeffreys I, Abad CCC, et al. Change-of-direction, speed and jump performance in soccer players: a comparison across different age-categories. J Sports Sci 2020; 38: 1279\u0026ndash;1285. doi:10.1080/02640414.2019.1574276\u003c/li\u003e\n \u003cli\u003eF\u0026iacute;lter A, Beltr\u0026aacute;n-Garrido V, Dos\u0026rsquo;Santos T, et al. The Relationship between Performance and Asymmetries in Different Multidirectional Sprint Tests in Soccer Players. J Hum Kinet 2021; 79: 155\u0026ndash;164. doi:10.2478/hukin-2021-0069\u003c/li\u003e\n \u003cli\u003eLoturco I, Bishop C, Freitas TT, et al. Vertical Force Production in Soccer: Mechanical Aspects and Applied Training Strategies. 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Gait Posture 2006; 24: 453\u0026ndash;458. doi:10.1016/j.gaitpost.2005.11.007\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eDescriptive data in the different tests, according to age categories.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"797\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.968553459119496%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.257861635220127%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eU-13 (n=25)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.257861635220127%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eU-15 (n=35)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.257861635220127%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eU-17 (n=31)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.257861635220127%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSENIOR (n=30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.893617021276595%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin-Max\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin-Max\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin-Max\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin-Max\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.893617021276595%\"\u003e\n \u003cp\u003eCSGS (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e3.10\u0026plusmn;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.86-3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.85\u0026plusmn;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.65-3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.74\u0026plusmn;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.53-2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.55\u0026plusmn;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.44-2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.893617021276595%\"\u003e\n \u003cp\u003eCSGS (m∙s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e5.48\u0026plusmn;0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e5.03-5.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e5.95\u0026plusmn;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e5.49-6.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e6.20\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e5.71-6.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e6.67\u0026plusmn;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e6.20-6.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.893617021276595%\"\u003e\n \u003cp\u003eCSWS (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e3.20\u0026plusmn;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.96-3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.95\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.88-3.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.85\u0026plusmn;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.56-3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.61\u0026plusmn;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.49-2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.893617021276595%\"\u003e\n \u003cp\u003eCSWS (m∙s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e5.32\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e4.95-5.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e5.78\u0026plusmn;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e5.35-6.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e5.98\u0026plusmn;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e5.51-6.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e6.51\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e6.07-6.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.893617021276595%\"\u003e\n \u003cp\u003eLinear 5m (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e1.14\u0026plusmn;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e1.03-1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e1.03\u0026plusmn;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.92-1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.95\u0026plusmn;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.84-1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.95\u0026plusmn;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.83-1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.893617021276595%\"\u003e\n \u003cp\u003eLinear 5m (m∙s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e4.38\u0026plusmn;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e4.01-4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e4.86\u0026plusmn;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e4.29-5.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e5.27\u0026plusmn;0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e4.58-5.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e5.27\u0026plusmn;0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e4.72-6.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.893617021276595%\"\u003e\n \u003cp\u003eLinear 20m (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e3.54\u0026plusmn;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e3.36-3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e3.15\u0026plusmn;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.83-3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.99\u0026plusmn;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.66-3.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.90\u0026plusmn;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.73-3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.893617021276595%\"\u003e\n \u003cp\u003eLinear 20m (m∙s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e5.65\u0026plusmn;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e5.32-5.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e6.37\u0026plusmn;0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e5.54-7.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e6.70\u0026plusmn;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e5.65-7.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e6.90\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e6.21-7.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.893617021276595%\"\u003e\n \u003cp\u003eCOD (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e5.64\u0026plusmn;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e5.17-6.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e5.34\u0026plusmn;0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e4.62-6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e5.07\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e4.75-5.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e4.71\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e4.30-5.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.893617021276595%\"\u003e\n \u003cp\u003eCOD (m∙s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e3.56\u0026plusmn;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e3.22-3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e3.76\u0026plusmn;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e3.33-4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e3.95\u0026plusmn;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e3.63-4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e4.26\u0026plusmn;0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e3.86-4.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.893617021276595%\"\u003e\n \u003cp\u003eCMJ (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e26.21\u0026plusmn;4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e20.20-34.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e32.61\u0026plusmn;4.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e25.60-43.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e38.00\u0026plusmn;3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e31.00-45.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e41.65\u0026plusmn;4.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e31.80-52.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: CSGS, Curve sprint \u0026lsquo;good side\u0026rsquo;; CSWS, Curve sprint \u0026lsquo;weak side\u0026rsquo;;COD, change-of-direction; CMJ, Countermovement jump;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Correlation coefficients (\u003cem\u003er\u003c/em\u003e) and 95% confidence intervals for the coefficients (CI95%) between performance variables.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.239669421487603%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.677685950413224%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurve Sprint\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.520661157024794%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLinear 5m\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(m∙s\u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.520661157024794%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLinear 20-m\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(m∙s\u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.520661157024794%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOD 20-m\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(m∙s\u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.520661157024794%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCMJ\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.239669421487603%\" rowspan=\"2\"\u003e\n \u003cp\u003eU-13\u003c/p\u003e\n \u003cp\u003e(n=25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.677685950413224%\"\u003e\n \u003cp\u003eGood side (m∙s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.520661157024794%\"\u003e\n \u003cp\u003e0.32\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(-0.08 to 0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.520661157024794%\"\u003e\n \u003cp\u003e0.57**\u003c/p\u003e\n \u003cp\u003e(0.23 to 0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.520661157024794%\"\u003e\n \u003cp\u003e0.23\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(-0.18 to 0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.520661157024794%\"\u003e\n \u003cp\u003e0.62**\u003c/p\u003e\n \u003cp\u003e(0.20 to 0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.042830540037244%\"\u003e\n \u003cp\u003eWeak side (m∙s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\"\u003e\n \u003cp\u003e0.22\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(-0.19 to 0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\"\u003e\n \u003cp\u003e0.70**\u003c/p\u003e\n \u003cp\u003e(0.43 to 0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\"\u003e\n \u003cp\u003e0.20\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(-0.21 to 0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\"\u003e\n \u003cp\u003e0.67**\u003c/p\u003e\n \u003cp\u003e(0.29 to 0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.239669421487603%\" rowspan=\"2\"\u003e\n \u003cp\u003eU-15\u003c/p\u003e\n \u003cp\u003e(n=35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.677685950413224%\"\u003e\n \u003cp\u003eGood side (m∙s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.520661157024794%\"\u003e\n \u003cp\u003e0.53**\u003c/p\u003e\n \u003cp\u003e(0.24 to 0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.520661157024794%\"\u003e\n \u003cp\u003e0.55**\u003c/p\u003e\n \u003cp\u003e(0.26 to 0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.520661157024794%\"\u003e\n \u003cp\u003e0.46**\u003c/p\u003e\n \u003cp\u003e(0.15 to 0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.520661157024794%\"\u003e\n \u003cp\u003e0.47**\u003c/p\u003e\n \u003cp\u003e(0.15 to 0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.042830540037244%\"\u003e\n \u003cp\u003eWeak side (m∙s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\"\u003e\n \u003cp\u003e0.56**\u003c/p\u003e\n \u003cp\u003e(0.28 to 0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\"\u003e\n \u003cp\u003e0.74**\u003c/p\u003e\n \u003cp\u003e(0.52 to 0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\"\u003e\n \u003cp\u003e0.59\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.32 to 0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\"\u003e\n \u003cp\u003e0.66**\u003c/p\u003e\n \u003cp\u003e(0.42 to 0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.239669421487603%\" rowspan=\"2\"\u003e\n \u003cp\u003eU-17\u003c/p\u003e\n \u003cp\u003e(n=31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.677685950413224%\"\u003e\n \u003cp\u003eGood side (m∙s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.520661157024794%\"\u003e\n \u003cp\u003e0.30\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(-0.06 to 0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.520661157024794%\"\u003e\n \u003cp\u003e0.38*\u003c/p\u003e\n \u003cp\u003e(0.03 to 0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.520661157024794%\"\u003e\n \u003cp\u003e0.49**\u003c/p\u003e\n \u003cp\u003e(0.16 to 0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.520661157024794%\"\u003e\n \u003cp\u003e0.10\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(-0.44 to 0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.042830540037244%\"\u003e\n \u003cp\u003eWeak side (m∙s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\"\u003e\n \u003cp\u003e0.23\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(-0.13 to 0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\"\u003e\n \u003cp\u003e0.55**\u003c/p\u003e\n \u003cp\u003e(0.24 to 0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\"\u003e\n \u003cp\u003e0.65**\u003c/p\u003e\n \u003cp\u003e(0.39 to 0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\"\u003e\n \u003cp\u003e0.30\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(-0.59 to 0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.239669421487603%\" rowspan=\"2\"\u003e\n \u003cp\u003eSenior\u003c/p\u003e\n \u003cp\u003e(n=30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.677685950413224%\"\u003e\n \u003cp\u003eGood side (m∙s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.520661157024794%\"\u003e\n \u003cp\u003e0.34\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(-0.02 to 0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.520661157024794%\"\u003e\n \u003cp\u003e0.76**\u003c/p\u003e\n \u003cp\u003e(0.55 to 0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.520661157024794%\"\u003e\n \u003cp\u003e0.22\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(-0.20 to 0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.520661157024794%\"\u003e\n \u003cp\u003e0.57**\u003c/p\u003e\n \u003cp\u003e(0.26 to 0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.042830540037244%\"\u003e\n \u003cp\u003eWeak side (m∙s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\"\u003e\n \u003cp\u003e0.46**\u003c/p\u003e\n \u003cp\u003e(0.12 to 0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\"\u003e\n \u003cp\u003e0.77**\u003c/p\u003e\n \u003cp\u003e(0.58 to 0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\"\u003e\n \u003cp\u003e0.11\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(-0.30 to 0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73929236499069%\"\u003e\n \u003cp\u003e0.58**\u003c/p\u003e\n \u003cp\u003e(0.28 to 0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: COD, change of direction; CMJ, Countermovement jump. *p\u0026lt;0.05; **p\u0026lt;0.01\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"sport-sciences-for-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssfh","sideBox":"Learn more about [Sport Sciences for Health](http://link.springer.com/journal/11332)","snPcode":"11332","submissionUrl":"https://submission.nature.com/new-submission/11332/3","title":"Sport Sciences for Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Multidirectional speed, Performance, Soccer, Youth. ","lastPublishedDoi":"10.21203/rs.3.rs-4649173/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4649173/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated whether curve sprint (CS) performance in soccer was related to linear sprint (LS), change of direction (COD), and countermovement jump (CMJ) in highly trained soccer players across different age categories. One hundred and twenty-one soccer players (U-13, U-15, U-17, and Senior) from the same professional club were recruited and performed all tests. One-way ANOVA and effect sizes were used to compare CS across age categories, while Pearson’s r correlation coefficient measured the relationships between all physical test performance. CS performance improved from the U-13 to the Senior category, exhibiting very large differences across all age categories (Cohen’s d \u0026gt; 2.0), except between the U-15 and U-17 categories. Moderate-large correlations (r = from 0.38 to 0.77) were found in most relationships between the CS and LS (5 and 20 meters), COD, and CMJ performance. Based on the findings CS improved gradually from the U-13 to the Senior category, with the smallest improvement occurring from the U-15 to the U-17. Regarding the association between CS and the speed-power abilities assessed, we suggest that specific training and assessments for young and professional soccer players should be utilized to develop such capacities (i.e., CS, LS, COD, and CMJ).\u003c/p\u003e","manuscriptTitle":"Is curve sprint performance in soccer related to other speed and power abilities across age categories?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-22 05:02:32","doi":"10.21203/rs.3.rs-4649173/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-24T11:49:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-23T03:15:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-08T19:45:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-03T14:21:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234179300743551371795141024543399563376","date":"2025-03-03T03:02:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"324470362170779403054855803823773861612","date":"2025-02-27T15:42:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90445463233905655615521642369306700297","date":"2025-02-25T15:26:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-25T14:59:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-28T11:07:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-28T11:05:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Sport Sciences for Health","date":"2024-06-27T14:22:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"sport-sciences-for-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssfh","sideBox":"Learn more about [Sport Sciences for Health](http://link.springer.com/journal/11332)","snPcode":"11332","submissionUrl":"https://submission.nature.com/new-submission/11332/3","title":"Sport Sciences for Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"74e8e39e-2d86-42a6-b8d7-3dc5e216d905","owner":[],"postedDate":"July 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T16:02:11+00:00","versionOfRecord":{"articleIdentity":"rs-4649173","link":"https://doi.org/10.1007/s11332-025-01570-z","journal":{"identity":"sport-sciences-for-health","isVorOnly":false,"title":"Sport Sciences for Health"},"publishedOn":"2025-10-17 15:57:44","publishedOnDateReadable":"October 17th, 2025"},"versionCreatedAt":"2024-07-22 05:02:32","video":"","vorDoi":"10.1007/s11332-025-01570-z","vorDoiUrl":"https://doi.org/10.1007/s11332-025-01570-z","workflowStages":[]},"version":"v1","identity":"rs-4649173","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4649173","identity":"rs-4649173","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}