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Article

Influence of Category and Sex in Game Structure Variables in Young Elite Tennis

by
Alejandro Zurano
1,
Jesús Ramón-Llín
2,
José Francisco Guzmán
2,* and
Rafael Martínez-Gallego
2
1
Physical Exercise and Performance Research Group, Department of Education Science, School of Humanities and Communication Sciences, Universidad Cardenal Herrera-CEU, CEU Universities, Calle Grecia 31, 12006 Castellon de la Plana, Spain
2
Research Group on Sports Technique and Tactics, University of Valencia, 46010 Valencia, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(10), 5496; https://doi.org/10.3390/app15105496
Submission received: 21 March 2025 / Revised: 8 May 2025 / Accepted: 12 May 2025 / Published: 14 May 2025
(This article belongs to the Special Issue Advances in Performance Analysis and Technology in Sports)
Review Reports Versions Notes

Abstract

:
Understanding the structure of play in competitive contexts is essential for an accurate performance analysis. This study examined the effects of sex and age category on the temporal and formal structure of play in youth tennis. Methods: Twenty-four semi-final and final matches were selected from under-12 (Open Super 12-Auray) and under-14 (Les Petits As) tournaments. Temporal structure was assessed through total match time, mean set and game duration, rally duration, and percentage of active time. Formal structure variables comprised the number of sets per match, games per set, points per game, strokes per rally, and stroke frequency. A structured, external, non-participant observational methodology was employed. No sex-based differences were detected. When grouped by age category, U-12 players recorded longer rally durations—9.48 s in boys and 8.76 s in girls (p < 0.001)—and a higher number of strokes per rally—6.75 strokes in boys and 6.14 strokes in girls (p < 0.001). In contrast, U-14 players exhibited a greater stroke frequency (46.44 strokes/min) (p < 0.008). The changes in game intensity aligned with expectations based on previous tactical investigations and the maturation processes characteristic of puberty.

1. Introduction

The praxiological analysis of sport games examines the set of systems that constitute the structure and dynamics of gameplay [1]. Alongside other performance indicators, it contributes to profiling the complexity of competitive performance in tennis [2,3], which is conditioned by tactical, technical, and physical factors [4]. Several authors [5,6,7] highlight that performance analysis should be conducted through field studies in natural environments, reflecting the sport’s inherent diversity and competitiveness by analyzing representative samples. Furthermore, such analysis must consider the influence of individual and interactive constraints on athletes’ behavior [8,9].
From a methodological standpoint, observational methodology [10,11] is well-suited to scientifically describe and analyze the sociomotor dynamics involved in sport performance. Notational analysis based on video recordings is the most commonly used approach to obtain quantitative data in natural contexts. This method is direct and non-participatory, with the added benefit that athletes are unaware they are being analyzed, thus eliminating potential bias in data collection [12,13].
In net and alternate participation sports—tennis being a prime example—temporal and structural variables are considered specific performance indicators derived from the game’s framework. These have been studied at various units of analysis such as the match, set, game, and point [14]. Regarding tennis’s intermittent dynamics, active time is characterized by repeated short-duration anaerobic efforts involving reactive acceleration–deceleration movements, multidirectional agility, and explosive jumps. Nevertheless, the high frequency of rest periods leads to prolonged match durations, underscoring the importance of the aerobic system for recovery [15]. Additionally, rest times are regulated by the International Tennis Federation (ITF), which stipulates a maximum of 20 s between points, 90 s during changeovers when the total number of games is odd, and 60 s between sets [16].
Currently, there is a growing interest in identifying and enhancing tactical variables that lead to match victories. As in other sports, tactical decisions in tennis are influenced by external game-derived parameters—such as ball type, court surface, match format (number of sets), and player ranking—as well as internal ones like sex–category, strategy employed, performance level, or physical attributes. Most research has focused on professional players, while studies involving youth athletes remain limited. When conducted, these studies often examine contexts removed from elite competition and do not comprehensively account for all surfaces and categories. Consequently, there is a knowledge gap concerning gameplay development during formative stages [15,17,18].
Concerning total match duration, similar values have been found across sex and category in U-18 players at both international and national competitive levels (77.2–85.0 min for males; 77.9–80.6 min for females) [18,19,20,21,22,23,24]. In contrast, national-level competitions report shorter durations for U-14 players (65.4 min for males; 59.8 min for females) and longer durations for U-16 (108.0 min for males; 99.7 min for females) [21,22,23].
Regarding average set and game duration—two underexplored parameters in the literature—studies have identified similarities between professional (41.2 min for males; 43.5 min for females) and U-18 players (34.6 min for males; 34.2 min for females) at the set level, and between 174.2–178.6 s for males and 183.8 s for females at the game level [18,25,26].
With respect to point duration, notable differences have been observed in U-12 players compared to other age categories (12.1 s in U-12 males; 7.3 s and 6.3 s in U-14; 9.1 s and 9.0 s in U-16; 7.2 s and 8.2 s in U-18) [20,21,22,23,27,28,29]. Sex-based analyses have found similarities in U-14 and U-16 [21,22]. However, among professionals, men displayed shorter point durations (4.4–7.0 s for males; 7.0–7.2 s for females) [20,21,26,27,28,30,31,32].
In terms of active time percentage, significant differences were reported across male categories (26.3% in U-12; 27.6% in U-14; 33.6% in U-16; and 21.9% in U-18) [19,20,21,22]. However, no significant differences were found across sex or competitive levels [19,20,21,22,23,26,31,33,34,35,36,37].
Regarding the number of sets per match, differences were observed in the U-18 category (2.2 for males; 2.3 for females) and in professional three-set matches (2.3–2.6), though similarities were noted when analyzed by sex [18,24,38].
When it comes to the number of games per set, this metric has been analyzed exclusively in professional players, revealing general differences (9.8 for males; 9.3 for females), as well as surface-specific differences: hard courts (9.6–9.9 for males; 9.2–9.3 for females), grass (10.1 for males; 9.6 for females), and clay (9.6 for males; 9.1 for females). Additional differences were observed for matches at sea level (10.1 on hard court; 9.9 on clay) and high altitude (9.9 on hard court; 10.1 on clay) in male professionals [38,39].
In terms of points per game, male professionals recorded similar values across surfaces: clay (6.3), grass (6.0), and hard court (6.2) [40]. Sex-based differences were found in general (6.3 for males; 6.5 for females), on hard court (6.3–6.4 for males; 6.5–6.6 for females), clay (6.4 for males; 6.6 for females), and grass (6.1 for males; 6.4 for females) [39].
As for strokes per point, average values ranged from 2.5 to 5.9 in formative stages [19,20,22,23,33,41]. A comparative study on hard courts between professionals and U-18 players found no significant differences (5.0 for professional males; 4.8 for U-18 males; 4.6 for professional females; 4.4 for U-18 females) [41]. U-18 females also showed similar averages on hard (2.7) and clay courts (2.5) [19,33], both lower than those seen in professional females on hard court (4.4) [41]. Sex comparisons revealed similar stroke counts on hard courts in U-16 (5.4 for males; 5.9 for females) [22] and U-14 (4.8–5.3 for males; 4.3 for females) [21,29], contrasting with U-12 males (5.7) [20].
Regarding stroke frequency (strokes per minute), professional data indicate significant differences based on sex and surface. Males demonstrated a higher frequency (44.5) than females (42.9). Stroke frequency was also higher on grass courts (45.1 for males; 44.1 for females) than on hard courts (44.0 for males; 42.2 for females) [26].
From an age and sex perspective, U-14 male players exhibited more demanding performance indicators. This aligns with the developmental stage corresponding to early and mid-puberty (ages 12–16 for males; 10–14 for females), which directly influences physiological variables [42] and plays a critical role in determining success through superior physical–motor performance in both upper and lower body segments [43]. Specifically, regarding the transition from U-12 to U-14, studies on physical conditioning report increases in strength, speed, and agility with age. Sex-wise, similar values were observed during early and mid-puberty, though a marked increase in physical capacities emerged in males once puberty had concluded [44,45,46,47,48,49,50,51].
In summary, the review of previous studies across different categories and sex during the formative stages reveals a notable lack of research focused on the youngest age groups (Table 1). Therefore, the objective of this study was to analyze the temporal and structural aspects of gameplay by sex in the U-12 and U-14 categories. It was hypothesized that performance differences would be primarily determined by category and would persist across sex.

2. Materials and Methods

This research, aimed at determining how sex and age category (U-12 versus U-14) modulate the temporal and structural aspects of gameplay in elite youth tennis, is methodologically grounded in notational analysis from a quantitative perspective. The study adopts a selective, cross-sectional, non-sequential group comparison design, specifically conceived to address the empirical gap in early developmental stages. To this end, an observational methodology based on video analysis is employed, conducted in a structured and externally non-participant manner, allowing for the recording and comparison of differentiated technical–tactical variables according to age (U-12 and U-14) and sex (male and female). The sample size was calculated using G*Power 3.1 (Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany). For a T-test with 2 groups of sexes (male and female) or 2 groups of categories (U-12 and U-14), with an effect size d = 0.8 and a statistical power of 0.8, it is adjusted for a sample of 42 matches, and our study recorded 35. Similarly, with an effect size of d = 0.7 and a statistical power of 0.8, this is appropriate for a sample of 52 sets, and our study recorded 57 matches. With an effect size of d = 0.25 and a statistical power of 0.9, it is adjusted for a sample of 550 games, and our study recorded 537 games. Finally, with an effect size of d = 0.1 and a statistical power of 0.9, it is adjusted for a sample of 3428 points, and our study recorded 3527 points. The detailed characteristics of the sample are presented in Table 2.
The decision-making process regarding the investigation of the dependent variables (see Table 3) was carried out through the consensus of a panel of experts composed of graduates in Sport Sciences, each with more than 10 years of experience in racket sports and extensive expertise in performance analysis. This process resulted in the establishment of clear and direct guidelines for the recording of each variable.
These metrics allow for a direct association between age and sex constraints and the actual competitive load. Specifically, temporal indicators quantify the intermittency and physiological demands of each category, while structural indicators describe the technical–tactical volume of exchanges. Thus, by crossing both dimensions, the central objective of detecting differences or similarities between sex and categories is addressed, providing an objective basis for adapting training content and load planning during formative stages.
Regarding the sample, it comprised a total of 24 semi-final and final matches, in both male and female modalities, from the international tournaments “Open Super 12 Auray” (U-12 category, under 12 years of age) and “Les Petits As” (U-14 category, under 14 years of age). These two tournaments are widely recognized as leading global benchmarks for competitive development at these stages and, therefore, require participants to either prove their level or qualify through a preliminary stage. All matches were played on hard courts under indoor conditions, ensuring consistent climatic conditions.
Participant selection was based on an arbitrary judgment by the panel of experts, employing purposive sampling to include elite players. The inclusion criteria required right-handed players participating in semi-final and final rounds between 2017 and 2019, resulting in a total of 35 players distributed across the U-12 (8 male and 9 female) and U-14 (8 male and 10 female) categories (see Table 2). All matches were played in best-of-three sets with tiebreaks in each set, conducted on indoor hard courts classified as Category 3 (high speed) according to the ITF CS 01/02 method described in the 2025 ITF Technical Booklet (International Tennis Federation, 2025). As shown in Table 2, a total of 24 matches, 57 sets, 537 games, and 3527 points were analyzed. Only rallies where the serve was neither a let nor a fault were included.
Match recordings were provided by the tournament organizations using a fixed cameras positioned at one end of the court. The videos were subsequently converted to AVI format with a resolution of 640 × 360 pixels and a frame rate of 30 frames per second, which was optimal for the software used. The analysis of match structure was conducted with VirtualHub 1.10.4 software (VirtualHub Software, New York, NY, USA), offering a precision of 0.04 s between frames (recorded at 25 frames per second). All variables were recorded following the conclusion of each point through a sequential notation system [14], specifically developed using Microsoft Excel version 16.16.7 (Microsoft, Redmond, WA, USA).
The computer setup used for video analysis consisted of a Dell Inspiron 15 laptop running Windows 8.1 (Microsoft, Redmond, WA, USA) connected via HDMI cable to a 27-inch LG 27UL500 monitor (LG Electronics Inc., Seoul, Republic of Korea). This dual-monitor configuration allowed one screen to be dedicated to match visualization and the other to data recording.
Data recording reliability was ensured by having the same observer carry out all measurements. Intra-observer reliability was calculated by re-coding a complete match after a one-month interval. Bivariate correlation [13] was used for both measurements, with the results presented in Table 4. Pearson’s r statistical test indicated a very high level of reliability; values above 0.9 are considered excellent [54].
The data obtained from the Excel spreadsheet recording system were exported to SPSS software, version 26 (SPSS Inc., Chicago, IL, USA). First, we analyzed the distribution of the data to assess normality. Depending on the sample size (n) for each variable, we applied the Kolmogorov–Smirnov test (n > 30) or the Shapiro–Wilk test (n < 30). For variables with a normal distribution, homogeneity of variances was tested using Levene’s Test.
To compare the effects of sex and age category on total match time, point duration, number of sets per match, number of games per set, and number of strokes per point, Mann–Whitney U tests were conducted. Furthermore, to evaluate the interaction effect of sex and category on these same variables, Kruskal–Wallis tests were performed, followed by pairwise Mann–Whitney U tests with Bonferroni-adjusted significance levels for post-hoc comparisons.
In addition, to analyze the effects of sex, category, and their interaction on total match duration, total set duration, percentage of active time relative to total match time, number of points per game, and stroke frequency per minute, two-way ANOVA tests were conducted. Post-hoc comparisons were also performed using the Bonferroni correction. To determine the effect size of the main effects, partial eta squared (ηp2) was used, with values between 0.04 and 0.24 interpreted as small effects, between 0.25 and 0.63 as medium effects, and values ≥ 0.64 as large effects [55]. The significance level was set at p < 0.05.

3. Results

Table 5 shows that sex did not have a significant effect on the temporal or structural aspects of gameplay, except for the number of strokes per point, where male players recorded significantly more strokes per point than female players (U = 2,012,114; Z = −2.088; p = 0.037).
When analyzing the effect of age category (Table 5), it was observed that U-12 players exhibited higher values in both temporal and structural gameplay variables. Specifically, the U-12 category showed significantly greater values than the U-14 category in total match time (U = 45,100; Z = −2.040; p = 0.041), point duration (U = 3,341,418; Z = −11.57; p < 0.001), percentage of active time (F = 19.4; p < 0.001, η2 = 0.432), number of strokes per point (U = 1,749,403; Z = −10.3; p < 0.001), and stroke frequency per minute (F = 21.8; p < 0.001, η2 = 0.524).
Finally, when analyzing the interaction effect of sex and category on the temporal and structural aspects of gameplay, a significant interaction was found for point duration (H3 = 138.3; p < 0.001) and for the number of strokes per point (H3 = 113; p < 0.001). Pairwise comparisons for point duration revealed that male players in the U-12 category recorded significantly longer point durations than those in the U-14 category (U = 575,341; Z = −8.99; p < 0.001), and the same pattern was observed for female players, with U-12 players showing longer point durations than U-14 players (U = 1,131,865; Z = −7.54; p < 0.001).
In the pairwise comparisons for the number of strokes per point, U-12 male players recorded significantly more strokes than U-12 female players (U = 522,847; Z = −2.64; p = 0.008) and U-14 male players (U = 289,613; Z = −8.24; p < 0.001). Similarly, U-12 female players also recorded significantly more strokes per point than their U-14 counterparts (U = 611,688; Z = −6.35; p < 0.001).

4. Discussion

4.1. Total Match Time

Overall, the results for U-12 and U-14 categories were consistent with the 90 min benchmark for three-set matches during formative stages [18,19,20,21,22,23,24,33]. In comparison with other studies at the international competitive level, the observed similarity in total match time between U-12 (77.2 min for males) and U-18 (77.2–85.0 min for males and 77.9–80.6 min for females) was evident [18,19,20,24]. However, our findings contrasted with national and regional competitions, which reported shorter times for U-14 (62.6 min overall, 65.4 min for males, and 59.8 min for females) and longer durations for U-16 (103.9 min overall, 108.0 min for males, and 99.7 min for females) [21,22]. Focusing on surface type, the total match time recorded on clay courts for U-12 males (77.2 min) [20] was unexpectedly lower, suggesting a limited influence of surface on match duration at this developmental stage, despite clay being a slower surface. When considering sex, the similarity in match duration between males and females aligns with prior research at both international and national levels [18,19,20,21,22,23,24].

4.2. Mean Set and Game Time

Similarly, the mean set time showed consistency between sex, reflecting what has been reported at the U-18 international level (34.6 min for males and 34.2 min for females) and among professionals (41.2 min for males and 43.5 min for females) [18]. In terms of mean game time, our results were comparable to previous findings in professional tennis (174.2–178.6 s for males and 183.8 s for females) [25,26]. Higher values were found in U-12, and slightly lower ones in U-14, possibly due to differences in point dynamics between the two stages.

4.3. Point Duration

With respect to point duration, both categories exhibited a decrease as players progressed from U-12 to higher categories, reflecting the influence of a more offensive playing style [37,55]. In terms of sex, point duration remained similar between males and females (12.1 s for U-12 males; 7.3 s and 6.3 s for U-14; 9.1 s and 9.0 s for U-16; 7.2 s and 8.2 s for U-18) [20,21,22,23,28,29,53], contrasting with the differentiated values found among professional players (4.4–7.1 s for males and 7.0–7.2 s for females) [21,26,31,32,52].
Moreover, surface type influenced point duration. U-12 males playing on hard courts exhibited shorter point durations compared to those on clay at the same age and sex category [20]. This surface effect mirrors patterns found in professional tennis, reinforcing its significance [20,21,26,28,30,31,32,53].

4.4. Active Time Percentage

Moving on to the active time percentage, U-14 males (18.6%) showed a trend toward lower values compared to U-12 males (26.3%), confirming a progressive decrease in active time across male categories (26.3% in U-12, 27.6% in U-14, 33.6% in U-16, and 21.9% in U-18) [19,20,21,22]. Compared to the commonly referenced 20–30% active time range [17], the values observed here matched those of female players in both categories and U-12 males, while U-14 males fell slightly below. Across developmental stages, variability in active time percentages was noted according to competitive level. National and regional competitions reported higher percentages in U-16 (30.1% for males and 31.1% for females) [22] and U-14 (27.6% for males and 23.5% for females) [21], exceeding the values found in this study. At the international level, however, U-18 females showed comparable active time values (21.6–21.9%) [19,33] to those of U-14 females. Additionally, similar active time percentages between hard and clay courts in U-12 suggest that surface influence is less pronounced in developmental stages compared to professional competition [20,21,26,31,35,36].

4.5. Number of Sets per Match

Turning to the number of sets per match, the similarity observed between categories aligned with results from the U-18 category (2.2 for males and 2–3 for females) and professional three-set matches (2.3–2.6) [18,24,38]. Nonetheless, these figures were lower compared to professional male players participating in five-set matches (3.7), indicating that neither category nor sex significantly influenced the number of sets when comparing developmental stages and professional three-set matches.

4.6. Number of Games per Set

In relation to the number of games per set, sex similarities contrasted with the significant differences reported among professional players by Carboch (2017) [38], both generally (9.8 for males and 9.3 for females) and across different surfaces—hard, grass, and clay. Meanwhile, the results observed for U-12 and U-14 males remained consistent with the stable trends reported from 1991 to 2009 for professional male players on hard courts (9.5–9.8) [40].

4.7. Number of Points per Game

As for the number of points per game, the results for U-12 players were slightly higher than those found for professional males (6.2) according to Cross and Pollard (2009) [39], whereas the U-14 values were more comparable. Despite this, a more recent study found narrowed differences between male (6.3–6.4) and female (6.5–6.6) professionals [39]. The observed sex similarities at youth levels contrast with the significant sex differences found in professional players [39], suggesting an evolution in sex influence during early development stages.

4.8. Number of Strokes per Point

In terms of the number of strokes per point, the findings followed the general trend of decreasing stroke counts with category advancement. U-12 males (5.7) recorded higher values than players in U-14 (4.8–5.3 for males and 4.3 for females), U-16, U-18, and professional categories [19,20,21,22,23,28,29,31,32,33,41,53]. Sex-wise, the observed similarities align with previous findings at the national competitive level [21,22]. Additionally, it is noteworthy that the stroke count for U-12 males on clay (5.7) [20] was lower than that recorded on hard courts, despite clay typically favoring longer rallies.

4.9. Stroke Frequency

Finally, stroke frequency varied by category. The trends identified were consistent with Smekal et al. (2001) [36], who observed higher stroke frequencies in offensive playstyles. Compared to professional players (44.0 for males and 45.1 for females) [26], U-12 players of both sex and U-14 females recorded lower frequencies, whereas U-14 males showed higher values, perhaps reflecting increased game intensity in contemporary youth tennis. Interestingly, sex similarities found at youth levels contrast with the differences previously observed among professionals [26], suggesting changes in sex influence during formative stages.

4.10. Study Limitations and Future Research Directions

Although the sample was drawn from prestigious international youth tournaments, the study analyzed only 24 semi-final and final matches played on indoor hard courts, limiting the generalizability of findings to other surfaces, earlier rounds, and competitive contexts. Future research should incorporate preliminary rounds, tournaments on different surfaces, and outdoor conditions, adopt a longitudinal design following the same cohorts across multiple seasons, and integrate a broader range of performance indicators to identify developmental patterns and project the athletic evolution of young tennis players.
Moreover, the cross-sectional design did not allow for the evaluation of individual developmental changes over time. A longitudinal approach would be necessary to confirm how the analyzed variables evolve according to category, sex, and physical maturation, providing greater depth and robustness to the analysis. Future perspectives include analyzing the full range of technical–tactical variables involved in serve and return dynamics, as well as integrating psychological variables for a deeper interpretative framework.

4.11. Practical Applications of the Findings

The findings provide coaches, physical trainers, and talent development specialists in youth tennis with valuable guidelines for planning training sessions and making tactical decisions based on competition integration into daily practice and individualized application of intensities and loads for both sexes in the U-12 and U-14 categories. In U-12, technical and tactical tasks should prioritize consistency and endurance in prolonged rallies, whereas in U-14, training should focus on developing a more aggressive playing style with shorter points. Accordingly, designing high-performance training sessions that replicate the real competition structure optimizes the physical and tactical performance of young players, maximizes their learning experience, and fosters their comprehensive and progressive development toward higher levels of competition.

5. Conclusions

The results indicate that, in the U-12 and U-14 categories, total match duration aligns with the reference standard of 90 min for three-set matches. In contrast, match duration tends to increase in the U-16 category and appears shorter in U-14 compared to national-level studies. Moreover, the court surface had a minimal influence on match duration, diverging from trends typically observed in professional tennis. As players progress through age categories, both point duration and strokes per point decrease, suggesting a transition toward more direct and aggressive playing styles. Among boys, the percentage of active time falls below the reference range starting in the U-14 category, indicating shorter and more intense rallies. Regarding active time percentage, the data are consistent with previous research reporting sex similarities during formative stages. However, these findings contrast with variations observed based on competitive level within youth stages. Stroke frequency in males varied by category, following the expected trend of being higher in offensive play styles. Notably, U-14 boys recorded higher stroke frequencies than those reported for professional players two decades ago, possibly reflecting increased game intensity in contemporary youth tennis. The number of sets, games, and points per game remained relatively stable across age categories and between sexes. This stability highlights the greater homogeneity of developmental stages, in contrast to the variability observed at the professional level, particularly in five-set matches. These findings provide practical guidance for tournament and training planning. It is recommended to schedule realistic match durations of approximately 90 min for U-12 and U-14 players (with longer durations for U-16) to support the technical–tactical shift toward an offensive playing style, to adapt physical preparation considering the reduction in active time observed in males from U-14 onward, and to leverage the similarity in performance metrics to design mixed training stimuli that promote balanced development across sexes.

Author Contributions

Conceptualization, A.Z., J.R.-L., J.F.G. and R.M.-G.; methodology, A.Z., J.R.-L., J.F.G. and R.M.-G.; software, A.Z. and R.M.-G.; validation A.Z., J.R.-L. and J.F.G.; formal analysis, A.Z. and J.R.-L.; investigation, A.Z., J.R.-L., J.F.G. and R.M.-G.; writing—original draft preparation A.Z., J.R.-L., J.F.G. and R.M.-G.; writing—review and editing, A.Z., J.R.-L., J.F.G. and R.M.-G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Lagardera Otero, F.; Lavega Burgués, P. Introducción a la Praxiología Motriz; Paidotribo: Barcelona, Spain, 2003. [Google Scholar]
  2. Davids. Complex Systems in Sport; Routledge: London, UK, 2013. [Google Scholar]
  3. Passos, P.; Araújo, D.; Davids, K. Competitiveness and the process of co-adaptation in team sport performance. Front. Psychol. 2016, 7, 1562. [Google Scholar] [CrossRef] [PubMed]
  4. Kovacs, M. Tennis physiology. Training the competitive athlete. Sport Med. 2007, 37, 189–198. [Google Scholar] [CrossRef] [PubMed]
  5. Janelle, C.M.; Hillman, C.H. Expert performance in sport: Current perspectives and critical issues. In Expert Performance in Sport: Advances in Research on Sport Expertise; Starkes, J.L., Ericsson, K.A., Eds.; Human Kinetics: Champaign, IL, USA, 2003; pp. 19–48. [Google Scholar]
  6. Martens, R. Science, knowledge and sport psychology. Sport Psychol. 1987, 1, 29–55. [Google Scholar] [CrossRef]
  7. Araújo, D.; Kirlik, A. Towards an ecological approach to visual anticipation for expert performance in sport. Int. J. Sport Psychol. 2008, 39, 157–165. [Google Scholar]
  8. Davids, K.; Glazier, P.; Araújo, D.; Bartlett, R. Movement systems as dynamical systems. Sports Med. 2003, 33, 245–260. [Google Scholar] [CrossRef]
  9. Sampaio, J.; Leite, N. Performance indicators in game sports. In Handbook of Sports Performance Analysis; McGarry, T., O’Donoghue, P., Sampaio, J., Eds.; Routledge: London, UK, 2013; pp. 115–127. [Google Scholar]
  10. Anguera, M.T.; Hernández, A. La metodología observacional en el ámbito del deporte. E-balonmano.com: Rev. Cienc. Deporte 2013, 9, 135–160. [Google Scholar]
  11. Anguera, M.T.; Hernández, A. Técnicas de análisis en estudios observacionales en ciencias del deporte. Cuad. Psicol. Deporte 2015, 15, 13–30. [Google Scholar] [CrossRef]
  12. Hughes, M.; Franks, I.M. Notational Analysis of Sport: Systems for Better Coaching and Performance in Sport; Routledge: London, UK, 2004. [Google Scholar]
  13. O’Donoghue, P. Research Methods for Sport Performance Analysis; Routledge: London, UK, 2010. [Google Scholar]
  14. Hughes, M.; Barlett, R. What is performance analysis? In The Essentials of Performance Analysis: An Introduction; Hughes, M., Franks, I., Eds.; Routledge: London, UK, 2007; pp. 8–21. [Google Scholar]
  15. Pluim, B.M.; Jansen, M.G.; Williamson, S.; Berry, C.; Camporesi, S.; Fagher, K.; Ardern, C.L. Physical demands of tennis across the different court surfaces, performance levels and sexes: A systematic review with meta-analysis. Sports Med. 2023, 53, 807–836. [Google Scholar] [CrossRef]
  16. ITF. ITF International Tennis Federation (2022). In Rules of Tennis; ITF: London, UK, 2024. [Google Scholar]
  17. Torres-Luque, G.; Sánchez-Pay, A.; Fernández-García, A.I.; Palao, J.M. Características de la estructura temporal en tenis. Una Revisión. J. Sport Health Res. 2014, 6, 117–128. [Google Scholar]
  18. Blanca-Torres, J.C.; Fernández-García, A.I.; Torres-Luque, G. Influencia de la categoría y el género en variables temporales en el tenis individual de élite. J. Sport Health Res. 2019, 11, 69–78. [Google Scholar]
  19. Fernández-Fernández, J.; Mendez-Villanueva, A.; Fernandez-García, B.; Terrados, N. Match activity and physiological responses during U-18 female singles tennis tournament. Br. J. Sport Med. 2007, 41, 711–716. [Google Scholar] [CrossRef] [PubMed]
  20. Kilit, B.; Arslan, E. Physiological responses and time-motion characteristics of young tennis players: Comparison of serve vs. return games and winners vs. losers matches. Int. J. Perform. Anal. Sport 2017, 17, 684–694. [Google Scholar] [CrossRef]
  21. Stare, M.; Filipcic, A.; Zibrat, U. Stroke effectivness in professional and U-18 tennis. Kinesiol. Slov. 2015, 21, 39–50. [Google Scholar]
  22. Torres-Luque, G.; Cabello, D.; Hernández, R.; Garatachea, N. An analysis of competition in young tennis players. Eur. J. Sport Sci. 2011, 11, 39–43. [Google Scholar] [CrossRef]
  23. Torres-Luque, G.; Sánchez-Pay, A.; Moya, M. Competitive analysis of requirement of young tennis players. J. Sport Health Res. 2011, 3, 71–78. [Google Scholar]
  24. Torres-Luque, G.; Fernández-García, A.I.; Sánchez-Pay, A.; Ramírez, A.; Nikolaidis, P.T. Diferencias en las estadísticas de competición en tenis individual en función de la superficie de juego en jugadores U-18 masculinos de alto nivel. SPORT TK Rev. Euroam. Cienc. Deporte 2017, 6, 75–80. [Google Scholar] [CrossRef]
  25. Martínez-Gallego, R.; Guzmán, J.F.; James, N.; Pers, J.; Ramón-Llin, J.; Vuckovic, G. Movement Characteristics of Elite Tennis Players on Hard Courts with Respect to the Direction of Ground Strokes. J. Sports Sci. Med. 2013, 12, 275–281. [Google Scholar]
  26. Morante, S.; Brotherhood, J. Match characteristics of professional singles tennis. Med. Sci. Tennis 2005, 10, 12–13. [Google Scholar]
  27. Carboch, J. Game characteristic in professional tennis at different levels of international tournaments. Int. J. Appl. Excercise Physiol. 2021, 10, 67–77. [Google Scholar]
  28. Kilit, B.; Arslan, C.; Akçınar, F.; Rad, A.G. A notational analysis of elite men’s tennis matches. J. Hum. Sci. 2012, 9, 1311–1320. [Google Scholar]
  29. Klaus, A.; Bradshaw, R.; Young, W.; O’Brien, B.; Zois, J. Success in national level U-18 tennis: Tactical perspectives. Int. J. Sports Sci. Coach. 2017, 12, 618–622. [Google Scholar] [CrossRef]
  30. Brown, E.; O’Donoghue, P. Sex and surface effect on elite tennis strategy. Coach. Sport Sci. Rev. 2008, 15, 11–13. [Google Scholar]
  31. Mendez-Villanueva, A.; Fernandez-Fernandez, J.; Bishop, D.; Fernandez-Garcia, B.; Terrados, N. Activity patterns, blood lactate concentrations and ratings of perceived exertion during a professional singles tennis tournament. Br. J. Sports Med. 2007, 41, 296–300. [Google Scholar] [CrossRef]
  32. Méndez-Villanueva, A.; Fernández-Fernández, J.; Bishop, D.; Fernández García, B. Ratings of perceived exertion-lactate association during actual singles tennis match play. J. Strength Cond. Res. 2010, 24, 165–170. [Google Scholar] [CrossRef]
  33. Fernández-Fernández, J.; Sanz, D.; Fernandez-García, B.; Mendez-Villanueva, A. Match activity and physiological load during a clay-court tennis tournament in elite female players. J. Sports Sci. 2008, 26, 1589–1595. [Google Scholar] [CrossRef]
  34. Torres-Luque, G. La exigencia competitiva individual en tenistas adolescentes. In Investigación en Deportes de Raqueta: Tenis y Bádminton; Torres, G., Carrasco, L., Eds.; Universidad Católica de San Antonio: Murcia, Spain, 2004. [Google Scholar]
  35. Fernandez-Fernandez, J.; Sanz-Rivas, D.; Sanchez-Muñoz, C.; Pluim, B.M.; Tiemessen, I.; Mendez-Villanueva, A. A comparison of the activity profile and physiological demands between advanced and recreational veteran tennis players. J. Strength Cond. Res. 2009, 23, 604–610. [Google Scholar] [CrossRef]
  36. Smekal, G.; Serge, P.V.D.; Claus, R.; Rochus, P.; Peter, H.; Ramon, B.; Harald, T.; Norbert, B. A physiological profile of tennis match play. Med. Sci. Sports Excercise 2001, 33, 999–1005. [Google Scholar] [CrossRef]
  37. Sánchez-Pay, A.; Ótalora-Murcia, F.J.; Sánchez-Alcaraz, B.J. Influencia de la altitud en las demandas de competición del tenis profesional en pista dura y tierra batida. Cult. Cienc. Deporte 2019, 14, 243–249. [Google Scholar]
  38. Carboch, J. Comparison of game characteristics of male and female tennis players at grand-slam tournaments in 2016. Sport Sci. 2017, 4, 151–155. [Google Scholar]
  39. Cross, R.; Pollard, G. Grand Slam men’s singles tennis 1991–2009 serve speeds and other related data. ITF Coach. Sport Sci. Rev. 2009, 16, 8–10. [Google Scholar]
  40. Kovalchik, S.A.; Reid, M. Comparing matchplay characteristics and physical demands of U-18 and professional tennis athletes in the era of big data. J. Sports Sci. Med. 2017, 16, 489–497. [Google Scholar] [PubMed]
  41. Armstrong, N.; McManus, A.M. Physiology of elite young male athletes. Med. Sport Sci. 2011, 56, 1–22. [Google Scholar]
  42. García-Rubio, J.; Ibáñez-Godoy, D.S.J.; Parejo-González, I.; Feu Molina, D.S.; Cañadas-Alonso, M. Diferencias entre nivel de juego y categoría de los jugadores en etapas de formación. Rev. Esp. Educ. Fís. Deporte 2011, 65, 13–28. [Google Scholar]
  43. Balsalobre-Fernández, C.; Nevado-Garrosa, F.; del Campo-Vecino, J.; Ganancias-Gómez, P. Repetición de esprints y salto vertical en jugadores jóvenes de baloncesto y fútbol de élite. Apunt. Educ. Fís. Deportes 2015, 120, 52–57. [Google Scholar]
  44. Courel-Ibáñez, J.; Llorca-Miralles, J. Physical fitness in young padel players: A cross-sectional study. Int. J. Environ. Res. Public Health 2021, 18, 2658. [Google Scholar] [CrossRef] [PubMed]
  45. Fernandez-Fernandez, J.; Loturco, I.; Pereira, L.A.; Del Coso, J.; Areces, F.; Gallo-Salazar, C.; Sanz-Rivas, D. Change of direction performance in young tennis players: A comparative study between sexes and age categories. J. Strength Cond. Res. 2022, 36, 1426–1430. [Google Scholar] [CrossRef]
  46. Fernandez-Fernandez, J.; Canós-Portalés, J.; Martinez-Gallego, R.; Corbi, F.; Baiget, E. Effects of Maturation on Lower-Body Neuromuscular Performance in Youth Tennis Players. J. Strength Cond. Res. 2023, 40, 867–876. [Google Scholar] [CrossRef]
  47. Kozinc, Z.; Sarabon, N. Bilateral deficit in countermovement jump and its association with change of direction performance in basketball and tennis players. Sports Biomech. 2021, 1, 1–14. [Google Scholar] [CrossRef]
  48. López-Ferrada, M.R.; Navarrete, F.J.; Navarrete, C.J.; Hernández, R. Estado nutricional y fuerza de tren inferior: Diferencias entre sexo y área geográfica entre niños y niñas. Retos: Nuevas Tend. Educ Fís Deporte Recreación 2021, 1, 612–617. [Google Scholar] [CrossRef]
  49. Lopez-Valenciano, A.; Ayala, F.; De Ste Croix, M.B.; Barbado, D.; Moreno-Perez, V.; Sanz-Rivas, D.; Fernandez-Fernandez, J. The association between chronological age and maturity status on lower body clinical measurements and asymmetries in elite youth tennis players. Sports Health 2022, 15, 250–259. [Google Scholar] [CrossRef]
  50. Sahin, G.; Koç, H.; Baydemir, B.; Abanoz, H.; Coşkun, A.; Günar, B.B. Analysis of some performance parameters of fencer according to sex and age. Kinesiol. Slov. 2019, 25, 27–34. [Google Scholar]
  51. Sonesson, S.; Lindblom, H.; Hägglund, M. Performance on sprint, agility and jump tests have moderate to strong correlations in youth football players but performance tests are weakly correlated to neuromuscular control tests. Knee Surg. Sports Traumatol. Arthrosc. 2020, 29, 1659–1669. [Google Scholar] [CrossRef] [PubMed]
  52. Fernández-García, A.I.; Torres-Luque, G.; Hernández-García, R.; Blanca-Torres, J.C. Análisis de las variables estadísticas relacionadas con el servicio en tenis masculino de alto rendimiento en categoría junior y absoluto. Cult. Cienc. Deporte 2019, 14, 285–295. [Google Scholar]
  53. Martin, C.; Bideau, B.; Touzard, P.; Kulpa, R. Identification of serve pacing strategies during five-set tennis matches. Int. J. Sports Sci. Coach. 2019, 14, 32–42. [Google Scholar] [CrossRef]
  54. Martínez Ortega, R.M.; Tuya Pendás, L.C.; Martínez Ortega, M.; Pérez Abreu, A.; Cánovas, A.M. El coeficiente de correlación de los rangos de Spearman caracterización. Rev. Habanera Cienc. Méd. 2009, 8. Available online: https://revhabanera.sld.cu/index.php/rhab/article/view/1531 (accessed on 11 May 2025).
  55. Pek, J.; Flora, D.B. Reporting effect sizes in original psychological research: A discussion and tutorial. Psychol. Methods 2018, 23, 208. [Google Scholar] [CrossRef]
Table 1. Synthesis of the variables analyzed in the studies consulted.
Table 1. Synthesis of the variables analyzed in the studies consulted.
Author (s)CATSEXSURFACETMTTTSTGTATPDNSMNGSNPGSFNSP
Smekal et al. (2001) [36]ProMaleClay---OF 20.3
DEF 29.3		Offensive 4.8
Defensive 8.2		---Offensive 47.1
Defensive 42.6		-
Torres-Luque et al. (2004) [34]U-16Male
Female		Hard108.3
99.6		--33.6
30.0		9.0
9.1		----5.4
5.9
Morante and Brotherhood (2005) [26]ProMale
Female		Hard/Grass154.2/137.0
113.5/65.3		-178.6/183.8
159/187.8		17.5/20.2
20.5/21.1		6.4/7
5.2/5.6		---44.0/42.2
45.1/44.1		-
Fernández-Fernández et al. (2007) [19]U-18FemaleClay80.6--21.98.2----2.8
Méndez-Villanueva et al. (2007) [31]ProMaleClay----7.5----2.7
Brown and O’Donoghue (2008) [30]ProMale
Female		Hard/Clay/Grass----6.5–6.9/7.3/5.4
6.6–6.8/7.3/6.2		-----
Fernández-Fernández et al. (2008) [33]ProFemaleClay65.0--21.06.3----2.5
Cross and Pollard (2009) [39]ProMaleHard/Clay/Grass------9.5–9.8/9.6/10.16.2/6.3/6.0--
Fernández-Fernández et al. (2009) [35]+45MaleClay---21.7-----2.1
Méndez-Villanueva et al. (2010) [32]ProMaleClay105.0--21.57.5----2.7
Torres-Luque et al. (2011) [22]U-16Male
Female		Hard108.3
99.6		--33.6
30.0		9.1
9.0		----5.95.4
Torres-Luque et al. (2011) [23]U-16MaleClay69.6--19.35.5----3.7
Kilit et al. (2012) [28]ProMaleHard---21.05.7----3.6
Martínez-Gallego et al. (2013) [25]ProMaleHard--174.234.9------
Stare et al. (2015) [21]Pro
U-14
U-14		Male
Male
Female		Hard81.0
65.4
59.8		--18.0
27.6
23.5		4.4
7.3
6.3		----3.0 5.34.3
Kilit et al. (2012) [28]ProMaleHard---26.36.7---43.33.9
Carboch (2017) [38]ProMale
Female		Hard/Clay/Grass------9.6–9.9/9.6/10.1
9.2–9.3/9.2/9.7		6.3–6.4/6.4/6.1
6.5–6.6/6.6/6.4		--
Klaus et al. (2017) [29]U-14MaleSurface---------4.8
Kovalchik and Reid (2017) [40]U-18Male FemaleHard/Clay/Grass---------4.8
4.4
Kilit and Arslan (2017) [20]U-12Male FemaleClay77.2--26.312.1----5.7
Torres-Luque et al. (2017) [24]U-18MaleHard/Clay85.2/103.3----2.2/2.4----
Blanca-Torres et al. (2019) [18]ProMale
Female		Hard/Clay/Grass158.2/148.8/144.0
100.2/97.4/96.2		41.2/40.2/40.5
43.5/41.7/40.6		---3.7/3.6/3.5
2.3/2.3/2.4		----
U-18Male
Female		Hard/Clay/Grass77.2/93.7/76.9
77.9/99.0/90.0		34.6/37.9/35.9
34.2/40.2/37.2		---2.2/2.5/2.2
2.3/2.4/2.4		----
Fernández-García et al. (2019) [52]ProMale
Female		Grass101.2
94.1		---------
Martín et al. (2019) [53]ProMaleHard/Clay/Grass212.2---------
Sánchez-Pay et al. (2019) [37]ProFemaleHardSea level 96.5----2.310.1---
Altitude 104.0-2.69.9---
CAT = category; TMT = total match time; TST = total set time; TGM = total game time; AT = active time; PD = point duration; NSM = number of sets per match; NGS = number of games per set; NPG = number of points per game; SF = stroke frequency; NSP = number of strokes per point.
Table 2. Sample characteristics.
Table 2. Sample characteristics.
SampleU-12U-12U-14U-14Total
MaleFemaleMaleFemaleSample
nnnnn
Players8981035
Matches666624
Sets1513151457
Games143145124125537
Points9689657778173527
Table 3. Relationship of the dependent variables with the game.
Table 3. Relationship of the dependent variables with the game.
Dependent VariableRegister
Total match time (min)The time from the first action of the match to the last
Total set time (min)The time from the first action of the set to the last
Total game time (s)The time from the first game action to the last
Duration of point (s) The time from the start of the point to its end
Percentage of active match time The percentage of time played
Number of sets per match The number of sets played in the match
Number of games per setThe games played in each set
Number of points per gameThe number of points played in a game
Number of hits per pointThe number of strokes made a point
Frequency per match (strokes/min)The relationship between number of hits and active time
Table 4. Results of reliability test.
Table 4. Results of reliability test.
VariableValueAgreement Strength Result
Number of sets1.000Very good
Number of games1.000Very good
Number of points1.000Very good
Number of strokes per point 0.980Very good
Point start time1.000Very good
Point completion time1.000Very good
Table 5. Effect of sex and category on the temporal and formal structure of the game.
Table 5. Effect of sex and category on the temporal and formal structure of the game.
VariablesU-12U-12U-14U-14SexCategory
Male (a)Female (b)Male (c)Female (d)EffectEffect
Mean (SD)Mean (SD)Mean (SD)Mean (SD)pp
Total Match Time (min)97.38 (23.44)94.01 (36.60)75.85 (19.17)77.90 (31.06)0.9220.091
Total Set Time (min)37.41 (12.72)35.67 (11.98)33.76 (7.81)31.92 (8.10)0.5030.205
Total Game Time (s)189.66 (91.89)171.27 (98.68)163.36 (95.45)162.07 (94.73)0.3260.041
Point Duration (s) 9.48 (6.98) c8.76 (6.57) a,d6.13 (6.49) a6.41 (7.06) b0.790<0.001
Percentage of Active Time 26.33 (3.99)25.03 (2.86)18.59 (4.52)20.15 (4.52)0.688<0.001
Number of Sets per Match 2.50 (0.55)2.50 (0.55)2.17 (0.41)2.33 (0.52)0.5830.149
Number of Games per Set9.53 (2.10)9.67 (2.32)9.54 (2.11)8.93 (1.69)0.8770.345
Number of Points per Game6.77 (2.64) 6.66 (2.80)6.25 (2.70) 6.54 (2.87)0.7190.105
Number of Strokes per Point6.75 (5.15) b,c6.14 (4.42) a,d4.94 (3.67) a5.12 (3.87) b0.037<0.001
Stroke Frequency per Match (strokes/min)42.20 (2.12) c42.09 (0.91)46.44 (2.02) a45.35 (2.67)0.459<0.001
SD = standard deviation; p = p-value; a = significant difference with U-12 male; b = significant difference with U-12 female; c = significant difference with U-14 male; d = significant difference with U-14 female.
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Zurano, A.; Ramón-Llín, J.; Guzmán, J.F.; Martínez-Gallego, R. Influence of Category and Sex in Game Structure Variables in Young Elite Tennis. Appl. Sci. 2025, 15, 5496. https://doi.org/10.3390/app15105496

AMA Style

Zurano A, Ramón-Llín J, Guzmán JF, Martínez-Gallego R. Influence of Category and Sex in Game Structure Variables in Young Elite Tennis. Applied Sciences. 2025; 15(10):5496. https://doi.org/10.3390/app15105496

Chicago/Turabian Style

Zurano, Alejandro, Jesús Ramón-Llín, José Francisco Guzmán, and Rafael Martínez-Gallego. 2025. "Influence of Category and Sex in Game Structure Variables in Young Elite Tennis" Applied Sciences 15, no. 10: 5496. https://doi.org/10.3390/app15105496

APA Style

Zurano, A., Ramón-Llín, J., Guzmán, J. F., & Martínez-Gallego, R. (2025). Influence of Category and Sex in Game Structure Variables in Young Elite Tennis. Applied Sciences, 15(10), 5496. https://doi.org/10.3390/app15105496

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