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Article

Cluster Set vs. Traditional Set in Plyometric Training: Effect on the Athletic Performance of Youth Football Players

by
Barışcan Öztürk
1,
Niyazi Sıdkı Adıgüzel
2,
Murat Koç
3,
Aydın Karaçam
2,
Umut Canlı
4,
Hakan Engin
1,
Bekir Erhan Orhan
5,
Peter Bartik
6,
Peter Sagat
6,
Jason Pérez
7,
Maria Isip
6 and
Pablo Prieto-González
6,*
1
Faculty of Sport Sciences, Çukurova University, Adana 01330, Türkiye
2
Faculty of Sport Sciences, Bandırma Onyedi Eylül University, Balıkesir 10200, Türkiye
3
Faculty of Sport Sciences, Erciyes University, Kayseri 38039, Türkiye
4
Faculty of Sport Sciences, Tekirdağ Namık Kemal University, Tekirdağ 59030, Türkiye
5
Faculty of Sport Sciences, İstanbul Aydın University, İstanbul 34295, Türkiye
6
Sport Sciences and Diagnostics Research Group, Prince Sultan University, Riyadh 11586, Saudi Arabia
7
Preparatory Year Program—College of Humanities and Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(3), 1282; https://doi.org/10.3390/app15031282
Submission received: 6 December 2024 / Revised: 20 January 2025 / Accepted: 21 January 2025 / Published: 26 January 2025
(This article belongs to the Special Issue Advances in Sports Science: Physical Performance Diagnostics and Enhancement)
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Versions Notes

Abstract

:
Aim: This study evaluated the effects of plyometric training with different set configurations on sprint speed, change of direction (COD), jump performance, and perceived exertion in youth football players. Method: Twenty-four U-19 players were randomized into three groups: Cluster Set (CLS, n = 8), Traditional Set (TRD, n = 8), and Control (CON, n = 8). CLS performed 8–10 sets of 2–3 repetitions, while TRD completed 2–3 sets of 8–10 repetitions in an 8-week plyometric program (2 sessions/week). The CON group did not train. Performance measures included 10 m, 20 m, and 30 m sprints, COD, Counter Movement Jump (CMJ), Reactive Strength Index (RSI), and Rate of Perceived Exertion (RPE). A repeated measures ANOVA analyzed group*time interactions. Results: Significant improvements were observed in CLS and TRD groups for sprints (10 m: f = 21.44; 20 m: f = 19.40; 30 m: f = 49.56; p < 0.001), COD (f = 14.66; p < 0.001), CMJ (f = 51.50; p < 0.001), and RSI (f = 24.91; p < 0.001). No changes occurred in CON (p > 0.05). Conclusions: CLS and TRD plyometric training improved sprint speed, COD, and jump performance, with CLS showing slightly superior results and better fatigue management.

1. Introduction

Football is a team sport characterized by brief, high-intensity actions, such as sprinting, jumping, and changing direction, which players use to gain an advantage over opponents [1]. These performance outputs are essential for athletes to demonstrate their full potential during training and competition, as they closely relate to an athlete’s capacity to generate strength and power [2,3]. Actions requiring rapid strength and power production—like sprints, changes of direction (COD), and jumps in horizontal and vertical planes—directly impact match performance. Various neuromuscular training methods, such as resistance, plyometric, and calisthenic training, are widely used for developing youth athletes [4,5]. Plyometric training, in particular, is well-documented as being effective in enhancing sprint performance, improving change of direction (COD) ability, and supporting overall athletic development [6,7,8].
Plyometric training comprises a series of explosive strength exercises to enhance physical capacity by increasing muscle-tendon stiffness and strength through muscle fibers’ stretch-shortening cycle (SSC) [9,10]. Plyometric training is believed to boost short-term power outputs—such as sprinting, COD, and jumping performance—by inducing neuromuscular adaptations. These adaptations include improved SSC function, enhanced motor unit recruitment, increased firing frequency, better inter- and intramuscular coordination, and morphological changes (e.g., variations in fiber type or pennation angle) [10,11,12]. Numerous studies have demonstrated that plyometric training improves sprinting, COD, and jumping performance [10,13,14,15,16,17]. However, despite the significant enhancements in athletic performance outputs attributed to plyometric training, the optimal design of training programs remains ambiguous. Developing effective plyometric training programs necessitates careful control of several variables, including training level, surface type, volume, intensity, and rest intervals between exercises and sets. Research often explores the effectiveness of various set configurations by manipulating rest intervals [18,19,20].
Traditional set configurations are commonly employed in training models to enhance sprinting, COD, and jumping performance outputs (e.g., resistance, plyometric, and complex training). A traditional set typically executes successive repetitions with extended rest intervals [21]. While this method has been widely used for years, sports scientists have developed new set configurations to evaluate their effectiveness in optimizing training sessions. One such innovation is the cluster-set method, which divides a traditional set into smaller subsets with shorter rest intervals [21]. These brief rest periods (10–30 s) facilitate ATP-CP resynthesis, reducing fatigue and enhancing force production [22].
Many studies have examined the effects of both set configurations on athletic performance outputs; however, most of the research has primarily focused on resistance training [23,24,25]. There is limited research on the impact of cluster versus traditional set configurations, specifically within plyometric training, and existing studies have mainly evaluated sprinting, change of direction, and jumping strength [10,26,27]. Although both set configurations share similar scopes and intensities, no research has investigated the perceptual difficulty levels they impose on athletes. Lower perceived exertion can contribute to reduced fatigue, better adherence to training programs, and a lower risk of overtraining [28]. Moreover, athletes who experience less fatigue during training can better maintain proper technique and execution, leading to more significant performance improvements.
These findings highlight the importance of optimizing athletic performance. The current lack of assessments regarding fatigue and perceived difficulty in existing studies indicates a critical need for further research into the design of optimal plyometric training. Moreover, as Schillaci and Ivaldi (2023) emphasized, the trainability of young athletes varies based on their age and stage of maturation, underscoring the importance of tailoring training protocols to suit this population [29]. Therefore, this research aims to determine the effects of plyometric training with different set configurations on sprinting, COD, jumping performance, and perceived exertion.

2. Materials and Methods

2.1. Research Design

This study utilized a randomized controlled trial design to investigate the effects of plyometric training with different set configurations on sprint performance, change of direction (COD), counter movement jump (CMJ), and reactive strength index (RSI). Adhering to CONSORT guidelines, the methodology ensured transparency and rigor in its design, implementation, and reporting. The intervention was conducted during the competition period to enhance the ecological validity of the findings.
A priori power analysis was conducted using G*Power (version 3.1) to determine the required sample size for the study. The analysis was based on an F-test for ANOVA with repeated measures and a within–between interaction design. The input parameters included an effect size f = 0.35 (moderate effect) [30], an alpha error probability (α) of 0.05, and a statistical power (1 − β) of 0.80. With three groups included in the study, the power analysis indicated that a minimum sample size of n participants was required to detect statistically significant differences [31].
Participants were randomly assigned to one of three groups: the Cluster-Set group (CLS), the Traditional-Set group (TRD), and the Control group (CON). Pre-test measurements were conducted before the start of the intervention. Over 8 weeks, the CLS and TRD groups participated in a plyometric training program designed with distinct set configurations alongside their standard football training. In contrast, the CON group continued standard football training without engaging in plyometric exercises. Post-test measurements were taken after the intervention to assess changes in performance.

2.2. Participants

The study involved 24 male football players competing in the U19 category. These athletes typically participated in 4 to 5 weekly training sessions, with an average training duration of 8.00 ± 2.00 h. During the competition period, the team also played one official match per week. Participants were selected based on two inclusion criteria: they had no prior experience with plyometric training programs and no history of injuries.
All participants and their parents were fully informed about the study’s aims, procedures, and potential benefits. Parental consent was obtained for athletes under 18, and all participants signed informed consent forms per the Declaration of Helsinki. Ethical approval for the study was obtained from the Ethics Committee of Çukurova University Faculty of Medicine (decision number 05.04.2024/143-77). Participants were randomly allocated to the CLS, TRD, or CON groups. A detailed flow chart illustrating participant allocation and study procedures is presented in Figure 1.

2.3. Test Procedure

The tests conducted in the study were spread across three sessions with a 36 h interval between them. The first session focused on the athletes’ anthropometric measurements and jump tests. In the second session, sprint measurements were taken at distances of 10 m, 20 m, and 30 m. The third session was dedicated to measuring change of direction (COD). Before each session, athletes completed a well-structured warm-up supervised [32], beginning with 5 min of running. Five minutes of running, followed by joint mobility exercises, including ankle, hip, and knee circles, torso rotations, lunges, and leg swings. Finally, athletes performed power and speed drills, such as progressive sprints over 10–15 m, acceleration runs, vertical jumps, and lateral bounds.
All measurements took place at the semi-professional Adana Vefa Sports Club facilities. The anthropometric measurements and jump tests were performed at sea level in the club’s athletic performance studio, where the average temperature was 24 °C, average humidity was 29%, and barometric pressure ranged from 1010 to 1025 mmHg. The field tests (10 m, 20 m, 30 m sprints, and COD) were conducted on the grass field used for the team’s official matches, which met international standards, with an average temperature of 26 °C, average humidity of 38%, and barometric pressure ranging from 1010 to 1025 mmHg. The final test measurements were also performed in three sessions with a 36 h interval. To minimize the influence of circadian rhythms on the athletes, all tests were administered by the same researcher at the same time of day (between 17:00 and 18:00). Athletes were encouraged to perform to the best of their ability [33,34].

2.4. Experimental Procedure

The plyometric training program spanned eight weeks and consisted of two sessions per week, a duration that has been demonstrated to effectively enhance physical fitness metrics, including sprinting, jumping, and COD performance [10,35]. A two-week adaptation phase was implemented to minimize the risk of potential injuries and familiarize athletes with the training regimen, consisting of two sessions per week before the pre-test measurements. The plyometric training was conducted immediately after a warm-up program that included 5 min of submaximal running, 5 min of stretching exercises, 10 submaximal vertical jumps, and 10 submaximal tuck jumps [36]. The Cluster-Set (CLS) and Traditional-Set (TRD) groups performed plyometric training twice weekly, with 48 h intervals between sessions. The training load was tailored to the developmental stages of the participants (Johnson et al., 2011), beginning with approximately 80 jumps per session [37] and progressively increasing every two weeks in alignment with the principle of progressive overload [38,39]. Both groups followed the same training program, maintaining identical intensity and volume, with the primary difference being the application of rest periods within and between sets.
Throughout all plyometric training sessions, athletes received verbal encouragement and feedback on their movement techniques from athletic performance coaches. The football players were instructed to maintain their normal daily physical activities and dietary habits throughout the 8-week intervention while avoiding additional strength or conditioning training. Notably, no injuries occurred during the training sessions. All sessions were conducted concurrently from 17:00 to 19:00. A detailed training program is presented in Figure 2.

2.5. Instruments

2.5.1. Body Mass and Height Measurement

The athletes’ height (measured with 0.5 cm sensitivity) and weight (measured with 100 g sensitivity) were recorded using a Seca stadiometer (Hamburg, Germany). The researchers prepared a questionnaire to assess the participants’ demographic characteristics, including age and sports experience.

2.5.2. Counter Movement Jump (CMJ)

The Counter Movement Jump (CMJ) test was conducted using the Witty Microgate jump mat. Athletes jumped with their hands on their hips, aiming to reach their highest point on the mat. The jump height was recorded during each measurement. Each athlete completed three jumps; the best height was calculated from these attempts [40].

2.5.3. 10 m, 20 m and 30 m Sprint Test

The 10 m, 20 m, and 30 m sprint tests were conducted using the Witty Microgate photocell system, which was positioned at the respective distances. The athletes began at the starting point, leaving 1 m behind the photocell. Once ready, they sprinted the full 30 m as quickly as possible. Each athlete completed three trials, and the best times for the 10 m, 20 m, and 30 m sprints were recorded [41].

2.5.4. Change of Direction Test (COD)

The Change of Direction (COD) test was conducted using the zigzag protocol, demonstrated in the literature to offer high reliability and repeatability [42,43]. The test data were captured with the Witty Microgate photocell system, known for its precision in timing and reliability in performance assessments. The course consisted of three slaloms arranged in a zigzag pattern, positioned 5 m apart at a 100-degree angle, covering a total distance of 20 m. Athletes commenced the test 1 m behind the starting line and navigated the slaloms at maximum speed. Each participant completed three attempts, with the best performance recorded for analysis. The test protocol was carefully designed to ensure validity and repeatability, contributing to its robustness as a measure of COD performance [42,43].

2.5.5. Reactive Strength Index (%)

The drop jump test determined the athletes’ Reactive strength index. The drop Jump test was measured using the Witty Microgate jump mat. The athletes waited, ready on a 40 cm platform next to the jump mat. When they felt good, they dropped onto the jump mat with one foot outside the platform, made contact with the ground in the shortest time possible, and jumped to the highest point they could. The measurements were repeated three times, and the best degree was recorded. The athlete’s contact time with the ground and the time he stayed in the air were evaluated during the jump, and their RSI levels were determined. Reactive Strength Index (%) = flight time (ms)/contact time (ms) [44].

2.5.6. Rate of Perceived Exertion (RPE)

The study assessed the athletes’ Rate of Perceived Exertion (RPE) using a modified Borg scale, ranging from 0 to 10 points. At the end of each training session, athletes in the cluster-set and traditional-set plyometric training groups were asked, “How difficult was the training?” Responding to this question, they provided a nominal score between 0 and 10. The score reflects the athlete’s perceived difficulty [45]. The average score obtained over the 8 weeks indicates the perceived difficulty associated with the different set configurations used in the training.

2.6. Statistical Analysis

The data set was first examined for erroneous values, outliers, and multicollinearity. It was confirmed that no incorrectly entered data were present. The analysis was conducted using SPSS version 25. Descriptive statistics were employed to analyze the demographic characteristics of the athletes, with results reported as arithmetic mean ± standard deviation (x ± ss). The normality of the distributions was assessed through skewness and kurtosis tests. Since the skewness and kurtosis coefficients fell within the range of −1.5 to +1.5, the data were deemed normally distributed [46]. Given that the assumptions for normal distribution were met, parametric tests were utilized. A repeated measures analysis of variance (ANOVA) was conducted to detect differences in the 10 m, 20 m, and 30 m sprints, CMJ, COD, and RSI parameters (3 groups × 2 time points). For variables exhibiting significant group–time interactions, the Bonferroni test was applied to compare changes across groups and time. Greenhouse–Geisser corrections were implemented for F tests when Mauchly’s test of sphericity was violated. Given the equality of group sizes and the homogeneity of variances, Tukey’s HSD test was employed for inter-group comparisons [47]. Additionally, partial eta squared (ηp2) was calculated to assess effect size, categorized as large (≥0.14), medium (≥0.06), and small (<0.06) according to Cohen (2013) and Espinosa et al. (2023) [48,49]. Descriptive analysis, including relative and absolute frequencies, was conducted with a 95% confidence interval [50]. An independent t-test was employed for the RPE measurements, with Cohen’s d calculated to assess effect size. Cohen’s effect size classifications were 0–0.19 as insignificant, 0.20–0.49 as small, 0.50–0.79 as medium, and >0.80 as large [48,51]. A significance level of p < 0.05 was established for this study.

3. Results

The soccer players had an average age of 18.25 ± 0.44 years, a height of 1.77 ± 0.05 m, a body weight of 67.30 ± 8.76 kg, and a sports age of 5.25 ± 1.41 years, resulting in a BMI of 21.33 ± 1.66 kg/m2 (Table 1).
A statistically significant difference was observed in the group*time interaction for the 10 m (F = 21.44; p < 0.001; η2 = 0.67), 20 m (F = 19.40; p < 0.001; η2 = 0.64), 30 m (F = 49.56; p < 0.001; η2 = 0.82), change of direction (COD) (F = 14.66; p < 0.001; η2 = 0.58), counter movement jump (CMJ) (F = 51.50; p < 0.001; η2 = 0.83), and reactive strength index (RSI) (F = 24.91; p < 0.001; η2 = 0.70) performances. In the control group, no statistically significant difference was found between pre-test and post-test performances (p > 0.05). After 8 weeks of plyometric training, the cluster-set (CLS) group demonstrated significant improvements of −9.26% in the 10 m sprint, −4.78% in the 20 m sprint, −3.40% in the 30 m sprint, −4.71% in COD, 12.66% in CMJ, and 11.90% in RSI (p < 0.05). Similarly, the traditional-set (TRD) group exhibited significant improvements of −9.94% in the 10 m sprint, −3.72% in the 20 m sprint, −2.89% in the 30 m sprint, −4.01% in COD, 12.15% in CMJ, and 9.02% in RSI (p < 0.05). (Table 2, Figure 3).
A statistically significant difference was observed in the rate of perceived exertion (RPE) levels of the football players, favoring the cluster-set (CLS) group (t = 12.42; p < 0.001; d = 6.38). (Table 3, Figure 4).

4. Discussion

This study primarily aimed to evaluate the effects of an 8-week plyometric training program on key athletic performance metrics, including sprint performance (10 m, 20 m, and 30 m), change of direction (COD) speed, Counter Movement Jump (CMJ), and Reactive Strength Index (RSI) in football players. The research compared two distinct training configurations, cluster sets (CLS), and traditional sets (TRD), alongside a control group (CON) that maintained regular football training without any additional plyometric exercises. The findings indicated significant performance improvements in both the CLS and TRD groups, with the CLS group frequently demonstrating superior results in several performance metrics and lower levels of perceived exertion.
The study’s findings demonstrated a significant enhancement in sprint performance across all measured distances (10 m, 20 m, and 30 m) for both the cluster-set (CLS) and traditional-set (TRD) training groups. The CLS group improved 9.26%, 4.78%, and 3.40% in the 10 m, 20 m, and 30 m sprints, respectively. Similarly, the TRD group recorded impressive gains of 9.94%, 3.72%, and 2.89%. These results highlight the efficacy of plyometric training in enhancing sprint acceleration and maximum speed, which are crucial for football players [8,52,53]. Conversely, the control group (CON) exhibited no significant changes, underscoring the importance of targeted training interventions to elicit meaningful performance adaptations. Notably, while marginal, the CLS group’s superior results are significant. The intermittent rest periods incorporated in cluster-set training likely contributed to better recovery, reduced fatigue, and enhanced power output over time. This finding aligns with prior research indicating that rest intervals within sets enable athletes to maintain higher power levels and manage neuromuscular fatigue more effectively [54,55,56].
Sprint performance is vital in football, particularly for actions such as chasing the ball, covering defensive gaps, and executing attacking plays; thus, these improvements directly affect game performance [57,58]. Additionally, improvements in change of direction (COD) speed emerged as another critical outcome of the study. The CLS group demonstrated a 4.71% enhancement in COD performance, while the TRD group improved by 4.01%. In contrast, the CON group showed no significant improvement. This outcome is particularly relevant to football, where rapid directional changes are frequently required during dribbling, tackling, and repositioning [59,60]. The ability to decelerate and reaccelerate quickly and with control is pivotal to performance, and plyometric training, which emphasizes explosive power and agility, directly supports these capabilities [61,62]. The COD improvements can likely be attributed to enhanced neuromuscular coordination and increased eccentric strength, essential for effectively controlling movement during rapid directional changes [63,64]. Once again, the slightly better performance of the CLS group highlights the advantages of intermittent rest, which allows for greater preservation of explosive power throughout training sessions. Previous studies have similarly shown that structured rest periods during high-intensity exercises can improve overall performance due to reduced fatigue [65,66]. The CLS and TRD groups significantly improved their Counter Movement Jump (CMJ) performance, showing 12.66% and 12.15% enhancements, respectively. These values approached those of the control group (UCT), which showed an improvement of 5.87%. Therefore, plyometric training greatly enhances lower body explosive power, essential for vertical jumps. This benefits football players involved in aerial challenges, headers, or defensive clearances. Jump height has been well-documented to improve with plyometric exercises, especially those that engage fast-twitch muscle fibers and are high in neuromuscular activation [67,68].
The similar performance improvements between the CLS and TRD groups suggest that both plyometric set configurations effectively enhance vertical jump performance. However, the slightly higher percentage improvement observed in the CLS group may still be attributed to better recovery mechanisms due to the rest intervals, which allow athletes to maintain higher power outputs during training [69,70]. The Reactive Strength Index (RSI), which reflects an athlete’s ability to produce rapid, explosive movements by calculating the ratio of flight time to ground contact time, also improved significantly in both training groups. The CLS group exhibited an 11.90% improvement, while the TRD group saw a 9.02% improvement. The RSI is essential in football, where players frequently need to react quickly, jump, and sprint with minimal ground contact time [51,71]. Plyometric training has consistently enhanced RSI by improving the stretch-shortening cycle efficiency, enabling athletes to produce more force in less time [10,72]. The fact that the CLS group outperformed the TRD group suggests that cluster-set training is effective for developing this neuromuscular efficiency due to controlled fatigue management, which allows athletes to execute higher-quality repetitions [40,73]. One unique finding of the study was that the athletes perceived the task’s difficulty significantly greater in one experimental group than the other. Athletes reported that cluster-set training was less physically demanding than traditional training, as evidenced by the finding that the CLS group had a significantly lower Rate of Perceived Exertion (RPE) than the TRD group. This difference is essential, especially when considering training programs in real life; lower perceived exertion will help improve adherence to training programs and reduce the risk of overtraining. By structuring rest intervals within the training session, cluster-set training likely reduces the cumulative fatigue experienced by athletes, enabling them to train at a high intensity without feeling overwhelmed [74,75]. The lower RPE scores observed in the CLS group may account for the superior improvements in physiological performance (e.g., maximal strength and endurance), psychological factors (such as motivation and enjoyment), and metabolic responses [76,77]. Athletes who feel less fatigued during training can maintain better technique and execution, contributing to more significant performance gains. This finding highlights the potential long-term benefits of cluster-set training in sports where consistent, high-quality performance is needed over extended periods.
Despite the promising results, this study has limitations that should be considered. Firstly, the relatively small sample size may limit the generalizability of the findings to broader populations of football players. Additionally, the study focused exclusively on physical performance metrics without accounting for psychological or tactical variables that could influence on-field performance. Future research should aim to increase the sample size and diversity of participants, incorporating football players of varying skill levels and positions. It would also be valuable to explore integrating plyometric training with other training methods and examine its effects on tactical and technical skills in real gameplay situations.

5. Conclusions

The results of this study demonstrate that both cluster-set and traditional-set plyometric training methods are highly effective in improving sprint performance, change of direction speed, jump height, and reactive strength in football players. However, the slightly superior performance outcomes observed in the cluster-set group and a significantly lower rate of perceived exertion suggest that cluster-set training may offer additional benefits regarding fatigue management and overall training efficiency. These findings have important implications for football coaches and trainers aiming to optimize athletic performance. Incorporating plyometric training into football training regimens, particularly through manipulating set configurations with cluster sets, can enhance athletes’ abilities to sprint, change direction, and jump while improving fatigue resistance. This enhancement in strength and neuromuscular control could lead to better match performance and a reduced risk of injury.
Further research should investigate the effects of cluster-set plyometric training over more extended timeframes and across various athlete populations and sports. Additionally, exploring plyometric training with other strength and conditioning methods (such as resistance training or agility drills) may yield valuable insights into potential performance improvements. Lastly, given the potential to significantly influence athlete development and prolong the careers of athletes in high-intensity sports like football, the impact of cluster-set plyometric training on athlete recovery and injury prevention warrants thorough exploration.

Author Contributions

Conceptualization, B.Ö., N.S.A. and M.K.; methodology, M.K., H.E. and N.S.A.; software, A.K. and U.C.; validation, H.E., B.E.O. and P.B.; formal analysis, M.K., N.S.A. and H.E.; investigation, B.Ö. and N.S.A.; resources, P.P.-G. and M.I.; data curation, U.C. and J.P.; writing—original draft preparation, B.Ö., H.E., P.B., P.S., J.P., M.I., P.P.-G., A.K. and U.C.; writing—review and editing, B.Ö., M.K., N.S.A., H.E., B.E.O., P.B., P.S., J.P., P.P.-G., M.I. and A.K.; visualization, P.S. and B.E.O.; supervision, B.Ö. and P.P.-G.; project administration, N.S.A.; funding acquisition, P.P.-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

The study was conducted in accordance with the Declartion of Helsinki and approved by the Institutional Review Board of Çukurova University Faculty of Medicine (protocol code 143 and date of approval 5 April 2024, decision number 80).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical committee restrictions and to preserve participant privacy.

Acknowledgments

The authors acknowledge Prince Sultan University for its support in covering the Article Processing Charges for this publication, assistance in paying publication fees, and allocation of research resources.

Conflicts of Interest

The authors declare no conflicts of interest.

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Applsci 15 01282 g001
Figure 1. Flow chart of the research.
Figure 1. Flow chart of the research.
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Figure 2. CLS and TRD plyometric training programs.
Figure 2. CLS and TRD plyometric training programs.
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Figure 3. Intra-Group Effects of Plyometric Training on Sprint, COD, CMJ, and RSI.
Figure 3. Intra-Group Effects of Plyometric Training on Sprint, COD, CMJ, and RSI.
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Figure 4. Intra-Group Effects of Plyometric Training on RPE.
Figure 4. Intra-Group Effects of Plyometric Training on RPE.
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Table 1. Demographic characteristics.
Table 1. Demographic characteristics.
CLS (n = 8)TRD (n = 8)CON (n = 8)Total (n = 24)
Age (year)18.12 ± 0.3518.50 ± 0.5318.12 ± 0.3518.25 ± 0.44
Height (m)1.74 ± 0.021.75 ± 0.041.77 ± 0.071.75 ± 0.05
Body mass (kg)73.50 ± 3.6272.25 ± 3.3273.50 ± 5.5873.08 ± 4.14
BMI (kg/m2)24.08 ± 1.4623.42 ± 1.5423.45 ± 1.1323.66 ± 1.36
Sport Age (year)8.12 ± 0.648.00 ± 0.927.75 ± 0.707.95 ± 0.75
Table 2. Effects of Plyometric Training on Sprint, COD, CMJ, and RSI.
Table 2. Effects of Plyometric Training on Sprint, COD, CMJ, and RSI.
ANOVA p
Pre TestPost Test Group × Time
x ¯ ± Std.
IC (95%)		 x ¯ ± Std.
IC (95%)		Δ%fpηp2
10 m (s)CLS1.77 ± 0.05
1.72–1.82		1.63 ± 0.08 Δa
1.56–1.70		−9.2621.44<0.0010.67
TRD1.77 ± 0.04
1.73–1.81		1.61 ± 0.07 Δa
1.55–1.68		−9.94
CON1.79 ± 0.06
1.73–1.85		1.78 ± 0.07 b
1.72–1.84		−0.56
20 m (s)CLS3.07 ± 0.09
2.99–3.15		2.93 ± 0.07 Δa
2.87–2.99		−4.7819.40<0.0010.64
TRD3.07 ± 0.05
3.02–3.11		2.96 ± 0.07 Δa
2.90–3.02		−3.72
CON3.09 ± 0.04
3.05–3.12		3.09 ± 0.07 b
3.05–3.12
30 m (s)CLS4.26 ± 0.06
4.21–4.32		4.12 ± 0.04 Δa
4.09–4.16		−3.4049.56<0.0010.82
TRD4.27 ± 0.04
4.23–4.31		4.15 ± 0.03 Δa
4.12–4.19		−2.89
CON4.29 ± 0.07
4.23–4.35		4.28 ± 0.05 b
4.23–4.33		−0.23
COD (s)CLS6.23 ± 0.07
6.17–6.29		5.95 ± 0.02 Δa
5.93–5.97		−4.7114.66<0.0010.58
TRD6.22 ± 0.10
6.14–6.30		5.98 ± 0.03 Δa
5.96–6.01		−4.01
CON6.22 ± 0.12
6.11–6.33		6.18 ± 0.10 b
6.10–6.27		−0.65
CMJ (cm)CLS38.63 ± 0.67
38.07–39.20		44.46 ± 0.63 Δa
43.93–45.00		12.6651.50<0.0010.83
TRD37.53 ± 1.07
36.63–38.42		42.72 ± 1.38 Δb
41.56–43.87		12.15
CON37.57 ± 1.13
36.62–38.52		37.57 ± 1.13 c
36.62–38.52
RSI (%)CLS1.11 ± 0.06
1.06–1.16		1.26 ± 0.04 Δa
1.23–1.29		11.9024.91<0.0010.70
TRD1.11 ± 0.03
1.08–1.14		1.22 ± 0.02 Δb
1.20–1.25		9.02
CON1.12 ± 0.04
1.08–1.16		1.13 ± 0.04 c
1.09–1.17		0.88
CMJ: Counter Movement Jump; COD: Change of Direction; RSI: Reactive Strength Index; CLS: Cluster; TRD: Traditional; Δ Significant difference (p < 0.05) compared with the pre-test results; a,b,c significant difference (p < 0.05) compared between groups.
Table 3. Effects of Plyometric Training on Rate of Perceived Exertion.
Table 3. Effects of Plyometric Training on Rate of Perceived Exertion.
n x ¯ Std.tpCohen’s d
RPECLS86.710.20−12.427<0.0016.38
TRD87.720.10
RPE: Rate of perceived exertion; CLS: Cluster; TRD: Traditional.
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Öztürk, B.; Adıgüzel, N.S.; Koç, M.; Karaçam, A.; Canlı, U.; Engin, H.; Orhan, B.E.; Bartik, P.; Sagat, P.; Pérez, J.; et al. Cluster Set vs. Traditional Set in Plyometric Training: Effect on the Athletic Performance of Youth Football Players. Appl. Sci. 2025, 15, 1282. https://doi.org/10.3390/app15031282

AMA Style

Öztürk B, Adıgüzel NS, Koç M, Karaçam A, Canlı U, Engin H, Orhan BE, Bartik P, Sagat P, Pérez J, et al. Cluster Set vs. Traditional Set in Plyometric Training: Effect on the Athletic Performance of Youth Football Players. Applied Sciences. 2025; 15(3):1282. https://doi.org/10.3390/app15031282

Chicago/Turabian Style

Öztürk, Barışcan, Niyazi Sıdkı Adıgüzel, Murat Koç, Aydın Karaçam, Umut Canlı, Hakan Engin, Bekir Erhan Orhan, Peter Bartik, Peter Sagat, Jason Pérez, and et al. 2025. "Cluster Set vs. Traditional Set in Plyometric Training: Effect on the Athletic Performance of Youth Football Players" Applied Sciences 15, no. 3: 1282. https://doi.org/10.3390/app15031282

APA Style

Öztürk, B., Adıgüzel, N. S., Koç, M., Karaçam, A., Canlı, U., Engin, H., Orhan, B. E., Bartik, P., Sagat, P., Pérez, J., Isip, M., & Prieto-González, P. (2025). Cluster Set vs. Traditional Set in Plyometric Training: Effect on the Athletic Performance of Youth Football Players. Applied Sciences, 15(3), 1282. https://doi.org/10.3390/app15031282

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