Effect of Three Pre-Season Training Protocols with Different Training Frequencies on Biochemical and Performance Markers in Professional Female Basketball Players

Mexis, Dimitrios; Nomikos, Tzortzis; Kostopoulos, Nikolaos

doi:10.3390/app15041833

Open AccessArticle

Effect of Three Pre-Season Training Protocols with Different Training Frequencies on Biochemical and Performance Markers in Professional Female Basketball Players

by

Dimitrios Mexis

^1,*,

Tzortzis Nomikos

²

and

Nikolaos Kostopoulos

¹

School of Physical Education and Sport Science, National and Kapodistrian University of Athens, 17237 Athens, Greece

²

Department of Nutrition and Dietetics, School of Health Sciences and Education, Harokopio University, 17676 Athens, Greece

^*

Author to whom correspondence should be addressed.

Appl. Sci. 2025, 15(4), 1833; https://doi.org/10.3390/app15041833

Submission received: 7 January 2025 / Revised: 1 February 2025 / Accepted: 6 February 2025 / Published: 11 February 2025

(This article belongs to the Special Issue Exercise, Fitness, Human Performance and Health: 2nd Edition)

Download Versions Notes

Abstract

:

This study examined the impact of three pre-season fitness training protocols with different training frequencies (heavy, moderate and light) on the physical abilities and the biochemical indicators of muscle damage and oxidative stress in female basketball athletes as expressed through physiological and biochemical assessments. Also, a comparison and a search for correlations were made between the physiological and biochemical changes that were caused by each protocol. The sample consisted of 28 professional female basketball athletes, who competed in three different teams, and each training protocol was assigned to each of the three teams. The somatometric and performance markers measured were as follows: weight, fat percentage, aerobic capacity, anaerobic capacity, vertical jumping ability, speed, acceleration, explosiveness and maximum lower body part strength. The biochemical measurements consisted of exercise-induced muscle damage (creatine kinase—CK, lactate dehydrogenase—LDH) and oxidative stress markers (protein carbonyls—PCs, glutathione peroxidase—GPx). The three fitness training protocols that were used improved the fitness status of high-level female basketball athletes after 6 weeks during the pre-season period, but they were also accompanied by some significant differences between them (fat percentage, anaerobic capacity, speed and acceleration). The results were also accompanied by an increase in CK and LDH for the three teams and by an increase in GPx for Teams 1 and 3. Also, we concluded that even 4 more or 4 less fitness training units (TUs) during a 6-week basketball preparation period can be considered important to differentiate the outcomes of physiological and biochemical markers. The purpose of this study was to provide more theoretical and practical knowledge to basketball coaches and trainers, so they can optimize the training process during the preparation period and thus maximize the performance of their athletes. Nevertheless, the present study was created with certain limitations, such as the small sample size for each team, the absence of a long-term follow-up or the lack of a control group. Future studies may need to turn their focus on fitness protocols with longer durations, try to differentiate the types of training protocols and attempt to achieve a better balance between maximizing performance and mitigating the muscle damage and oxidative stress levels.

Keywords:

basketball; pre-season training; physiological assessment; biochemical evaluation; muscle damage; oxidative stress; functional training; plyometric training

1. Introduction

Athletes competing in high-level basketball must possess certain abilities and physical characteristics, such as endurance, speed, strength and explosiveness. These abilities are typically used in dynamic, demanding environments where players must move and act quickly and decisively [1,2,3,4]. Therefore, a certain level of mental and physical readiness is necessary to meet the psychological and physiological requirements for the entire competitive basketball season [3,5].

Many fitness and basketball coaches are aiming to improve these physical abilities mainly during the pre-season period by using training protocols with increased frequency, duration and loads [6]. This approach may cause some positive effects for improving certain physical attributes, but on the other hand, it focuses more on the quantitative expression of training rather than the qualitative, something that may lead to overtraining, which will significantly increase the physical and mental fatigue and thus the probability of potential injuries [7,8,9].

Considering these facts, our main goal was to investigate if some training protocols with a smaller training frequency but with an increased focus on qualitative training in relation to quantitative can cause similar or greater improvements in the physical abilities of female basketball athletes, resulting in the optimum outcome with the least possible physiological and psychological fatigue. However, it is possible that a training program with reduced training frequency will cause negative effects on the physical abilities of basketball athletes [10], but meanwhile, it can also lead to significant improvements in mental or physical fatigue [5,11,12]. In this case, the trainers and the coaches must reflect and process those alterations, find the appropriate balance between them and decide which plan is more suitable for every occasion.

Pre-season period is the most important part in terms of getting basketball athletes physically ready for the competitive season, as it is the first training period of the whole season where athletes must achieve a certain level of physical, technical and tactical abilities, so they can sustain the heavy and long schedule of matches, reducing at the same time the risk of any possible injuries [3]. Also, there is a very limited amount of studies that have examined how various training aspects can affect the female basketball athletes, especially in a pre-season period [3]. Training adaptations may differ between male and female athletes, so there is a need for more studies to turn their focus on female athletes [12]. Importantly, it has not yet been researched if different training frequencies affect the physiological and biochemical markers in female basketball players during a pre-season period. The present study hopes to fill in this gap in the research area.

More specifically, the main purpose of the present study is to apply three pre-season training protocols in professional female basketball athletes, each with a different weekly frequency of training units (TUs). Accordingly, we will examine the effect of each of the three protocols on the physical abilities and the biochemical markers of muscle damage and oxidative stress, as expressed through a physiological and biochemical evaluation. Muscle damage and oxidative stress will be evaluated using the CK and LDH enzymes, which are broadly recognized as two of the best muscle damage markers, along with the determination of GPx and PC, which are two of the most approved markers with antioxidant status. It was very important for our research to implement these specific biochemical markers, so we can better understand the effects that they may have on improving or reducing performance. Subsequently, there will be a comparison between the physiological and the biochemical alterations caused by each protocol and also an attempt to locate and demonstrate all the necessary correlations between the physiological and biochemical characteristics of the volunteers.

2. Materials and Methods

2.1. Participants

The sample of this study originally consisted of 35 professional female athletes competing in three different teams in the Greek National A2 Female Division. However, there were 28 athletes that finally managed to successfully complete the training protocols as well as the physiological and biochemical assessments. The main characteristics of the participants for each team are presented in Table 1. All athletes participated voluntarily in the study and had to satisfy 2 specific inclusion requirements (at least 4 years of basketball experience and no injuries for at least 3 months before the start of the study). Moreover, the volunteers were briefed thoroughly before the start of the study on the purpose, analytical procedures, conditions and benefits of this inquiry, and they signed the required consent documents. The Bioethics Committee of the School of Physical Education and Sport Science of the National and Kapodistrian University of Athens granted the ethics approval for the current study (1209/16 September 2020) in accordance with the ethical criteria of the Helsinki Declaration.

2.2. Study Design

This is a non-randomized experimental study during which the athletes were evaluated before and after the 6-week pre-season period. The intervention training protocols were the independent variables, while the physiological and biochemical markers that were evaluated were the dependent variables. Each of the 3 training protocols that were used was assigned to each of the 3 teams that voluntarily participated in this study. Weight, height, fat percentage, aerobic capacity, anaerobic capacity, speed, acceleration, explosiveness, lower body part strength and vertical jumping ability were the physiological variables that were evaluated, whilst creatine kinase (CK), lactate dehydrogenase (LDH), protein carbonyls (PCs) and glutathione peroxidase (GPx) were the examined biochemical markers. All the measurements were carried out twice (except for the height), on the first day before the start of the pre-season period and one day after the last training of the pre-season period.

2.3. Intervention Protocols

Throughout the course of this study, we applied 3 fitness training programs, each with a different weekly frequency of TUs (heavy, moderate and light). The heavy protocol was assigned to Team 1; the moderate protocol was allocated to Team 2, and the light protocol was assigned to Team 3. The 3 fitness protocols were carried out for the whole 6 weeks of the pre-season period, and the teams were separated in terms of strength and conditioning TUs. The strength TUs were always executed before the main tactical and technical basketball TUs, while the conditioning TUs were executed after the warm-up and the stretching of the main tactical and technical basketball TUs. The total number of tactical and technical basketball TUs as well as friendly games was the same for the 3 teams (47 TUs); the total number of strength and conditioning TUs was 24 (12 strength + 12 conditioning) for Team 1, 20 (10 strength + 10 conditioning) for Team 2 and 16 (8 strength + 8 conditioning) for Team 3. Exercises, intensity and duration of all the TUs were exactly the same for the 3 teams. The training load for each athlete was calculated using the 1RM for the upper (bench press) and the lower (squat) body parts. Additionally, the exercises that were used during the strength training were based on functional and plyometric training patterns, while the conditioning exercises were exclusively basketball-related fitness drills (side steps; turns; back steps; 5, 10 or 20 m sprints and jumping). The total number of the strength and conditioning training units including the intensity and duration that were executed by each team during the 6-week pre-season period are presented in detail in Table 2.

Moreover, the detailed fitness training protocols that were performed by each team during the first and the last weeks of the pre-season period are displayed in Table 3.

2.4. Biochemical Assessment

The participants arrived at the laboratory of Harokopio University two times, on the morning of the first day of the pre-season period and on the morning after the last training day of the pre-season period, after 8–12 hours of fasting. Blood samples were collected by a trained phlebotomist under the supervision of a doctor. Ten (10) mL of blood was collected in vacutainers without anticoagulant and left at room temperature for 30 min. The tubes were then centrifuged at 1500× g for 20 min at a temperature of 4 °C, and the supernatant (serum) was collected and aliquoted in Eppendorff tubes. Six (6) mL of blood was also collected in vacutainers with the EDTA anticoagulant. These tubes were also centrifuged at 1500× g for 10 min at a temperature of 4 °C, and the supernatant (plasma) was also collected and aliquoted. Serum CK and LDH activities were analyzed using an automated chemistry analyzer (Konelab 60i, Thermo Fisher Scientific, Waltham, MA, USA) in a private diagnostic center. PC and GPx assays were carried out at the Biology, Biochemistry and Human Physiology’s specialized laboratory of Harokopio University. PC serum activity was measured using the method of protein carbonyl derivatization with 2,4-dinitrophenylhydrazine (DNPH) and a consequent spectrophotometric assay [13], while GPx serum activity was quantified spectrophotometrically using the method that involves monitoring the consumption of NADPH as reduced glutathione (GSH) is oxidized to glutathione disulfide (GSSG) [14].

2.5. Somatometric Assessment

Height was measured with a portable stadiometer (ADE MZ10042, Ade, Hamburg, Germany), while weight was measured with a precision electronic scale (Beurer BF 1000 Super Precision, Beurer, Ulm, Germany). The 7-spot (chest, midaxilar, triceps, subscapular, abdominal, suprailiac and thigh) Jackson and Pollock formula was used for the calculation of fat percentage, performed with a precision skinfold caliper (Harpenden Skinfold Caliper, Baty International, Sheffield, UK). All the above measurements took place inside the basketball gym.

2.6. Aerobic and Anaerobic Capacity Assessment

A VO2max test was used for the assessment of aerobic capacity. Firstly, the heart rate and the maximum oxygen uptake in a resting state were measured. Then, the volunteers began the test which initially included running at 8 km/h for 2 min, and after every other 2 min, the speed increased by 1 km/h until the athletes reached exhaustion. The exhaled gas of the participants was recorded for each breath during the run via an open spirometry circuit (Sensormedics vmax229, SensorMedics, Yorba Linda, CA, USA). At the same time, during the test, the heart rate was measured with a portable heart rate monitor (Polar H10, Polar Electro Oy, Kempele, Finland). The test was considered successful when (a) the participants showed the inability to continue the protocol due to exhaustion or when (b) their heart rate reached at least 10 bpm lower than the maximum heart rate for their age, as calculated using the equation (220—age). For VO2max, we recorded the mean value of the three highest reported values of VO2 [15,16]. Furthermore, anaerobic capacity was evaluated through the 30 s Wingate test in a cycle ergometer (Monark, Monark Exercise, Vansbro, Sweden). The volunteers started the test protocol after 2 min of trial cycling at a slow and fast pace followed by another 2 min of resting. Initially, they cycled for a short period of time without any further load in order to minimize the phenomena of inactivity and wheel resistance. Afterward, the researchers applied the chosen load for this protocol (0.075 kp·kg⁻¹ body weight); as the time began to count, the participants began to cycle at a maximum level for 30 s. The frequency of leg rotation during cycling for every athlete was recorded every 10 s. The recorded values were written down on a specific spreadsheet so as to calculate the maximal work performed over the period of 30 s and thus the anaerobic capacity for each athlete [17]. The VO2max test was conducted at the specialized Exercise Physiology laboratory of Harokopio University, while the 30 s Wingate test was performed inside the basketball gym.

2.7. Speed, Acceleration, Explosiveness and Vertical Jumping Ability Assessment

Speed, acceleration and explosiveness were assessed through linear sprint tests of 20, 10 and 5 m, respectively, and the time was recorded electronically using photocells (Witty, Microgate, Bolzano, Italy). The first 2 photocells were placed on the starting line, and the next 6 photocells were placed 5, 10 and 20 m away from the starting line. The participants began to sprint 20 cm before the start line, and the time began to count when they passed through the first 2 photocells next to the starting line and stopped when they passed through the last 2 photocells that were 20 m away. The times held through the photogates were for 5, 10 and 20 m [18,19]. Finally, vertical jumping ability was assessed using the counter movement jump (CMJ) test with free arms swing in a portable photocell device (Optojump, Microgate, Bolzano, Italy). Every athlete started the test in a standing position, and after making a deep squat, they tried to jump as high as possible. Also, the athletes had to stretch their arms up during the squat, and they had to continue the opposite arm movement during the propulsive phase in order to give their bodies a greater boost [2]. These 4 measurements were executed inside the basketball gym.

2.8. Lower Body Part Strength Assessment

Lower body part strength was evaluated using a protocol in the dominant foot of the participants, during which we measured the maximum strength of the quadriceps and the hamstring muscles with an isokinetic dynamometer (Biodex System 3, Biodex Medical Systems, Shirley, NY, USA). At the start of the test, the researchers made the necessary modifications on the adjustable chair of the dynamometer and gave the required instructions to the participants. Every athlete was stabilized in the adjustable chair using specific straps so that only the examined extension and flexor muscles of the knee were able to move at a certain range. The dominant foot of each athlete was originally placed in such a position that the knee joint was at 90°, and the isokinetic dynamometer was set so that the extension range was also 90° (range from 90° to 180°). All the participants executed some trial movements of extension and flexion, and then they started to perform the regular procedure, executing 3 consecutive attempts of extension and flexion at maximum intensity with a 15 s break between each attempt [20]. This protocol was carried out at the specialized Exercise Physiology laboratory of Harokopio University.

2.9. Statistical Analysis

The analysis of the data was executed using the SPSS software (version 25.0, IBM Corp., Armonk, NY, USA). The normal distribution of the data was assessed using the Kolmogorov–Smirnov test. When the variables followed a parametric distribution, the results were presented as mean ± standard deviation, while in the case of a non-parametric distribution, the results were presented as the median (25–75° percentile). A t-test analysis was used to compare the mean values of each variable for the 3 teams at the same time point, while a paired t-test was used to assess the post-training vs. pre-training differences (Δ) of the dependent variables for each team separately. In the cases of non-parametric values, the Wilcoxon test and the Wilcoxon signed-rank test were used accordingly. The magnitude of percentage change was assessed using the Cohen d effect size (ES) model (<0.1 → trivial effect, 0.1–0.3 → small effect, 0.3–0.5 → moderate effect and >0.5 → large effect). The effects of each team on each dependent variable were evaluated using a two-way analysis of variance with repeated measures across time. The Levene homogeneity of variance test was performed before the ANOVA test to assess the equality of variances of the examined variables. Due to multiple comparisons, a post hoc bonferroni correction was executed in order to correct the type I error and approach the level of significance that was set for the statistical analyses of this study. The correlations between the variables were carried out using the Pearson correlation test for a parametric distribution, and the Spearman correlation test was carried out for a non-parametric distribution. The correlation magnitude was determined according to Cohen’s provided guidelines (r = 0.1–0.3 → small, 0.3–0.5 → medium and >0.5 → large). The effect size for the ANOVA test was evaluated using the η_p² model, while all statistical differences between the mean values of the variables were calculated using a p = 0.05 significance level and a 95% confidence interval (CI).

3. Results

3.1. Descriptive Characteristics

The descriptive characteristics of the participants as recorded before the study are displayed on Table 1. Regarding age, we noticed that Team 3 showed greater values compared to Teams 1 and 2 (p = 0.007). That was the only significant difference between the three teams before the start of the study.

3.2. Effect of Pre-Season Training on Body Composition

We observed that only the average value of all three teams together showed a significant decrease in weight [T (Weight): p = 0.012, η_p² = 0.229]. On the other hand, the fat percentage was significantly reduced in Teams 1 (−0.3 ± 0.32, p = 0.024) and 2 (−1.29 ± 0.59, p < 0.001), while the ANOVA test between the three teams showed a greater reduction for Team 2 compared to Teams 1 and 3 [TG (Fat Perc): p <0.001, η_p² = 0.472] (Table 4).

3.3. Effect of Pre-Season Training on Aerobic and Anaerobic Capacity

No differences were found in aerobic and anaerobic capacity between the three teams before the start and after the end of the pre-season period. All the three training protocols that were used in this study led to significant improvements in aerobic [(Team 1: 1.19 ± 0.76, p = 0.002), (Team 2: 1.03 ± 0.73, p = 0.002) and (Team 3: 0.83 ± 0.55, p = 0.002)] and anaerobic capacity [(Team 1: 30.8 ± 11.3, p < 0.001), (Team 2: 22.17 ± 9.15, p < 0.001) and (Team 3: 15.48 ± 5.91, p < 0.001)]. Finally, the training protocol that was executed by Team 1 resulted in a greater improvement in anaerobic capacity compared to the training protocol that was executed by Team 3 [TG (Anaerobic Capacity): p = 0.006, η_p² = 0.340] (Table 5).

3.4. Effect of Pre-Season Training on Speed, Acceleration, Explosiveness and Vertical Jumping Ability

Explosiveness [(Team 1: −0.011 ± 0, p < 0.001), (Team 2: −0.011 ± 0, p < 0.001) and (Team 3: −0.007 ± 0, p = 0.022)] and vertical jumping ability [(Team 1: 2.28 ± 1.32, p < 0.001), (Team 2: 1.68 ± 1.14, p < 0.001) and (Team 3: 1.47 ± 1.05, p = 0.003)] were improved similarly for the three teams after the end of the pre-season period. Acceleration was improved for Teams 1 and 2 [(Team 1: −0.015 ± 0.01, p = 0.015) and (Team 2: −0.009 ± 0.01, p = 0.041)], and speed was improved only for Team 1 (−0.03 ± 0.03, p = 0.038). We did not discover any other significant differences before and after, or between, the three teams (Table 6).

3.5. Effect of Pre-Season Training on Lower Body Part Strength

Pre-season training favorably affected the maximum strength of quadriceps [(Team 1: 3.65 ± 2.41, p = 0.002), (Team 2: 3.06 ± 2.35, p = 0.003) and (Team 3: 2.95 ± 2.09, p = 0.003)] and hamstrings [(Team 1: 2.83 ± 1.56, p < 0.001), (Team 2: 2.08 ± 1.88, p = 0.007), (Team 3: 1.76 ± 1.03, p < 0.001)] for every team that participated in the current study, with no subsequent differences between the teams (Table 7).

3.6. Effect of Pre-Season Training on CK, LDH

Before the start of the study, Team 2 showed lower values of CK compared to Team 3 (p = 0.020), as well as greater values of LDH compared to Teams 1 and 3 (p < 0.001). Additionally, after the end of the study, Team 2 kept having greater values of LDH compared to Teams 1 and 3 (p < 0.001), and that is the reason why the ANOVA test for each group indicated a significant value [G (LDH): p < 0.001, η_p² = 0.728]. Finally, CK and LDH were significantly elevated for all the teams after the end of the pre-season period: (Team 1 → CK: 82 ± 77.63, p = 0.013), (Team 2 → CK: 70.6 ± 26.32, p < 0.001) and (Team 3 → CK: 46.88 ± 42.86, p = 0.011) and (Team 1 → LDH: 43.88 ± 45.28, p = 0.020), (Team 2 → LDH: 18.4 ± 24.74, p = 0.043) and (Team 3 → LDH: 14 ± 9.57, p = 0.002) (Table 8).

3.7. Effect of Pre-Season Training on PC and GPx

The mean value of the three teams for GPx was found to be significant [Τ (GPx): p = 0.008, η_p² = 0.270], but after the end of the training period, GPx increased only for Teams 1 (10 ± 11.73, p = 0.034) and 3 (6 ± 6.32, p = 0.022). No differences were observed regarding the PC measurements (Table 9).

3.8. Correlations

The correlations that were formulated were based on the differences (Δ) between all the physiological and biochemical variables that were examined in this study. Strong and positive correlations were found between CK and LDH (r = 0.638, p < 0.001), as well as between speed and acceleration (r = 0.912, p < 0.001). GPx and speed (r = 0.408, p = 0.039), as well as aerobic capacity and hamstrings maximum strength (r = 0.413, p = 0.029), were also positively correlated (Table 10).

4. Discussion

This was the first study that tried to explore the effect of three pre-season training protocols with different training frequencies on performance, muscle damage and oxidative stress markers. Firstly, after observing the somatometric and performance characteristics of the participants of the three teams, it seems that there are no significant differences between the three teams before the start of the study. This finding is considered very important, as it shows that all three teams started from the same level of physiological attributes, and consequently, we were able to better comprehend any alterations that were caused by the three different training protocols. Nevertheless, the present study was created with certain limitations, such as the small sample size for each team, the absence of a long-term follow-up or the lack of a control group.

Fat percentage was reduced for Teams 1 (−0.3 ± 0.32%) and 2 (−1.29 ± 0.59%) but not for team 3. Team 3 performed the light protocol with the lowest training units compared to Teams 1 and 2, so this may explain why we did not find a significant reduction in the fat percentage of Team 3. In addition, Team 2 showed a significantly greater decrease in fat percentage compared to Teams 1 and 3 [TG (Fat Perc): p < 0.001, η_p² = 0.472], indicating that the moderate protocol had finally shown better results in reducing fat. The fat percentage reductions that we demonstrated in our study after 6 weeks of pre-season training agree with previous studies that were performed in other basketball teams, which had also shown significant reductions after 6 (−3.49%) or 8 (−3.1%) weeks of pre-season training [21,22]. Finally, it is worth mentioning that the decrease in fat percentage was not accompanied by weight loss. This finding indicates that although some players experienced a notable reduction in body fat, they simultaneously built more muscle mass, which explains why their body weight remained comparable to levels as those that were observed before the start of the preparation period. [22].

Aerobic capacity was similarly improved for each one of the three teams [(Team 1: 1.19 ± 0.76, p = 0.002), (Team 2: 1.03 ± 0.73, p = 0.002) and (Team 3: 0.83 ± 0.55, p = 0.002)], without any significant differences between them. Nevertheless, a trend for a gradual decrease as we proceed from the heavy to the light protocol is evident, but yet again, these data were found to be not statistically significant. The study by Gantois et al., 2018 [23] was the only one that attempted to evaluate aerobic capacity before and after a 6-week pre-season training period using the VO2max test, and they demonstrated slightly greater improvements (~3 mL/min/kg) compared to our study. However, the main difference between the two studies was that Gantois et al. used a fitness protocol based on repeated sprint ability training, compared to our research that used a mixed fitness protocol based on functional and plyometric training. Also, significant improvements were found in three more studies in basketball athletes, where aerobic capacity was examined via the Yo-Yo test after 3 (476 m), 7 (485 m) and 12 (49 m) weeks of pre-season training [11,24,25]. On the other hand, while anaerobic capacity was also improved for every team in our study [(Team 1: 30.8 ± 11.3, p < 0.001), (Team 2: 22.17 ± 9.15, p < 0.001) and (Team 3: 15.48 ± 5.91, p < 0.001)], the ANOVA test between the teams showed that Team 1 had a significantly greater increase compared to Team 3 [TG (Anaerobic Capacity): p = 0.006, η_p² = 0.340]. This finding clearly suggests that the heavy protocol that was executed by Team 1 produced better results compared to the light protocol that was used by Team 3. Only one study that tried to examine anaerobic capacity in professional basketball athletes before and after the pre-season period through the anaerobic power step test (APST) was found, which showed significant improvements (497 watts) after 6 weeks of a mixed aerobic and anaerobic fitness training protocol [21]. The rest of the studies have either simply evaluated anaerobic capacity through a single measurement [17,26] or have investigated the relationship between anaerobic ability with other performance markers [27,28,29,30].

From a practical point of view, the increases in aerobic capacity for the three teams are not considered large, as it is validated also by the effect size. A few years ago, the fitness programs in basketball were more focused on improving aerobic endurance, which is a contrast with the last few years when the game of basketball became much faster, and therefore, anaerobic capacity is the basis of a basketball player’s endurance. This was also confirmed by the highest values of VO2max that were observed in older studies [31,32] compared to newer [33,34]. On the contrary, it is normal to observe a larger effect size in the increases in anaerobic capacity, as this was the main goal of our training protocols. We also witnessed the values of anaerobic capacity in our study being higher than the values of other studies in the past [21,26,35], confirming that in recent years, the fitness protocols are mainly focused on improving anaerobic endurance rather than aerobic.

The measurements of speed, acceleration and explosiveness through the 20, 10 and 5 m sprints, respectively, have returned some quite interesting results, as we noticed that speed only improved for Team 1 (−0.03 ± 0.03 s), and acceleration improved for Teams 1 (−0.015 ± 0.01 s) and 2 (−0.009 ± 0.01 s), while explosiveness improved for all the teams: (Team 1 → −0.011 ± 0 s), (Team 2 → −0.011 ± 0 s) and (Team 3 → −0.007 ± 0 s). This finding shows that as the distance was getting longer, the protocols with the less training units did not give significant results. Thus, the heavy protocol improved speed, acceleration and explosiveness; the moderate protocol improved acceleration and explosiveness, and the light protocol only improved explosiveness. Some of the studies that have examined similar performance markers in basketball athletes through a 20 or a 10 m sprint have largely verified the findings of our own research, after displaying analogous improvements after 3 (−0.03 s) and 6 (−0.028 s) weeks of pre-season training [11,36]. Nevertheless, the study by Lukonaitienė et al., 2020 [11] showed an improvement in a 10 m sprint measurement, despite the short duration of the training period, while the study by Borin et al., 2019 [10] in high-level female basketball athletes showed that the results in a 20 m sprint test got worse (0.05 s) after a 27-day training period. These results confirm our previous allegation, since it is understood that the protocols with fewer training units may produce positive results mostly in shorter sprints. In addition, we identified two more studies that evaluated the effect of certain training programs on performance markers via a 5 m sprint test. The first one examined the effect of an 8-week mixed plyometric and balance training protocol in young female basketball athletes [37], while the other investigated the effect of a 10-week plyometric training protocol in young male basketball athletes [38]. The study by Bouteraa et al., 2020 [37] did not found any significant alterations, while the study by Aksović et al., 2019 [38] found a positive effect (−0.07 s).

The improvements in speed (0.03 s), acceleration (0.015 s) and explosiveness (0.011 s) for Team 1 were found to have a small effect size, but under real conditions, if someone considers that a sprint in basketball can start from 5 m, can reach up to 20 m and can last for ~1–5 s [3,18,39], then it is understood that our athletes will have a much longer time to perform a sprint or a specific basketball movement than they had before. Consequently, the improvements that we demonstrated in relation to speed (Team 1), acceleration (Teams 1 and 2) and explosiveness (Teams 1, 2 and 3) can contribute significantly to the effectiveness of the athletes to perform specialized basketball movements faster than before, thus maximizing their performance.

Vertical jumping ability showed similar large improvements for the three teams, with no accompanied significant differences between them. Vertical ability has been examined mainly after plyometric training protocols, which have caused similar improvements with those of our own study (3.1–6.3 cm) [4,40]. The improvements of 2.28 cm (Team 1), 1.68 cm (Team 2) and 1.47 cm (Team 3) that we found in our study are considered to be more than sufficient to enhance vertical jumping ability, as previous studies have shown that a basketball athlete can perform an average of 46 jumps during a match, while the jump height for professional female and male basketball athletes ranges from ~35 to ~75 cm [3,20,39,41].

Regarding the lower body part maximum strength, since it was expressed through the dynamometer measurements for quadriceps and hamstrings, we noticed increases for every team that participated in our study, with no significant differences between the teams. Additionally, we found one more study that tried to investigate the maximum strength of the quadriceps and hamstrings in female basketball athletes with an isokinetic dynamometer, before and after the pre-season period. In their study, a significant improvement was found in the maximum strength of the hamstrings (7.31 N.m) but not for the quadriceps [42]. The duration of their research was the same as ours (6 weeks), but the fitness protocols that were used in their study were different compared to ours, as Wilkerson et al., 2004 [42] used a plyometric fitness protocol in parallel with isotonic strengthening exercises, as opposed to our mixed plyometric and functional fitness protocol, which led to improvements not only to the maximum strength of the hamstrings: (Team 1 → 2.83 ± 1.56 Ν.m), (Team 2 → 2.08 ± 1.88 Ν.m) and (Team 3 → 1.76 ± 1.03 Ν.m) but also in the maximum strength of the quadriceps: (Team 1 → 3.65 ± 2.41 Ν.m), (Team 2 → 3.06 ± 2.35 Ν.m) and (Team 3 → 2.95 ± 2.09 Ν.m). Several epidemiological studies have shown that most of the muscle injuries in basketball athletes occur in the hamstring muscles [43,44]. For this reason, one of the main goals of the fitness training protocols that we used in this research was the proper and adequate training of the hamstrings in order to avoid or minimize the risk for any potential injuries. If we observe the effect sizes of the two lower extremity indices that we examined, we will identify that the maximum strength of the quadriceps had mild increases, while the maximum strength of the hamstrings had large increases for all the teams. Accordingly, we can safely state that the training protocols that were used in our research have managed to produce large improvements in the maximum strength of the hamstring muscles for every team that participated in this study.

The determination of CK and LDH demonstrated similar elevated levels for the three teams: (CK: Team 1 → 82 ± 77.63 U/L, Team 2 → 70.6 ± 26.32 U/L and Team 3 → 46.88 ± 42.86 U/L) and (LDH: Team 1 → 43.88 ± 45.28 U/L, Team 2 → 18.4 ± 24.74 U/L and Team 3 → 14 ± 9.57 U/L). Observing these values, it can be noted that Team 3 had a trend for a lower increase in CK and LDH than Teams 1 and 2, but this trend was not found to be statistically significant. Several other studies have demonstrated higher levels of CK (615–1250 U/L) and LDH (59–89.37 U/L) in basketball athletes after 2 months of training and until the end of the season [45,46,47], or after a single official match (~125–160%) [48,49]. The repeated plyometric movements and the body collisions that are occurring during basketball training or official matches are the main reasons for the increased muscle damage of these athletes [45,50].

Plyometric muscle contractions are known to cause muscle damage, particularly when performed without a sufficient training background [51]. The game of basketball involves repeated high-intensity movements, continuous jumps and changes in direction. Given these demands, it is very likely that such specific movements will lead to an extended load on the lower extremity muscles, resulting in subsequent muscle damage [49,50]. According to these data, it was very important for our research to analyze the potential muscle damage that was caused by each protocol. The analyses of CK and LDH are broadly recognized as two of the best procedures for identifying muscle damage, though not always fully reflective of the extent of the damage. Our findings indicate a mild muscle damage of the participants, something that it is also validated if we take into account the regular reference values for CK and LDH. Nonetheless, equivalent CK and LDH values have been linked to bone and muscle injuries [52,53], highlighting the importance of continuing to investigate the mechanisms behind these procedures and to achieve a deeper understanding of the functions of CK and LDH activities. Additionally, CK and LDH had proven to be effective markers for a better planning of the recovery strategies, which are very important for maximizing athletic performance [54,55]. Consequently, their variations following a pre-season training period should be carefully analyzed, particularly to minimize potential injuries and to organize the appropriate recovery strategies.

No significant results were found after the pre-season period or between the teams, regarding the PC measurements, while in the case of GPx, Teams 1 and 3 showed significant increases after the end of the preparation period: (GPx: Team 1 → 10 ± 11.73 U/L) and (GPx: Team 3 → 6 ± 6.32 U/L). Oxidative stress can manifest when the generation of reactive oxygen species exceeds the capacity of the antioxidant defense system [56]. Also, it is already established that high oxidative stress values can impair athletic performance through various metabolic paths [56]. PC and GPx are two of the most approved markers with antioxidant status, and that is the reason they were chosen for the biochemical assessment of this study [56,57]. The increases that we reported for Teams 1 and 3 indicate a mild oxidative stress presence, which is maybe directly related to the delayed inflammatory response that originated from the muscle damage, and can unfavorably contribute in the development of fiber strains, or negatively affect the reconstruction of the damaged muscle fibers [56,58]. On the other hand, low oxidative stress values may also have a positive effect on athletic performance, as the low number of free oxygen radicals plays an important role in the exercise-induced adaptation of the muscle fibers [56]. The exact number of free radicals that are directly related to positive or negative effects on athletic performance varies from person to person and is based on many factors, such as gene polymorphism [59]. Finally, our results showed a mild increase in GPx for Teams 1 and 3 but not for Team 2. This finding can be explained from the outcomes of previous studies, which have demonstrated that the type, the frequency and the intensity of training can significantly affect the production of free oxygen radicals, which in turn leads to different oxidative stress values for every athlete [57,60,61].

The positive correlation between CK and LDH confirms an increased muscle damage of the participants after the end of the pre-season period. This finding comes in agreement with the study by Khajehlandi and Janbozorgi, 2018 [62], which showed a similar increase in CK and LDH enzymes in female basketball athletes after a single exercise session. Another significant correlation that was found between GPx and speed has shown that as the oxidative stress increases, as it is expressed by the GPX values [63], so do the times of the athletes in the 20 m sprint. This increase in the sprint times means that their speed decreases as the levels of oxidative stress are increasing. Oxidative stress can cause impairment to the metabolism of lipids, proteins and DNA procedures and therefore reduce athletic performance [64]. Regarding the performance markers, aerobic capacity has been positively correlated with the maximum strength of the hamstring muscles. In a recent study in female cyclists, a positive correlation between maximum oxygen intake and muscle strength [65] was also highlighted. Strength training improves the maximum strength of athletes and anaerobic ability, but it has also been found to improve their aerobic capacity. On the other hand, endurance training besides aerobic capacity can also improve anaerobic capacity. Both procedures when combined will lead to an overall improvement of athletic performance, mutually supporting each other. According to all the above, it is understood that through strength training, it is also possible to improve aerobic capacity or vice versa [66]. Finally, the improvement of speed was positively correlated with the improvement of acceleration. This outcome suggests that the sprint times when athletes run in a straight line from 10 and up to 20 m were improved significantly. The study by Shalfawi et al., 2011 [67] also demonstrated a strong correlation between the same 10 and 20 m sprints in basketball athletes. This result is considered quite critical, given that basketball athletes run between 10 and 20 m very often during training or official matches [68].

From another point of view, the effects that were caused by each protocol after the end of the pre-season period can be regarded as crucial not only for the in-season performance of these athletes but also for managing their injury prevention strategies [3]. The pre-season period is much shorter in duration compared to the in-season period, but nevertheless, during the pre-season period, basketball athletes must create a sufficient physical, technical, tactical and psychological background for the upcoming in-season period [69], which will ultimately maximize their performance and reduce the risk of potential injuries [70,71]. These improvements can occur mostly during this phase due to the lack of official matches and competitions, a fact that allows the fitness coaches to distribute a heavier training load across the entire preparation training schedule [72]. On the other hand, the main objective during the in-season period is the preservation of the improvements that were achieved during the pre-season period [69]. Our study led to significant improvements on performance markers after the end of the preparation period, which we hope will be beneficial for creating a solid training background, reducing the risk for injuries and maximizing the performance of the female basketball athletes during the competitive season.

As we have witnessed by the studies that have been previously displayed, the female basketball population is not well investigated. There are critical differences between males and females, which are directly related to sports and exercise. These differences are mainly associated with the physical characteristics and the metabolic responses [12,73], but there are also some more studies that have examined and identified gender differences regarding injuries, oxidative stress and muscle damage [12,74,75,76]. Under this perspective, it is necessary that the focus of future studies should be equally allocated to both male and female athletes. This study highlights this literature gap, as it is very important for sports science to further involve the female population in the research process.

In conclusion, this study demonstrates important findings on how to improve the physiological profile of high-level female basketball athletes through the training protocols that were used. It also shows that the quantitative manifestation of training is not always important, but it is possible through qualitative training to also create a good training background for basketball. Additionally, it gives a strong stimulus so that basketball coaches and trainers will better understand the importance of biochemical evaluation and how the metabolic procedures of every athlete can affect their performance. Future studies may need to turn their focus on fitness protocols with longer durations, try to differentiate the types of the training protocols and attempt to achieve a better balance between maximizing performance and mitigating the muscle damage and oxidative stress levels.

5. Practical Applications

The plyometric and functional training protocols that were used in this study were capable of improving the fitness status of professional female basketball athletes after 6 weeks of pre-season basketball training. Also, body fat percentage was significantly reduced for every team that participated in this study. More specifically, the heavy protocol improved all the physiological markers that were evaluated, accompanied by a mild increase in muscle damage and oxidative stress markers. The moderate protocol improved seven of the eight performance markers (except for speed) and demonstrated a mild increase only for the muscle damage markers. The light protocol improved six of the eight performance markers (except for speed and acceleration) and showed a smaller increase in anaerobic capacity compared to the heavy protocol. The alterations that were caused by the light protocol were also accompanied by a mild increase in muscle damage and oxidative stress markers. Consequently, it is understood that even 4 more or 4 less fitness training units during a 6-week basketball preparation period can be considered very important to differentiate the outcomes of physiological and biochemical markers. Basketball coaches should wisely choose the training protocols they will use by considering the fatigue–training balance, and deciding which protocol they will follow should always be in relation to the methodologies and the ways of their technical and tactical basketball training.

Author Contributions

Conceptualization, D.M.; Data curation, D.M.; Formal analysis and interpretation, D.M. and T.N.; Funding acquisition, N.K.; Methodology, D.M. and T.N.; Writing—Original Draft Preparation, D.M.; Writing—Review and Editing, D.M.; Visualization, T.N. and D.M.; Resources, D.M. and T.N.; Supervision, D.M. and N.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National and Kapodistrian University of Athens—Special Account for Research Grants (SARG).

Institutional Review Board Statement

The Bioethics Committee of the School of Physical Education and Sport Science of the National and Kapodistrian University of Athens granted the ethics approval for the current study (1209/16 September 2020) in accordance with the ethical criteria of the Helsinki Declaration.

Informed Consent Statement

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

Data Availability Statement

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

Conflicts of Interest

The authors report no conflict of interest.

References

Ferioli, D.; Bosio, A.; Zois, J.; La Torre, A.; Rampinini, E. Seasonal changes in physical capacities of basketball players according to competitive levels and individual responses. PLoS ONE 2020, 15, e0230558. [Google Scholar] [CrossRef] [PubMed]
Lehnert, M.; Hůlka, K.; Malý, T.; Fohler, J.; Zahálka, F. The effects of a 6 week plyometric training programme on explosive strength and agility in professional basketball players. Acta Univ. Palacki. Olomuc. Gymn. 2013, 43, 7–15. [Google Scholar] [CrossRef]
Mexis, D.; Nomikos, T.; Kostopoulos, N. Effect of Pre-Season Training on Physiological and Biochemical Indices in Basketball Players—A Systematic Review. Sports 2022, 10, 85. [Google Scholar] [CrossRef] [PubMed]
Asadi, A.; Ramirez-Campillo, R.; Meylan, C.; Nakamura, F.Y.; Cañas-Jamett, R.; Izquierdo, M. Effects of volume-based overload plyometric training on maximal-intensity exercise adaptations in young basketball players. J. Sports Med. Phys. Fit. 2017, 57, 1557–1563. [Google Scholar] [CrossRef] [PubMed]
Heishman, A.D.; Curtis, M.A.; Saliba, E.; Hornett, R.J.; Malin, S.K.; Weltman, A.L. Non-invasive Assessment of Internal and External Player Load: Implications for Optimizing Athletic Performance. J. Strength Cond. Res. 2018, 32, 1280–1287. [Google Scholar] [CrossRef]
Petway, A.; Freitas, T.; Calleja-González, J.; Medina Leal, D.; Alcaraz, P. Training load and match-play demands in basketball based on competition level: A systematic review. PLoS ONE 2020, 15, e0229212. [Google Scholar] [CrossRef] [PubMed]
Lewis, M. It’s a Hard-Knock Life: Game Load, Fatigue, and Injury Risk in the National Basketball Association. J. Athl. Train. 2018, 53, 503–509. [Google Scholar] [CrossRef] [PubMed]
Pero, R.; Brancaccio, M.; Mennitti, C.; Gentile, L.; Franco, A.; Laneri, S.; De Biasi, M.; Pagliuca, C.; Colicchio, R.; Salvatore, P.; et al. HNP-1 and HBD-1 as Biomarkers for the Immune Systems of Elite Basketball Athletes. Antibiotics 2020, 9, 306. [Google Scholar] [CrossRef]
Agrotou, S.; Mexis, D.; Dorokofikis, P.; Armenis, E. Injuries in Basketball. Int. J. Latest Res. Humanit. Soc. Sci. 2024, 7, 41–48. [Google Scholar]
Borin, J.P.; Haddad, C.R.R.; Daniel, J.F.; Pinheiro, A.M.; Beneli, L.D.M.; Fachina, R.J.; Montagner, P.C. Short-term effects of combined training on the performance of the Brazilian women’s basketball team. Rev. Bras. Cineantropom. Desempenho. Hum. 2019, 21, e59839. [Google Scholar] [CrossRef]
Lukonaitiene, I.; Kamandulis, S.; Paulauskas, H.; Domeika, A.; Pliauga, V.; Kreivyte, R.; Stanislovaitiene, J.; Conte, D. Investigating the workload, readiness and physical performance changes during intensified 3-week preparation periods in female national Under 18 and Under 20 basketball teams. J. Sports Sci. 2020, 38, 1018–1025. [Google Scholar] [CrossRef] [PubMed]
Mexis, D.; Nomikos, T.; Mitsopoulos, N.; Kostopoulos, N. Effect of a 6-Week Preseason Training Protocol on Physiological and Muscle Damage Markers in High-Level Female and Male Basketball Players. Sports 2023, 11, 229. [Google Scholar] [CrossRef] [PubMed]
Colombo, G.; Clerici, M.; Garavaglia, M.; Giustarini, D.; Rossi, R.; Milzani, A.; Dalle-Donne, I. A step-by-step protocol for assaying protein carbonylation in biological samples. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2016, 1019, 178–190. [Google Scholar] [CrossRef]
Ferreira-Cravo, M.; Moreira, D.; Hermes-Lima, M. Glutathione Depletion Disrupts Redox Homeostasis in an Anoxia-Tolerant Invertebrate. Antioxidants 2023, 12, 1197. [Google Scholar] [CrossRef]
Faulkner, J.; Mauger, A.; Woolley, B.; Lambrick, D. The Efficacy of a Self-Paced VO2max Test during Motorized Treadmill Exercise. Int. J. Sports Physiol. Perform. 2015, 10, 99–105. [Google Scholar] [CrossRef] [PubMed]
Gantois, P.; Batista, G.; Aidar, F.; Nakamura, F.; Araujo de Lima-Junior, D.; de Souza, M.; Matos, D.; Cabral, B. Repeated sprint training improves both anaerobic and aerobic fitness in basketball players. Isokinet. Exerc. Sci. 2019, 27, 97–105. [Google Scholar] [CrossRef]
Delextrat, A.; Cohen, D. Physiological Testing of Basketball Players: Toward a Standard Evaluation of Anaerobic Fitness. J. Strength Cond. Res. 2008, 22, 1066–1072. [Google Scholar] [CrossRef] [PubMed]
Delextrat, A.; Trochym, E.; Calleja-González, J. Effect of a typical in-season week on strength jump and sprint performances in national-level female basketball players. J. Sports Med. Phys. Fit. 2012, 52, 128–136. [Google Scholar]
Boone, J.; Bourgois, J. Morphological and Physiological Profile of Elite Basketball Players in Belgium. Int. J. Sports Physiol. Perform. 2013, 8, 630–638. [Google Scholar] [CrossRef]
Rouis, M.; Coudrat, L.; Jaafar, H.; Filliard, J.R.; Vandewalle, H.; Barthelemy, Y.; Driss, T. Assessment of isokinetic knee strength in elite young female basketball players: Correlation with vertical jump. J. Sports Med. Phys. Fit. 2015, 55, 1502–1508. [Google Scholar]
Tavino, L.; Bowers, C.; Archer, C. Effects of Basketball on Aerobic Capacity, Anaerobic Capacity, and Body Composition of Male College Players. J. Strength Cond. Res. 1995, 9, 75–77. [Google Scholar]
Marzilli, T. The effects of a preseason strength training program on a division II collegiate women’s basketball team. Int. J. Fit. 2008, 4, 7–14. [Google Scholar]
Gantois, P.; Aidar, F.J.; Dantas, M.P.; da Silva, L.M.; Paes, P.P.; Santana, E.E.; Dantas, P.M.d.S.; Cabral, B.G.d.A.T. Aerobic fitness is associated with improved repeated sprints ability of basketball players after six weeks of training during preseason. Rev. Bras. Cineantropom. Desempenho Hum. 2018, 20, 114–124. [Google Scholar] [CrossRef]
Nunes, J.A.; Moreira, A.; Crewther, B.T.; Nosaka, K.; Viveiros, L.; Aoki, M.S. Monitoring training load, recovery-stress state, immune-endocrine responses, and physical performance in elite female basketball players during a periodized training program. J. Strength Cond. Res. 2014, 28, 2973–2980. [Google Scholar] [CrossRef] [PubMed]
Ferioli, D.; Bosio, A.; La Torre, A.; Carlomagno, D.; Connolly, D.R.; Rampinini, E. Different training loads partially influence physiological responses to preparation period in basketball. J. Strength Cond. Res. 2017, 32, 790–797. [Google Scholar] [CrossRef] [PubMed]
Woolstenhulme, M.T.; Bailey, B.K.; Allsen, P.E. Vertical jump, anaerobic power, and shooting accuracy are not altered 6 hours after strength training in collegiate women basketball players. J. Strength Cond. Res. 2004, 18, 422–425. [Google Scholar] [CrossRef]
Hoffman, J.; Epstein, S.; Einbinder, M.; Weinstein, Y. The Influence of Aerobic Capacity on Anaerobic Performance and Recovery Indices in Basketball Players. J. Strength Cond. Res. 1999, 13, 407–411. [Google Scholar]
Stauffer, K.; Nagle, E.; Goss, F.; Robertson, R. Assessment of anaerobic power in female Division I collegiate basketball players. J. Exerc. Physiol. Online 2010, 13, 1–10. [Google Scholar]
Changela, P.; Bhatt, S. The correlational study of the Vertical Jump test and Wingate Cycle test as a method to assess Anaerobic Power in High School Basketball players. Int. J. Sci. Res. Publ. 2012, 2, 1–6. [Google Scholar]
Alemdaroglu, U. The Relationship between Muscle Strength, Anaerobic Performance, Agility, Sprint Ability and Vertical Jump Performance in Professional Basketball Players. J. Hum. Kinet. 2012, 31, 149–158. [Google Scholar] [CrossRef]
Stojanovic, M.D.; Ostojic, S.M.; Calleja-González, J.; Milosevic, Z.; Mikic, M. Correlation between explosive strength, aerobic power and repeated sprint ability in elite basketball players. J. Sports Med. Phys. Fit. 2012, 52, 375–381. [Google Scholar]
Kucsa, R.; Mačura, P. Physical characteristics of female basketball players according to playing position. Acta Fac. Educ. Phys. Univ. Comen. 2015, 55, 46–53. [Google Scholar] [CrossRef]
Tokatlidou, C.; Xirouchaki, E.; Armenis, E.; Apostolidis, N. Hematologic, biochemical, and physiologic characteristics of elite and professional basketball players. J. Phys. Educ. Sport 2020, 20, 3384–3390. [Google Scholar] [CrossRef]
Mourgan, F.H.A.E.; AlMatar, M.; Al-Shamli, A.; Al-Kitani, M.; Al-Yaaribi, A.; Albarri, O. The Effect of Specific High-intensity Exercises on Cardiovascular Balance, Vascularity, and Performance in Female Youth Basketball Players. Open Bioch. J. 2024, 18, e1874091X281813. [Google Scholar] [CrossRef]
De Araujo, G.G.; de Barros Manchado-Gobatto, F.; Papoti, M.; Camargo, B.H.F.; Gobatto, C.A. Anaerobic and aerobic performances in elite basketball players. J. Hum. Kinet. 2014, 42, 137–147. [Google Scholar] [CrossRef]
Pliauga, V.; Lukonaitiene, I.; Kamandulis, S.; Skurvydas, A.; Sakalauskas, R.; Scanlan, A.; Stanislovaitiene, J.; Conte, D. The effect of block and traditional periodization training models on jump and sprint performance in collegiate basketball players. Biol. Sport 2018, 35, 373–382. [Google Scholar] [CrossRef]
Bouteraa, I.; Negra, Y.; Shephard, R.; Chelly, M. Effects of Combined Balance and Plyometric Training on Athletic Performance in Female Basketball Players. J. Strength Cond. Res. 2020, 34, 1967–1973. [Google Scholar] [CrossRef]
Aksović, N.; Berić, D.; Kocić, M.; Jakovljević, S.; Milanović, F. Plyometric training and sprint abilities of young basketball players. Phys. Educ. Sport 2019, 17, 539–548. [Google Scholar] [CrossRef]
Gottlieb, R.; Shalom, A.; Calleja-Gonzalez, J. Physiology of Basketball—Field Tests. Rev. Article. J. Hum. Kinet. 2021, 77, 159–167. [Google Scholar] [CrossRef] [PubMed]
Khlifa, R.; Aouadi, R.; Hermassi, S.; Chelly, M.S.; Jlid, M.C.; Hbacha, H.; Castagna, C. Effects of a plyometric training program with and without added load on jumping ability in basketball players. J. Strength Cond. Res. 2010, 24, 2955–2961. [Google Scholar] [CrossRef] [PubMed]
Dawes, J.; Marshall, M.; Spiteri, T. Relationship between pre-season testing performance and playing time among NCAA DII basketball players. Sports Exerc. Med. 2016, 2, 47–54. [Google Scholar] [CrossRef]
Wilkerson, G.B.; Colston, M.A.; Short, N.I.; Neal, K.L.; Hoewischer, P.E.; Pixley, J.J. Neuromuscular Changes in Female Collegiate Athletes Resulting from a Plyometric Jump-Training Program. J. Athl. Train. 2004, 39, 17–23. [Google Scholar] [PubMed] [PubMed Central]
Bigoni, M.; Gaddi, D.; Piatti, M. Basketball: Epidemiology and Injury Mechanism. Arthrosc. Sport Inj. 2016, 33–39. [Google Scholar] [CrossRef]
Eftekhari, A.; Cogan, C.; Pandya, N.; Feeley, B. Hamstring Injury Epidemiology in the National Basketball Association over a Five-Year Period. Muscles Ligaments Tendons J. 2022, 12, 79–93. [Google Scholar] [CrossRef]
Gocentas, A.; Landõr, A. Stress and muscle damage monitoring in high-level basketball players. Pap. Anthropol. 2012, 21, 72–83. [Google Scholar] [CrossRef]
Mennitti, C.; Brancaccio, M.; Gentile, L.; Ranieri, A.; Terracciano, D.; Cennamo, M.; La Civita, E.; Liotti, A.; D’Alicandro, G.; Mazzaccara, C.; et al. Athlete’s Passport: Prevention of Infections, Inflammations, Injuries and Cardiovascular Diseases. J. Clin. Med. 2020, 9, 2540. [Google Scholar] [CrossRef] [PubMed]
Mohammed, B.; Bachir, K.; Nour Eddine, S.; Adel, B. Study of LDH adaptations associated with the development of Speed endurance in basketball players U19. Int. J. Appl. Exerc. Physiol. 2018, 7, 35–43. [Google Scholar] [CrossRef]
Kostopoulos, N.; Apostolidis, N.; Mexis, D.; Mikellidi, A.; Nomikos, T. Dietary intake and the markers of muscle damage in elite basketball players after a basketball match. J. Phys. Educ. Sport 2017, 58, 394–401. [Google Scholar] [CrossRef]
Moreira, A.; Nosaka, K.; Nunes, J.; Viveiros, L.; Jamurtas, A.Z.; Aoki, M.S. Changes in Muscle Damage Markers in Female Basketball Players. Biol. Sport 2014, 31, 3–7. [Google Scholar] [CrossRef]
Booth, M.A.; Orr, R. Effects of plyometric training on sports performance. Strength Cond. J. 2016, 38, 30–37. [Google Scholar] [CrossRef]
Nosaka, K.; Newton, M. Difference in the magnitude of muscle damage between maximal and submaximal eccentric loading. J. Strength Cond. Res. 2002, 16, 202–208. [Google Scholar]
Miyamoto, T.; Oguma, Y.; Sato, Y.; Kobayashi, T.; Ito, E.; Tani, M.; Miyamoto, K.; Nishiwaki, Y.; Ishida, H.; Otani, T.; et al. Elevated Creatine Kinase and Lactic Acid Dehydrogenase and Decreased Osteocalcin and Uncarboxylated Osteocalcin are Associated with Bone Stress Injuries in Young Female Athletes. Sci. Rep. 2018, 8, 18019. [Google Scholar] [CrossRef] [PubMed]
Lippi, G.; Schena, F.; Ceriotti, F. Diagnostic biomarkers of muscle injury and exertional rhabdomyolysis. Clin. Chem. Lab. Med. 2018, 57, 175–182. [Google Scholar] [CrossRef] [PubMed]
Doeven, S.H.; Brink, M.S.; Kosse, S.J.; Lemmink, K. Postmatch recovery of physical performance and biochemical markers in team ball sports: A systematic review. BMJ Open Sport Exerc. Med. 2018, 4, e000264. [Google Scholar] [CrossRef]
Plumb, M. Use of Post-Exercise Recovery Strategies in Team and Individual Sports. J. Sci. Med. Sport 2023, 26, 139–140. [Google Scholar] [CrossRef]
Nocella, C.; Cammisotto, V.; Pigozzi, F.; Borrione, P.; Fossati, C.; D’Amico, A.; Cangemi, R.; Peruzzi, M.; Gobbi, G.; Ettorre, E.; et al. Impairment between oxidant and antioxidant systems: Short-and long-term implications for athletes’ health. Nutrients 2019, 11, 1353. [Google Scholar] [CrossRef]
Bloomer, R.; Davis, P.; Consitt, L.; Wideman, L. Plasma protein carbonyl response to increasing exercise duration in aerobically trained men and women. Int. J. Sports Med. 2007, 28, 21–25. [Google Scholar] [CrossRef]
Butterfield, T.A. Eccentric exercise in vivo: Strain-induced muscle damage and adaptation in a stable system. Exerc. Sport Sci. Rev. 2010, 38, 51–60. [Google Scholar] [CrossRef] [PubMed]
Reid, M.B. Free radicals and muscle fatigue: Of ROS, canaries, and the IOC. Free Radic. Biol. Med. 2008, 44, 169–179. [Google Scholar] [CrossRef] [PubMed]
Goto, C.; Nishioka, K.; Umemura, K.; Jitsuiki, D.; Sakagutchi, A.; Kawamura, M.; Chayama, K.; Yoshizumi, M.; Higashi, Y. Acute moderate-intensity exercise induces vasodilation through an increase in nitric oxide bioavailability in humans. Am. J. Hypertens. 2007, 20, 825–830. [Google Scholar] [CrossRef] [PubMed]
Hadžović-Džuvo, A.; Valjevac, A.; Lepara, O.; Pjanić, S.; Hadžimuratović, A.; Mekić, A. Oxidative stress status in elite athletes engaged in different sport disciplines. Bosn. J. Basic Med. Sci. 2014, 14, 56. [Google Scholar] [CrossRef] [PubMed]
Khajehlandi, M.; Janbozorgi, M. Effect of one session of resistance training with and without blood flow restriction on serum levels of creatine kinase and lactate dehydrogenase in female athletes. J. Clin. Basic Res. 2018, 2, 5. [Google Scholar] [CrossRef]
Mocan, M.; Vesa, S.; Suciu, S.; Blaga, S. Systemic markers of oxidative stress in relation to metabolic syndrome components. Clujul Med. 2013, 86, 227–234. [Google Scholar] [PubMed]
Deaton, C.; Marlin, D. Exercise-associated oxidative stress. Clin. Tech. Equine Pract. 2003, 2, 278–291. [Google Scholar] [CrossRef]
Sezer, F.; Akbaş, D.; Çakır, V.O.; Bayrakdar, A.; Bayraktar, I. The Relationship between FTP and VO2max Ability and Strength and Flexibility in Cyclists. Int. J. Relig. 2024, 5, 118–125. [Google Scholar] [CrossRef]
Stone, M.H.; Stone, M.E.; Sands, W.A.; Pierce, K.C.; Newton, R.U.; Haff, G.G.; Carlock, J. Maximum Strength and Strength Training—A Relationship to Endurance? Strength Cond. J. 2006, 28, 44–53. [Google Scholar] [CrossRef]
Shalfawi, S.A.; Sabbah, A.; Kailani, G.; Tønnessen, E.; Enoksen, E. The relationship between running speed and measures of vertical jump in professional basketball players: A field-test approach. J. Strength Cond. Res. 2011, 25, 3088–3092. [Google Scholar] [CrossRef] [PubMed]
Wen, N.; Dalbo, V.J.; Burgos, B.; Pyne, D.B.; Scanlan, A.T. Power testing in basketball: Current practice and future recommendations. J. Strength Cond. Res. 2018, 32, 2677–2691. [Google Scholar] [CrossRef] [PubMed]
Díaz-Martínez, A.S.; Vaquero-Cristóbal, R.; Albaladejo-Saura, M.; Esparza-Ros, F. Effect of pre-season and in-season training on anthropometric variables, somatotype, body composition and body proportion in elite basketball players. Sci. Rep. 2024, 14, 7537. [Google Scholar] [CrossRef] [PubMed]
Arede, J.; Ferreira, A.P.; Esteves, P.; Gonzalo-Skok, O.; Leite, N. Preparing for the youth basketball European Championship: Perceptual response and acute effects of an injury prevention program. Int. J. Sports Sci. Coach. 2024, 19, 306–314. [Google Scholar] [CrossRef]
Taylor, J.B.; Ford, K.R.; Nguyen, A.D.; Terry, L.N.; Hegedus, E.J. Prevention of lower extremity injuries in basketball: A systematic review and meta-analysis. Sports Health 2015, 7, 392–398. [Google Scholar] [CrossRef] [PubMed]
Lidor, R.; Blumenstein, B.; Tenenbaum, G. Psychological aspects of training in European basketball: Conceptualization, periodization, and planning. Sport Psychol. 2007, 21, 353–367. [Google Scholar] [CrossRef]
Kang, J.; Hoffman, J.R.; Chaloupka, E.C.; Ratamess, N.A.; Weiser, P.C. Gender differences in the progression of metabolic responses during incremental exercise. J. Sports Med. Phys. Fit. 2006, 46, 71. [Google Scholar]
Nuzzo, J.L. Narrative review of sex differences in muscle strength, endurance, activation, size, fiber type, and strength training participation rates, preferences, motivations, injuries, and neuromuscular adaptations. J. Strength Cond. Res. 2023, 37, 494–536. [Google Scholar] [CrossRef] [PubMed]
Goldfarb, A.H.; McKenzie, M.J.; Bloomer, R.J. Gender comparisons of exercise-induced oxidative stress: Influence of antioxidant supplementation. Appl. Physiol. Nutr. Metab. 2007, 32, 1124–1131. [Google Scholar] [CrossRef] [PubMed]
Clarkson, P.M.; Hubal, M.J. Are women less susceptible to exercise-induced muscle damage? Curr. Opin. Clin. Nutr. Metab. Care 2001, 4, 527–531. [Google Scholar] [CrossRef]

Table 1. Descriptive characteristics.

	TEAM 1 n = 9	TEAM 2 n = 10	TEAM 3 n = 9	p
AGE (years)	22.7 ± 5.9 ^a	24.8 ± 8.2 ^b	33.4 ± 6.2 ^a,b	0.007 *
HEIGHT (cm)	171 ± 0.04	168 ± 0.06	170 ± 0.04	0.562
WEIGHT (kg)	71.2 ± 9.5	62.9 ± 6.2	69.3 ± 9.5	0.102
FAT PERC (%)	23.43 ± 3.5	26.99 ± 5	24.14 ± 4.1	0.179

^a,b Significant differences between the teams. * Significant differences between the 3 teams before the pre-season period.

Table 2. Strength and conditioning training units.

1st WEEK	2nd WEEK	3rd WEEK	4th WEEK	5th WEEK	6th WEEK	Total TUs
TEAM 1
2 strength (60′ medium intensity) and 1 conditioning (30′ low intensity)	2 strength (60′ medium intensity) and 2 conditioning (30′ medium intensity)	3 strength (50′ high intensity) and 2 conditioning (40′ medium intensity and 30′ high intensity)	2 strength (50′ high intensity) and 3 conditioning (30′ high intensity × 2 and 40′ medium intensity)	2 strength (50′ high to medium intensity) and 2 conditioning (30′ high to medium intensity)	1 strength (50′ medium to low intensity) and 2 conditioning (20′ medium to low intensity)	24 (12 strength + 12 conditioning)
TEAM 2
2 strength (60′ medium intensity) and 1 conditioning (30′ low intensity)	2 strength (60′ medium intensity) and 1 conditioning (30′ medium intensity)	2 strength (50′ high intensity) and 2 conditioning (40′ medium intensity and 30′ high intensity)	2 strength (50′ high intensity) and 2 conditioning (40′ medium intensity and 30′ high intensity)	1 strength (50′ high to medium intensity) and 2 conditioning (30′ high to medium intensity)	1 strength (50′ medium to low intensity) and 2 conditioning (20′ medium to low intensity)	20 (10 strength + 10 conditioning)
TEAM 3
1 strength (60′ medium intensity) and 1 conditioning (30′ low intensity)	2 strength (60′ medium intensity) and 1 conditioning (30′ medium intensity)	2 strength (50′ high intensity) and 1 conditioning (30′ high intensity)	1 strength (50′ high intensity) and 2 conditioning (40′ medium intensity and 30′ high intensity)	1 strength (50′ high to medium intensity) and 2 conditioning (30′ high to medium intensity)	1 strength (50′ medium to low intensity) and 1 conditioning (20′ medium to low intensity)	16 (8 strength + 8 conditioning)

Table 3. First and sixth weeks’ analytical fitness protocols.

	1st WEEK	6th WEEK
	TEAMS 1 and 2
Monday	(1) Fitball planks 3 × 40 s. (2) Forward lunges on bosu 2 × 15 for each leg. (3) TRX back row 3 × 15. (4) Arm biceps with exercise band 3 × 20. (5) Fitball pull overs with dumbbell 3 × 12. (6) Smith squat 3 × 12-10-8. (7) Push-ups 3 × 12 (8). Shoulders press with dumbbells 3 × 10. (9) Abs exercises (upper-lower-side) 3 × 15. (10) Back exercises 3 × 15. + Basketball training	(1) Ladder drills 5′. (2) Nebraska agility cone drill 45″ × 5. (3) Wheel bag barrier drill 4 sets. (4) Four corner cone drill 40″ × 4. + Basketball training
Tuesday	Basketball training (morning) + Basketball training (afternoon)	(1) Wall squat 3 × 40 s. (2) Fitball planks 3 × 40 s. (3) TRX back row 3 × 15. (4) Single-leg RDL with dumbbells 3 × 10. (5) Dumbbell chest press on a fitball 3 × 12. (6) Bulgarian split squat with dumbbells 3 × 10. (7) Arnold press exercise 3 × 12-10-8. (8) Abs exercises (upper-lower-side) 3 × 15. (9) Back exercises 3 × 15. + Basketball training
Wednesday	(1) Ladder drills 5′. (2) Cone ladder drill 5-10-5 50″ × 5. (3) Four corner cone drill 45″ × 5. (4) Tap bag barrier drill 5 sets. (5) Change in pace cone drill 40″ × 4. + Basketball training	Basketball—friendly game
Thursday	Basketball training	Basketball training (morning) + Basketball training (afternoon)
Friday	Basketball training (morning) + Basketball training (afternoon)	(1) Ladder drills 5′. (2) Sprint and shuffle cone drill 40″ × 4. (3) Crossover and step barrier drill 4 sets. (4) V cone drill 35″ × 4. + Basketball training
Saturday	(1) Single-leg balance exercises on bosu 3 × 40 s. (2) Shoulder external and internal rotation with exercise band 3 × 15 for each arm. (3) Bosu squat 3 × 15. (4) Closed grip push-ups 3 × 12. (5) TRX front shoulder raises 3 × 15. (6) Bulgarian split squat with dumbbells 3 × 12. (7) Bench press 3 × 12-10-8. (8) Single-arm dumbbell back row 3 × 12. (9) Abs exercises (upper-lower-side) 3 × 15. (10) Back exercises 3 × 15. + Basketball training	Basketball training
Sunday	Rest	First game of the regular season
	TEAM 3
Monday	Basketball training	Basketball training
Tuesday	(1) Fitball planks 3 × 40 s. (2) Forward lunges on bosu 2 × 15 for each leg. (3) TRX back row 3 × 15. (4) Arm biceps with exercise band 3 × 20. (5) Fitball pull overs with dumbbell 3 × 12. (6) Smith squat 3 × 12-10-8. (7) Push-ups 3 × 12 (8) Shoulders press with dumbbells 3 × 10. (9) Abs exercises (upper-lower-side) 3 × 15. (10) Back exercises 3 × 15. + Basketball training	(1) Wall squat 3 × 40 s. (2) Fitball planks 3 × 40 s. (3) TRX back row 3 × 15. (4) Single-leg RDL with dumbbells 3 × 10. (5) Dumbbell chest press on a fitball 3 × 12. (6) Bulgarian split squat with dumbbells 3 × 10. (7) Arnold press exercise 3 × 12-10-8. (8) Abs exercises (upper-lower-side) 3 × 15. (9) Back exercises 3 × 15. + Basketball training
Wednesday	Basketball training (morning) + Basketball training (afternoon)	Basketball—friendly game
Thursday	Basketball training	Basketball training (morning) + Basketball training (afternoon)
Friday	(1) Ladder drills 5′. (2). Cone ladder drill 5-10-5 50″ × 5. (3) Four corner cone drill 45″ × 5. (4) Tap bag barrier drill 5 sets. (5) Change in pace cone drill 40″ × 4. + Basketball training	(1) Ladder drills 5′. (2) Sprint and shuffle cone drill 40″ × 4. (3) Crossover and step barrier drill 4 sets. (4) V cone drill 35″ × 4. + Basketball training
Saturday	Basketball training (morning) + Basketball training (afternoon)	Basketball training
Sunday	Rest	First game of the regular season

Table 4. Effects on somatometric characteristics.

		Before	After	Δ (p)	ES (95% CI)	T p (η_p²)	TG p (η_p²)	G P (η_p²)
Weight (kg)	Team 1 (Heavy)	71.2 ± 9.5	70.8 ± 9.1	−0.41 ± 0.66 (0.101)	−0.04 (−1.35, 1.26)	0.012 ^† (0.229)	0.722 (0.026)	0.100 (0.068)
	Team 2 (Moderate)	62.9 ± 6.2	62.6 ± 6.5	−0.26 ± 0.78 (0.325)	−0.04 (−1.28, 1.2)
	Team 3 (Light)	69.3 ± 9.5	68.8 ± 9	−0.56 ± 0.9 (0.103)	−0.06 (−1.36, 1.24)
	p	0.102	0.099
Fat Perc (%)	Team 1 (Heavy)	23.43 ± 3.5	23.13 ± 3.3	−0.3 ± 0.32 ^a (0.024) ^#	−0.09 (−1.39, 1.21)	<0.001 ^† (0.652)	<0.001 ^† (0.472)	0.264 (0.101)
	Team 2 (Moderate)	26.99 ± 5	25.7 ± 5	−1.29 ± 0.59 ^a,b (<0.001) ^#	−0.26 (−1.5, 0.99)
	Team 3 (Light)	24.14 ± 4.1	23.75 ± 3.6	−0.39 ± 0.55 ^b (0.067)	−0.1 (−1.41, 1.21)
	p	0.179	0.379

T, time; TG, time × group; G, group. ^{a, b} Significant differences between the teams. ^# Significant differences before and after the pre-season period for each team separately. ^† Significant interaction between the 3 teams (two-way repeated measures ANOVA).

Table 5. Effects on aerobic and anaerobic capacity.

		Before	After	Δ (p)	ES (95% CI)	T p (η_p²)	TG p (η_p²)	G p (η_p²)
Aerobic Capacity—VO2max (ml/kg/min)	Team 1 (Heavy)	36.74 ± 3.67	37.93 ± 3.12	1.19 ± 0.76 (0.002) ^#	0.35 (−0.97, 1.67)	<0.001 ^† (0.708)	0.561 (0.045)	0.772 (0.021)
	Team 2 (Moderate)	38.04 ± 3.97	39.07 ± 3.68	1.03 ± 0.73 (0.002) ^#	0.27 (−0.98, 1.51)
	Team 3 (Light)	37.79 ± 4.03	38.63 ± 4.02	0.83 ± 0.55 (0.002) ^#	0.21 (−1.1, 1.52)
	p	0.751	0.791
Anaerobic Capacity (Watts)	Team 1 (Heavy)	571.42 ± 29.7	602.22 ± 28.5	30.8 ± 11.3 ^a (<0.001) ^#	1.06 (−0.34, 2.45)	<0.001 ^† (0.876)	0.006 ^† (0.340)	0.657 (0.033)
	Team 2 (Moderate)	566.97 ± 29.4	589.14 ± 29.9	22.17 ± 9.15 (<0.001) ^#	0.75 (−0.54, 2.03)
	Team 3 (Light)	580.93 ± 23.8	596.42 ± 20.3	15.48 ± 5.91 ^a (<0.001) ^#	0.7 (−0.65, 2.05)
	p	0.549	0.572

T, time; TG, time × group; G, group. ^a Significant differences between the teams. ^# Significant differences before and after the pre-season period for each team separately. ^† Significant interaction between the 3 teams (two-way repeated measures ANOVA).

Table 6. Effects on speed, acceleration, explosiveness and vertical jumping ability.

		Before	After	Δ (p)	ES (95% CI)	T p (η_p²)	TG p (η_p²)	G p (η_p²)
Speed 20 m. (s)	Team 1 (Heavy)	3.67 ± 0.26	3.638 ± 0.25	−0.03 ± 0.03 (0.038) ^#	−0.13 (−1.43, 1.18)	<0.001 ^† (0.350)	0.726 (0.025)	0.625 (0.037)
	Team 2 (Moderate)	3.6 ± 0.22	3.58 ± 0.22	−0.02 ± 0.03 (0.088)	−0.09 (−1.33, 1.15)
	Team 3 (Light)	3.71 ± 0.3	3.69 ± 0.29	−0.02 ± 0.03 (0.094)	−0.07 (−1.38, 1.24)
	p	0.627	0.623
Acceleration 10 m. (s)	Team 1 (Heavy)	2.3 ± 0.12	2.284 ± 0.12	−0.015 ± 0.01 (0.015) ^#	−0.13 (−1.44, 1.18)	<0.001 ^† (0.410)	0.439 (0.064)	0.540 (0.048)
	Team 2 (Moderate)	2.27 ± 0.09	2.261 ± 0.09	−0.009 ± 0.01 (0.041) ^#	−0.1 (−1.34, 1.14)
	Team 3 (Light)	2.333 ± 0.15	2.325 ± 0.14	−0.008 ± 0.01 (0.133)	−0.06 (−1.36, 1.25)
	p	0.556	0.523
Explosiveness 5 m. (s)	Team 1 (Heavy)	1.452 ± 0.07	1.441 ± 0.07	−0.011 ± 0 (<0.001) ^#	−0.16 (−1.47, 1.15)	<0.001 ^† (0.686)	0.305 (0.091)	0.749 (0.023)
	Team 2 (Moderate)	1.439 ± 0.05	1.428 ± 0.04	−0.011 ± 0 (<0.001) ^#	−0.24 (−1.49, 1)
	Team 3 (Light)	1.461 ± 0.08	1.454 ± 0.07	−0.007 ± 0 (0.022) ^#	−0.1 (−1.4, 1.21)
	p	0.790	0.704
Vertical Jumping Ability (cm)	Team 1 (Heavy)	34.59 ± 3.26	36.87 ± 3.04	2.28 ± 1.32 (<0.001) ^#	0.72 (−0.63, 2.07)	<0.001 ^† (0.725)	0.334 (0.084)	0.686 (0.030)
	Team 2 (Moderate)	35.87 ± 2.54	37.55 ± 2.47	1.68 ± 1.14 (<0.001) ^#	0.67 (−0.6, 1.95)
	Team 3 (Light)	35 ± 2.83	36.47 ± 3.16	1.47 ± 1.05 (0.003) ^#	0.5 (−0.84, 1.81)
	p	0.618	0.714

T, time; TG, time × group; G, group. ^# Significant differences before and after the pre-season period for each team separately. ^† Significant interaction between the 3 teams (two-way repeated measures ANOVA).

Table 7. Effects on lower body part strength.

		Before	After	Δ (p)	ES (95% CI)	T p (η_p²)	TG p (η_p²)	G p (η_p²)
Quadriceps (N.m)	Team 1 (Heavy)	174.64 ± 15.87	178.3 ± 15.57	3.65 ± 2.41 (0.002) ^#	0.23 (−1.08, 1.54)	<0.001 ^† (0.688)	0.784 (0.019)	0.576 (0.043)
	Team 2 (Moderate)	180.09 ± 12.74	183.15 ± 11.73	3.06 ± 2.35 (0.003) ^#	0.25 (−0.99, 1.49)
	Team 3 (Light)	181.05 ± 11.06	184.01 ± 11.32	2.95 ± 2.09 (0.003) ^#	0.27 (−1.05, 1.58)
	p	0.552	0.605
Hamstrings (N.m)	Team 1 (Heavy)	95.22 ± 4.6	98.05 ± 3.7	2.83 ± 1.56 (<0.001) ^#	0.68 (−0.67, 2.02)	<0.001 ^† (0.698)	0.339 (0.083)	0.712 (0.027)
	Team 2 (Moderate)	96.28 ± 4	98.36 ± 3.73	2.08 ± 1.88 (0.007) ^#	0.54 (−0.72, 1.8)
	Team 3 (Light)	97.1 ± 2.14	98.86 ± 2.08	1.76 ± 1.03 (<0.001) ^#	0.83 (−0.53, 2.19)
	p	0.574	0.870

T, time; TG, time × group; G, group. ^# Significant differences before and after the pre-season period for each team separately. ^† Significant interaction between the 3 teams (two-way repeated measures ANOVA).

Table 8. Effects on muscle damage markers.

		Before	After	Δ (p)	ES (95% CI)	T p (η_p²)	TG p (η_p²)	G p (η_p²)
CK (U/L)	Team 1 (Heavy)	77.11 ± 10.86	159.11 ± 75.43	82 ± 77.63 (0.013) ^#	1.52 (0.04, 3.01)	<0.001 ^† (0.641)	0.366 (0.077)	0.353 (0.080)
	Team 2 (Moderate)	67.5 ± 18.1 ^a	138.1 ± 39.54	70.6 ± 26.32 (<0.001) ^#	2.29 (0.7, 3.89)
	Team 3 (Light)	101.11 ± 37.68 ^a	148 ± 43.2	46.88 ± 42.86 (0.011) ^#	1.16 (−0.26, 2.57)
	p	0.020 *	0.708
LDH (U/L)	Team 1 (Heavy)	134.22 ± 27.76 ^a	178.11 ± 56.07 ^a	43.88 ± 45.28 (0.020) ^#	0.99 (−0.39, 2.38)	<0.001 ^† (0.444)	0.093 (0.173)	<0.001 ^† (0.728)
	Team 2 (Moderate)	242 ± 38.81 ^a,b	260.4 ± 31.53 ^a,b	18.4 ± 24.74 (0.043) ^#	0.52 (−0.74, 1.78)
	Team 3 (Light)	133 ± 20.29 ^b	147 ± 27.49 ^b	14 ± 9.57 (0.002) ^#	0.58 (−0.75, 1.91)
	p	<0.001 *	<0.001 *

T, time; TG, time × group; G, group. ^{a, b} Significant differences between the teams. * Significant differences between the two teams before and after the pre-season period. ^# Significant differences before and after the pre-season period for each team separately. ^† Significant interaction between the 3 teams (two-way repeated measures ANOVA).

Table 9. Effects on oxidative stress markers.

		Before	After	Δ (p)	ES (95% CI)	T p (η_p²)	TG p (η_p²)	G p (η_p²)
PC (nmol/mg)	Team 1 (Heavy)	1.54 ± 0.7	1.29 ± 0.44	−0.25 ± 0.58 (0.288)	−0.43 (−1.75, 0.89)	0.139 (0.106)	0.469 (0.073)	0.359 (0.097)
	Team 2 (Moderate)	1.64 ± 0.5	1.66 ± 0.19	0.02 ± 0.56 (0.929)	0.05 (−1.19, 1.29)
	Team 3 (Light)	1.54 ± 0.31	1.27 ± 0.48	−0.27 ± 0.4 (0.124)	−0.67 (−2.01, 0.67)
	p	0.915	0.090
GPx (U/L)	Team 1 (Heavy)	107.78 ± 11.89	117.78 ± 17.16	10 ± 11.73 (0.034) ^#	0.68 (−0.67, 2.02)	0.008 ^† (0.270)	0.815 (0.018)	0.571 (0.048)
	Team 2 (Moderate)	115.13 ± 21.16	125.13 ± 23.21	10 ± 23.39 (0.266)	0.45 (−0.81, 1.71)
	Team 3 (Light)	110.33 ± 14.25	116.33 ± 14.78	6 ± 6.32 (0.022) ^#	0.41 (−0.91, 1.73)
	p	0.640	0.590

T, time; TG, time × group; G, group. ^# Significant differences before and after the pre-season period for each team separately. ^† Significant interaction between the 3 teams (two-way repeated measures ANOVA).

Table 10. Correlations between the differences (Δ) of the measured variables.

Variables r Correlation (p Value)	r Magnitude
CK (↑) and LDH (↑) 0.638 (<0.001)	Large
GPx (↑) and Speed 20 m. (↑) (Speed ↓) 0.408 (0.039)	Medium
Aerobic Capacity (↑) and Hamstrings Maximum Strength (↑) 0.413 (0.029)	Medium
Speed 20 m. (↓) (Speed ↑) and Acceleration 10 m. (↓) (Acceleration ↑) 0.912 (<0.001)	Large

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Share and Cite

MDPI and ACS Style

Mexis, D.; Nomikos, T.; Kostopoulos, N. Effect of Three Pre-Season Training Protocols with Different Training Frequencies on Biochemical and Performance Markers in Professional Female Basketball Players. Appl. Sci. 2025, 15, 1833. https://doi.org/10.3390/app15041833

AMA Style

Mexis D, Nomikos T, Kostopoulos N. Effect of Three Pre-Season Training Protocols with Different Training Frequencies on Biochemical and Performance Markers in Professional Female Basketball Players. Applied Sciences. 2025; 15(4):1833. https://doi.org/10.3390/app15041833

Chicago/Turabian Style

Mexis, Dimitrios, Tzortzis Nomikos, and Nikolaos Kostopoulos. 2025. "Effect of Three Pre-Season Training Protocols with Different Training Frequencies on Biochemical and Performance Markers in Professional Female Basketball Players" Applied Sciences 15, no. 4: 1833. https://doi.org/10.3390/app15041833

APA Style

Mexis, D., Nomikos, T., & Kostopoulos, N. (2025). Effect of Three Pre-Season Training Protocols with Different Training Frequencies on Biochemical and Performance Markers in Professional Female Basketball Players. Applied Sciences, 15(4), 1833. https://doi.org/10.3390/app15041833

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

Effect of Three Pre-Season Training Protocols with Different Training Frequencies on Biochemical and Performance Markers in Professional Female Basketball Players

Abstract

1. Introduction

2. Materials and Methods

2.1. Participants

2.2. Study Design

2.3. Intervention Protocols

2.4. Biochemical Assessment

2.5. Somatometric Assessment

2.6. Aerobic and Anaerobic Capacity Assessment

2.7. Speed, Acceleration, Explosiveness and Vertical Jumping Ability Assessment

2.8. Lower Body Part Strength Assessment

2.9. Statistical Analysis

3. Results

3.1. Descriptive Characteristics

3.2. Effect of Pre-Season Training on Body Composition

3.3. Effect of Pre-Season Training on Aerobic and Anaerobic Capacity

3.4. Effect of Pre-Season Training on Speed, Acceleration, Explosiveness and Vertical Jumping Ability

3.5. Effect of Pre-Season Training on Lower Body Part Strength

3.6. Effect of Pre-Season Training on CK, LDH

3.7. Effect of Pre-Season Training on PC and GPx

3.8. Correlations

4. Discussion

5. Practical Applications

Author Contributions

Funding

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Conflicts of Interest

References

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI