The research design for this study is a quantitative experimental approach that uses a controlled trial to investigate the impact of structured training loads on new basketball athletes. A comparison of the two-month period’s changes in performance attributes between an experimental group and a control group can be made possible by this approach.

The study started with the selection of young basketball players who are representative of different playing positions and have been on a team for one to three years. To find athletes that fit the inclusion requirements and ensure a representative sample that can offer pertinent data for the study, a purposive sampling technique was employed. To get baseline data, an initial evaluation was given to each of the forty male basketball players who fit the requirements. In order to record the respondents’ age, playing experience, and current position, a profile of them will be created during this phase. After that, the athletes underwent preliminary testing to assess their technical abilities (like throw and free throw accuracy) as well as their physical characteristics (such running speed and jumping height). Additionally, the Cooper Test was used to evaluate general physical fitness.

In order to reduce selection bias, athletes were randomly allocated using simple random sampling to two groups of twenty players each after the initial assessment. The control group followed the team’s standard operating procedures and routines by attending the regular training sessions. On the other hand, the experimental group engaged in a two-month basketball training program that was specifically created and organized into four Macrocycles, each having a pattern of four-1 microcycles. The training regimen’s intensity and attention were adjusted in this pattern to maximize performance improvements.

Throughout the two months, the study employed rigorous observation and data collection methods. Training loads for both groups were closely monitored using training logs. These logs recorded the details of each training session, including the type of exercise, duration, intensity from the athletes. Performance outcomes were assessed through on-court drills and tests to objectively measure improvements and responses to the training loads.

At the end of the two-month period, final testing was conducted, mirroring the initial tests to measure any improvements or changes in the athletes’ physical and technical capabilities. The data from both testing periods were compared to evaluate the effectiveness of the training programs.

The collected data was statistically analyzed to draw difference between the training loads and performance outcomes. The analysis seeks to determine if the specialized training program provides a significant improvement in performance compared to the control group’s standard training.

This experimental research design, rooted in the comparison of a control and an experimental group, enabled a robust investigation into the efficacy of structured training programs for new basketball athletes. The findings will contribute valuable insights into sports science, with the potential to impact training protocols and athlete development programs.

The study was conducted in Anhui University of Chinese Medicine where the researcher is a faculty member who specializes in teaching basketball. The researcher made use of its locality also as a coach of the Anhui University of Chinese Medicine Basketball Team. Purposive sampling was used in the conduct of the study. Respondents are members of the Anhui University of Chinese Medicine Basketball Team who have been part of a team for one to three years and are representative of various playing positions. Simple random sampling was also applied in determining the control and experimental groups. Researchers can choose a subset of people from a broader population using this basic statistical sampling strategy, which ensures that any potential sample of the desired size has an equal chance of being chosen.

This method is used with the justification that it reduces bias in the process of choosing the control and experimental groups, which is essential to the validity of the experimental studies. With the exception of the intervention—the customized training program created for the experimental group, the study seeks to establish groups that are equivalent in every way by offering every member of the population an equal chance of being included in the sample.

This study intentionally chooses to use simple random sampling in order to improve the experimental design’s scientific rigor. As it addresses any selection bias and encourages the equal distribution of any confounding variables among the control and experimental groups, it is a fundamental component that upholds the validity of the study’s conclusions. By using this method, the study hopes to offer solid and trustworthy findings regarding how training loads affect novice basketball players.

The experimental group used a 4-1 micro cycle during the training period, which consists of 4 working days and 1 day off. A total of 4 macrocycles were performed in the period of 2 months. During the training period, basketball players were observed to determine the level of their physical and technical preparedness before and after training. The following tests were used:

The training load will be monitored using the sRPE method, which researchers have shown to be a valid, reliable, inexpensive, and very simple method for monitoring the training load in various exercise activities ( Foster et al., 2021 ; Singh, Foster, Tod, & McGuigan, 2007 ; Williams, Trewartha, Cross, Kemp, & Stokes, 2016 ), as well as in team sport settings ( Impellizzeri, Rampinini, Coutts, Sassi, & Marcora, 2004 ; Lambert & Borresen, 2010 ). The RPE data was collected for 15 - 30 minutes following each training or game, which was suggested to be the best timeframe by Singh et al. (2007) . To obtain sRPE values, the RPE grade (1-10) will be multiplied by the duration of a training session. The sRPE method will be applied after all training sessions. The researcher made use of the following training programs, each designed for the experimental and control groups.

All the players were familiarized with the use of the scale during the preparatory period. Lastly, the researcher used a Likert-scale questionnaire to determine the players’ satisfaction, from both the experimental and the control groups, on the program they have been under to during the eight-week training program.

The data gathered on the profile of the respondents, which includes age, length of service as a player in the team and current playing position in the team. Table 1 presents the tabular summary of the 20 players who belong to the experimental group.

The average age of the players in the experimental group is 18.4 years, indicating that the group primarily consists of late adolescents and young adults. The standard deviation of age is approximately 1.67, suggesting a moderate spread in the ages of the players. The youngest player is 16 years old, and the oldest is 21 years old. Half of the players (50^th percentile or median) are 18.5 years old or younger, indicating a slightly younger demographic in the upper half of the group.

The average length of service in the team is 2 years, reflecting a balance between relatively new and more experienced players within the team. The standard

deviation for length of service is around 0.79, pointing to a reasonable spread in the years of experience among the players. The duration of the service is one to three years. There is a good mix of experience levels among the players; half have served for up to two years, and the other half have served for longer.

The players’ positions on the playing field are distributed reasonably evenly. With five players apiece, point guards and power forwards are the most prevalent positions. Four players represent small forwards, three men each for shooting guards and centers. This distribution suggests that different basketball roles are fairly represented, which is crucial for an in-depth analysis of training loads.

The distributions of ages and lengths of service show that the experimental group, which included both novice and seasoned players, is a fair representation of young basketball athletes. Examining the effects of training loads on players at various developmental and experience levels requires this diversity. The study benefits from the relatively uniform distribution of playing positions. It guarantees that the results might be used too many playing roles, each of which might have distinct physical and skill requirements. The experimental group’s demographic composition offers a strong starting point for evaluating the impact of the training loads. The diversity of players’ ages, backgrounds, and positions on the court should strengthen the study’s conclusions and provide a more complex picture of how training influences players at various phases of their basketball and athletic development.

The age, duration of service, and playing positions of the participants are broken out in depth in the frequency distribution table for the experimental group’s demographics in the study “Training Loads Among New Basketball Athletes,” as shown in Table 2 .

The gamers in the experimental group span late childhood to early adulthood, with ages ranging from 16 to 21. The proportion of participants is evenly distributed across the age range, with 20% (4 players) of the group belonging to each of the age groups of 16, 18, 19, and 20. This displays a fair sample of both younger and more experienced athletes. With only two players, the ages of seventeen and twenty-one are underrepresented. The reduced presence of these age groups may indicate that during the sampling frame, fewer players at these ages were participating in or beginning the sport. The study’s findings can be applied to a wide range of ages commonly found among beginning basketball players because the main age groups are equally represented.

The team members have been there for one to three years. Thirty percent (6 players) of the group are players with one year and three years of service, showing a solid balance between relatively new and more seasoned players. The largest group of players, or eight players, comprise those with two years of service. This suggests that the second year is a critical period for the growth and continuation of athletes on the squad. This distribution makes it possible for the study to investigate the response of players with different service durations to training loads, which is essential for comprehending how experience affects training adaption.

There is a good balance and diversity in the distribution of playing positions. With 25% (5 players) each, point guards and power forwards are the positions with the most representation in the group. This means that positions that are critical to both offense and defense should be prioritized. Shooting guards and centers each make up 15% of the group, with small forwards making up 20% (4 players). A thorough analysis requires a balanced representation of players at all positions on the court in order to comprehend the effects of training loads on players in diverse roles.

The demographic frequency distribution of the experimental group indicates a well-balanced composition with respect to age, experience level, and position on the field. This variety is advantageous to the research since it can shed light on how various player groups react to varying training loads. The study’s findings are applicable to a typical range of ages for beginning basketball players due to the balanced age distribution. The range of service durations demonstrates how athletes at various phases of their athletic development are included on the squad. Last but not least, the variety of playing positions enables position-specific analysis, an essential ability in a sport like basketball where various roles have varying physical requirements. This comprehensive demographic makeup strengthens the potential impact and relevance of the study’s outcomes.

Table 3 presents the tabular summary of the 20 players who belong to the control group.

The average age of players in the control group is 18.3 years. The ages range from 16 to 21 years, similar to the experimental group, with a standard deviation of approximately 1.69. This indicates a comparable spread across the age range. The 25^th percentile is at 17 years, the median (50^th percentile) is at 18 years, and the 75^th percentile is at 20 years. Half of the players are thus between the ages of 17 and 20.

The average length of service among the players in the control group is 2 years, which is identical to the experimental group. The lengths of service range from 1 to 3 years, with a standard deviation of approximately 0.86. This indicates a slightly higher variability in the years of service compared to the experimental group. The median length of service is 2 years, with half of the players having 1 to 3 years of service, showing a balanced distribution of experience in the group.

The distribution of playing positions in the control group is as follows: Point Guard and Small Forward positions have the highest representation, each with 5 players (25% each). The positions of center and shooting guard are occupied by four players each (20% each). With two players (10%), power forward is the position with the lowest representation.

An important consideration for analyzing the effects of conventional training loads at different ages is the age distribution within the control group, which represents a wide variety of developmental stages. In comparison to the experimental group, there is a minor skew towards younger players in the distribution, with a higher percentage of 16-year-olds.

Similar to the experimental group, the length of service implies that there is a healthy variety of experience levels, from relatively new to more seasoned participants. Valid comparison between the two groups in the study requires that the control group have a parallel structure in terms of player experience, which is supported by the data.

With the exception of the Power Forward position, which is underrepresented in the control group relative to the experimental group, the playing position distribution exhibits a rather balanced representation across several roles. It’s possible that the usual training program places more emphasis on speed and agility given the higher proportion of Point Guards and Small Forwards.

The age distribution of the control group is pretty even, with 20% of the group being 16 and 18 years old. Teenagers aged 17 and 19 make up 15% of the group, while those aged 20 make up 20% once again. The age group with the lowest representation, at 10%, is 21. According to this distribution, the control group encompasses a wide variety of developmental phases, from the middle of adolescence to the end.

In terms of service duration, the group is slightly more skewed toward the extremes, with those with 1 - 3 years of service accounting for 35% of the total, and those with 2 - 3 years of service for 30%. This points to a bimodal distribution in which a sizable portion of players are either seasoned veterans of the game or relatively fresh to competitive basketball. This intriguing combination could shed light on how individuals at various phases of their athletic development are affected by training loads in a team environment ( Table 4 ).

There is a wide range of playing positions represented in the group; small forwards and point guards account for 25% of each. This suggests that players who are usually more engaged in ball handling and perimeter play are well-represented. Twenty percent of the group are Shooting Guards and Centers, which guarantees a strong representation of the jobs that usually call for shooting ability and an inside presence. At under 10%, the Power Forward position is the least common, which may restrict the applicability of findings to this position but also represents a normal variation in team makeup.

The control group’s age distribution shows a healthy range that is appropriate for evaluating the effects of typical training loads at various phases of maturity. The age distribution is such that studying the complex impacts of training in a youth to young adult cohort is made easier by the slightly lower representation at the oldest end of the age spectrum.

The players who are taking on leadership roles with more experience and those who are probably just getting to know the team’s processes make up the length of service distribution, which makes it more important. The study will be able to identify possible changes in adaptation to training loads based on team tenure because of the even distribution at the 1- and 3-year marks, with a slightly smaller group at the 2-year point.

With a solid mix of centers, guards, and forwards, the playing positions represented in the control group offer a cross-section of the team dynamics. This balance is essential since the physical and technical requirements for each position varies, and the impact of the training load may also change.

In conclusion, the control group’s frequency distribution of demographic data provides a thorough overview of what is typical in a basketball team environment. The range of playing positions and differences in age and experience offer a strong foundation for comparison with the experimental group. Additionally, it guarantees that the study’s conclusions have a wide range of applications, possibly influencing how different basketball team roles are trained.

1) Level of the Players’ Physical and Technical Preparedness before and After Training

Table 5 provides a comprehensive overview of the training load and performance metrics for players 1 through 20 in the experimental group. The data include total time spent in training (in minutes), total Rated Perceived Exertion (RPE), total load (sRPE), and their respective averages.

duration of the training session. It provides an estimate of the training load. The total sRPE ranges from 7965 (Player 2) to 11,115 (Player 20), highlighting differences in perceived training intensity among players. Players 20, 14, 16, 19, 7, and 17 have the highest total loads, suggesting they experienced the most intense training regimens.

The variance in sRPE and RPE among players points to variations in how players experience and manage training-related stress. Fitness levels, recuperation, mental toughness, and individual characteristics may all have an impact on this. Given that players react differently to varied training volumes and intensities, the statistics point to the necessity of personalized training approaches. Comprehending these metrics can aid coaches and trainers in optimizing training regimens, guaranteeing that athletes experience challenge without undue strain, and aiding in the identification of players who might require extra assistance or recuperation measures. Table 6 provides important information about the training loads and perceived effort of the players in the experimental group, to sum up.

A detailed statistical analysis of the training load for players 1 through 20 in the basketball study’s control group is given in Table 6 . This data includes the entire load (Session RPE or sRPE), total Rated Perceived Exertion (RPE), and the overall amount of time spent training (in minutes).

Total Training Time: The 4720 minutes all players receive in training are the same. This consistency shows that the group’s training length is consistent, guaranteeing that each participant gets the same amount of time to train.

Total and Average RPE: A person’s subjective perception of how hard they are working during an activity is measured using the Rated Perceived Exertion (RPE) scale. It’s an important measure of perceived effort and varies greatly from person to person. Players’ cumulative RPE scores range from 186 (Player 1) to 308 (Player 16), with significant differences between them. This variance shows that players’ perceptions of the same training regimen’s intensity vary. Additionally variable is the average RPE, with Player 1 having the lowest RPE (3.88) and Player 16 having the highest RPE (6.42). Higher RPEs may suggest a higher perceived exertion during training, while lower average RPEs may suggest a lesser perceived effort or higher physical conditioning.

Session RPE (sRPE) is computed by multiplying the RPE by the length of the training session, yielding the total and average sRPE. It offers a rough assessment of the training load. Players 1 through 16 had total sRPE values ranging from 18,240 to 30,100, indicating notable variations in how intensely they perceive their training. Additionally, the average sRPE varies per session, ranging from 380.00 (Player 1) to 627.08 (Player 16). This shows that even if each participant receives the same amount of training time, they all perceive a different amount of training.

Players may perceive and react to training intensity extremely differently even though they are following a similar training routine in terms of duration, based on the significant disparities in RPE and sRPE. Individual fitness levels, mental toughness, and physiological reactions to exercise may all have an impact on this.

Coaches and trainers must comprehend these metrics. It enables them to more successfully adapt training programs to the demands of specific individuals. For example, players with greater sRPE and RPE may need to pay more attention to fatigue management and recovery, whereas players with lower scores may be able to play at a higher intensity or volume. The information emphasizes how crucial it is to personalize training plans in a group environment. Based on each player’s RPE and sRPE values, modifications might be made to maximize training benefits and lower the chance of overtraining or injury.

It’s possible that players with lower RPE and sRPE ratings are fitter and need less work to finish the same training program as other players. On the other hand, players with higher scores may find the training more difficult, either because they are less fit or because they put in more effort throughout the sessions.

To sum up, the statistical examination of the control group’s training load provides important information on individual variations in the perceived level of training. This knowledge is essential for creating customized training plans that take into accounts the distinct psychological and physiological reactions of every athlete, eventually increasing the program’s efficacy.

2) Difference in the Initial and Final Tests of the Control Group

The findings of paired t-tests used to determine whether test scores for different performance measures in the control group differed significantly at first and at the end are shown in Table 7 . These metrics include the Cooper Test results, throwing performance (throws out of 40), free throw accuracy (free throws out of 30), jumping height, running endurance (running 3 × 40 sec), and running timings at 6 and 20 meters.

The 6-meter running times for the control group did not significantly change between the first and final tests, according to the p-value of 0.522. This suggests that there was little variation in the short-distance run’s performance over time. With a p-value of 0.177, there is no significant difference in the 20-meter running times between the initial and final tests. This suggests that the participants’ performance in the 20-meter run remained fairly consistent.

The negative t-statistic and a p-value of 0.697 indicate that there is no significant difference in jumping height between the initial and final tests. This implies that the control group’s ability to jump did not change significantly. The p-value of 0.258 suggests no significant difference in the endurance running performance measured by Running 3 × 40 sec between the initial and final tests. Endurance levels remained relatively stable over time. The negative t-statistic and a p-value of 0.545 indicate that there is no significant difference in the throwing performance between the initial and final tests. Participants’ throwing abilities remained consistent.

The p-value of 0.404 suggests that there is no significant difference in free throw accuracy between the initial and final tests. The control group’s performance in free throws did not change significantly. The p-value of 0.698 indicates no significant difference in the results of the Cooper Test between the initial and final tests. This suggests that participants’ aerobic endurance levels remained consistent over time.

3) Difference in the Initial and Final Tests of the Control Group

Table 8 summarizes the results of the paired t-tests conducted on various performance measures for the experimental group. The t-statistic, p-value, and whether a significant difference was observed, are provided for each measure.

Running at 6 m (sec) and running at 20 m (sec) both showed a significant improvement in running times, with very low p-values indicating high statistical significance. Jumping Height (cm) demonstrated a significant increase in jumping height, with a p-value of 0.000135. Running 3 × 40 sec (m) showed a significant improvement in endurance, with a p-value of 0.00599.

Throws out of 40 exhibited a significant improvement in throwing performance, with a p-value of 0.0351. Free Throws out of 30 indicated a significant improvement in free throw performance, with a p-value of 0.00404. Cooper Test (m) displayed a very significant improvement in aerobic capacity and endurance, with an extremely low p-value of less than 0.000001 (2.945e−10).