Are Contextual Factors Determinate to Weekly Training Load in Basketball? Analysis in a Professional Team

This study explores how contextual factors opponent level, match location, and previous game results influence weekly training load (WTL) in professional basketball.

Twelve male professional basketball players from the Spanish second division participated in this observational study. Training load metrics included total distance (TD), player load (PL), high-speed running distance (HSR), high-intensity accelerations (HI ACC), and high-intensity decelerations (HI DEC). Contextual factors such as match location (home vs. away), opponent level (high, medium, low), and match outcome (win vs. loss) were analyzed. All participants were fully informed about the purpose, risks, and potential benefits of the research and provided written informed consent for the collection of data for scientific purposes.

The study was approved by the Ethics Committee of the European University of Madrid (CIPI/18/195), and all procedures were conducted in accordance with the Declaration of Helsinki. No significant differences in training load metrics were found based on opponent level, match outcome, or match location.

Significant differences were observed in training load as match day approached, with reductions in load on the day before the match (MD-1) compared to earlier days. The lack of significant differences in WTL based on contextual factors suggests that professional basketball teams may employ a consistent training approach, focusing on overall season performance and minimizing injury risk rather than adjusting loads based on recent game outcomes or opponent level.

Key Words: Professional Basketball, External Load, Season Performance, Observational Study

Training load can be defined as the input that can be intentionally manipulated to achieve specific training adaptations (19). It facilitates the identification of the relationship between load and injury risk, provides insights into appropriate physiological adaptations, and helps assess the individual load tolerance of players (24, 33). Furthermore, load management enables practitioners to customize training load based on the unique physical demands of each player (26), ultimately enhancing overall performance (14).

The total load in basketball is represented by the sum of the load accumulated during all training sessions and the load imposed by the games played (36). Both training and game load can be influenced by various factors. In training, the use of diverse game-based drills, combined with technical and tactical content, can significantly affect the physical and psychological demands on basketball players (9, 28, 38). Additionally, playing position (3, 12) playing time (4, 27, 37), season phase (40), and overtime periods (30) have been shown to influence load. Moreover, research in soccer indicates that key contextual factors such as opponent level, match location, and game outcome can have a significant and impact on training load (10, 17, 22).

In basketball, there is limited research investigating the influence of opponent level, match location, and game outcome on weekly training load (WTL). Sansone et al. (27) demonstrated that the level of the upcoming opponent influenced weekly training loads, with higher loads observed when the next opponent was of a lower level compared to high and medium-level opponents. Conversely, contextual factors from the previous game, such as the game outcome and the level of the previous opponent, did not impact weekly training loads. Additionally, in a study of female basketball players, Piñar et al. (25) reported that players experienced significantly higher weekly loads after winning a game compared to losing one, and that the lower the level of the previous opponent, the higher the subsequent training load. The discrepancies in study results highlight the need for further research to better understand the influence of these factors on training load, particularly in professional basketball. Despite the significant effect that home-court advantage has on game outcomes (1, 2) there is currently no data on how match location affects WTL.

Therefore, this paper aims to investigate how opponent level, match location, and previous game results influence the WTL of professional basketball players. Additionally, it seeks to provide insights that could lead to more effective and individualized training load management, ultimately enhancing performance.

Subjects
Twelve male professional basketball players from the Spanish second division participated in this observational study (mean age: 26.3±3.8 years; mean height: 196.7 cm±8.8; mean body mass: 91.5 kg±10.1). Players were required to participate in at least 80% of the training sessions to be included in the study (29)). All participants were fully informed about the purpose, risks, and potential benefits of the research and provided written informed consent for the collection of data for scientific purposes. The study was approved by the Ethics Committee of the European University of Madrid (CIPI/18/195), and all procedures were conducted in accordance with the Declaration of Helsinki.

Procedures
Measurements were conducted during the second half of the 2022-2023 regular season, spanning from February to June. A total of 46 basketball sessions were recorded over a 13-week period. All training sessions were designed and organized by the coaching staff. To ensure comprehensive data collection, warm-up activities were included in the analysis, allowing for results that represent the entire training session. The team typically held approximately five basketball sessions per week (~5 times·wk-1), alongside three strength training sessions (~3 times·wk-1), and played one game per week. Prior to each training session, players wore a vest equipped with a GPS device positioned on their upper back.

Training Workload
Weekly load was measured using several metrics: total distance (TD) in meters (m), player load (PL), high-speed running distance (HSR) above 18 km·h-1 (15), the number of high-intensity accelerations (HI ACC), and the number of high-intensity decelerations (HI DEC) exceeding 3.5 m·s-2 (34). Player load (PL) is a metric that quantifies the total body load on athletes across three axes vertical, anterior-posterior, and medial-lateral. This variable is widely used to assess neuromuscular load in various player positions (16).

Each weekly block consists of the match day (MD) and four training days leading up to it: match day minus 4 (MD-4), which is training held four days before the match, followed by MD-3, MD-2, and MD-1 (8).

The external load data were captured using the WIMU PRO™ system (Realtrack Systems S.L., Almería, Spain), which has been validated for test-retest reliability (%TEM: 1.19), inter-unit reliability (bias: 0.18), and intraclass correlation coefficients (ICC) of 0.65 and 0.88 for the x and y coordinates, respectively (5). The system incorporates four 3-axis accelerometers, a gyroscope, a 3D magnetometer, a barometer (sampled at 100 Hz), and an ultra-wideband positioning system (sampled at 18 Hz). For each training session, antennas were consistently positioned in the same locations, with sequential activation, and the master antenna was always activated last (32). SPROTM software (version 950, RealTrack Systems, Almería, Spain) was used for GPS data analysis. The data were then exported to Microsoft Excel, where the analysis was conducted.

Contextual Factors
Three independent variables were examined in this research. In line with previous studies the contextual variable of match location was categorized as either home or away (21, 27). For opponent level, physical performance was analyzed based on whether the team played against highly successful teams (ranked in the top 6 league positions=high), moderately successful teams (ranked 7th to 12th=medium), or less successful teams (ranked in the bottom 6=low). These categories are consistent with prior research (8, 27). The final variable, match outcome, was classified as win or loss.

Statistical Analysis 
Data are presented as the mean±standard deviation (±SD). Before using parametric tests, the assumption of normality was verified using Kolmogorov–Smirnov. Subsequently, comparisons were made between the categorical groups using Student’s t-test for independent samples when the assumption of normality was met (p>0.05), and the Mann-Whitney U test when it was not met (p<0.05), considering the variables Result, Previous result, and Location. Likewise, a one-factor analysis of variance (ANOVA) was carried out for the TD, PL, and HSR variables, based on the Day Code and Opponent Ranking factors. For the HI ACC and HI DEC variables, where normality was not assumed, the non-parametric Kruskal-Wallis test was applied. When a significant p-value was found, Tukey post hoc tests were applied. The level of statistical significance was set at p<0.05. Analyses were performed using SPSS for Mac version 24.0 (SPSS Inc., Chicago, IL, USA).

In the obtained results, no significant differences were found (p>0.05) for the variables TD, PL, HSR, HIACC, and HIDEC when comparing the categories of location, result, and previous result (table 1).

However, in relation to the variable match day, significant differences were identified (p<0.005) in all variables of workload training on day MD-1 compared to MD-2, MD-3, and MD-4 (figure 2). Additionally, for the variable HSR (p=0.002), further significant differences were found between days MD-2 and MD-4 (figure 2).

Regarding the variable Opponent Ranking, table 2 shows that no significant differences were found in any of the analyzed variables (p>0.05).

The purpose of this study was to assess the impact of contextual factors on WTL in professional male basketball players. To our knowledge, this is the first investigation specifically focusing on how factors such as the opponent’s level, match outcome, and location influence WTL in this population.

In football, studies have shown lower training loads in preparation for high-level opponents due to the perceived difficulty (8), while higher loads were associated with lower-level opponents in basketball (27). Contrary to these findings in football and semi-professional basketball, we did not observe significant differences in WTL based on opponent level. This inconsistency could stem from differences in coaching philosophies and preparation strategies. Coaches might emphasize consistent load management regardless of the opponent, focusing more on overall season performance rather than fluctuating preparation levels (27). Furthermore, when playing against lower-level teams, less external load is expected, and the likelihood of winning is higher (25). Moreover, Piñar et al. (25) found that in female basketball, medium-level opponents may induce more evenly matched games, requiring greater preparation. The lack of observed variation in our study could imply that professional teams apply a standardized training approach to minimize the risk of overexertion or injury (8), particularly as the season progresses and playoff qualification becomes a priority. Although the physical demands of games change depending on the opponent’s level (20), with increasing demands for HSR and high-intensity actions (23), the absence of differences in WTL based on opponent level can be explained by different coaching styles and the continually evolving team rankings (25).

Previous research on the influence of match outcomes on WTL in soccer showed that higher loads were observed after a loss (6). Conversely, Piñar et al. (25) found that the highest WL occurred after a win. These findings are significant, as game results can impact the physical demands on players (13). In our study, we found no such differences, which aligns with research in basketball and football (10, 27). The variation in results could be attributed to differences in coaching philosophies and psychosocial factors (31). The absence of differences related to opponent level suggests that the coaches in our study took a forward-looking approach, focusing on upcoming game factors when designing training (27), especially since it was the latter part of the season, with playoffs approaching. Rather than using increased training loads as punishment for losses, training loads should be planned based on technical and tactical considerations (25).

Home-court advantage is a well-documented phenomenon across sports, with research in both football (8) and basketball (7) showing that home games typically result in higher performance metrics and more wins, especially for lower-level teams (2). However, our study did not find significant differences in WTL based on match location. One possible explanation for this disparity is that training strategies may not be heavily influenced by whether a game is played at home or away, particularly as higher-level teams tend to perform well in both home and away venues (2). Coaches might implement consistent training plans that focus on long-term athlete development and conditioning, rather than adjusting based on location. Additionally, the findings of (13), which demonstrated higher physical loads during away games, could be due to increased travel demands and unfamiliar surroundings. However, modern professional teams often employ sophisticated travel and recovery management strategies to mitigate the negative effects of travel, which might explain the lack of observed differences in our study. This further emphasizes the importance of integrating travel management with training load planning (18).

A significant decrease in training load as match day approaches, or tapering, was evident in our study. This tapering strategy has been previously documented in elite basketball (35), and its role in optimizing performance is well understood. By reducing load in the final days before a game, players can recover and adapt, resulting in better performance on game day (39). The clear tapering trend seen in our data emphasizes the importance of load management, especially as a means to prevent injuries and enhance recovery. This finding highlights the need for strategic planning across the microcycle. Coaches appear to prioritize rest and recovery leading up to match day, particularly later in the season when cumulative fatigue and injury risk are higher (11).

In the current study, several limitations must be acknowledged. Firstly, total training load consists of both external and internal components; however, only external load data were collected, limiting our ability to fully evaluate the training responses and adaptations in professional players (26). Additionally, the absence of official game data restricts our understanding of the actual game load and its contextual influences on the WTL.

Moreover, the lack of individual player monitoring may have influenced our findings, as training load responses can vary based on physiological and tactical factors (9). Different training philosophies may yield different results, limiting their generalizability. Despite these constraints, our findings highlight the impact of small changes in game preparation on performance. Future research should integrate individual monitoring and varied training approaches to refine load-management strategies.

In conclusion, this study found no significant differences in weekly training load (WTL) based on contextual factors such as opponent level, match location, and previous game results in professional basketball players. These findings suggest that, in contrast to other sports, training loads in professional basketball may not be significantly influenced by factors like the level of the opponent or match outcomes. The results could reflect the proactive approach of coaching staff, particularly later in the season, focusing on technical and tactical preparation for upcoming matches rather than adjusting training loads based on previous results.

Furthermore, the consistency of professional players’ performance, regardless of match location, may contribute to the lack of significant differences in WTL by location. These insights highlight the importance of individualized and strategic load management practices to optimize performance and reduce injury risk in professional basketball. Further research is needed to explore how other contextual variables, such as player fatigue or team dynamics, may impact training loads in this setting.

Conflict of Interest
The authors have no conflict of interest.

Ethical Approval
All participants were fully informed about the purpose,
risks, and potential benefits of the research and provided written informed consent for the collection of data for scientific purposes. The study was approved by the Ethics Committee of the European University of Madrid (CIPI/18/195), and all procedures were conducted in accordance with the Declaration of Helsinki.

Acknowledgments
All authors are grateful to basketball players, for participation in study and coaching staff for collaboration.

Weekly training load (WTL) in professional basketball is influenced by multiple factors, yet this study found no significant differences based on opponent level, match location, or previous game results. Training loads remained consistent, suggesting a structured and standardized approach by coaching staff, independent of contextual factors. However, a significant tapering effect was observed, with training loads decreasing as match day approached, highlighting the importance of strategic load management to optimize performance and recovery.