The Association Between Training Load and Performance in Team Sports: A Systematic Review

Intensity Zones and Intensity Thresholds Used to Quantify External Load in Competitive Basketball: A Systematic Review

BACKGROUND

Despite widespread use of intensity zones to quantify external load variables in basketball research, the consistency in identifying zones and accompanying intensity thresholds using predominant monitoring approaches in training and games remains unclear.

OBJECTIVES

The purpose of this work was to examine the external load intensity zones and thresholds adopted across basketball studies using video-based time-motion analysis (TMA), microsensors, and local positioning systems (LPS).

METHODS

PubMed, MEDLINE, and SPORTDiscus databases were searched from inception until 31 January 2023 for studies using intensity zones to quantify external load during basketball training sessions or games. Studies were excluded if they examined players participating in recreational or wheelchair basketball, were reviews or meta-analyses, or utilized monitoring approaches other than video-based TMA, microsensors, or LPS.

RESULTS

Following screening, 86 studies were included. Video-based TMA studies consistently classified jogging, running, sprinting, and jumping as intensity zones, but demonstrated considerable variation in classifying low-intensity (standing and walking) and basketball-specific activities. Microsensor studies mostly utilized a single, and rather consistent, threshold to identify only high-intensity activities (> 3.5 m·s-2 for accelerations, decelerations, and changes-in-direction or > 40 cm for jumps), not separately quantifying lower intensity zones. Similarly, LPS studies predominantly quantified only high-intensity activities in a relatively consistent manner for speed (> 18.0 m·s-1) and acceleration/deceleration zones (> 2.0 m·s-2); however, the thresholds adopted for various intensity zones differed greatly to those used in TMA and microsensor research.

CONCLUSIONS

Notable inconsistencies were mostly evident for low-intensity activities, basketball-specific activities, and between the different monitoring approaches. Accordingly, we recommend further research to inform the development of consensus guidelines outlining suitable approaches when setting external load intensity zones and accompanying thresholds in research and practice.

The impact of internal and external loads on player performance in Chinese basketball association

BACKGROUND

Limited research has investigated the association between training load and performance of basketball players during games. Little is known about how different indicators of player performance are affected by internal and external loads. The purpose of this study was to determine whether external and internal loads influence basketball players' performance during games.

METHOD

This longitudinal study involved 20 professional male basketball players from a single team, classified as first-level athletes by the Chinese Basketball Association. During 34 games, external load was measured as PlayerLoad using micro-sensors, while internal load was assessed using session rating of perceived exertion (sRPE). Player performance was quantified using three metrics: Efficiency, Player Index Rating (PIR), and Plus-Minus (PM). Pearson correlation coefficients were calculated to assess the strength of the relationships between training loads and performance metrics. Linear mixed-effects models were applied to further analyze the influence of internal and external loads on basketball performance.

RESULTS

Pearson correlation analysis revealed moderate positive correlations between both sRPE and PlayerLoad with Efficiency and PIR. Specifically, sRPE (r = 0.52) and PlayerLoad (r = 0.54) were both significantly correlated with Efficiency. For PIR, sRPE (r = 0.50) and PlayerLoad (r = 0.56) also demonstrated moderate correlations. These correlations were further substantiated by linear mixed-effects models, which showed that sRPE (β = 2.21, p < 0.001) and PlayerLoad (β = 1.87, p = 0.004) had significant independent effects on Efficiency. Similarly, sRPE (β = 2.15, p < 0.001) and PlayerLoad (β = 2.36, p < 0.001) significantly predicted PIR. Additionally, a significant interaction effect between PlayerLoad and sRPE was found on Plus-Minus (β = -2.49, p < 0.001), indicating that the combination of high physical and psychological loads negatively impacted overall team performance. However, the correlation strengths for Plus-Minus were relatively low (sRPE: r = 0.16; PlayerLoad: r = 0.10).

CONCLUSION

Both external and internal loads positively contribute to performance, the integration of objective (accelerometry) and subjective (sRPE) measures of load provides a comprehensive understanding of the physiological and psychological demands on athletes, contributing to more effective training regimens and performance optimization.

Player Activity and Load Profiling with Hidden Markov Models: A Novel Application in Rugby League

Player movement in rugby league is complex, being spatiotemporal and multifaceted. Modeling this complexity to provide robust measures of player activity and load has proved difficult, with important aspects of player movement yet to be considered. These include the influence of time-varying covariates on player activity and the combination of different dimensions of player movement. Few studies have simultaneously categorized player activity into different activity states and investigated factors influencing the transition between states, or compared player activity and load profiles between matches and training. This study applied hidden Markov models (HMMs)-a data-driven, multivariate approach-to rugby league training and match GPS data to i) demonstrate how HMMs can combine multiple variables in a data-driven way to effectively categorize player movement states, ii) investigate the influence of two time-varying covariates, score difference and elapsed match time on player activity states, and iii) compare player activity and load profiles within and between training and match modalities. HMMs were fitted to player GPS, accelerometer and heart rate data of one English Super League team across 60 training sessions and 35 matches. Distinct activity states were detected for both matches and training, with transitions between states in matches influenced by score difference and elapsed time and clear differences in activity and load profiles between training and matches. HMMs can model the complexity of player movement to effectively profile player activity and load in rugby league and have the potential to facilitate new research across several sports.

Associations Between Physical Characteristics and Golf Clubhead Speed: A Systematic Review with Meta-Analysis

BACKGROUND

Historically, golf does not have a strong tradition of fitness testing and physical training. However, in recent years, both players and practitioners have started to recognise the value of a fitter and healthier body, owing to its potential positive impacts on performance, namely clubhead speed (CHS).

OBJECTIVE

The aim of this meta-analysis was to examine the associations between CHS (as measured using a driver) and a variety of physical characteristics.

METHODS

A systematic literature search with meta-analysis was conducted using Medline, SPORTDiscus, CINAHL and PubMed databases. Inclusion criteria required studies to have (1) determined the association between physical characteristics assessed in at least one physical test and CHS, (2) included golfers of any skill level but they had to be free from injury and (3) been peer-reviewed and published in the English language. Methodological quality was assessed using a modified version of the Downs and Black Quality Index tool and heterogeneity assessed via the Q statistic and I2. To provide summary effects for each of the physical characteristics and their associations with CHS, a random effects model was used where z-transformed r values (i.e. zr) were computed to enable effect size pooling within the meta-analysis.

RESULTS

Of the 3039 studies initially identified, 20 were included in the final analysis. CHS was significantly associated with lower body strength (zr = 0.47 [95% confidence intervals {CI} 0.24-0.69]), upper body strength (zr = 0.48 [95% CI 0.28-0.68]), jump displacement (zr = 0.53 [95% CI 0.28-0.78]), jump impulse (zr = 0.82 [95% CI 0.63-1.02]), jumping peak power (zr = 0.66 [95% CI 0.53-0.79]), upper body explosive strength (zr = 0.67 [95% CI 0.53-0.80]), anthropometry (zr = 0.43 [95% CI 0.29-0.58]) and muscle capacity (zr = 0.17 [95% CI 0.04-0.31]), but not flexibility (zr = - 0.04 [95% CI - 0.33 to 0.26]) or balance (zr =  - 0.06 [95% CI - 0.46 to 0.34]).

CONCLUSIONS

The findings from this meta-analysis highlight a range of physical characteristics are associated with CHS. Whilst significant associations ranged from trivial to large, noteworthy information is that jump impulse produced the strongest association, upper body explosive strength showed noticeably larger associations than upper body strength, and flexibility was not significant. These findings can be used to ensure practitioners prioritise appropriate fitness testing protocols for golfers.

Loading or Unloading? This Is the Question! A Multi-Season Study in Professional Football Players

This study examined the impact of training load periodization on neuromuscular readiness in elite football players using the Locomotor Efficiency Index (LEI) as a measure of performance optimization. Throughout the 2021/22 and 2022/23 seasons, 106 elite male players (age: 19.5 ± 3.9 years) from an Italian professional football club were monitored using Global Positioning Systems (GPS) external load data. The LEI was derived from a machine learning model, specifically random forest regression, which compared predicted and actual PlayerLoad™ values to evaluate neuromuscular efficiency. Players were categorized by weekly LEI into three readiness states: bad, normal, and good. Analysis focused on the variation in weekly LEI relative to weekly load percentage variation (large decrease, moderate decrease, no variation, moderate increase, large increase), which included total distance, high-speed distance (above 25.2 km/h), and mechanical load, defined as the sum of accelerations and decelerations. Statistical analysis showed significant differences only with variations in total distance and mechanical load. Specifically, reducing weekly loads improved LEI in players in lower readiness states, while maintaining or slightly increasing loads promoted optimal readiness. This approach enables coaches to tailor training prescriptions more effectively, optimizing workload and recovery to sustain player performance throughout a demanding season.

Influence of the time-task constraint on ocular metrics of semi-elite soccer players

This study novelty aimed to investigate the influence of manipulating the available time to perform the training tasks on soccer players´ ocular metrics, following training. Specifically, pupillary response (pupil diameter) and saccadic features (latency, accuracy, velocity, and number) were measured with a portable eye tracker following training to reflect the mental load accumulated by players during the training sessions. Nine semi-elite soccer players performed two training sessions, based on large-sided games, on an artificial grass field. These two sessions were composed of the same tasks but varying the required time to complete the task goals (Session 1: No time limitations to perform the tasks; Session 2: Limited time to perform the tasks). The participants performed, before (pre-test) and after (post-test) each training session, a prosaccade task in a room near the playing field. Findings revealed a differentiated effect of the available time to complete the training tasks on ocular metrics because significant differences were found in all variables after training (p < .001 for pupil diameter; p < .01 for saccade accuracy and number of saccades; p < .05 for saccade velocity and latency). Ocular metrics could be a promising tool to evaluate mental load following practice because they were sensitive to the time-task constraint, providing researchers a valuable information for a better planning of the mental workload when designed training tasks.

Calculating Load and Intensity Using Muscle Oxygen Saturation Data

The study aimed to calculate training intensity and load using muscle oxygen saturation (SmO2) during two differentiated physical tasks. 29 university athletes participated in a 40-m Maximal Shuttle Run Test (MST, 10 × 40-m with 30 s recovery between sprints) and a 3000-m time trial run. Distance and time were used to calculate external load (EL). Internal load indicators were calculated based on percentage of maximum heart rate (%HRMAX) and SmO2 variables: muscle oxygen extraction (∇%SmO2) and the cardio-muscle oxygen index (CMOI) was also provided by relating ∇%SmO2 ÷ %HRMAX, and the training load were calculated as the product of speed (m/min × IL) and the efficiency index [Effindex (m/min ÷ IL)]. A student t test was applied based on Bayesian factor analysis. As expected, EL differed in the 40-m MST (331 ± 22.8) vs. 3000-m trials (222 ± 56.8) [BF10 = 6.25e+6; p = <0.001]. Likewise, IL showed higher values in 40-m MST (39.20 ± 15.44) vs. 3000-m (30.51 ± 8.67) in CMOI: [BF10 = 1.70; p = 0.039]. Training load was greater in 40-m MST (85.77 ± 27.40) vs. 3000-m (15.55 ± 6.77) [(m/min × ∇%SmO2): BF10 = 12.5; p = 0.003] and 40-m MST (129.27 ± 49.44) vs. 3000-m (70.63 ± 32.98) [(m/min × CMOI): BF10 = 169.6; p = <0.001]. Also, the Effindex was higher in 40-m MST (10.19 ± 4.17) vs. 3000-m (6.06 ± 2.21) [(m/min × ∇%SmO2): BF10 = 137.03; p = <0.001] and 40-m MST (9.69 ± 4.11) vs. 3000-m (7.55 ± 1.87) [(m/min × CMOI): BF10 = 1.86; p = 0.035]. This study demonstrates calculations of training intensity and load based on SmO2 as an internal load indicator along with speed as an external load indicator during two differentiated exercises.

Intensity Thresholds for External Workload Demands in Basketball: Is Individualization Based on Playing Positions Necessary?

Currently, basketball teams use inertial devices for monitoring external and internal workload demands during training and competitions. However, the intensity thresholds preset by device manufacturers are generic and not adapted for specific sports (e.g., basketball) and players' positions (e.g., guards, forwards, and centers). Using universal intensity thresholds may lead to failure in accurately capturing the true external load faced by players in different positions. Therefore, the present study aimed to identify external load demands based on playing positions and establish different intensity thresholds based on match demands in order to have specific reference values for teams belonging to the highest competitive level of Spanish basketball. Professional male players (n = 68) from the Spanish ACB league were monitored during preseason official games. Three specific positions were used to group the players: guards, forwards, and centers. Speed, accelerations, decelerations, impacts/min, and player load/min were collected via inertial devices. Two-step clustering and k-means clustering categorized load metrics into intensity zones for guards, forwards, and centers. Guards covered more distance at high speeds (12.72-17.50 km/h) than forwards and centers (p < 0.001). Centers experienced the most impacts/min (p < 0.001). Guards exhibited greater accelerations/decelerations, albeit mostly low magnitude (p < 0.001). K-means clustering allowed the setting of five zones revealing additional thresholds. All positions showed differences in threshold values (p < 0.001). The findings provide insights into potential disparities in the external load during competition and help establish position-specific intensity thresholds for optimal monitoring in basketball. These data are highly applicable to the design of training tasks at the highest competitive level.

Training stress, neuromuscular fatigue and well-being in volleyball: a systematic review

BACKGROUND

Volleyball, with its unique calendar structure, presents distinct challenges in training and competition scheduling. Like many team sports, volleyball features an unconventional schedule with brief off-season and pre-season phases, juxtaposed against an extensive in-season phase characterized by a high density of matches and training. This compact calendar necessitates careful management of training loads and recovery periods. The effectiveness of this management is a critical factor, influencing the overall performance and success of volleyball teams. In this review, we explore the associations between training stress measures, fatigue, and well-being assessments within this context, to better inform future research and practice.

METHODS

A systematic literature search was conducted in databases including PsycINFO, MEDLINE/PubMed, SPORTDiscus, Web of Science, and Scopus. Inclusion criteria were original research papers published in peer-reviewed journals involving volleyball athletes.

RESULTS

Of the 2535 studies identified, 31 were thoroughly analysed. From these 31 articles, 22 included professional athletes, seven included collegiate-level volleyball athletes, and two included young athletes. Nine studies had female volleyball players, while the remaining 22 had male volleyball athletes.

CONCLUSIONS

Internal training load should be collected daily after training sessions and matches with the session rating of perceived exertion method. External training load should also be measured daily according to the methods based on jump height, jump count, and kinetic energy. If force platforms are available, neuromuscular fatigue can be assessed weekly using the FT:CT ratio of a countermovement jump or, in cases where force platforms are not available, the average jump height can also be used. Finally, the Hooper Index has been shown to be a measure of overall wellness, fatigue, stress, muscle soreness, mood, and sleep quality in volleyball when used daily.

Relationship Between Athlete-Reported Outcome Measures and Subsequent Match Performance in Team Sports: A Systematic Review

ABSTRACT

Sansone, P, Rago, V, Kellmann, M, and Alcaraz, PE. Relationship between athlete-reported outcome measures and subsequent match performance in team sports: A systematic review. J Strength Cond Res 37(11): 2302-2313, 2023-Athlete-reported outcome measures (AROMs; e.g., fatigue, stress, readiness, recovery, and sleep quality) are commonly implemented in team sports to monitor the athlete status. However, the relationship between AROMs and match performance indicators is unclear and warrants further investigation. This systematic review examined the relationship between precompetitive AROMs and subsequent match performances of team sport athletes. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 3 (PubMed, Scopus, and Web of Science) databases were systematically searched to retrieve studies investigating the effects or association of AROMs and match: (a) technical-tactical performance (match-related statistics), (b) physical performance, (c) physiological and (d) perceptual demands, and (e) other measures of performance in adult team sport athletes. Quality assessment of included studies was performed using a modified Black and Downs checklist. Fifteen articles representing 289 team sport athletes were included. Mean quality of included studies was 7.6 ± 1.0 (of 11). Across the included studies, 22 AROMs parameters were used, and 16 different statistical approaches were identified. Approximately 11 of 15 studies used nonvalidated AROMs. Overall, associations or effects of AROMs were found consistently for match-related statistics (7/9 studies), whereas results were unclear for physical performances (3/7 studies), perceptual demands (1/2 studies), or other measures of performance (2/4 studies). Considering the importance of key match-related statistics for success in team sports, this review suggests that monitoring precompetitive AROMs has potential to provide valuable information to coaches. However, it is indispensable to validate AROMs questionnaires and to uniform data collection and statistical procedures before substantiated indications to practitioners can be made.

Internal and external workload in national and international netball competition

Differences in workload exist between netball playing positions and competition levels, but no research has compared workloads experienced by the same elite players during national and international competitions. This study collected internal (heart rate) and external (PlayerLoad·min-1) workload data per match quarter from 44 players during a national competition and 12 players during an international competition. Nine players played in both competitions. Linear mixed models compared percentage of match quarter in each heart rate zone and PlayerLoad·min-1 between competitions for each playing position. Workloads against low- and high-ranked international opponents were also compared. Internal workloads were greater in national compared to international competition for GD and WD positions. PlayerLoad·min-1 was significantly higher by 8-13% in the national competition for positions WD and C, and by 5-8% in the international competition for GD and GA. Positional differences may indicate a role of the team's tactical style of play. Workloads were generally greater against higher- rather than lower-ranked international opponents. These results indicate that tactical factors in combination with playing position and opposition characteristics should be considered when preparing physically for matches.

Methods of Monitoring Internal and External Loads and Their Relationships with Physical Qualities, Injury, or Illness in Adolescent Athletes: A Systematic Review and Best-Evidence Synthesis

BACKGROUND

With the increasing professionalisation of youth sports, training load monitoring is increasingly common in adolescent athletes. However, the research examining the relationship between training load and changes in physical qualities, injury, or illness in adolescent athletes is yet to be synthesised in a systematic review.

OBJECTIVE

The aim of this review was to systematically examine the research assessing internal and external methods of monitoring training load and physical qualities, injury, or illness in adolescent athletes.

METHODS

Systematic searches of SPORTDiscus, Web of Science, CINAHL and SCOPUS were undertaken from the earliest possible records to March 2022. Search terms included synonyms relevant to adolescents, athletes, physical qualities, injury, or illness. To be eligible for inclusion, articles were required to (1) be original research articles; (2) be published in a peer-reviewed journal; (3) include participants aged between 10 and 19 years and participating in competitive sport; (4) report a statistical relationship between a measure of internal and/or external load and physical qualities, injury or illness. Articles were screened and assessed for methodological quality. A best-evidence synthesis was conducted to identify trends in the relationships reported.

RESULTS

The electronic search yielded 4125 articles. Following screening and a review of references, 59 articles were included. The most commonly reported load monitoring tools were session ratings of perceived exertion (n = 29) and training duration (n = 22). Results of the best-evidence synthesis identified moderate evidence of positive relationships between resistance training volume load and improvement in strength, and between throw count and injury. However, evidence for other relationships between training load and change in physical qualities, injury, or illness were limited or inconsistent.

CONCLUSIONS

Practitioners should consider monitoring resistance training volume load for strength training. Additionally, where appropriate, monitoring throw counts may be useful in identifying injury risk. However, given the lack of clear relationships between singular measures of training load with physical qualities, injury, or illness, researchers should consider multivariate methods of analysing training load, as well as factors that may mediate the load-response relationship, such as maturation.

The Perception of Volleyball Student-Athletes: Evaluation of Well-Being, Sport Workload, Players' Response, and Academic Demands

Physical activity has been shown to improve the health and well-being of students, athletes and the general population, especially when it is properly monitored and responses are evaluated. However, data are mostly gathered without considering a valuable element, participants' perceptions. Therefore, the objective was to know the perception of volleyball student-athletes when using different monitoring and response tools that assess well-being, workloads, responses to workloads, and academic demands. A qualitative study using semi-structured interviews with female volleyball student-athletes (n = 22) was used to know players' perceptions when using a wellness/well-being questionnaire, session ratings of perceived exertion (sRPE), and countermovement jumps (CMJ), and consider academic demands. Results show that the wellness questionnaire and sRPE increased student-athletes' awareness of well-being and readiness to perform, improved self-evaluation, self-regulation, and self-demand. However, motivation and overcoming challenges were based on the CMJ. Academic demands affected 82% of student-athletes, altering stress, fatigue, and sleep quality. Nonetheless, sport was seen as an activity that helped with academic commitments. Therefore, the wellness questionnaires and the sRPE facilitated self-awareness and positive dispositions toward self-regulation. Simultaneous intensive academic demands and training can produce mutual positive effects if the variables of physical and mental loads are harmonized in the critical academic and sports periods.

Rating of Perceived Exertion in Professional Volleyball: A Systematic Review

The rating of perceived exertion (RPE) is a non-invasive, cost effective, and time efficient strategy to measure training loads. However, data can be collected without following specific procedures and across a range of methods (e.g., different RPE scales and/or different operational questions). Consequently, practitioners working in professional volleyball can use this information in various ways with different assessment standards between them. Therefore, the purpose of the current review was to systematically and critically evaluate the use of RPE-based methods in professional volleyball athletes. Electronic searches were conducted in four databases (PubMed, SPORTDiscus, Scopus, and Web of Science). The electronic search yielded 442 articles, from which 14 articles were included in the systematic review. All included studies used the BORG-CR10 scale to calculate the session RPE. The main findings indicate that, to minimize the effect of the last exercise of the session, the athlete should be presented with the RPE question 10 to 30 minutes after the session is finished. Additionally, in order to evaluate the intensity of the training session, the question should be "how hard/intense was your session?", avoiding questions without these adverbs or adjectives such as "how was your training session/workout?". Future studies should analyse the collection of the localized RPE responses in professional volleyball athletes and their relationships with objective markers such as the number of jumps and accelerations.

Correlations between horizontal jump and sprint acceleration and maximal speed performance: a systematic review and meta-analysis

Objectives

The purpose of this study is to determine the associations between horizontal jump and sprint acceleration, as well as maximal speed performance.

Methods

A systematic literature search was performed using PubMed, MEDLINE (EBSCOhost), and Web of Science. The studies that were included in this review must meet the following criteria: (1) well-trained individuals over the age of 18 years old; (2) Pearson's correlation coefficients between sprint time and horizontal jump distance were provided; (3) the sprint distance was limited to 0-100 m. The quality of the studies was assessed using a modified version of the Downs and Black Quality Index test. A random-effects model was used to determine the effect sizes, and heterogeneity between studies was examined using the Q statistic and I².

Results

From the identified 2,815 studies, 27 studies were included in this study (two from reference lists). The sprint time of the sprint acceleration phase was moderately and negatively correlated with the standing long jump (r =  - 0.45, z = 7.48, p < 0.001), single leg standing long jump (r =  - 0.48, z = 3.49, p < 0.001) and horizontal drop jump distance (r =  - 0.48, z = 3.49, p < 0.001), and was largely and negatively correlated with multiple jump distance (r =  - 0.69, z = 6.02, p < 0.001). Out of five studies assessed the standing triple jump, three studies reported significant positive association with the sprint acceleration performance. The sprint time of maximal speed phase was very largely and negatively associated with standing long jump distance (r =  - 0.73, z = 4.44, p < 0.001) and multiple jump distance (r =  - 0.76, z = 6.86, p < 0.001).

Conclusions

This review indicates the moderate to very large associations between horizontal jump and sprint acceleration and maximal speed performance, and the highest magnitude of associations between them is found in the multiple jump. Moreover, compared to the sprint acceleration performance, there are greater associations between maximal speed performance and standing long jump and multiple jump distance.

External and Internal Load Variables Encountered During Training and Games in Female Basketball Players According to Playing Level and Playing Position: A Systematic Review

Background

Despite the growing global participation of females in basketball and number of studies conducted on the topic, no research has summarized the external and internal load variables encountered by female basketball players during training and games.

Objective

To collate existing literature investigating external and internal load variables during training and games in female basketball players according to playing level (club, high-school, representative, collegiate, semi-professional, and professional) and playing position (backcourt and frontcourt players).

Methods

A systematic review of the literature was performed using PubMed, SPORTDiscus, and Web of Science to identify studies published from database inception until June 11, 2021. Studies eligible for inclusion were observational and cross-sectional studies, published in English, reporting external and/or internal load variables during training sessions and/or games. Methodological quality and bias were assessed for each study prior to data extraction using a modified Downs and Black checklist. Weighted means according to playing level and playing position were calculated and compared if a load variable was reported across two or more player samples and were consistent regarding key methodological procedures including the seasonal phase monitored, minimum exposure time set for including player data (playing time during games), approach to measure session duration, and approach to measure session intensity.

Results

The search yielded 5513 studies of which 1541 studies were duplicates. A further 3929 studies were excluded based on title and abstract review, with 11 more studies excluded based on full-text review. Consequently, 32 studies were included in our review. Due to the wide array of methodological approaches utilized across studies for examined variables, comparisons could only be made according to playing level for blood lactate concentration during games, revealing backcourt players experienced higher lactate responses than frontcourt players (5.2 ± 1.9 mmol·L−1 vs. 4.4 ± 1.8 mmol·L−1).

Conclusions

Inconsistencies in the methods utilized to measure common load variables across studies limited our ability to report and compare typical external and internal loads during training and games according to playing level and position in female basketball players. It is essential that standardized methodological approaches are established for including player data as well as measuring session duration (e.g., total time, live time) and intensity (e.g., consistent rating of perceived exertion scales, intensity zone cut points) in future female basketball research to permit meaningful interpretation and comparisons of load monitoring data across studies.

Within-week differences in external training load demands in elite volleyball players

Purpose

The aim of this study was to analyze the within-week differences in external training intensity in different microcycles considering different playing positions in women elite volleyball players.

Methods

The training and match data were collected during the 2020–2021 season, which included 10 friendly matches, 41 league matches and 11 champions league matches. The players’ position, training/match duration, training/match load, local positioning system (LPS) total distance, LPS jumps, accelerations, decelerations, high metabolic load distance (HMLD), acute and chronic (AC) mean and AC ratio calculated with the rolling average (RA) method and the exponentially weighted moving average (EWMA) method, monotony and strain values were analyzed.

Results

All the variables except strain, Acc/Dec ratio and acute mean (RA) showed significant differences among distance to match days. Regarding the players’ positions, the only difference was found in the AC ratio (EWMA); in all microcycles, the middle blocker player showed workload values when compared with the left hitter, setter and libero.

Conclusion

Overall, the analysis revealed that the intensity of all performance indicators, except for strain, acc/dec and acute mean load (RA), showed significant differences among distance to match day with moderate to large effect sizes. When comparing players’ positions, the middle blocker accumulated the lowest loads. There were no significant differences among other positions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13102-022-00568-1.

Load-velocity relationships and predicted maximal strength: A systematic review of the validity and reliability of current methods

Maximal strength can be predicted from the load-velocity relationship (LVR), although it is important to understand methodological approaches which ensure the validity and reliability of these strength predictions. The aim of this systematic review was to determine factors which influence the validity of maximal strength predictions from the LVR, and secondarily to highlight the effects of these factors on the reliability of predictions. A search strategy was developed and implemented in PubMed, Scopus, Web of Science and CINAHL databases. Rayyan software was used to screen titles, abstracts, and full texts to determine their inclusion/eligibility. Eligible studies compared direct assessments of one-repetition maximum (1RM) with predictions performed using the LVR and reported prediction validity. Validity was extracted and represented graphically via effect size forest plots. Twenty-five eligible studies were included and comprised of a total of 842 participants, three different 1RM prediction methods, 16 different exercises, and 12 different velocity monitoring devices. Four primary factors appear relevant to the efficacy of predicting 1RM: the number of loads used, the exercise examined, the velocity metric used, and the velocity monitoring device. Additionally, the specific loads, provision of velocity feedback, use of lifting straps and regression model used may require further consideration.

Modeling Match Performance in Elite Volleyball Players: Importance of Jump Load and Strength Training Characteristics

In this study, we investigated the relationships between training load, perceived wellness and match performance in professional volleyball by applying the machine learning techniques XGBoost, random forest regression and subgroup discovery. Physical load data were obtained by manually logging all physical activities and using wearable sensors. Daily wellness of players was monitored using questionnaires. Match performance was derived from annotated actions by a video scout during matches. We identified conditions of predictor variables that related to attack and pass performance (p < 0.05). Better attack performance is related to heavy weights of lower-body strength training exercises in the preceding four weeks. However, worse attack performance is linked to large variations in weights of full-body strength training exercises, excessively heavy upper-body strength training, low jump heights and small variations in the number of high jumps in the four weeks prior to competition. Lower passing performance was associated with small variations in the number of high jumps in the preceding week and an excessive amount of high jumps performed, on average, in the two weeks prior to competition. Differences in findings with respect to passing and attack performance suggest that elite volleyball players can improve their performance if training schedules are adapted to the position of a player.

RELACIÓN ENTRE METODOLOGÍAS DE CONTROL DE LA CARGA EN EL BALONCESTO PROFESIONAL

El objetivo de este trabajo fue determinar la relación existente entre diferentes metodologías de medición de la carga de entrenamiento en un equipo profesional de baloncesto. 12 jugadores de un mismo equipo fueron sometidos a estructuras de entrenamiento iguales en cuanto a diseño y fueron monitorizados diariamente con frecuencia cardiaca, GPS y percepción subjetiva de esfuerzo. Los resultados indicaron diferencias significativas entre todas las metodologías de control utilizadas. RPE (x2=12.4; P=.015), sRPE (x2=21.5; P<.001), TRIMP (x2=23.5; P<.001), SHRZ (x2=19.3; P<.001) y distancia (x2=21.7; P<.001). Se observó una correlación entre todas ellas exceptuando la variable distancia recorrida; Borg (p=.19; P=79), sRPE (p=.14; P=.299) y sHR-Z (p=.17; P=.197). Estos resultados sugieren que los diferentes métodos de evaluación utilizados para el control de la carga de entrenamiento determinan resultados variables en un equipo de baloncesto profesional. Se consideró que el ratio de esfuerzo percibido por sesión resulta el instrumento más eficaz para el control de la carga de entrenamiento.

Validation of Internal and External Load Metrics in NCAA D1 Women's Beach Volleyball

ABSTRACT

Tometz, MJ, Jevas, SA, Esposito, PM, and Annaccone, AR. Validation of internal and external load metrics in NCAA D1 women's beach volleyball. J Strength Cond Res 36(8): 2223-2229, 2022-The purpose of this study was to determine the validity of internal and external load metrics in NCAA D1 women's beach volleyball. Subjects included 13 NCAA D1 women's beach volleyball players (age: 20.3 ± 1.4 years). A total of 578 data points were analyzed from 51 team training sessions, including practice, games, and sport-specific conditioning during the pre-season semester (15 weeks). Data points included Edward's training impulse (TRIMP) (228.0 ± 80.7 arbitrary units [AU]), session rating of perceived exertion (sRPE) Load (532.5 ± 232.8 [AU]), distance covered (DC) in meters (2,635.4 ± 884.3 [m]), and daily environmental condition variables {(temperature (76.5 ± 13.7 [°F]), relative humidity (72.5 ± 13.2 [%]), and wet-bulb globe temperature (52.9 ± 19.9 [°F])}. The subjects wore Polar Team Pro heart rate monitors with global positioning system during each session. Subjects completed an sRPE questionnaire after every session. Pearson product moment correlations yielded statistically significant relationships (p < 0.01) between TRIMP and sRPE Load (r = 0.81), TRIMP and DC (r = 0.78), and sRPE Load and DC (r = 0.82). A forward selection multiple regression yielded that sRPE Load could predict TRIMP with the equation: TRIMPTometz = 78.735 + (sRPE Load * 0.28) (p < 0.001). These findings support sRPE Load as a valid alternative to TRIMP when monitoring internal loads in NCAA D1 women's beach volleyball. Session rating of perceived exertion Load may be more practical and accessible for teams. Distance covered should be considered when periodizing and monitoring training loads because of its relationship with internal loads.

Relationship Between Game Load and Player's Performance in Professional Basketball

PURPOSE

The purpose of the study was to examine the relationships between external and internal loads, and their ratio (efficiency index), with game performance between backcourt and frontcourt professional basketball players.

METHODS

Game loads of 14 basketball players were monitored during 6 games. External load variables measured were total distance (TD); distance >18 km·h-1, commonly known as high-speed running (HSR); and number of accelerations (ACC) and decelerations (DEC) >3 m·s-2, whereas the internal load variable measured was average heart rate (HRmean). The ratio between external and internal load variables was calculated and defined through 4 efficiency indexes (TD:HRmean, HSR:HRmean, ACC:HRmean, and DEC:HRmean). Furthermore, basketball performance was quantified using game-related statistics.

RESULTS

TD presented a small association with basketball performance, whereas the other external load variables and the 4 efficiency indexes calculated showed trivial relationships with game-related statistics. Furthermore, HRmean showed the greatest (small) associations with individual performance (P = .01-.02; r = .19 to .22). Regarding specific positions, the only 2 variables that presented significant differences were DEC (P = .01; d = 0.86) and DEC:HRmean (P = .01; d = 0.81), which showed higher values in backcourt players compared with frontcourt players.

CONCLUSIONS

The results suggest that the best performances of basketball players during official competition are not associated with higher game loads. This illustrates the necessity to assess basketball performance from a holistic approach and consider more than just external and internal variables to better understand the players' performance during basketball competition.

Eastward Jet Lag is Associated with Impaired Performance and Game Outcome in the National Basketball Association

Objectives: Elite athletes are often required to travel across time zones for national and international competitions, causing frequent jet lag. The aim of this study was to examine whether the direction of travel-related jet lag is associated with performance in the National Basketball Association (NBA), and if so, to explore potential mechanisms. Methods: Ten seasons comprising of 11,481 games of NBA data from the 2011/2012 to the 2020/2021 regular season were analyzed using multi-level mixed models with one fixed factor (three levels; jet lag direction: eastward vs westward vs no jet lag) and three random factors (team, opponent, game time). Predicted circadian resynchronization rate was accounted for, and home and away games were analysed separately. Mediation analyses were performed to examine potential mechanisms. Results: Among home teams, eastward (but not westward) jet lag was associated with reduced winning (Δ (i.e., change) = -6.03%, p = 0.051, marginal), points differential (Δ = -1.29 points, p = 0.015), rebound differential (Δ = -1.29 rebounds, p < 0.0001), and effective field goal percentage differential (Δ = -1.2%, p < 0.01). As the magnitude of eastward jet lag increased, home team points differential decreased (2 h Δ = -4.53 points, p < 0.05; 1 h Δ = -0.72 points, p = 0.07). No significant associations were found between jet lag and away team performance. Conclusion: Eastward jet lag was associated with impaired performance for home (but not away) teams. Sleep and circadian disruption associated with advancing phase following eastward travel may have significant adverse consequences on performance in the NBA, particularly when recovery time is limited. Sports organisations could consider chronobiology-informed scheduling and interventions to maximise recovery and performance of their athletes.

Impact of sports participation on components of metabolic syndrome in adolescents: ABCD growth study

OBJECTIVES

This study aimed to analyze the impact of sports participation (12 months of practice) on the components of metabolic syndrome (MetS) in both sexes.

METHODS

This is an observational longitudinal study, a part of which is entitled "Analysis of Behaviors of Children During Growth" (ABCD Growth Study), Presidente Prudente, São Paulo, Brazil. The sample was composed of 171 adolescents (112 boys and 59 girls), divided into non-sports and sports groups. High-density lipoprotein-cholesterol (HDL-c), triglycerides, and glucose were analyzed by the colorimetric method of dry chemistry and processed biochemically. Systolic blood pressure and diastolic blood pressure were measured using an automatic device. Body fat was estimated using a densitometry scanner.

RESULTS

Adolescents who practiced sports were younger (p-value=0.001) and had a lower peak height velocity (p-value=0.001) than the non-sports group. The differences (Δ) after 12 months were of greater magnitude for the sports group when compared to the non-sports group (p-value=0.013), glucose (moderate magnitude in favor of the sports group; p-value=0.001), HDL-c (small magnitude in favor of the sports group; p-value=0.0015), and MetS (moderate magnitude in favor of the sports group; p-value=0.001).

CONCLUSIONS

The practice of sports in adolescents had a protective effect on the metabolic components of MetS.

The Current State of Subjective Training Load Monitoring: Follow-Up and Future Directions

This article addresses several key issues that have been raised related to subjective training load (TL) monitoring. These key issues include how TL is calculated if subjective TL can be used to model sports performance and where subjective TL monitoring fits into an overall decision-making framework for practitioners. Regarding how TL is calculated, there is conjecture over the most appropriate (1) acute and chronic period lengths, (2) smoothing methods for TL data and (3) change in TL measures (e.g., training stress balance (TSB), differential load, acute-to-chronic workload ratio). Variable selection procedures with measures of model-fit, like the Akaike Information Criterion, are suggested as a potential answer to these calculation issues with examples provided using datasets from two different groups of elite athletes prior to and during competition at the 2016 Olympic Games. Regarding using subjective TL to model sports performance, further examples using linear mixed models and the previously mentioned datasets are provided to illustrate possible practical interpretations of model results for coaches (e.g., ensuring TSB increases during a taper for improved performance). An overall decision-making framework for determining training interventions is also provided with context given to where subjective TL measures may fit within this framework and the determination if subjective measures are needed with TL monitoring for different sporting situations. Lastly, relevant practical recommendations (e.g., using validated scales and training coaches and athletes in their use) are provided to ensure subjective TL monitoring is used as effectively as possible along with recommendations for future research.

Understanding load in netball – An analysis of multiple seasons, phases, and teams

Studies of training and competition load in sport are usually based on data that represents a sample of a league and or annual training program. These studies sometimes explore important factors that are affected by load, such as training adaptations and injury risk. The generalisability of the conclusions of these studies, can depend on how much load varies between seasons, training phases and teams. The interpretation of previous load studies and the design of future load studies should be influenced by an understanding of how load can vary across seasons, training phases and between teams. The current study compared training loads (session rating of perceived exertion x session duration) between all (8) teams in an elite Netball competition for multiple (2) season phases and (2) seasons. A total of 29,545 records of athlete session training loads were included in the analysis. Linear mixed models identified differences between seasons and training phases (p < .05). There were also differences between teams and a complex set of interactions between these three factors (season, phase, and team) (p < .05). While the absolute value of the training loads reported here are only relevant to elite netball, these results illustrate that when data is sampled from a broader context, the range and variation in load may increase. This highlights the importance of cautiously interpreting and generalisation of findings from load studies that use limited data sets.

External load of the tasks planned by teachers for learning handball

The load in tasks planned for sports teaching in physical education classes has received little attention. The purpose of this study was therefore to analyze the external load, eTL, in the tasks designed by physical education teachers from the in-service and pre-service stages, for teaching handball in primary education, and to compare them with the tasks included in the lesson plans designed for handball using the tactical games teaching model. An associative, comparative and cross-sectional methodology was used. Twenty-three teachers, five in the in-service phase and eighteen in the pre-service phase, designed lesson plans for teaching handball, which were compared with lesson plans validated by a panel of experts. The analysis was performed on 1,232 tasks or analysis units. eTL was categorized using the Integrated analysis system of training tasks (SIATE) instrument. A descriptive and associative analysis was made of the variables that make up the eTL and an inferential analysis of the eTL using non-parametric tests. The total eTL of the tasks planned by the in-service and pre-service teachers was low, and significantly lower than the tasks planned using the tactical games model, which showed a high level.

Are Subjective Intensities Indicators of Player Load and Heart Rate in Physical Education?

Physical education teachers need valid, low-cost, subjective techniques as an alternative to high-cost new technologies to monitor students’ intensity monitoring. This study aimed to investigate the correlations between both objective and subjective external (eTL) and internal (iTL) intensities. A total of 95 primary education students participated in this study. In this regard, 40 played soccer, and 55 performed basketball tasks, recording a total of 3956 units of analysis. The intensities caused by the different soccer and basketball tasks were measured using objective techniques (inertial devices and heart rate monitors) and subjective techniques (a sheet of task analysis and ratings of perceived exertion). Matrix scatter plots were made to show the values of two variables for a dataset. In this regard, adjustment lines were plotted to determine the trend of the correlations. Then, Spearman’s correlation was calculated to measure the association between two variables. Despite the low correlation levels obtained, the main results showed significant positive correlations between the intensities. This means that the high intensity values recorded by objective techniques also implied high intensity values recorded by subjective techniques, and vice versa. Negative correlations (r Rho = −0.19; p = 0.00) were only found between the following eTL variables: task eTL per minute (subjective technique) and player load per minute (objective technique). This negative correlation occurred when students played in the same 3 vs. 3 game situation without variability in subjective eTL (M ± SD, 28.00 ± 0.00). Therefore, subjective eTL and iTL techniques could be proposed as a suitable alternative for planning and monitoring the intensities supported by students in physical education classes. Moreover, these subjective techniques are easy to use in schools.

Training Progression in Recreational Cyclists: No Linear Dose-Response Relationship With Training Load

ABSTRACT

Vermeire, KM, Vandewiele, G, Caen, K, Lievens, M, Bourgois, JG, and Boone, J. Training progression in recreational cyclists: no linear dose-response relationship with training load. J Strength Cond Res 35(12): 3500-3505, 2021-The purpose of the study was to assess the relationship between training load (TL) and performance improvement in a homogeneous group of recreational cyclists, training with a self-oriented training plan. Training data from 11 recreational cyclists were collected over a 12-week period. Before and after the training period, subjects underwent a laboratory incremental exercise test with blood lactate measurements to determine the power output associated with the aerobic threshold (PAT) and the anaerobic threshold (PANT), and the maximal power output (PMAX) was also determined. Mean weekly TL (calculated using the training impulse (TRIMP) of Banister, Edwards TRIMP, Lucia TRIMP and the individualized TRIMP) were correlated to the progression in fitness parameters using Pearson Correlation. Training intensity distribution (TID) was also determined (% in zone 1 as ANT). No significant correlations between mean weekly TRIMP values and the improvement on PMAX (r = -0.22 to 0.08), PANT (r = -0.56 to -0.31) and PAT (r = -0.08 to 0.41) were found. The TID was significant in a multiple regression with PANT as dependent variable (y = 0.0088 + 0.1094 × Z1 - 0.2704 × Z2 + 1.0416 × Z3; p = 0.02; R2 = 0.62). In conclusion, this study shows that the commonly used TRIMP methods to quantify TL do not show a linear dose-response relationship with performance improvement in recreational cyclists. Furthermore, the study shows that TID might be a key factor to establish a relationship with performance improvement.

The Application of Accelerometer-Derived Moving Averages to Quantify Peak Demands in Basketball: A Comparison of Sample Duration, Playing Role, and Session Type

ABSTRACT

Fox, JL, Conte, D, Stanton, R, McLean, B, and Scanlan, AT. The application of accelerometer-derived moving averages to quantify peak demands in basketball: A comparison of sample duration, playing role, and session type. J Strength Cond Res 35(12S): S58-S63, 2021-The purpose of this was to study compare peak external workload intensities in basketball using accelerometer-derived moving averages between different sample durations (0.5-5 minutes), session types (training vs. game-play), and playing roles (starting vs. bench players). Five starting and 3 bench players were monitored over a 15-week competitive season using accelerometers. For all training sessions and games, peak external workload intensities were determined using accelerometer-derived moving averages for PlayerLoad per minute (PL·min-1) across sample durations of 0.5, 1, 2, 3, 4, and 5 minutes. Linear mixed-models and effect sizes (ESs) were used to compare peak PL·min-1 between sample durations, session type, and playing role. Peak PL·min-1 was significantly different between all sample durations (p < 0.05; ES = 0.88-5.45), with higher intensities evident across shorter sample durations. In starting players, peak intensities were significantly higher during games compared with training for all sample durations (p < 0.05; ES = 0.69-0.93). Peak game intensities were higher in starting players using all sample durations (p > 0.05; ES = 0.69-1.43) compared with bench players. Shorter sample durations produced higher peak PL·min-1. Peak intensities were higher during games than training in starting players, indicating training may not adequately prepare players for the most demanding passages of game-play.

Managing the Training Process in Elite Sports: From Descriptive to Prescriptive Data Analytics

Elite sport practitioners increasingly use data to support training process decisions related to athletes' health and performance. A careful application of data analytics is essential to gain valuable insights and recommendations that can guide decision making. In business organizations, data analytics are developed based on conceptual data analytics frameworks. The translation of such a framework to elite sport may benefit the use of data to support training process decisions. Purpose: The authors aim to present and discuss a conceptual data analytics framework, based on a taxonomy used in business analytics literature to help develop data analytics within elite sport organizations. Conclusions: The presented framework consists of 4 analytical steps structured by value and difficulty/complexity. While descriptive (step 1) and diagnostic analytics (step 2) focus on understanding the past training process, predictive (step 3) and prescriptive analytics (step 4) provide more guidance in planning the future. Although descriptive, diagnostic, and predictive analytics generate insights to inform decisions, prescriptive analytics can be used to drive decisions. However, the application of this type of advanced analytics is still challenging in elite sport. Thus, the current use of data in elite sport is more focused on informing decisions rather than driving them. The presented conceptual framework may help practitioners develop their analytical reasoning by providing new insights and guidance and may stimulate future collaborations between practitioners, researchers, and analytics experts.

Effects of and Response to Mechanical Loading on the Knee

Mechanical loading to the knee joint results in a differential response based on the local capacity of the tissues (ligament, tendon, meniscus, cartilage, and bone) and how those tissues subsequently adapt to that load at the molecular and cellular level. Participation in cutting, pivoting, and jumping sports predisposes the knee to the risk of injury. In this narrative review, we describe different mechanisms of loading that can result in excessive loads to the knee, leading to ligamentous, musculotendinous, meniscal, and chondral injuries or maladaptations. Following injury (or surgery) to structures around the knee, the primary goal of rehabilitation is to maximize the patient's response to exercise at the current level of function, while minimizing the risk of re-injury to the healing tissue. Clinicians should have a clear understanding of the specific injured tissue(s), and rehabilitation should be driven by knowledge of tissue-healing constraints, knee complex and lower extremity biomechanics, neuromuscular physiology, task-specific activities involving weight-bearing and non-weight-bearing conditions, and training principles. We provide a practical application for prescribing loading progressions of exercises, functional activities, and mobility tasks based on their mechanical load profile to knee-specific structures during the rehabilitation process. Various loading interventions can be used by clinicians to produce physical stress to address body function, physical impairments, activity limitations, and participation restrictions. By modifying the mechanical load elements, clinicians can alter the tissue adaptations, facilitate motor learning, and resolve corresponding physical impairments. Providing different loads that create variable tensile, compressive, and shear deformation on the tissue through mechanotransduction and specificity can promote the appropriate stress adaptations to increase tissue capacity and injury tolerance. Tools for monitoring rehabilitation training loads to the knee are proposed to assess the reactivity of the knee joint to mechanical loading to monitor excessive mechanical loads and facilitate optimal rehabilitation.

Reactive Strength Index and its Associations with Measures of Physical and Sports Performance: A Systematic Review with Meta-Analysis

BACKGROUND

Reactive strength index (RSI) is used frequently in the testing and monitoring of athletes. Associations with sports performance measures may vary dependent on the task but a literature synthesis has not been performed.

OBJECTIVES

The aim of this meta-analysis was to examine associations between RSI measured during rebound jumping tasks and measures of strength, linear and change of direction speed, and endurance performance.

METHODS

A systematic literature search with meta-analysis was conducted using databases PubMed, SPORTDiscus, Web of Science, and Ovid. Inclusion criteria required studies to (1) examine the relationship between RSI and an independent measure of physical or sporting performance for at least one variable; and (2) provide rebound test instructions to minimise ground contact time and maximise displacement of the jump. Methodological quality was assessed using a modified version of the Downs and Black Quality Index tool. Heterogeneity was examined via the Q statistic and I². Pooled effect sizes were calculated using a random-effects model, with Egger's regression test used to assess small study bias (inclusive of publication bias).

RESULTS

Of the 1320 citations reviewed, a total of 32 studies were included in this meta-analysis. RSI was significantly and moderately associated with strength (isometric: r = 0.356 [95% CI 0.209-0.504]; isotonic: r = 0.365 [0.075-0.654]; pooled strength measures: r = 0.339 [0.209-0.469]) and endurance performance (r = 0.401 [0.173-0.629]). Significant moderate and negative associations were indicated for acceleration (r = - 0.426 [- 0.562 to - 0.290]), top speed (r = - 0.326 [- 0.502 to - 0.151]), and significant large negative associations were noted for change of direction speed (r = - 0.565 [- 0.726 to - 0.404]). Heterogeneity was trivial to moderate across all measures (I² = 0-66%), and significant for isotonic strength and change of direction speed (p < 0.1). Evidence of small study bias was apparent for both acceleration and change of direction speed (p < 0.05).

CONCLUSIONS

We identified primarily moderate associations between RSI and independent measures of physical and sporting performance, and the strength of these relationships varied based on the task and physical quality assessed. The findings from this meta-analysis can help practitioners to develop more targeted testing and monitoring processes. Future research may wish to examine if associations are stronger in tasks that display greater specificity.

Monitoring Accumulated Training and Match Load in Football: A Systematic Review

(1) Background: Training load monitoring has become a relevant research-practice gap to control training and match demands in team sports. However, there are no systematic reviews about accumulated training and match load in football. (2) Methods: Following the preferred reporting item for systematic reviews and meta-analyses (PRISMA), a systematic search of relevant English-language articles was performed from earliest record to March 2020. The search included descriptors relevant to football, training load, and periodization. (3) Results: The literature search returned 7972 articles (WoS = 1204; Pub-Med = 869, SCOPUS = 5083, and SportDiscus = 816). After screening, 36 full-text articles met the inclusion criteria and were reviewed. Eleven of the included articles analyzed weekly training load distribution; fourteen, the weekly training load and match load distribution; and eleven were about internal and external load relationships during training. The reviewed articles were based on short-telemetry systems (n = 12), global positioning tracking systems (n = 25), local position measurement systems (n = 3), and multiple-camera systems (n = 3). External load measures were quantified with distance and covered distance in different speed zones (n = 27), acceleration and deceleration (n = 13) thresholds, accelerometer metrics (n = 11), metabolic power output (n = 4), and ratios/scores (n = 6). Additionally, the internal load measures were reported with perceived exertion (n = 16); heart-rate-based measures were reported in twelve studies (n = 12). (4) Conclusions: The weekly microcycle presented a high loading variation and a limited variation across a competitive season. The magnitude of loading variation seems to be influenced by the type of week, player's starting status, playing positions, age group, training mode and contextual variables. The literature has focused mainly on professional men; future research should be on the youth and female accumulated training/match load monitoring.

Is the Acute: Chronic Workload Ratio (ACWR) Associated with Risk of Time-Loss Injury in Professional Team Sports? A Systematic Review of Methodology, Variables and Injury Risk in Practical Situations

BACKGROUND

The acute: chronic workload ratio (ACWR) is an index of the acute workload relative to the cumulative chronic workloads. The monitoring of physical workloads using the ACWR has emerged and been hypothesized as a useful tool for coaches and athletes to optimize performance while aiming to reduce the risk of potentially preventable load-driven injuries.

OBJECTIVES

Our goal was to describe characteristics of the ACWR and investigate the association of the ACWR with the risk of time-loss injuries in adult elite team sport athletes.

DATA SOURCES

PubMed, EMBASE and grey literature databases; inception to May 2019.

ELIGIBILITY CRITERIA

Longitudinal studies that assess the relationship of the ACWR and time-loss injury risk in adult professional or elite team sports.

METHODS

We summarized the population characteristics, workload metrics and ACWR calculation methods. For each workload metric, we plotted the risk estimates for the ACWR in isolation, or when combined with chronic workloads. Methodological quality was assessed using a modified version of the Downs and Black scale.

RESULTS

Twenty studies comprising 2375 injuries from 1234 athletes (all males and mean age of 24 years) from different sports were included. Internal (65%) and external loads (70%) were collected in more than half of the studies and the session-rating of perceived exertion and total distance were the most commonly collected metrics. The ACWR was commonly calculated using the coupled method (95%), 1:4 weekly blocks (95%) and subsequent week injury lag (80%). There were 14 different binning methods with almost none of the studies using the same binning categories.

CONCLUSION

The majority of studies suggest that athletes are at greater risk of sustaining a time-loss injury when the ACWR is higher relative to a lower or moderate ACWR. The heterogenous methodological approaches not only reflect the wide range of sports studied and the differing demands of these activities, but also limit the strength of recommendations.

PROSPERO REGISTRATION NUMBER

CRD42017067585.

The Association Between Sleep and In-Game Performance in Basketball Players

Purpose:To investigate the associations between sleep and competitive performance in basketball.Methods:A total of 7 semiprofessional, male players were monitored across the in-season. On nights prior to competition, sleep duration and quality were assessed using actigraphs and sleep diaries. The data were accumulated over 1 (night 1), 2 (nights 1–2 combined), 3 (nights 1–3 combined), and 4 (nights 1–4 combined) nights prior to competition. Performance was reported as player statistics (field goal and free-throw accuracy, rebounds, assists, steals, blocks, and turnovers) and composite performance statistics (offensive rating, defensive rating, and player efficiency). Linear regression analyses with cluster-robust standard errors using bootstrapping (1000 replications) were performed to quantify the association between sleep and performance.Results:The night before competition, subjective sleep quality was positively associated with offensive rating and player efficiency (P < .05).Conclusions:Strategies to increase subjective sleep quality the night before competition should be considered to increase the likelihood of successful in-game performance, given its association with composite performance metrics.

Relationships Between Different Internal and External Training Load Variables and Elite International Women's Basketball Performance

PURPOSE

To investigate the relationships between internal and external training load (TL) metrics with elite international women's basketball performance.

METHODS

Sessional ratings of perceived exertion, PlayerLoad™/minute, and training duration were collected from 13 elite international-level female basketball athletes (age 29.0 [3.7] y, stature 186.0 [9.8] cm, body mass 77.9 [11.6] kg) during the 18 weeks prior to the International Basketball Federation Olympic qualifying event for the 2016 Rio Olympic Games. Training stress balance, differential load, and the training efficiency index were calculated with 3 different smoothing methods. These TL metrics and their change in the last 21 days prior to competition were examined for their relationship to competition performance as coach ratings of performance.

RESULTS

For a number of TL variables, there were consistent significant small to moderate correlations with performance and significant small to large differences between successful and unsuccessful performances. However, these differences were only evident for external TL when using exponentially weighted moving averages to calculate TL. The variable that seemed most sensitive to performance was the change in training efficiency index in the last 21 days prior to competition (performance r = .47-.56, P < .001 and difference between successful and unsuccessful performance P < .001, f2 = 0.305-0.431).

CONCLUSIONS

Internal and external TL variables were correlated with performance and distinguished between successful and unsuccessful performances among the same players during international women's basketball games. Manipulating TL in the last 3 weeks prior to competition may be worthwhile for basketball players' performance, especially in internal TL.

The Business End of the Season: A Comparison Between Playoff and Regular-Season Workloads in Professional Basketball Players

PURPOSE

To quantify and compare the internal workloads experienced during the playoffs and regular season in basketball.

METHODS

A total of 10 professional male basketball players competing in the Italian first division were monitored during the final 6 weeks of the regular season and the entire 6-week playoff phase. Internal workload was quantified using the session rating of perceived exertion (s-RPE) method for all training sessions and games. A 2-way repeated-measures analysis of variance (day type × period) was utilized to assess differences in daily s-RPE between game days, days within 24 hours of games, and days >24 hours from games during the playoffs and regular season. Comparisons in weekly training, game, and total workloads were made between the playoffs and regular season using paired t tests and effect sizes.

RESULTS

A significant interaction between day and competitive period for s-RPE was found (P = .003, moderate). Lower s-RPE was apparent during playoff and regular-season days within 24 hours of games than all other days (P < .001, very large). Furthermore, s-RPE across days >24 hours from playoff games was different than all other days (P ≤ .01, moderate-very large). Weekly training (P = .009, very large) and total (P < .001, moderate) s-RPE were greater during the regular season than playoffs, whereas weekly game s-RPE was greater during the playoffs than the regular season (P < .001, very large).

CONCLUSIONS

This study presents an exploratory investigation of internal workload during the playoffs in professional basketball. Players experienced greater training and total weekly workloads during the regular season than during the playoffs with similar daily game workloads between periods.

Influence of Contextual Factors, Technical Performance, and Movement Demands on the Subjective Task Load Associated With Professional Rugby League Match-Play

PURPOSE

To identify the association between several contextual match factors, technical performance, and external movement demands on the subjective task load of elite rugby league players.

METHODS

Individual subjective task load, quantified using the National Aeronautics and Space Administration Task Load Index (NASA-TLX), was collected from 29 professional rugby league players from one club competing in the European Super League throughout the 2017 season. The sample consisted of 26 matches (441 individual data points). Linear mixed modeling revealed that various combinations of contextual factors, technical performance, and movement demands were associated with subjective task load.

RESULTS

Greater number of tackles (effect size correlation ± 90% confidence intervals; η2 = .18 ± .11), errors (η2 = .15 ± .08), decelerations (η2 = .12 ± .08), increased sprint distance (η2 = .13 ± .08), losing matches (η2 = .36 ± .08), and increased perception of effort (η2 = .27 ± .08) led to most likely-very likely increases in subjective total task load. The independent variables included in the final model for subjective mental demand (match outcome, time played, and number of accelerations) were unclear, excluding a likely small correlation with technical errors (η2 = .10 ± .08).

CONCLUSIONS

These data provide a greater understanding of the subjective task load and their association with several contextual factors, technical performance, and external movement demands during rugby league competition. Practitioners could use this detailed quantification of internal loads to inform recovery sessions and current training practices.

Internal and External Training Load in Under-19 versus Professional Soccer Players during the In-Season Period

This study aimed to compare the training load of a professional under-19 soccer team (U-19) to that of an elite adult team (EAT), from the same club, during the in-season period. Thirty-nine healthy soccer players were involved (EAT [n = 20]; U-19 [n = 19]) in the study which spanned four weeks. Training load (TL) was monitored as external TL, using a global positioning system (GPS), and internal TL, using a rating of perceived exertion (RPE). TL data were recorded after each training session. During soccer matches, players' RPEs were recorded. The internal TL was quantified daily by means of the session rating of perceived exertion (session-RPE) using Borg's 0-10 scale. For GPS data, the selected running speed intensities (over 0.5 s time intervals) were 12-15.9 km/h; 16-19.9 km/h; 20-24.9 km/h; >25 km/h (sprint). Distances covered between 16 and 19.9 km/h, > 20 km/h and >25 km/h were significantly higher in U-19 compared to EAT over the course of the study (p = 0.023, d = 0.243, small; p = 0.016, d = 0.298, small; and p = 0.001, d = 0.564, small, respectively). EAT players performed significantly fewer sprints per week compared to U-19 players (p = 0.002, d = 0.526, small). RPE was significantly higher in U-19 compared to EAT (p = 0.001, d = 0.188, trivial). The external and internal measures of TL were significantly higher in the U-19 group compared to the EAT soccer players. In conclusion, the results obtained show that the training load is greater in U19 compared to EAT.

Usefulness of Linear Mixed-Effects Models to Assess the Relationship between Objective and Subjective Internal Load in Team Sports

Internal load can be objectively measured by heart rate-based models, such as Edwards' summated heart rate zones, or subjectively by session rating of perceived exertion. The relationship between internal loads assessed via heart rate-based models and session rating of perceived exertion is usually studied through simple correlations, although the Linear Mixed Model could represent a more appropriate statistical procedure to deal with intrasubject variability. This study aimed to compare conventional correlations and the Linear Mixed Model to assess the relationships between objective and subjective measures of internal load in team sports. Thirteen male youth beach handball players (15.9 ± 0.3 years) were monitored (14 training sessions; 7 official matches). Correlation coefficients were used to correlate the objective and subjective internal load. The Linear Mixed Model was used to model the relationship between objective and subjective measures of internal load data by considering each player individual response as random effect. Random intercepts were used and then random slopes were added. The likelihood-ratio test was used to compare statistical models. The correlation coefficient for the overall relationship between the objective and subjective internal data was very large (r = 0.74; ρ = 0.78). The Linear Mixed Model using both random slopes and random intercepts better explained (p < 0.001) the relationship between internal load measures. Researchers are encouraged to apply the Linear Mixed Models rather than correlation to analyze internal load relationships in team sports since it allows for the consideration of the individuality of players.

Measuring Physical Demands in Basketball: An Explorative Systematic Review of Practices

BACKGROUND

Measuring the physical work and resultant acute psychobiological responses of basketball can help to better understand and inform physical preparation models and improve overall athlete health and performance. Recent advancements in training load monitoring solutions have coincided with increases in the literature describing the physical demands of basketball, but there are currently no reviews that summarize all the available basketball research. Additionally, a thorough appraisal of the load monitoring methodologies and measures used in basketball is lacking in the current literature. This type of critical analysis would allow for consistent comparison between studies to better understand physical demands across the sport.

OBJECTIVES

The objective of this systematic review was to assess and critically evaluate the methods and technologies used for monitoring physical demands in competitive basketball athletes. We used the term 'training load' to encompass the physical demands of both training and game activities, with the latter assumed to provide a training stimulus as well. This review aimed to critique methodological inconsistencies, establish operational definitions specific to the sport, and make recommendations for basketball training load monitoring practice and reporting within the literature.

METHODS

A systematic review of the literature was performed using EBSCO, PubMed, SCOPUS, and Web of Science to identify studies through March 2020. Electronic databases were searched using terms related to basketball and training load. Records were included if they used a competitive basketball population and incorporated a measure of training load. This systematic review was registered with the International Prospective Register of Systematic Reviews (PROSPERO Registration # CRD42019123603), and approved under the National Basketball Association (NBA) Health Related Research Policy.

RESULTS

Electronic and manual searches identified 122 papers that met the inclusion criteria. These studies reported the physical demands of basketball during training (n = 56), competition (n = 36), and both training and competition (n = 30). Physical demands were quantified with a measure of internal training load (n = 52), external training load (n = 29), or both internal and external measures (n = 41). These studies examined males (n = 76), females (n = 34), both male and female (n = 9), and a combination of youth (i.e. under 18 years, n = 37), adults (i.e. 18 years or older, n = 77), and both adults and youth (n = 4). Inconsistencies related to the reporting of competition level, methodology for recording duration, participant inclusion criteria, and validity of measurement systems were identified as key factors relating to the reporting of physical demands in basketball and summarized for each study.

CONCLUSIONS

This review comprehensively evaluated the current body of literature related to training load monitoring in basketball. Within this literature, there is a clear lack of alignment in applied practices and methodological framework, and with only small data sets and short study periods available at this time, it is not possible to draw definitive conclusions about the true physical demands of basketball. A detailed understanding of modern technologies in basketball is also lacking, and we provide specific guidelines for defining and applying duration measurement methodologies, vetting the validity and reliability of measurement tools, and classifying competition level in basketball to address some of the identified knowledge gaps. Creating alignment in best-practice basketball research methodology, terminology and reporting may lead to a more robust understanding of the physical demands associated with the sport, thereby allowing for exploration of other research areas (e.g. injury, performance), and improved understanding and decision making in applying these methods directly with basketball athletes.

Training load, recovery and game performance in semiprofessional male basketball: influence of individual characteristics and contextual factors

This study examined the effects of individual characteristics and contextual factors on training load, pre-game recovery and game performance in adult male semi-professional basketball. Fourteen players were monitored, across a whole competitive season, with the session-RPE method to calculate weekly training load, and the Total Quality Recovery Scale to obtain pre-game recovery scores. Additionally, game-related statistics were gathered during official games to calculate the Performance Index Rating (PIR). Individual characteristics and contextual factors were grouped using k-means cluster analyses. Separate mixed linear models for repeated measures were performed to evaluate the single and combined (interaction) effects of individual characteristics (playing experience; playing position; playing time) and contextual factors (season phase; recovery cycle; previous game outcome; previous and upcoming opponent level) on weekly training load, pre-game recovery and PIR. Weekly load was higher in guards and medium minute-per-game (MPG) players, and lower for medium-experienced players, before facing high-level opponents, during later season phases and short recovery cycles (all p < 0.05). Pre-game recovery was lower in centers and high-experience players (p < 0.05). Game performance was better in high-MPG players (p < 0.05) and when facing low and medium-level opponents (p < 0.001). Interestingly, players performed better in games when the previous week's training load was low (p = 0.042). This study suggests that several individual characteristics and contextual factors need to be considered when monitoring training load (playing experience, playing position, playing time, recovery cycle, upcoming opponent level), recovery (playing experience, playing position) and game performance (opponent level, weekly training load, pre-game recovery) in basketball players during the competitive season.