The Effect of Match Schedule on Accelerometry-Derived Exercise Dose during Training Sessions throughout a Competitive Basketball Season

Contextual factors influencing basketball training and competition demands: a systematic review

This systematic review described the effects of contextual factors on basketball training and competition demands. A comprehensive search and process led to the inclusion of 28 articles representing 646 basketball players. Fourteen contextual factors were identified. A decrease in external load variables was evident in the fourth quarter of games compared to the first quarter (effect sizes: small-large). The impact of game locations on load variables was inconsistent. Game outcomes did not influence external or internal loads. Conversely, close games were associated with higher physical and physiological demands than unbalanced games (effect sizes: moderate-very large). Higher external game loads were found in won quarters (effect size: small) and during scoring streaks (effect size: moderate). In youth male teams, those with superior performance covered less distances and exhibited better locomotor ratios compared to lower-level counterparts. Weekly external and internal training loads were adjusted according to the opponent's level in adult males. Internal game loads were found to be consistent across different season phases. Weekly total loads were higher during periods of congested schedules (effect sizes: moderate-very large), with training loads being reduced to offset the increased demands of game loads (effect size: moderate). This review offers basketball practitioners' insights into the external and internal loads that can be anticipated based on the contextual factors of training and competition.

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.

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.

Training and match demands differ between the regular season and finals in semi-professional basketball

Basketball competitions often include a scheduled regular season followed by knock-out finals. Understanding training and match demands through the season can help optimize performance and reduce injury risk. This study investigated whether training and/or match demands differed between the regular season and finals, and whether these differences were dependent on player role. Average session intensity and volume and durations of relative exercise intensities (inactive, light, moderate-vigorous, maximal, supramaximal) were quantified during training sessions and matches using accelerometry in two semi-professional basketball teams (n = 23; 10 women, 13 men). Training and match demands were compared between the regular season (training: 445 observations; matches: 387 observations) and finals (training: 113 observations, matches: 75 observations) with consideration of player role (starters, in-rotation bench, out-rotation bench). During finals matches, starters received 4.4 min more playing time (p = 0.03), performed 14% more absolute maximal activity (p < 0.01) and had 8% less relative inactive time (p = 0.02) when compared to the regular season. Out-rotation bench players received 2.1 min less playing time (p < 0.01), performed 33% less absolute maximal activity (p = 0.01) and 57% less absolute supramaximal activity (p < 0.01) in finals when compared to the regular season. During finals training sessions, average training intensity was 5% higher (p = 0.02), absolute moderate-vigorous activity was 3% higher (p = 0.04), relative maximal activity was 12% higher (p < 0.01), and relative inactive time was 5% lower (p = 0.03) when compared to the regular season. These findings suggest starters need to be physically prepared for greater match demands during finals, while out-rotation bench players should supplement their training during finals with extra supramaximal activity to maintain their conditioning levels for matches.

Development Status and Influencing Factors of Competitive Basketball Management System under the Background of Deep Learning

Competitive basketball is one of the most popular sports in the world. With the development of China's sports power strategy, the national movement has strengthened the status of basketball in sports. However, China's competitive basketball ranking is not high in the world, and the analysis of the reasons should start with the management system. Therefore, this paper aims to explore the development status and influencing factors of China's competitive basketball management system under the background of deep learning. For the background of deep learning, this paper describes the application of deep learning algorithms in basketball strategy. It adopts the expert interview method for the competitive basketball management system and elaborates in detail on five aspects: target mechanism, competition mechanism, selection mechanism, market mechanism, and incentive mechanism. The experimental results of the article believe that, based on the suggestions of 10 experts, the current Chinese competitive basketball selection mechanism is the most influential factor, with a weight of 16.1%, and the smallest impact is the level of athletes, accounting for 11.4%.

Residual neuromuscular fatigue influences subsequent on-court activity in basketball

AbstractThe aim of this study was to determine if residual neuromuscular fatigue influenced subsequent match and training activity in professional women's basketball. Prior to matches and training sessions throughout a season, players performed countermovement jumps while wearing a magnetic, angular rate and gravity (acceleration) sensor on their upper back. Flight time to contraction time ratio was used to determine neuromuscular performance and to identify neuromuscular fatigue. Average session intensity and volume, proportion of live time spent in different intensity bands (matches), and absolute and relative time spent in different intensity bands (training) were quantified using accelerometry. Residual neuromuscular fatigue was deemed to be present when the decrement in neuromuscular performance relative to pre-season baseline was greater than the smallest worthwhile change. Players displayed residual neuromuscular fatigue before 16% of matches and 33% of training sessions. When players were fatigued prior to matches, the proportion of live time undertaking supramaximal activity was 5.7% less (p = 0.02) and moderate-vigorous activity was 3.7% more than when not fatigued (p = 0.02). When fatigued prior to training, the players displayed a 2.6% decrement in average intensity (p = 0.02), 2.8% decrement in absolute (p = 0.01) and 5.0% decrement in relative (p = 0.01) maximal activity, as well as 13.3% decrement in absolute (p < 0.01) and 6.8% decrement in relative (p < 0.01) supramaximal activity when compared to not being fatigued. These findings suggest that residual neuromuscular fatigue influences players' ability to perform supramaximal activity, which highlights the importance of monitoring neuromuscular performance throughout a professional season.

The Effect of Rifle Carriage on the Physiological and Accelerometer Responses During Biathlon Skiing

Purpose

Investigate the effect of biathlon rifle carriage on physiological and accelerometer-derived responses during biathlon skiing.

Methods

Twenty-eight biathletes (11F, 17M) completed two XC skiing time-trials (~2,300 m), once with and once without the biathlon rifle, with concurrent measurements of HR, skiing speed and accelerations recorded from three triaxial accelerometers attached at the Upper-spine, Lower-spine and Pelvis. Exercise intensity was quantified from HR, skiing speed as well from accelerometry-derived PlayerLoad™ per minute (PL·min^-1) and average net force (AvF_Net). All metrics were analyzed during Uphill, Flat and Downhill sections of the course. Relationships between accelerometry-derived metrics and skiing speed were examined.

Results

Time-trials were faster for males compared with females (mean difference: 97 ± 73 s) and No-Rifle compared to With-Rifle (mean difference: 16 ± 9 s). HR was greatest during Downhill (183 ± 5 bpm), followed by Uphill (181 ± 5 bpm) and was lowest in the Flat sections (177 ± 6 bpm, p <0.05). For PL·min^-1 and AvF_Net there were 3-way Rifle x Gradient x Sensor-Position interactions. Typically, these metrics were greatest during Uphill and Flat sections and were lowest during Downhill sections. Rifle carriage had no impact on the AvF_Net at the Lower-Spine or Pelvis. Significant positive linear relationships were identified between skiing speed and accelerometer-derived metrics during Uphill, Flat and Downhill skiing (r = 0.12-0.61, p < 0.05).

Conclusions

The accelerometry-derived approach used in this study provides the potential of a novel method of monitoring the external demands during skiing. In particular, AvF_Net with sensors located close to the center of mass displayed greatest utility because it followed the expected response of external intensity where responses were greatest during uphill sections, followed by flats and lowest during downhills. In addition, there were significant positive relationships between AvF_Net and skiing speed ranging from small to large. Accelerometry-derived measures could provide useful estimates of the external demands in XC skiing and biathlon.

Comparison of the most demanding scenarios during different in-season training sessions and official matches in professional basketball players

The purpose of this study was to compare physical demands during the most demanding scenarios (MDS) of different training sessions and official matches in professional basketball players across playing positions. Thirteen professional basketball players were monitored over a 9-week competitive season using a local positioning system. Peak physical demands included total distance, distance covered at > 18 km·h^-1, distance and number of accelerations (≥ 2 m∙s^-2) and decelerations (≤ -2 m∙s^-2) over a 60-second epoch. Analysis of variance for repeated measures, Bonferroni post-hoc tests and standardised Cohen's effect size (ES) were calculated. Overall, almost all physical demands during the MDS of training were lower (-6.2% to -35.4%) compared to official matches. The only variable that surpassed competition demands was distance covered at > 18 km·h^-1, which presented moderate (ES = 0.61, p = 0.01) and small (ES = 0.48, p > 0.05) increases during training sessions four and three days before a competition, respectively. Conversely, the two previous practices before match day presented trivial to very large decreases (ES = 0.09-2.66) in all physical demands. Furthermore, centres achieved the lowest peak value in total distance covered during matches, forwards completed the greatest peak distance at > 18 km·h^-1, and guards performed the greatest distance and number of high-intensity accelerations and decelerations. In conclusion, physical demands during the MDS of different training sessions across the microcycle failed to match or surpass peak values during official matches, which should be considered when prescribing a training process intended to optimise the MDS of match play.

Simplifying External Load Data in NCAA Division-I Men's Basketball Competitions: A Principal Component Analysis

The primary purpose was to simplify external load data obtained during Division-I (DI) basketball competitions via principal component analysis (PCA). A secondary purpose was to determine if the PCA results were sensitive to load demands of different positional groups (POS). Data comprised 229 observations obtained from 10 men's basketball athletes participating in NCAA DI competitions. Each athlete donned an inertial measurement unit that was affixed to the same location on their shorts prior to competition. The PCA revealed two factors that possessed eigenvalues >1.0 and explained 81.42% of the total variance. The first factor comprised total decelerations (totDEC, 0.94), average speed (avgSPD, 0.90), total accelerations (totACC, 0.85), total mechanical load (totMECH, 0.84), and total jump load (totJUMP, 0.78). Maximum speed (maxSPD, 0.94) was the lone contributor to the second factor. Based on the PCA, external load variables were included in a multinomial logistic regression that predicted POS (Overall model, p < 0.0001; AUC_centers = 0.93, AUC_guards = 0.88, AUC_forwards = 0.80), but only maxSPD, totDEC, totJUMP, and totMECH were significant contributors to the model's success (p < 0.0001 for each). Even with the high significance, the model still had some issues differentiating between guards and forwards, as in-game demands often overlap between the two positions. Nevertheless, the PCA was effective at simplifying a large external load dataset collected on NCAA DI men's basketball athletes. These data revealed that maxSPD, totDEC, totJUMP, and totMECH were the most sensitive to positional differences during competitions. To best characterize competition demands, such variables may be used to individualize training and recovery regimens most effectively.

The Relationship Between Cardiorespiratory and Accelerometer-Derived Measures in Trail Running and the Influence of Sensor Location

PURPOSE

To examine the relationship between cardiorespiratory and accelerometer-derived measures of exercise during trail running and determine the influence of accelerometer location.

METHODS

Eight trail runners (7 males and 1 female; age 26 [5] y; maximal oxygen consumption [V˙O2] 70 [6] mL·kg-1·min-1) completed a 7-km trail run (elevation gain: 486 m), with concurrent measurements of V˙O2, heart rate, and accelerations recorded from 3 triaxial accelerometers attached at the upper spine, lower spine, and pelvis. External exercise intensity was quantified from the accelerometers using PlayerLoad™ per minute and accelerometry-derived average net force. External exercise volume was calculated using accumulated PlayerLoad and the product of average net force and duration (impulse). Internal intensity was calculated using heart rate and V˙O2-metrics; internal volume was calculated from total energy expenditure (work). All metrics were analyzed during both uphill (UH) and downhill (DH) sections of the trail run.

RESULTS

PlayerLoad and average net force were greater during DH compared with UH for all sensor locations (P ≤ .004). For all accelerometer metrics, there was a sensor position × gradient interaction (F2,1429.003; P <.001). The upper spine was lower compared with both pelvis (P ≤ .003) and lower spine (P ≤ .002) for all accelerometer metrics during both UH and DH running. Relationships between accelerometer and cardiorespiratory measures during UH running ranged from moderate negative to moderate positive (r = -.31 to .41). Relationships were stronger during DH running where there was a nearly perfect correlation between work and impulse (r = .91; P < .001).

CONCLUSIONS

Simultaneous monitoring of cardiorespiratory and accelerometer-derived measures during trail running is suggested because of the disparity between internal and external intensities during changes in gradient. Sensor positioning close to the center of mass is recommended.

Criterion Validity of an Automated Method of Detecting Live Play Periods in Basketball

This study aimed to develop an automated method to detect live play periods from accelerometry-derived relative exercise intensity in basketball, and to assess the criterion validity of this method. Relative exercise intensity (% oxygen uptake reserve) was quantified for two men's semi-professional basketball matches. Live play period durations were automatically determined using a moving average sample window and relative exercise intensity threshold, and manually determined using annotation of video footage. The sample window duration and intensity threshold were optimised to determine the input parameters for the automated method that would result in the most similarity to the manual method. These input parameters were used to compare the automated and manual active play period durations in another men's semi-professional match and a women's professional match to assess the criterion validity of the automated method. The optimal input parameters were a 9-s sample window and relative exercise intensity threshold of 31% oxygen uptake reserve. The automated method showed good relative (ρ = 0.95–0.96 and ICC = 0.96–0.98, p < 0.01) and absolute (median bias = 0 s) agreement with the manual method. These findings support the use of an automated method using accelerometry-derived relative exercise intensity and a moving average sample window to detect live play periods in basketball.

Misuse of the term 'load' in sport and exercise science

Despite the International System of Units (SI), as well as several publications guiding researchers on correct use of terminology, there continues to be widespread misuse of mechanical terms such as 'work' in sport and exercise science. A growing concern is the misuse of the term 'load'. Terms such as 'training load' and 'PlayerLoad' are popular in sport and exercise science vernacular. However, a 'load' is a mechanical variable which, when used appropriately, describes a force and therefore should be accompanied with the SI-derived unit of the newton (N). It is tempting to accept popular terms and nomenclature as scientific. However, scientists are obliged to abide by the SI and must pay close attention to scientific constructs. This communication presents a critical reflection on the use of the term 'load' in sport and exercise science. We present ways in which the use of this term breaches principles of science and provide practical solutions for ongoing use in research and practice.

Influence of Match Congestion on Performances in the National Basketball Association

The ability to recover from official match-play across a single and multiple matches is often considered a key factor in subsequent performance for modern professional basketball. The aims of this study were to: (i) explore the differences in match performances between different match congestion cycles (i.e., matches separated by zero, one, or two or greater days of rest); and (ii) identify the key performance indicators (KPIs) discriminating between winning and losing during different match congestion cycles. The current study indicated that scoring close to (i.e., within the paint) (ES = 0.08) or very far away (i.e., Three-point, ES = 0.05) was significantly greater for winning matches separated by 1- and 2-days of rest compared to consecutive matches (i.e., 0 rest days between matches). Additionally, shooting efficiency (P < 0.001), and attaining Defensive Rebounds (P < 0.001) and Steals (P < 0.001), were significant offensive and defensive KPIs that differentiated winning and losing teams. Similarly, opponent quality and match pace were important situational variables that affected match outcome during different match congestion cycles. While match location had an impact on winning following 1- and 2-days of rest, it had no impact for back-to-back matches (i.e., 0 days between matches). The current results will support coaches' offensive, defensive and recovery strategies during various match congestion cycles for a greater probability of winning NBA matches.

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.

Discrepancies Exist between Exercise Prescription and Dose in Elite Women's Basketball Pre-Season

This study assessed the influence of exercise prescription on the objectively measured exercise dose in basketball. Intensity (RPE) and volume (sRPE) were prescribed by a professional coach on a drill-by-drill basis during pre-season training for nine elite basketball players. Training drills were classified by prescribed intensity (easy-moderate, moderate-hard, hard–very hard, and very hard-maximal) and type (warm-up, skill-development, offensive- and defensive-technical/tactical, or match-simulation). Exercise intensity was objectively quantified using accelerometry-derived average net force (AvF_Net) and time spent in accelerometry-derived relative intensity zones. The volume of exercise (exercise dose) was objectively quantified using accumulated impulse (AvF_Net × duration). Relationships between prescribed volume and exercise dose were explored by correlations between sRPE and drill-by-drill accumulation of sRPE (dRPE) with impulse. Very hard-maximal drill intensity was greater than hard-very hard (p = 0.011), but not moderate-hard (p = 0.945). Very hard-maximal drills included the most time performing Supra-maximal intensity (>100% V˙O₂R) efforts (p < 0.001), suggesting that intensity prescription was based upon the amount of high-intensity exercise. Correlations between impulse with sRPE and dRPE were moderate (r = 0.401, p = 0.197) and very-large (r = 0.807, p = 0.002), respectively, demonstrating that the coach misinterpreted the accumulative effect of drill volume over an entire training session. Overall, a mismatch existed between exercise prescription and exercise dose. Objective monitoring might assist coaches to improve precision of exercise prescription.

Monitoring External Training Loads and Neuromuscular Performance for Division I Basketball Players over the Preseason

Limited research has paralleled concomitant changes in external training load (eTL) and countermovement jump (CMJ) performance. Therefore, this investigation characterized eTL and CMJ performance changes across preseason training in Division 1 male collegiate basketball athletes, while examining the influence of position (Guard vs. Forward/Center) and scholarship status (Scholarship = S vs. Walk-on = WO). During 22 practices, eTL was monitored in 14 male athletes, with weekly CMJs performed to quantify neuromuscular performance (Jump Height [JH], Flight Time:Contraction Time [FT:CT], Reactive Strength Index Modified [RSIMod ]). PlayerLoad per minute was significantly higher during W1 and W2 (5.4 ± 1.3au and 5.3 ± 1.2au, respectively; p < 0.05) compared to subsequent weeks, but no additional differences in eTL parameters across time were observed. Scholarship athletes displayed greater PlayerLoad (S = 777.1 ± 35.6, WO = 530.1 ± 56.20; Inertial Movement Analysis (IMA) IMA_High (S = 70.9 ± 15.2, WO = 41.3 ± 15.2); IMA_Medium (S = 159.9 ± 30.7, WO = 92.7 ± 30.6); and IMA_Low (S = 700.6 ± 105.1, WO = 405 ± 105.0;) (p < 0.05), with no observed differences in eTL by position. Moderate decreases in FT:CT and RSIMod paralleled increased eTL. Significant increases in practice intensity (W1 and W2) did not impact CMJ performance, suggesting athletes could cope with the prescribed training loads. However, moderate perturbations in FT:CT and RSIMod paralleled the weeks with intensified training. Cumulatively, scholarship status appears to influence eTL while player position does not.

Setting Kinematic Parameters That Explain Youth Basketball Behavior: Influence of Relative Age Effect According to Playing Position

Pino-Ortega, J, Gómez-Carmona, CD, Nakamura, FY, Rojas-Valverde, D, and Effect, RA. Setting kinematic parameters that explain youth basketball behavior: Influence of relative age effect according to playing position. J Strength Cond Res XX(X): 000-000, 2020-The aims of the present study were to: (a) set kinematic behavior parameters during official matches by principal component analysis (PCA), (b) examine the distribution of birth dates in competitive basketball, differentiating by playing position and, (c) analyze the relative age effect (RAE) on kinematic performance according to playing position. A total of 94 young elite athletes participated in an official U18 Euroleague tournament (8 clubs, 4 days, 3 games). Kinematic motion variables were measured using an inertial device worn by all players during matches. A total of 252 variables were measured, a PCA was performed to select them for final analysis and 3 principal components and 6 variables were extracted (maximum acceleration [MAcc], average acceleration [PAcc], landing 8-100 G [Ldg 8-100 g·min], relative distance [RD], jump average take off [MJumpsTO], and jump average landing [MJumpsLdg]). These variables explained 66.3% of total variance. Differences were found in RD (p = 0.04; ωp = 0.02), PAcc (p = 0.04; ωp = 0.02), MAcc (p < 0.01; ωp = 0.03), and Ldg 8-100 g·min (p = 0.02; ωp = 0.02) because of RAE. There were differences by playing position in Ldg 8-100 g·min (guards, p = 0.04; ωp = 0.03), MAcc (forwards, p < 0.01; ωp = 0.07; centers, p < 0.01; ωp = 0.44), PAcc (centers, p < 0.01; ωp = 0.34) and in MJumpsLdg (centers, p = 0.03; ωp = 0.13). Results suggested that RAE does have an impact on kinematic variables, and is affected by playing position in variables such as MAcc, PAcc, MJumpsLdg, and Ldg 8-100 g·min. The extracted variables are well-known intensity indicators and fundamental performance variables. This evidence should be taken into account by sport scientists and coaches to develop individualized training programs and match tactics.

Longitudinal Changes of Functional Capacities Among Adolescent Female Basketball Players

Background: The interpretation of young athletes' performance during pubertal years is important to support coaches' decisions, as performance may be erroneously interpreted due to the misalignment between chronological age (CA), biological age (BA) and sport age (SA). Aim: Using a Bayesian multilevel approach, the variation in longitudinal changes in performance was examined considering the influence of CA, BA (age at menarche), SA, body size, and exposure to training among female basketball players. Method: The study had a mixed-longitudinal design. Thirty eight female basketball players (aged 13.38 ± 1.25 years at baseline) were measured three times per season. CA, BA and SA were obtained. Anthropometric and functional measures: countermovement jump, Line drill (LD), Yo-Yo (Yo-Yo IR1). Based on the sum of the z-scores, an index of overall performance was estimated. The effects of training on longitudinal changes in performance were modeled. Results: A decrease in the rate of improvements was apparent at about 14 years of age. When aligned for BA, the slowing of the rate of improvements is apparent about 2 years after menarche for LD. For countermovement jump longitudinal changes, when performance was aligned for BA improvements became linear. For Yo-Yo IR1 and performance index, both indicators showed a linear trend of improvement when aligned for CA and BA, separately. Older players showed higher rates of improvement for Yo-Yo IR1 and performance index from pre-season to end-season. When considering performance changes aligned for BA it was apparent an improvement of performance as players became biologically mature. Conclusions and Implications: The alignment of CA with BA and SA provides important information for coaches. Human growth follows a genetically determined pattern, despite variation in both tempo and timing. When the effects of maturation reach their end, all the girls went through the same process. Hence, there is no need to artificially manipulate youth competitions in order to accelerate gains that sooner or later reach their peak and tend to flat their improvement curve.