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Practical Applications of External Peak Demands in Basketball to Optimize the Compensatory Training with Unselected or Fringe Players
Since the analysis of most demanding scenarios (MDS) in basketball has improved the practical knowledge about match demands and possible impacts for the training process, it seems important to summarize the scientific evidence providing useful information and future directions related to MDS. This review assesses the results reflected in the available literature about the MDS in basketball, synthesizing and discussing data from scientific papers, and then providing relevant insights about terminology, sex and sample size, competition category, workload variables recorded, technology used, method of calculation, time windows analyzed, and activities evaluated related to MDS. Therefore, the present narrative review would be of practical use for coaches, scientists, athletes as well as strength and conditioning trainers exploring the current trends and future directions related to MDS in basketball.
The aim of this study was (I) to establish absolute specific velocity thresholds during basketball games using local positional system (LPS) and (II) to compare the speed profiles between various levels of competitions. The variables recorded were total distance (TD); meters per minute (m·min); real time (min); maximum speed (Km h−1), distance (m), percentage distance, and percentage duration invested in four speed zones (standing–walking; jogging; running; and high-speed running). Mean and standard deviation (±SD) were calculated, and a separate one-way analysis of variance was undertaken to identify differences between competitions. TD (3188.84 ± 808.37 m) is covered by standing–walking (43.51%), jogging (36.58%), running (14.68%), and sprinting (5.23%) activities. Overall, 75.22% of the time is invested standing–walking, jogging (18.43%), running (4.77%), and sprinting (1.89%). M·min (large effect size), % duration zone 2 (moderate effect size); distance zone 4 (large effect size), and % distance zone 4 (very large effect size) are significantly higher during junior than senior. However, % distance zone 1 (large effect size) and % duration zone 1 (large effect size) were largely higher during senior competition. The findings of this study reveal that most of the distance and play time is spent during walking and standing activities. In addition, the proportion of time spent at elevated intensities is higher during junior than in senior competition.
The purpose of this study was to compare peak external intensities across game quarters in basketball. Eight semi-professional male players were monitored using accelerometers. For all quarters, peak intensities were determined via moving averages for PlayerLoad/minute (PL·min) using sample durations of 15 s, 30 s, 1 min, 2
min, 3 min, 4 min, and 5 min. Linear mixed models and effect sizes (ES) were used to compare peak intensities between quarters for each sample duration. Small decreases in peak PL·min occurred between Quarters 1 and 4 for all sample durations (ES = 0.21-0.49). Small decreases in peak PL·min were apparent between quarters 1 and 2 for 30-s, 1-min, and 3-min sample durations (ES = 0.24-0.33), and between quarters 3 and 4 for 2-5-min sample durations (ES = 0.20-0.24). Peak intensities decline across quarters with game progression in basketball, providing useful insight for practitioners to develop game-specific training and tactical strategies.
The main purpose of this study was to describe the most demanding scenarios of match play in basketball through a number of physical demand measures (high-intensity accelerations and decelerations, relative distance covered, and relative distance covered in established speed zones) for four different rolling average time epochs (30, 60, 180, and 300 s) during an official international tournament. A secondary purpose was to identify whether there were significant differences in physical demand measures among playing positions (centers, guards, and forwards) and levels (two best classified teams in the tournament and remaining teams), match scoring (winning, losing, and drawing), and playing periods (match quarter) at the moment of the most demanding scenarios. Data were collected from 94 male under 18 (U18) elite basketball players (age: 17.4 ± 0.7 years; stature: 199.0 ± 11.9 cm; body mass: 87.1 ± 13.1 kg) competing in a Euroleague Basketball Tournament. Measures were compared via a Bayesian inference analysis. The results revealed the presence of position-related differences [Bayesian factor (BF) > 10 (at least strong evidence) and standardized effect size (δ) > 0.6 (at least moderate)] so that centers covered a lower relative distance at speed zone 1 and had lower high-intensity accelerations and decelerations than guards. However, the Bayesian analysis did not demonstrate the existence of significant differences in any physical demand measure in relation to the playing level, match scoring, and playing periods at the moment of the most demanding scenarios. Therefore, this study provides coaches and strength and conditioning specialists with a most demanding scenario reference on physical demands that can be used as an upper limit threshold in the training and rehabilitation monitoring processes.
Background:
The aim of this study is to describe the peak match demands and compare them with average demands in basketball players, from an external load point of view, using different time windows. Another objective is to determine whether there are differences between positions and to provide an approach for practical applications.
Methods:
During this observational study, each player wore a micro technology device. We collected data from 12 male basketball players (mean ± SD: age 17.56 ± 0.67 years, height 196.17 ± 6.71 cm, body mass 90.83 ± 11.16 kg) during eight games. We analyzed intervals for different time windows using rolling averages (ROLL) to determine the peak match demands for Player Load. A separate one-way analysis of variance (ANOVA) was used to identify statistically significant differences between playing positions across different intense periods.
Results:
Separate one-way ANOVAs revealed statistically significant differences between 1 min, 5 min, 10 min, and full game periods for Player Load, F (3,168) = 231.80, ηp2 = 0.76, large, p < 0.001. It is worth noting that guards produced a statistically significantly higher Player Load in 5 min (p < 0.01, ηp2 = -0.69, moderate), 10 min (p < 0.001, ηp2 = -0.90, moderate), and full game (p < 0.001, ηp2 = -0.96, moderate) periods than forwards.
Conclusions:
The main finding is that there are significant differences between the most intense moments of a game and the average demands. This means that understanding game demands using averages drastically underestimates the peak demands of the game. This approach helps coaches and fitness coaches to prepare athletes for the most demanding periods of the game and present potential practical applications that could be implemented during training and rehabilitation sessions.
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.
The sentiments and feelings like the aforementioned may clearly affect the balance between happiness and wellness (Calleja-Gonzalez et al., 2018). In that way, coaches focus on respecting, valuing, involving, engaging in dialogue with, listening to, and supporting players, as well as treating them as human beings, giving them the confidence and feelings of responsibility to try (BarkerRuchti et al., 2014). There is a clear need for more research in this area, although some advances were already made by examining empathy using qualitative methods and identifying factors of empathy between athletes and coaches (David and Larson, 2018). Furthermore, a period of constructive reflection considering the relationship between performance analysis and recovery is strongly recommended (Calleja-González et al., 2018). Thus, there is a gap between research and reality (Buchheit, 2017), because players express that they are more fatigued from traveling than from training or competition, which is the focus of this letter.A shift in the approach to sports performance research seems to be necessary. For example, sleep quality and quantity (Gupta et al., 2017), burden associated to traveling (Fowler et al., 2014), chronobiological disturbance (Drust et al., 2005) are often cited as limiting factors of performance in high level sport, and their impact should be considered and assessed. Further, the additive effect or the means by which one factor influence another should be taken into account (Tobias et al., 2013).Elite athletes are exposed to substantial training loads , however, that is only a (small) part of the key determinants of performance. Current trends in expertise describe the concept as a dynamically varying relationship captured by the constraints of the environment and those of the performer of a task (RW.ERROR -Unable to find reference:4304). Using this approach, the context is key and should not be detached from the content, thus, the guidelines for designing and implementation of a training program will benefit from incorporating environmental information, integrated periodization, mental performance, skill acquisition, or nutrition (Mujika et al., 2018). In addition, using the aforementioned methods in combination with athlete monitoring of training, competition and psychological load, and pooled with assessments of recovery, well-being, and illness . It may enable the achievement of enhanced performance levels.Since extended traveling is common in elite sport (Flatt et al., 2019), it is recommended that coaches and applied sports scientists consider the following key points in order to minimize injury risk, enhance recovery, optimize performance and bring down the effect of traveling and sleep disturbance on performance (Vitale et al., 2019):-Monitor external training load (before, during and after competition) using tracking systems (Fox et al., 2017) with the least possible invasion.-Monitor Internal responses using heart rate measures and biomarkers in blood, saliva and/or urine before, during and after competition (Halson, 2014).-Monitor daily sleep quality, sleep duration, and player wellbeing to inform same day adjustments to training and competition workload (Fox et al., 2019).-Arrive early to competition destination in order to include sufficient time on-site to recover from traveling and adjust to new time-zones, altitudes, climates and environments (Lastella et al., 2019).-Avoid environmental changes because changing physical sleep environments may increase susceptibility to altered sleep responses, which may negatively affect performance (Pitchford et al., 2017).-Develop and apply consistent strategies (pre, during and post-traveling) that may help prevent or ease jet lag (Fowler et al., 2014).-Develop and apply an ad-hoc nutrition plan for traveling .Stress on the body is probably cumulative (Issurin, 2009). Therefore, the development of new variables, such as ratios, that might relate player's fatigue, training demands, match performance, environmental conditions, at home or away, could be an interesting open window to explore. Further, the creation and validation of a travel fatigue scale would enhance an understanding of the travelling effect. Also, a scale of mental fatigue (Russell et al., 2019) that informs about the stress derived from training, competition and environmental stress would be most useful.With the increasing popularity of sport, number of contests, and travel demands on the rise, the importance of athlete load monitoring in combination with nutritional programming, implementation of recovery methods, and proper sleep practices cannot be underestimated. Taking these steps will make for a more effective travel experience and support athlete health and playing career longevity. In the same page, rationalizing the use of measurement instruments and procedures seems also a need, as anecdotally suggests that "strict data-led regimes undermine trust and stifle creativity, shackling a player's natural empathy with the game", thus, "it is vital that those who oversee performance in elite sport consider the consequences on players of such intense surveillance". • Bus/plane traveling (seats ergonomic, number of disposable seats in bus/plane).• Seating positions/dangerous seating positions (players education and control).• Muscle activation during traveling.• Intellectual activity during traveling.• Problem with sleep medicaments (hypotonic effects).• Sleep banking between travels and games.• Designing individual players traveling profile.• Plane/bus vibration effect on athlete's bodies.• Plane/bus engine noise stressor effect.
The primary purpose of this study was to identify potential risk factors for sports injuries in professional basketball. An observational retrospective cohort study involving a male professional basketball team, using game tracking data was conducted during three consecutive seasons. Thirty-three professional basketball players took part in this study. A total of 29 time-loss injuries were recorded during regular season games, accounting for 244 total missed games with a mean of 16.26 ± 15.21 per player and season. The tracking data included the following variables: minutes played, physiological load, physiological intensity, mechanical load, mechanical intensity, distance covered, walking maximal speed, maximal speed, sprinting maximal speed, maximal speed, average offensive speed, average defensive speed, level one acceleration, level two acceleration, level three acceleration, level four acceleration, level one deceleration, level two deceleration, level three deceleration, level four deceleration, player efficiency rating and usage percentage. The influence of demographic characteristics, tracking data and performance factors on the risk of injury was investigated using multivariate analysis with their incidence rate ratios (IRRs). Athletes with less or equal than 3 decelerations per game (IRR, 4.36; 95% CI, 1.78-10.6) and those running less or equal than 1.3 miles per game (lower workload) (IRR, 6.42 ; 95% CI, 2.52-16.3) had a higher risk of injury during games (p < 0.01 in both cases). Therefore, unloaded players have a higher risk of injury. Adequate management of training loads might be a relevant factor to reduce the likelihood of injury according to individual profiles.
Background: Basketball is a popular, court-based team sport that has been extensively studied over the last decade.
Objective: The purpose of this article was to provide a systematic review regarding the activity demands and physiological responses experienced during basketball match-play according to playing period, playing position, playing level, geographical location, and sex.
Methods: The electronic database search of relevant articles published prior to 30 September 2016 was performed with PubMed, MEDLINE, ERIC, Google Scholar, SCIndex, and ScienceDirect. Studies that measured activity demands and/or physiological responses during basketball match-play were included.
Results: Following screening, 25 articles remained for review. During live playing time across 40-min matches, male and female basketball players travel 5-6 km at average physiological intensities above lactate threshold and 85% of maximal heart rate. Temporal comparisons show a reduction in vigorous activities in the fourth quarter, likely contributing to lower blood lactate concentrations and heart rate (HR) responses evident towards the end of matches. Guards tend to perform a higher percentage of live playing time sprinting and performing high-intensity shuffling compared with forwards and centers. Guards also perform less standing and walking during match-play compared with forwards and centers. Variations in activity demands likely account for the higher blood lactate concentrations and HR responses observed for guards compared to forwards and centers. Further, higher level players perform a greater intermittent workload than lower level players. Moreover, geographical differences may exist in the activity demands (distance and frequency) and physiological responses between Australian, African and European basketball players, whereby Australian players sustain greater workloads. While activity demands and physiological data vary across playing positions, playing levels, and geographical locations, male and female players competing at the same level experience similar demands.
Conclusion: The current results provide a detailed description of the specific requirements placed on basketball players during match-play according to playing period, playing level, playing position, geographical location, and sex, which may be useful in the development of individualized basketball training drills.
Purpose:
To quantify the accumulative training and match load during an annual season in English Premier League soccer players classified as starters (n=8, started ≥60% of games), fringe players (n=7, started 30-60% of games) and non-starters (n=4, started <30%% of games).
Methods:
Players were monitored during all training sessions and games completed in the 2013-2014 season with load quantified using GPS and Prozone technology, respectively.
Results:
When including both training and matches, total duration of activity (10678 ± 916, 9955 ± 947, 10136 ± 847 min; P=0.50) and distance covered (816.2 ± 92.5, 733.8 ± 99.4, 691.2 ± 71.5 km; P=0.16) was not different between starters, fringe and non-starters, respectively. However, starters completed more (all P<0.01) distance running at 14.4-19.8 km/h (91.8 ± 16.3 v 58.0 ± 3.9 km; ES=2.5), high speed running at 19.9-25.1 km/h (35.0 ± 8.2 v 18.6 ± 4.3 km; ES=2.3) and sprinting at >25.2 km/h (11.2 ± 4.2, v 2.9 ± 1.2 km; ES=2.3) than non-starters. Additionally, starters also completed more sprinting (P<0.01. ES=2.0) than fringe players who accumulated 4.5 ± 1.8 km. Such differences in total high-intensity physical work done were reflective of differences in actual game time between playing groups as opposed to differences in high-intensity loading patterns during training sessions.
Conclusions:
Unlike total seasonal volume of training (i.e. total distance and duration), seasonal high-intensity loading patterns are dependent on players' match starting status thereby having potential implications for training programme design.
To describe the physiological and activity demands experienced by Australian female basketball players during competition.
A between-subjects (positional comparison) repeated measures (playing periods) observational experimental design was followed.
State-level basketball players (n=12; age: 22.0±3.7 yr; body mass: 72.9±14.2 kg; stature: 174.2±6.9 cm; body fat: 17.2±5.6%; estimated V˙O(2max):43.3±5.7 ml kg⁻¹ min⁻¹) volunteered to participate. Heart rate (HR) and blood lactate concentration ([BLa]) were collected across eight competitive matches. Overall and positional player activity demands were calculated across three matches using time-motion analysis methodology. Activity frequencies, total durations and total distances were determined for various activity categories.
Mean (±SD) HR responses of 162±3b min⁻¹ (82.4±1.3% HR(max)) and 136±6b min⁻¹ (68.6±3.1% HR(max)) were evident across live and total time during matches. A mean [BLa] of 3.7±1.4 mmol L⁻¹ was observed across competition. Player activity demands were unchanged across match periods, with 1752±186 movements performed and 5214±315 m travelled across total live match time. Furthermore, 39±3%, 52±2%, 5±1% and 4±1% of total live time was spent performing low-intensity, moderate-intensity, high-intensity and dribbling activity. Positional comparisons revealed backcourt players performed more ball dribbling (p<0.001) and less standing/walking (p<0.01) and running (p<0.05) than frontcourt players.
Together, these findings highlight the high intermittent demands and important contributions of both anaerobic and aerobic metabolic pathways during state-level female basketball competition.
The aim of this study was to examine the training load (TL) profile of professional elite level basketball players during the crucial parts of the competitive season (pre-play-off finals). Subjects were 8 full-time professional basketball players (age 28 +/- 3.6 years, height 199 +/-7.2 cm, body mass 102 +/- 11.5 kg, and body fat 10.4 +/- 1.5%) whose heart rate (HR) was recorded during each training session and their individual response to TL monitored using the session-rate of perceived exertion (RPE) method (200 training sessions). The association between the session-RPE method and training HR was used to assess the population validity of the session-RPE method. Significant relationships were observed between individual session-RPE and all individual HR-based TL (r values from 0.69 to 0.85; p < 0.001). Coaches spontaneously provided a tapering phase during the competitive weeks irrespective of the number of games played during it (i.e., 1 or 2 games). The individual weekly players' TL resulted in being not significantly different from each other (p > 0.05). Elite male professional basketball imposes great physiological and psychological stress on players through training sessions and official competitions (1-2 per week). Consequently, the importance of a practical and valid method to assess individual TL is warranted. In this research, we demonstrated that session-RPE may be considered as a viable method to asses TL without the use of more sophisticated tools (i.e., HR monitors). The session-RPE method enabled the detection of periodization patterns in weekly planning in elite professional basketball during the crucial part of the competitive season (1 vs. 2 weekly fixtures model).
Understanding the most demanding scenarios of basketball match-play can optimise training prescription. We established physical demand differences in total distance covered, distance covered at high-speed running, distance covered at high-intensity accelerations and decelerations, number of high-speed running actions and number of high-intensity accelerations comparing the traditional average method with the most demanding scenarios based on 1-minute rolling averages. Physical demand parameters were analysed from 21 elite basketball players according to playing position during a friendly game via local positioning system microtechnology. The results showed that players covered a total distance of 141.3 m·min⁻¹ (p <0.001; ES = 7.80) and 25.4 m·min⁻¹ (p <0.001; ES = 4.52) at high-speed running using rolling averages, compared to 66.3 m·min⁻¹ and 3.2 m·min⁻¹, respectively, using the traditional average approach. These data represent a very large increase of 113.1% for total distance per minute and 686.4% for high-speed running distance per minute, 252% for the number of high-intensity accelerations and 290.5% for the number of high-intensity decelerations, respectively, demonstrating the relevance of this novel approach. In conclusion, this investigation indicated that the traditional average method underestimates peak physical demands over a 1-minute period during a basketball game. Thus, the average approach should be complemented by analysing the most demanding scenarios in order to have a better understanding of physical demands during basketball competition.
Purpose:
this study aimed to characterize the weekly training load (TL) and well-being in college basketball players during the in-season phase.
Methods:
Ten (6 guards and 4 forwards) male basketball players (age: 20.9 ± 0.9 years; stature: 195.0 ± 8.2 cm; body mass: 91.3 ± 11.3 kg) from the same Division I National Collegiate Athletic Association team were recruited to participate in this study. Individualized training and game loads were assessed using the session rating of perceived exertion (sRPE) at the end of each training and game session, while well-being status was collected before each session. Weekly changes (%) in TL, acute:chronic workload ratio (ACWR) and well-being were determined. Differences in TL and well-being between starting and bench players and between 1-game and 2-game weeks were calculated using magnitude-based statistics.
Results:
Total weekly TL and ACWR demonstrated high week-to-week variation with spikes up to 226% and 220%, respectively. Starting players experienced a higher (most likely negative) total weekly TL and similar (unclear) well-being status compared to bench players. Game scheduling influenced TL with 1-game weeks demonstrating a higher (likely negative) total weekly TL and similar (most likely trivial) well-being status compared to 2-game weeks.
Conclusions:
These findings provide college basketball coaches information to optimize training strategies during the in-season phase. Basketball coaches should concurrently consider the number of weekly games and player status (starting vs. bench player) when creating individualized periodization plans, with increases in TL potentially needed in bench players, especially in 2-game weeks.
Purpose:
The purpose of this paper was to study the structure of interrelationships among external training load measures and how these vary among different positions in elite basketball.
Methods:
Eight external variables of jumping (JUMP), acceleration (ACC), deceleration (DEC) and change of direction (COD), and two internal load variables (RPE and sRPE) were collected from 13 professional players with 300 session records. Three playing positions were considered: guards (n=4), forwards (n=4) and centers (n=5). High and total external variables (hJUMP and tJUMP, hACC and tACC, hDEC and tDEC, hCOD and tCOD) were used for the principal component analysis. Extraction criteria were set at the eigenvalue of greater than one. Varimax rotation mode was used to extract multiple principal components.
Results:
The analysis showed that all positions had two or three principal components (explaining almost all of the variance), but the configuration of each factor was different: tACC, tDEC, tCOD and hJUMP for centers, hACC, tACC, tCOD and hJUMP for guards, and tACC, hDEC, tDEC, hCOD, and tCOD for forwards are specifically demanded in training sessions and, therefore, these variables must be prioritized in load monitoring. Furthermore, for all playing positions, RPE and sRPE have high correlation with the total amount of ACC, DEC and COD. This would suggest that, although players perform the same training tasks, the demands of each position can vary.
Conclusion:
A particular combination of external load measures is required to describe training load of each playing position, especially to better understand internal responses among players.
Time-Motion Analysis and Physiological Data of Elite Under-19-Year-Old Basketball Players During Competition
- Nidhal Abdelkrim
- Saloua El Ben
- Jalila El Fazaa
- Ati
Abdelkrim, Nidhal Ben, Saloua El Fazaa, and Jalila El Ati. 2007. "Time-Motion Analysis
and Physiological Data of Elite Under-19-Year-Old Basketball Players During
Competition." British Journal of Sports Medicine 41(2):69-75. doi:
10.1136/bjsm.2006.032318.
Performance Changes in NBA Basketball Players Vary in Starters vs Nonstarters Over a Competitive Season
- Adam M Gonzalez
- R Jay
- Joseph P Hoffman
- William Rogowski
- Edwin Burgos
- Manalo
Gonzalez, Adam M., Jay R. Hoffman, Joseph P. Rogowski, William Burgos, Edwin
Manalo, Keon Weise, Maren s Fragala, and Jeffrey r Stout. 2013. "Performance
Changes in NBA Basketball Players Vary in Starters vs Nonstarters Over a
Competitive Season." Journal of Strength and Conditioning Research / National
Strength & Conditioning Association 27(3):611-15.