Comparing Weekly Training and Game Demands According to Playing Position in a Semiprofessional Basketball Team

2020 ◽  
pp. 1-7
Author(s):  
Markus N.C. Williams ◽  
Vincent J. Dalbo ◽  
Jordan L. Fox ◽  
Cody J. O’Grady ◽  
Aaron T. Scanlan
Keyword(s):  
Heart Rate ◽  
Mixed Models ◽  
Perceived Exertion ◽  
Movement Analysis ◽  
Rating Of Perceived Exertion ◽  
Effect Sizes ◽  
Basketball Players ◽  
Several Variables ◽  
Longitudinal Observational Study ◽  
Weekly Training

Purpose: To compare weekly training and game demands according to playing position in basketball players. Methods: A longitudinal, observational study was adopted. Semiprofessional, male basketball players categorized as backcourt (guards; n = 4) and frontcourt players (forwards/centers; n = 4) had their weekly workloads monitored across an entire season. External workload was determined using microsensors and included PlayerLoad™ (PL) and inertial movement analysis variables. Internal workload was determined using heart rate to calculate absolute and relative summated-heart-rate-zones workload and rating of perceived exertion (RPE) to calculate session-RPE workload. Comparisons between weekly training and game demands were made using linear mixed models and effect sizes in each positional group. Results: In backcourt players, higher relative PL (P = .04, very large) and relative summated-heart-rate-zones workload (P = .007, very large) were evident during training, while greater session-RPE workload (P = .001, very large) was apparent during games. In frontcourt players, greater PL (P < .001, very large), relative PL (P = .019, very large), peak PL intensities (P < .001, moderate), high-intensity inertial movement analysis events (P = .002, very large), total inertial movement analysis events (P < .001, very large), summated-heart-rate-zones workload (P < .001, very large), RPE (P < .001, very large), and session-RPE workload (P < .001, very large) were evident during games. Conclusions: Backcourt players experienced similar demands between training and games across several variables, with higher average workload intensities during training. Frontcourt players experienced greater demands across all variables during games than training. These findings emphasize the need for position-specific preparation strategies leading into games in basketball teams.

Weekly Training Demands Increase, but Game Demands Remain Consistent Across Early, Middle, and Late Phases of the Regular Season in Semiprofessional Basketball Players

2021 ◽  
pp. 1-8
Author(s):  
Markus N.C. Williams ◽  
Jordan L. Fox ◽  
Cody J. O’Grady ◽  
Samuel Gardner ◽  
Vincent J. Dalbo ◽  
...  
Keyword(s):  
Mixed Models ◽  
Perceived Exertion ◽  
Late Phase ◽  
Movement Analysis ◽  
Rating Of Perceived Exertion ◽  
Basketball Players ◽  
External Monitoring ◽  
Weekly Training ◽  
The Stability ◽  
Training Demands

Purpose: To compare weekly training, game, and overall (training and games) demands across phases of the regular season in basketball. Methods: Seven semiprofessional, male basketball players were monitored during all on-court team-based training sessions and games during the regular season. External monitoring variables included PlayerLoad™ and inertial movement analysis events per minute. Internal monitoring variables included a modified summated heart rate zones model calculated per minute and rating of perceived exertion. Linear mixed models were used to compare training, game, and overall demands between 5-week phases (early, middle, and late) of the regular season with significance set at P ≤ .05. Effect sizes were calculated between phases and interpreted as: trivial, <0.20; small, 0.20 to 0.59; moderate, 0.60 to 1.19; large, 1.20 to 1.99; very large, ≥2.00. Results: Greater (P > .05) overall inertial movement analysis events (moderate–very large) and rating of perceived exertion (moderate) were evident in the late phase compared with earlier phases. During training, more accelerations were evident in the middle (P = .01, moderate) and late (P = .05, moderate) phases compared with the early phase, while higher rating of perceived exertion (P = .04, moderate) was evident in the late phase compared with earlier phases. During games, nonsignificant, trivial–small differences in demands were apparent between phases. Conclusions: Training and game demands should be interpreted in isolation and combined given overall player demands increased as the season progressed, predominantly due to modifications in training demands given the stability of game demands. Periodization strategies administered by coaching staff may have enabled players to train at greater intensities late in the season without compromising game intensity.

Working Overtime: The Effects of Overtime Periods on Game Demands in Basketball Players

2019 ◽  
Vol 14 (10) ◽  
pp. 1331-1337 ◽  
Author(s):  
Aaron T. Scanlan ◽  
Robert Stanton ◽  
Charli Sargent ◽  
Cody O’Grady ◽  
Michele Lastella ◽  
...  
Keyword(s):  
Heart Rate ◽  
Effect Size ◽  
Perceived Exertion ◽  
Movement Analysis ◽  
Rating Of Perceived Exertion ◽  
Intensity Change ◽  
Basketball Players ◽  
Change Of Direction ◽  
Low Intensity ◽  
Medium Intensity

Purpose: To quantify and compare internal and external workloads in regular and overtime games and examine changes in relative workloads during overtime compared with other periods in overtime games in male basketball players. Methods: Starting players for a semiprofessional male basketball team were monitored during 2 overtime games and 2 regular games (nonovertime) with similar contextual factors. Internal (rating of perceived exertion and heart-rate variables) and external (PlayerLoad and inertial movement analysis variables) workloads were quantified across games. Separate linear mixed-models and effect-size analyses were used to quantify differences in variables between regular and overtime games and between game periods in overtime games. Results: Session rating-of-perceived-exertion workload (P = .002, effect size 2.36, very large), heart-rate workload (P = .12, 1.13, moderate), low-intensity change-of-direction events to the left (P = .19, 0.95, moderate), medium-intensity accelerations (P = .12, 1.01, moderate), and medium-intensity change-of-direction events to the left (P = .10, 1.06, moderate) were higher during overtime games than during regular games. Overtime periods also exhibited reductions in relative PlayerLoad (first quarter P = .03, −1.46, large), low-intensity accelerations (first quarter P = .01, −1.45, large; second quarter P = .15, −1.22, large), and medium-intensity accelerations (first quarter P = .09, −1.32, large) compared with earlier periods. Conclusions: Overtime games disproportionately elevate perceptual, physiological, and acceleration workloads compared with regular games in starting basketball players. Players also perform at lower external intensities during overtime periods than earlier quarters during basketball games.

Not All About the Effort? A Comparison of Playing Intensities During Winning and Losing Game Quarters in Basketball

2020 ◽  
pp. 1-4
Author(s):  
Jordan L. Fox ◽  
Jesse Green ◽  
Aaron T. Scanlan
Keyword(s):  
Heart Rate ◽  
Mixed Models ◽  
High Intensity ◽  
Movement Analysis ◽  
Effect Sizes ◽  
Basketball Players ◽  
Heart Rate Monitors ◽  
Competitive Season ◽  
Team Success ◽  
Minute Sample

Purpose: To compare peak and average intensities encountered during winning and losing game quarters in basketball players. Methods: Eight semiprofessional male basketball players (age = 23.1 [3.8] y) were monitored during all games (N = 18) over 1 competitive season. The average intensities attained in each quarter were determined using microsensors and heart-rate monitors to derive relative values (per minute) for the following variables: PlayerLoad, frequency of high-intensity and total accelerations, decelerations, changes of direction, jumps, and total inertial movement analysis events combined, as well as modified summated-heart-rate-zones workload. The peak intensities reached in each quarter were determined using microsensors and reported as PlayerLoad per minute over 15-second, 30-second, 1-minute, 2-minute, 3-minute, 4-minute, and 5-minute sample durations. Linear mixed models and effect sizes were used to compare intensity variables between winning and losing game quarters. Results: Nonsignificant (P > .05), unclear–small differences were evident between winning and losing game quarters in all variables. Conclusions: During winning and losing game quarters, peak and average intensities were similar. Consequently, factors other than the intensity of effort applied during games may underpin team success in individual game quarters and therefore warrant further investigation.

The Impact of Contextual Factors on Game Demands in Starting, Semiprofessional, Male Basketball Players

2020 ◽  
Vol 15 (4) ◽  
pp. 450-456 ◽  
Author(s):  
Jordan L. Fox ◽  
Robert Stanton ◽  
Charli Sargent ◽  
Cody J. O’Grady ◽  
Aaron T. Scanlan
Keyword(s):  
Heart Rate ◽  
High Intensity ◽  
Perceived Exertion ◽  
Contextual Factors ◽  
Relative Number ◽  
Movement Analysis ◽  
Rating Of Perceived Exertion ◽  
Basketball Players ◽  
Mixed Effect ◽  
The Impact

Purpose: To quantify and compare external and internal game workloads according to contextual factors (game outcome, game location, and score-line). Methods: Starting semiprofessional, male basketball players were monitored during 19 games. External (PlayerLoad™ and inertial movement analysis variables) and internal (summated-heart-rate-zones and rating of perceived exertion [RPE]) workload variables were collected for all games. Linear mixed-effect models and effect sizes were used to compare workload variables based on each of the contextual variables assessed. Results: The number of jumps, absolute and relative (in min−1) high-intensity accelerations and decelerations, and relative changes-of-direction were higher during losses, whereas session RPE was higher during wins. PlayerLoad™ the number of absolute and relative jumps, high-intensity accelerations, absolute and relative total decelerations, total changes-of-direction, summated-heart-rate-zones, session RPE, and RPE were higher during away games, whereas the number of relative high-intensity jumps was higher during home games. PlayerLoad™, the number of high-intensity accelerations, total accelerations, absolute and relative decelerations, absolute and relative changes-of-direction, summated-heart-rate-zones, sRPE, and RPE were higher during balanced games, whereas the relative number of total and high-intensity jumps were higher during unbalanced games. Conclusions: Due to increased intensity, starting players may need additional recovery following losses. Given the increased external and internal workload volumes encountered during away games and balanced games, practitioners should closely monitor playing times during games. Monitoring playing times may help identify when players require additional recovery or reduced training volumes to avoid maladaptive responses across the in-season.

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

2020 ◽  
pp. 1-8
Author(s):  
Davide Ferioli ◽  
Aaron T. Scanlan ◽  
Daniele Conte ◽  
Emanuele Tibiletti ◽  
Ermanno Rampinini
Keyword(s):  
Repeated Measures ◽  
Significant Interaction ◽  
Perceived Exertion ◽  
Rating Of Perceived Exertion ◽  
Effect Sizes ◽  
Basketball Players ◽  
Professional Basketball ◽  
Weekly Training ◽  
Exploratory Investigation ◽  
Repeated Measures Analysis

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.

The Concurrent Validity of Session-Rating of Perceived Exertion Workload Obtained Face-to-Face Versus Via an Online Application: A Team Case Study

2020 ◽  
Vol 15 (10) ◽  
pp. 1476-1479
Author(s):  
Jordan L. Fox ◽  
Cody J. O’Grady ◽  
Aaron T. Scanlan
Keyword(s):  
Heart Rate ◽  
Repeated Measures ◽  
Concurrent Validity ◽  
Perceived Exertion ◽  
State Level ◽  
Rating Of Perceived Exertion ◽  
Basketball Players ◽  
Online Application ◽  
Face To Face

Purpose: To compare the concurrent validity of session-rating of perceived exertion (sRPE) workload determined face-to-face and via an online application in basketball players. Methods: Sixteen semiprofessional, male basketball players (21.8 [4.3] y, 191.2 [9.2] cm, 85.0 [15.7] kg) were monitored during all training sessions across the 2018 (8 players) and 2019 (11 players) seasons in a state-level Australian league. Workload was reported as accumulated PlayerLoad (PL), summated-heart-rate-zones (SHRZ) workload, and sRPE. During the 2018 season, rating of perceived exertion (RPE) was determined following each session via individualized face-to-face reporting. During the 2019 season, RPE was obtained following each session via a phone-based, online application. Repeated-measures correlations with 95% confidence intervals were used to determine the relationships between sRPE collected using each method and other workload measures (PL and SHRZ) as indicators of concurrent validity. Results: Although all correlations were significant (P < .05), sRPE obtained using face-to-face reporting demonstrated stronger relationships with PL (r = .69 [.07], large) and SHRZ (r = .74 [.06], very large) compared with the online application (r = .29 [.25], small [PL] and r = .34 [.22], moderate [SHRZ]). Conclusions: Concurrent validity of sRPE workload was stronger when players reported RPE in an individualized, face-to-face manner compared with using a phone-based online application. Given the weaker relationships with other workload measures, basketball practitioners should be cautious when using player training workloads predicated on RPE obtained via online applications.

The Relationships Between External and Internal Workloads During Basketball Training and Games

2020 ◽  
Vol 15 (8) ◽  
pp. 1081-1086
Author(s):  
Jordan L. Fox ◽  
Cody J. O’Grady ◽  
Aaron T. Scanlan
Keyword(s):  
Heart Rate ◽  
High Intensity ◽  
Perceived Exertion ◽  
Movement Analysis ◽  
Rating Of Perceived Exertion ◽  
Large Games ◽  
Workload Management ◽  
Low Intensity ◽  
Medium Intensity ◽  
Selection Of

Purpose: To investigate the relationships between external and internal workloads using a comprehensive selection of variables during basketball training and games. Methods: Eight semiprofessional, male basketball players were monitored during training and games for an entire season. External workload was determined as PlayerLoad™: total and high-intensity accelerations, decelerations, changes of direction, and jumps and total low-intensity, medium-intensity, high-intensity, and overall inertial movement analysis events. Internal workload was determined using the summated-heart-rate zones and session rating of perceived exertion models. The relationships between external and internal workload variables were separately calculated for training and games using repeated-measures correlations with 95% confidence intervals. Results: PlayerLoad was more strongly related to summated-heart-rate zones (r = .88 ± .03, very large [training]; r = .69 ± .09, large [games]) and session rating of perceived exertion (r = .74 ± .06, very large [training]; r = .53 ± .12, large [games]) than other external workload variables (P < .05). Correlations between total and high-intensity accelerations, decelerations, changes of direction, and jumps and total low-intensity, medium-intensity, high-intensity, and overall inertial movement analysis events and internal workloads were stronger during training (r = .44–.88) than during games (r = .15–.69). Conclusions: PlayerLoad and summated-heart-rate zones possess the strongest dose–response relationship among a comprehensive selection of external and internal workload variables in basketball, particularly during training sessions compared with games. Basketball practitioners may therefore be able to best anticipate player responses when prescribing training drills using these variables for optimal workload management across the season.

scholarly journals Different intensities of basketball drills affect jump shot accuracy of expert and junior players

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4250 ◽  
Author(s):  
Giuseppe Marcolin ◽  
Nicola Camazzola ◽  
Fausto Antonio Panizzolo ◽  
Davide Grigoletto ◽  
Antonio Paoli
Keyword(s):  
Heart Rate ◽  
High Intensity ◽  
Perceived Exertion ◽  
Acute Effect ◽  
Rating Of Perceived Exertion ◽  
Basketball Players ◽  
Maximum Accuracy ◽  
Counter Movement Jump ◽  
Different Levels

Background In basketball a maximum accuracy at every game intensity is required while shooting. The aim of the present study was to investigate the acute effect of three different drill intensity simulation protocols on jump shot accuracy in expert and junior basketball players. Materials & Methods Eleven expert players (age 26 ± 6 yrs, weight 86 ± 11 kg, height 192 ± 8 cm) and ten junior players (age 18 ± 1 yrs, weight 75 ± 12 kg, height 184 ± 9 cm) completed three series of twenty jump shots at three different levels of exertion. Counter Movement Jump (CMJ) height was also measured after each series of jump shots. Exertion’s intensity was induced manipulating the basketball drills. Heart rate was measured for the whole duration of the tests while the rating of perceived exertion (RPE) was collected at the end of each series of shots. Results Heart rate and rating of perceived exertion (RPE) were statistically different in the three conditions for both expert and junior players. CMJ height remained almost unchanged in both groups. Jump shot accuracy decreased with increasing drills intensity both in experts and junior players. Expert players showed higher accuracy than junior players for all the three levels of exertion (83% vs 64%, p < 0.001; 75% vs 57%, p < 0.05; 76% vs 60%, p < 0.01). Moreover, for the most demanding level of exertion, experts showed a higher accuracy in the last ten shots compared to the first ten shots (82% vs 70%, p < 0.05). Discussion Experts coped better with the different exertion’s intensities, thus maintaining a higher level of performance. The introduction of technical short bouts of high-intensity sport-specific exercises into skill sessions should be proposed to improve jump shot accuracy during matches.

Reduced Fatigue in Passive Versus Active Recovery: An Examination of Repeated-Change-of-Direction Sprints in Basketball Players

2018 ◽  
Vol 13 (8) ◽  
pp. 1034-1041
Author(s):  
Maria C. Madueno ◽  
Vincent J. Dalbo ◽  
Joshua H. Guy ◽  
Kate E. Giamarelos ◽  
Tania Spiteri ◽  
...  
Keyword(s):  
Heart Rate ◽  
Oxygen Consumption ◽  
Perceived Exertion ◽  
Lactate Concentration ◽  
Blood Lactate Concentration ◽  
Rating Of Perceived Exertion ◽  
Active Recovery ◽  
Basketball Players ◽  
Change Of Direction ◽  
Passive Recovery

Purpose: To investigate the physiological and performance effects of active and passive recovery between repeated-change-of-direction sprints. Methods: Eight semiprofessional basketball players (age: 19.9 [1.5] y; stature: 183.0 [9.6] cm; body mass: 77.7 [16.9] kg; body fat: 11.8% [6.3%]; and peak oxygen consumption: 46.1 [7.6] mL·kg−1·min−1) completed 12 × 20-m repeated-change-of-direction sprints (Agility 5-0-5 tests) interspersed with 20 seconds of active (50% maximal aerobic speed) or passive recovery in a randomized crossover design. Physiological and perceptual measures included heart rate, oxygen consumption, blood lactate concentration, and rating of perceived exertion. Change-of-direction speed was measured during each sprint using the change-of-direction deficit (CODD), with summed CODD time and CODD decrement calculated as performance measures. Results: Average heart rate (7.3 [6.4] beats·min−1; P = .010; effect size (ES) = 1.09; very likely) and oxygen consumption (4.4 [5.0] mL·kg−1·min−1; P = .12; ES = 0.77; unclear) were moderately greater with active recovery compared with passive recovery across sprints. Summed CODD time (0.87 [1.01] s; P = .07; ES = 0.76, moderate; likely) and CODD decrement (8.1% [3.7%]; P < .01; ES = 1.94, large; almost certainly) were higher with active compared with passive recovery. Trivial–small differences were evident for rating of perceived exertion (P = .516; ES = 0.19; unclear) and posttest blood lactate concentration (P = .29; ES = 0.40; unclear) between recovery modes. Conclusions: Passive recovery between repeated-change-of-direction sprints may reduce the physiological stress and fatigue encountered compared with active recovery in basketball players.

The perceptual, heart rate and technical-tactical characteristics of 3 × 3 basketball

2020 ◽  
Vol 15 (5-6) ◽  
pp. 772-782
Author(s):  
Riley B McGown ◽  
Nick B Ball ◽  
Jan S Legg ◽  
Jocelyn K Mara
Keyword(s):  
Heart Rate ◽  
Mixed Models ◽  
Perceived Exertion ◽  
Ratings Of Perceived Exertion ◽  
Peak Heart Rate ◽  
Basketball Players ◽  
Preparation Programs ◽  
Dependent Variables ◽  
Physical Preparation ◽  
Pool Game

The aim of this study was to investigate the perceptual, heart rate and technical-tactical characteristics of elite male and female 3 × 3 basketball games. Eleven male and twenty-two female elite basketball players were monitored using heart rate telemetry, Ratings of Perceived Exertion (RPE) and video analysis across three 3 × 3 basketball tournaments. Linear mixed models were performed to determine the influence of round (pool game, quarter-final, semi-final, final, classification game) and sex on all dependent variables (alpha = 0.05). There was no difference between sexes for heart rate variables (p = 0.53 - 0.85). The greatest percentage (56.9 ± 20.1%) of game time was spent in heart rate zone 5 (90-100% peak heart rate). Overall RPE was higher in semi-finals (7.2 ± 1.5, p ≤ 0.001, ES: 1.27) and finals (7.7 ± 1.6 p ≤ 0.001, ES: 1.67) compared to pool games (5.1 ± 1.5). An analysis of the technical-tactical actions revealed that there were less steals in semi-finals (p = 0.01, ES = 0.56) and finals (p = 0.01, ES = 0.71) compared to pool games, with no sex-related differences present (p = 0.06 - 0.97). Players generally spent one minute on the bench for every three minutes on the court, creating a 3:1 work to rest ratio. Physical preparation programs for elite 3 × 3 basketball athletes should include exposure to high-intensity activity in which heart rates ≥80% of peak heart rate are reached for periods of time similar to that experienced during gameplay. A 3:1 work-to-rest ratio may be beneficial during conditioning training for this population.

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