Jordan L. Fox

A Review of Player Monitoring Approaches in Basketball: Current Trends and Future Directions

Fox, JL, Scanlan, AT, and Stanton, R. A review of player monitoring approaches in basketball: current trends and future directions. J Strength Cond Res 31(7): 2021-2029, 2017-Effective monitoring of players in team sports such as basketball requires an understanding of the external demands and internal responses, as they relate to training phases and competition. Monitoring of external demands and internal responses allows coaching staff to determine the dose-response associated with the imposed training load (TL), and subsequently, if players are adequately prepared for competition. This review discusses measures reported in the literature for monitoring the external demands and internal responses of basketball players during training and competition. The external demands of training and competition were primarily monitored using time-motion analysis, with limited use of microtechnology being reported. Internal responses during training were typically measured using hematological markers, heart rate, various TL models, and perceptual responses such as rating of perceived exertion (RPE). Heart rate was the most commonly reported indicator of internal responses during competition with limited reporting of hematological markers or RPE. These findings show a large discrepancy between the reporting of external and internal measures and training and competition demands. Microsensors, however, may be a practical and convenient method of player monitoring in basketball to overcome the limitations associated with current approaches while allowing for external demands and internal responses to be recorded simultaneously. The triaxial accelerometers of microsensors seem well suited for basketball and warrant validation to definitively determine their place in the monitoring of basketball players. Coaching staff should make use of this technology by tracking individual player responses across the annual plan and using real-time monitoring to minimize factors such as fatigue and injury risk.

A Comparison of Training and Competition Demands in Semiprofessional Male Basketball Players

ABSTRACT Purpose: The purpose of this study was to quantify and compare training and competition demands in basketball. Methods: Fifteen semiprofessional male basketball players wore microsensors during physical conditioning training (PCT), games-based training (GBT), and competition to measure absolute and relative (·min−1) PlayerLoadTM (PL) and estimated equivalent distance (EED). Internal responses were calculated using absolute and relative session rating of perceived exertion (sRPE) and summated heart rate zones (SHRZ). Integrated measures were calculated as sRPE:PL and SHRZ:PL ratios. Results: PlayerLoad (arbitrary units [AU]) and EED (m) were statistically significantly (p < .05) higher during PCT (632 ± 139 AU, d = 1.36; 5,964 ± 1,312 m, d = 1.36; 6.50 ± 0.81 AU·min−1, d = 2.44; 61.88 ± 7.22 m·min−1, d = 2.60) and GBT (624 ± 113 AU, d = 1.54; 5,892 ± 1,080 m, d = 1.53; 6.10 ± 0.77 AU·min−1, d = 2.14; 56.76 ± 6.49 m·min−1, d = 2.22) than they were during competition (449 ± 118 AU; 3,722 ± 1474 m; 4.35 ± 1.09 AU·min−1; 41.01 ± 10.29 m·min−1). Summated heart rate zones were statistically significantly (p < .05) higher during PCT (314 ± 86 AU, d = 1.05; 3.22 ± 0.50 AU·min−1, d = 1.94) and GBT (334 ± 79 AU, d = 1.38; 3.19 ± 0.54 AU·min−1, d = 1.83) than they were during competition (225 ± 77 AU; 2.17 ± 0.69 AU·min−1). The ratio of sRPE:PL was statistically significantly (p < .05) higher during competition (1.58 ± 0.85) than during PCT (0.98 ± 0.22, d = 1.44) and GBT (0.91 ± 0.24, d = 1.90). Conclusion: Training demands exceeded competition demands.

A comparison of traditional and modified Summated-Heart-Rate-Zones models to measure internal training load in basketball players

ABSTRACT The Summated-Heart-Rate-Zones training load (SHRZ TL) model is used to measure internal loading; however, a major limitation of this approach is the use of broad heart rate (HR) zones to quantify exercise intensity. Therefore, this study aimed to compare SHRZ TL outcomes derived using the traditional model and modified approaches using smaller HR zones. HR responses were monitored in 15 semi-professional basketball players during preparatory training to calculate SHRZ TL using the traditional approach with 10%HRmax zones (SHRZ10) and modified approaches with 5%HRmax zones (SHRZ5) and 2.5%HRmax zones (SHRZ2.5). Significant (P < 0.001) differences were evident in SHRZ TL between SHRZ10 (254.2 ± 41.7 AU) and SHRZ5 (275.9 ± 43.3 AU, unclear, small) as well as SHRZ2.5 (286.7 ± 44.3 AU, very likely, moderate). Use of SHRZ2.5 provides novel insight regarding internal loading in basketball players and may carry greater sensitivity for detection of maladaptive and adaptive responses to training.

Cumulative Training Dose Alters the Interrelationships Between Common Training Load Models During Basketball Activity.

PURPOSE The influence of various factors on training-load (TL) responses in basketball has received limited attention. This study aimed to examine the temporal changes and influence of cumulative training dose on TL responses and interrelationships during basketball activity. METHODS Ten state-level Australian male junior basketball players completed 4 × 10-min standardized bouts of simulated basketball activity using a circuit-based protocol. Internal TL was quantified using the session rating of perceived exertion (sRPE), summated heart-rate zones (SHRZ), Banister training impulse (TRIMP), and Lucia TRIMP models. External TL was assessed via measurement of mean sprint and circuit speeds. Temporal TL comparisons were performed between 10-min bouts, while Pearson correlation analyses were conducted across cumulative training doses (0-10, 0-20, 0-30, and 0-40 min). RESULTS sRPE TL increased (P < .05) after the first 10-min bout of basketball activity. sRPE TL was only significantly related to Lucia TRIMP (r = .66-.69; P < .05) across 0-10 and 0-20 min. Similarly, mean sprint and circuit speed were significantly correlated across 0-20 min (r = .67; P < .05). In contrast, SHRZ and Banister TRIMP were significantly related across all training doses (r = .84-.89; P < .05). CONCLUSIONS Limited convergence exists between common TL approaches across basketball training doses lasting beyond 20 min. Thus, the interchangeability of commonly used internal and external TL approaches appears dose-dependent during basketball activity, with various psychophysiological mediators likely underpinning temporal changes.

The Commonality Between Approaches to Determine Jump Fatigue During Basketball Activity in Junior Players: In-Game Versus Across-Game Decrements

PURPOSE Declines in high-intensity activity during game play (in-game approach) and performance tests measured pre- and postgame (across-game approach) have been used to assess player fatigue in basketball. However, a direct comparison of these approaches is not available. Consequently, this study examined the commonality between in- and across-game jump fatigue during simulated basketball game play. METHODS Australian, state-level, junior male basketball players (n = 10; 16.6 ± 1.1 y, 182.4 ± 4.3 cm, 68.3 ± 10.2 kg) completed 4 × 10-min standardized quarters of simulated basketball game play. In-game jump height during game play was measured using video analysis, while across-game jump height was determined pre-, mid-, and postgame play using an in-ground force platform. Jump height was determined using the flight-time method, with jump decrement calculated for each approach across the first half, second half, and entire game. RESULTS A greater jump decrement was apparent for the in-game approach than for the across-game approach in the first half (37.1% ± 11.6% vs 1.7% ± 6.2%; P = .005; d = 3.81, large), while nonsignificant, large differences were evident between approaches in the second half (d = 1.14) and entire game (d = 1.83). Nonsignificant associations were evident between in-game and across-game jump decrement, with shared variances of 3-26%. CONCLUSIONS Large differences and a low commonality were observed between in- and across-game jump fatigue during basketball game play, suggesting that these approaches measure different constructs. Based on our findings, it is not recommended that basketball coaches use these approaches interchangeably to monitor player fatigue across the season.

Validity of a Smartphone-Based Application for Determining Sprinting Performance

Recent innovations in smartphone technology have led to the development of a number of applications for the valid and reliable measurement of physical performance. Smartphone applications offer a number of advantages over laboratory based testing including cost, portability, and absence of postprocessing. However, smartphone applications for the measurement of running speed have not yet been validated. In the present study, the iOS smartphone application, SpeedClock, was compared to conventional timing lights during flying 10 m sprints in recreationally active women. Independent samples t-test showed no statistically significant difference between SpeedClock and timing lights (t(190) = 1.83, p = 0.07), while intraclass correlations showed excellent agreement between SpeedClock and timing lights (ICC (2,1) = 0.93, p = 0.00, 95% CI 0.64–0.97). Bland-Altman plots showed a small systematic bias (mean difference = 0.13 seconds) with SpeedClock giving slightly lower values compared to the timing lights. Our findings suggest SpeedClock for iOS devices is a low-cost, valid tool for the assessment of mean flying 10 m sprint velocity in recreationally active females. Systematic bias should be considered when interpreting the results from SpeedClock.

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

BackgroundAdequate training loads promote favorable physical and physiological adaptations, reduce the likelihood of illness and injury, and, therefore, increase the possibility of success during competition.ObjectivesOur objective was to systematically examine the association between training load and performance outcomes in team sports.MethodsWe systematically searched the PubMed, SPORTDiscus, and PsycINFO databases for original research published before July 2018. The search included terms relevant to training load, performance, and team sports. Articles were screened using pre-defined selection criteria, and methodological quality was assessed independently by two authors before data were extracted by the lead author.ResultsThe electronic search yielded 5848 articles, 2373 of which were duplicates. A further 17 articles were retrieved from additional sources. In total, 26 articles met the inclusion criteria for this review, with quality scores ranging from 6 to 10 out of 11. Training exposure was more strongly associated with aerobic performance than other external training load measures. High-intensity activity (≥ 90% of maximum heart rate) was strongly associated with aerobic performance. The individualized training impulse model was strongly associated with aerobic performance, whereas various other training impulse models and perceptual training load measures showed weak associations with aerobic performance. There were no clear associations between training load and neuromuscular variables or game-related statistics.ConclusionWe found no consistent associations between external training load measures and performance. High-intensity internal training load appears to be the most prominent indicator of aerobic performance.

Decrements in knee extensor and flexor strength are associated with performance fatigue during simulated basketball game-play in adolescent, male players

ABSTRACT This study quantified lower-limb strength decrements and assessed the relationships between strength decrements and performance fatigue during simulated basketball. Ten adolescent, male basketball players completed a circuit-based, basketball simulation. Sprint and jump performance were assessed during each circuit, with knee flexion and extension peak concentric torques measured at baseline, half-time, and full-time. Decrement scores were calculated for all measures. Mean knee flexor strength decrement was significantly (P < 0.05) related to sprint fatigue in the first half (R = 0.65), with dominant knee flexor strength (R = 0.67) and dominant flexor:extensor strength ratio (R = 0.77) decrement significantly (P < 0.05) associated with sprint decrement across the entire game. Mean knee extensor strength (R = 0.71), dominant knee flexor strength (R = 0.80), non-dominant knee flexor strength (R = 0.75), mean knee flexor strength (R = 0.81), non-dominant flexor:extensor strength ratio (R = 0.71), and mean flexor:extensor strength ratio (R = 0.70) decrement measures significantly (P < 0.05) influenced jump fatigue during the entire game. Lower-limb strength decrements may exert an important influence on performance fatigue during basketball activity in adolescent, male players. Consequently, training plans should aim to mitigate lower-limb fatigue to optimise sprint and jump performance during game-play.

Temporal changes in physiological and performance responses across game-specific simulated basketball activity

Purpose The aims of this study were to: (1) provide a comprehensive physiological profile of simulated basketball activity and (2) identify temporal changes in player responses in controlled settings. Methods State-level male basketball players (n = 10) completed 4 × 10 min simulated quarters of basketball activity using a reliable and valid court-based test. A range of physiological (ratings of perceived exertion, blood lactate concentration ([BLa−]), blood glucose concentration ([BGlu]), heart rate (HR), and hydration) and physical (performance and fatigue indicators for sprint, circuit, and jump activity) measures were collected across testing. Results Significantly reduced [BLa−] (6.19 ± 2.30 vs. 4.57 ± 2.33 mmol/L; p = 0.016) and [BGlu] (6.91 ± 1.57 vs. 5.25 ± 0.81 mmol/L; p = 0.009) were evident in the second half. A mean HR of 180.1 ± 5.7 beats/min (90.8% ± 4.0% HRmax) was observed, with a significant increase in vigorous activity (77%–95% HRmax) (11.31 ± 6.91 vs. 13.50 ± 6.75 min; p = 0.024) and moderate decrease in near-maximal activity (>95% HRmax) (7.24 ± 7.45 vs. 5.01 ± 7.20 min) in the second half. Small increases in performance times accompanied by a significantly lower circuit decrement (11.67% ± 5.55% vs. 7.30% ± 2.16%; p = 0.032) were apparent in the second half. Conclusion These data indicate basketball activity imposes higher physiological demands than previously thought and temporal changes in responses might be due to adapted pacing strategies as well as fatigue-mediated mechanisms.