Measuring Decrement in Change-of-Direction Speed Across Repeated Sprints in Basketball: Novel vs. Traditional Approaches.

ABSTRACT: Scanlan, AT, Madueno, MC, Guy, JH, Giamarelos, K, Spiteri, T, and Dalbo, VJ. Measuring decrement in change-of-direction speed across repeated sprints in basketball: Novel vs. traditional approaches. J Strength Cond Res 35(3): 841-845, 2021-Approaches to quantify decrement in change-of-direction speed during repeated sprints in basketball players have traditionally used total performance time, which is strongly influenced by linear speed. The purpose of this study was to compare performance decrement across change-of-direction sprints using total performance time and a novel approach that better isolates change-of-direction speed, termed change-of-direction deficit (CODD). Semiprofessional basketball players (N = 8; 19.9 ± 1.5 years; 183.0 ± 9.6 cm; 77.7 ± 16.9 kg) completed 12 × 20-m change-of-direction sprints (Agility 5-0-5 trials) with 20-second recoveries between each sprint. Agility 5-0-5 performance time was taken as the duration to cover 5 m immediately before and after (10 m in total) a 180° directional change. Change-of-direction deficit was calculated as the difference between mean 10- and 20-m split time determined during reference 20-m linear sprints in a separate session and Agility 5-0-5 time in each sprint. Performance decrement was calculated for each approach as: ([total time/ideal time] × 100) - 100. Comparisons between approaches were made using a paired-sample t-test, effect size analyses, and magnitude-based inferences. A significantly greater (P < 0.001; effect size = 2.16, very large; almost certainly higher) performance decrement was apparent using CODD (5.99 ± 1.88%) than Agility 5-0-5 performance time (2.84 ± 0.84%). The present findings indicate that change-of-direction speed measured with CODD shows promise in providing different insight and deteriorates more than total performance time during repeated sprints in basketball players. Change-of-direction deficit has potential to better isolate decrements in change-of-direction speed across repeated sprints compared with total performance time.

Influence of Different Methods to Determine Maximum Heart Rate on Training Load Outcomes in Basketball Players.

Berkelmans, DM, Dalbo, VJ, Fox, JL, Stanton, R, Kean, CO, Giamarelos, KE, Teramoto, M, and Scanlan, AT. Influence of different methods to determine maximum heart rate on training load outcomes in basketball players. J Strength Cond Res 32(11): 3177-3185, 2018-The summated-heart-rate-zones (SHRZ) approach uses heart rate (HR) responses relative to maximum HR (HRmax) to calculate the internal training load (TL). Age-predicted, test-derived, and session-based approaches have all been used to determine HRmax in team sports. The purpose of this study was to determine the effects of using age-predicted, test-derived, and session-based HRmax responses on SHRZ TL in basketball players. Semiprofessional, male basketball players (N = 6) were analyzed during the preparatory training phase. Six age-based approaches were used to predict HRmax including Fox (220 - age); Hossack (206 - [0.567 × age]); Tanaka (208 - [0.7 × age]); Nikolaidis (223 - [1.44 × age]); Nes (211 - [0.64 × age]); and Faff (209.9 - [0.73 × age]). Test-derived HRmax was taken as the highest HR during the Yo-Yo intermittent recovery test (Yo-Yo IRT), whereas session-based HRmax was taken as the higher HR seen during the Yo-Yo IRT or training sessions. Comparisons in SHRZ TL were made at group and individual levels. No significant group differences were evident between SHRZ approaches. Effect size analyses revealed moderate (d = 0.60-0.79) differences between age-predicted, test-derived, and session-based methods across the group and individually in 2 players. The moderate differences between approaches suggest age-predicted, test-derived, and session-based methods to determine HRmax are not interchangeable when calculating SHRZ. Basketball practitioners are encouraged to use individualized HRmax directly measured during field-based tests supplemented with higher HR responses evident during training sessions and games when calculating the SHRZ TL to ensure greatest accuracy.

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

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.