No abstract

Examination of activity demands and stoppage durations across game periods provides useful insight concerning fatigue, tactical strategies, and playing pace in team sports such as basketball. Therefore, the aims of this study were to quantify and compare game activity fluctuations across quarters in professional and semiprofessional basketball players. Video-based time-motion analyses were conducted across multiple games. Frequencies, total durations (in seconds), total distances (in meters), and mean velocities (in meters per second) were calculated for low-intensity movement (≤3 m·s), high-intensity movement (>3 m·s), shuffling, and dribbling activity. Frequencies were determined for jumping and upper-body activity; stoppage durations were also calculated. Separate repeated-measures analysis of variance and Cohen's d were used to identify significant differences and quantify the effect sizes between game quarters for all outcome measures, respectively. Pearson correlation analyses were performed to determine the relationship between stoppage duration and all activity measures. The results showed significantly (p ≤ 0.05) reduced dribbling (3.09 ± 0.03 m·s vs. 2.81 ± 0.01 m·s) and total (2.22 ± 0.04 m·s vs. 2.09 ± 0.03 m·s) activity velocities during the third compared with the first quarter in professional players. Furthermore, effect size analyses showed greater decreases in high-intensity (professional: d = 1.7-5.4; semiprofessional: d = 0.3-1.7), shuffling (professional: d = 2.3-3.2; semiprofessional: d = 1.4-2.1), and total (professional: d = 1.0-4.9; semiprofessional: d = 0.3-0.8) activity and increases in dribbling (professional: d = 1.4-4.7; semiprofessional: d = 2.5-2.8) with game progression in professional players. In semiprofessional players, stoppage duration was significantly (p ≤ 0.05) related to various low-intensity (R = 0.64-0.72), high-intensity (R = 0.65-0.72), and total (R = 0.63-0.73) activity measures. Although not directly measured, the observed game activity fluctuations were likely because of a combination of physiological (e.g., muscle glycogen depletion, dehydration), tactical (e.g., ball control, game pace), and game-related (e.g., time-outs, player fouls) factors. Basketball coaches can use the provided data to (a) develop more precise training plans and management strategies, (b) elevate semiprofessional player performance closer to the professional level, and (c) incorporate tactical strategies to maximize the benefits of stoppages.

Berkelmans, DM, Dalbo, VJ, Kean, CO, Milanović, Z, Stojanović, E, Stojiljković, N, and Scanlan, AT. Heart rate monitoring in basketball: applications, player responses, and practical recommendations. J Strength Cond Res 32(8): 2383-2399, 2018-The aims of this review were to collate the existing literature encompassing heart rate (HR) monitoring in basketball to (a) identify the applications of HR measurement; (b) report HR responses in male and female players during training and game-play; (c) evaluate use of current HR-based training load models; and (d) provide recommendations for future research and best practice approaches for basketball practitioners. Heart rate monitoring in basketball carries 3 primary applications: (a) monitoring exercise intensity; (b) assessing player fatigue status; and (c) quantifying internal training load. When interpreting the available training and game-play HR data in basketball players, key differences have been observed between playing positions and playing levels. Sex- and age-based differences in HR responses during basketball training and game-play are apparent across separate studies; however, further research exploring HR responses in wider player groups is needed, especially in female and junior players. There is also a lack of research directly comparing player HR responses during training and game-play to ascertain the effectiveness of different drills in preparing players for competition. Heart rate-based models have been frequently used to quantify the internal training load in basketball players, including Banister's Training Impulse (TRIMP), Lucia's TRIMP, and Edwards' Summated-Heart-Rate-Zones (SHRZ). The SHRZ model seems to hold practical advantages and better detect changes in player responses across training cycles compared with other approaches. Practical outcomes of this review center on recommendations for position-specific training plans, drills to promote desired cardiovascular stress, analysis of HR outcome measures, and ideal training load monitoring approaches.

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.

Cricket is a popular international team sport with various game formats ranging from long-duration multiday tests to short-duration Twenty20 game play. The role of batsmen is critical to all game formats, with differing physiological demands imposed during each format. Investigation of the physiological demands imposed during cricket batting has historically been neglected, with much of the research focusing on bowling responses and batting technique. A greater understanding of the physiological demands of the batting role in cricket is required to assist strength and conditioning professionals and coaches with the design of training plans, recovery protocols, and player-management strategies. This brief review provides an updated synthesis of the literature examining the internal (eg, metabolic demands and heart rate) and external (eg, activity work rates) physiological responses to batting in the various game formats, as well as simulated play and small-sided-games training. Although few studies have been done in this area, the summary of data provides important insight regarding physiological responses to batting and highlights that more research on this topic is required. Future research is recommended to combine internal and external measures during actual game play, as well as comparing different game formats and playing levels. In addition, understanding the relationship between batting technique and physiological responses is warranted to gain a more holistic understanding of batting in cricket, as well as to develop appropriate coaching and training strategies.