ECSS Paris 2023: CP-AP22
INTRODUCTION: Although the Lane Agility Test (LAT) has been widely used for assessing change-of-direction speed in basketball, its ability to evaluate and discriminate players competing at different standards and positions is unknown [1]. The aim of this study was to compare change-of-direction speed in male basketball players according to playing standards and positions determined using the LAT. METHODS: This study adopted a cross-sectional, descriptive research design, where data collected from highly trained adolescent basketball players (n = 53; age: 17.3 ± 1.0 years; height: 185.0 ± 7.8 cm; body mass: 78.3 ± 10.7 kg) and participants in the 2024 NBA Draft Combine (n = 77; age: 21.4 ± 1.6 years; height: 198.9 ± 8.7 cm; body mass: 96.2 ± 9.9 kg) were grouped and compared based on playing standards and positions (backcourt: n = 54; age: 19.1 ± 2.5 years; height: 184.9 ± 7.0 cm; body mass: 79.4 ± 10.1 kg; frontcourt: n = 76; age: 20.1 ± 2.4 years; height: 199.1 ± 9.0 cm; body mass: 95.7 ± 11.4 kg). Generalized linear model was performed on the LAT values, while using playing standard and position as predictors. RESULTS: According to the generalized linear model, both predictors, playing standard (p = 0.046, Wald x2 = 3.977) and playing position (p = 0.001, Wald x2 = 10.783), had a significant main effect on the time in the LAT. Data collected at the NBA Draft Combine (compared to highly trained adolescents) and from backcourt positions (compared to frontcourt) accounted for -0.389 seconds (95% CI: -0.682 to -0.097) and -0.533 seconds (95% CI: -0.860 to -0.206) faster LAT times, respectively. CONCLUSION: Our findings suggest basketball researchers and practitioners may use the LAT to confidently assess and discriminate change-of-direction speed between male players competing at different standards and positions. REFERENCES 1. Morrison, M., et al., A systematic review on fitness testing in adult male basketball players: Tests adopted, characteristics reported and recommendations for practice. Sports Medicine, 2022. 52(7): p. 1491-1532.
Read CV Emilija StojanovicECSS Paris 2023: CP-AP22
INTRODUCTION: Basketball requires high-intensity efforts across various parameters. Collegiate seasons often expose players to congested schedules with multiple games per week. Internal training load (ITL) reflects physiological stress experienced by the players during practices and games (1). Investigating the impact of differing game schedules such as a 3-game week vs. 2-game week could provide insight on variation of ITL. ITL distribution between practice and games could further assist planning to optimize players’ readiness for games (1). While external training load has been studied well, we are unaware of research on ITL using modified summated heart rate zones (SHRZ) in collegiate womens basketball. SHRZ quantifies ITL by accounting for time spent at different heart rate intensities (2). Our aim was to examine how a congested two-week microcycle affects ITL distribution between practices and games and the overall weekly demands in collegiate women’s basketball. METHODS: A two-week microcycle with three games and three practices in week 1 (W1), and two games and four practices in week 2 (W2) was monitored. Players who played ≥ 10 live minutes in each game of the microcycle were included. Four players with mean age 22.75±0.5 years and mean height 180.97±8.89 cm met the criteria. Catapult S7 sensors (indoor mode) and Openfield Console 3.13.0 captured heart rates which were exported in 1-minute epochs into Microsoft Excel. SHRZ involves calculating heart rate bands in 2.5% increments of maximum heart rate (HRmax) (starting from 50% HRmax), assigning each band a weighting factor which was multiplied by the time spent in that band and finally summed to get absolute SHRZ (aSHRZ) in arbitrary units (AU) (2). HRmax for each player was the maximum heart rate observed from preseason to the end of the microcycle. We identified that 7 observations were missing for 3 players. The mean of the data from the remaining days in the microcycle was used for imputation. Descriptive statistics were calculated using Jamovi 2.6.17. RESULTS: The mean aSHRZ for W1 (3-game week) was 247±110 AU and W2 (2-game week) was 217±86 AU. W1 practices averaged 185±47.2 AU and games averaged 308±121 AU. W2 had practices averaging at 201±69.7 and games at 297±83.1 AU. The total aSHRZ for W1 was 106% of W2. Practices and games were 37.5% and 62.5% of the total aSHRZ in W1. In W2, practices were 57.52% whereas games were at 42.48% of the total aSHRZ. CONCLUSION: Our study found a slightly higher overall ITL during the 3-game week compared to the 2-game week consistent with previous literature (1). While games had a higher mean aSHRZ than practices in both weeks we observed that the practices in the 2-game week exhibited a higher total aSHRZ than the games. This suggests that strategic scheduling could be implemented to manage ITL, recovery and may improve performance. 1. Fox J. L, et al. doi.org/10.5114/biolsport.2020.91499 2. Scanlan A. T, et al. doi.org/10.1080/1091367X.2018.1445089
Read CV Priyanshi MehtaECSS Paris 2023: CP-AP22
INTRODUCTION: Change of direction speed (CODS) which has a very important role in basketball performance, is a complex and multidirectional skill that involves planned decelerations and accelerations in different directions1. On the other hand, change of direction deficit (CODD) is the additional time that one directional change requires when compared to pure linear sprint over an equivalent distance1. It is also known that for young athletes both sex and maturation are two important factors that affect performance variables throughout growth2. Hence the purpose of this study was to determine CODS, speed and CODD variations in young basketball players according to sex and maturation. METHODS: A total of 181 young basketball players (Females: n=97, Males: n=84) with at least two years of basketball experience constituted the sample of this study. Participants were classified into three groups based on their biological maturation levels determined by their distance from the peak height velocity (PHV) periods as pre-PHV (n=59), circa-PHV (n=46) and post-PHV (n=76). Each participant performed two Y-drill tests (planned) for determination of CODS to both left and right sides and two 10-m sprint tests for determination of speed in random order. CODD was calculated as the difference between best COD performance and 10-m sprint time. To determine the variations in CODS, speed and CODD according to sex and maturation a 2x3 (sex x maturation) two-way ANOVA was applied with Scheffe’s post hoc analysis. RESULTS: Results indicated significant sex (p=.000) and maturation effect (p=.000) with no significant sex x maturation interaction (p=.159) in CODS. Males had faster COD than females and post-PHV was the fastest compared to the other maturation groups. Similarly, in 10-m speed there was significant sex (p=.000) and maturation (p=.000) effect, while sex x maturation interaction was not significant (p=.425). Again males were faster than females and post-PHV group was the fastest compared to other groups. Finally, in CODD no significant sex and maturation effects were found together with sex x maturation interaction (p>.05). CONCLUSION: The findings of this study indicated that sex and biological maturation resulted in significant variations in CODS and speed, whereas these two variables did not affect CODD. 1Nimphius et al 2016, 2Giuriato et al 2021
Read CV Ayse Kin IslerECSS Paris 2023: CP-AP22