Force-velocity profiling in elite luge athletes during the simulated luge start

Introduction & Purpose The idea of some athletes having high strength levels but being too slow for a particular movement while others are fast but not strong enough for the demands of their sport exists among many coaches. Therefore, planning decisions are often based on the measurement results of speed and force capabilities and the interest in the relationships between additional load, force, velocity and power during the required movement is high. The luge start is a ballistic movement that requires both high forces and velocities to reach maximal performance (i.e. fast starting times). Examining the load, force, velocity and power capabilities of elite luge athletes during the luge start could deliver insights for training prescription to improve individual weak points and improve performance. This study investigated the relationship between the calculated maxima of force-velocity (FV), load-velocity (LV) and power-velocity (PV) profiles during a simulated luge start and on-ice start times in elite luge athletes. Methods Eleven Austrian national luge team athletes (1 female, 10 male, 25 [4.4] y, 182.4 [7.3] cm, 86.65 [10.4] kg) participated in this study. Measurements were conducted during a single testing session on a specially designed start simulator. Each athlete performed starts against 5 increasing resistances relative to their bodyweight (0%, 25%, 50%, 75%, 100%). For each pull, relative maximum force (Fmax), maximum power (Pmax) and maximum velocity (Vmax) were calculated using the measurements of a linear position transducer (GymAware PowerTool). An experimental approach was used to create the FV-, LV- and PV-profiles showing the highest R2. On-ice start times [s] were measured by photoelectric sensors (ALGE). A correlation matrix (p < 0.05) was used to analyze the relationships between the theoretical Pmax, Fmax (i.e. theoretical isometric maximum force), theoretically unloaded Vmax (Vmax(F0)), theoretical relative maximum loads (Lmax(V0)) and starting times obtained from the profiles. Results Ten linear FV profiles and 11 exponential LV were created (Figure 1; FV: R2mean = 0.9532 ± 0.06; LV: R2mean = 0.9918 ± 0.01). There were significant negative relationships between Vmax(F0) and on-ice starting times (r(9) = -.74, p < .05; r(9) = -.71, p < .05; r(9) = -.74, p < .05) and significant positive correlations between Lmax(V0) and ice start times (r(9) = .71, p < .05; r(9) = .71, p < .05; r(9) = .72, p < .05). Discussion The FV relationship during a simulated luge start can be represented mostly through a linear regression line, supporting the findings of former studies which showed a quasilinear relationship between force and velocity in multi-joint movements (Bobbert, 2012; Jaric, 2015). In line with earlier approaches, PV relationships could be represented quite accurately by use of second-degree polynomial regressions (Allison et al., 2013; Padulo et al., 2017). Contradictory to the linear LV relationships found in other movements (García-Ramos et al., 2018), the luge start LV profile can be better represented by exponential regression curves which could be due to the minimized friction in the luge start setup. Conclusion The abilities of accelerating high external loads and producing high velocities both affect luge start time. References Allison, S. J., Brooke-Wavell, K., & Folland, J. P. (2013). Multiple joint muscle function with ageing: The force-velocity and power-velocity relationships in young and older men. Aging Clinical and Experimental Research, 25(2), 159-166. https://doi.org/10.1007/s40520-013-0024-y Bobbert, M. F. (2012). Why is the force-velocity relationship in leg press tasks quasi-linear rather than hyperbolic? Journal of Applied Physiology, 112(12), 1975-1983. https://doi.org/10.1152/japplphysiol.00787.2011 García-Ramos, A., Pestaña-Melero, F. L., Pérez-Castilla, A., Rojas, F. J., & Haff, G. G. (2018). Differences in the load–velocity profile between 4 bench-press variants. International Journal of Sports Physiology and Performance, 13(3), 326–331. https://doi.org/10.1123/ijspp.2017-0158 Jaric, S. (2015). Force-velocity relationship of muscles performing multi-joint maximum performance tasks. International Journal of Sports Medicine, 36(9), 699-704. https://doi.org/10.1055/s-0035-1547283 Padulo, J., Migliaccio, G. M., Ardigò, L. P., Leban, B., Cosso, M., & Samozino, P. (2017). Lower limb force, velocity, power capabilities during leg press and squat movements. International Journal of Sports Medicine, 38(14), 1083-1089. https://doi.org/10.1055/s-0043-118341

Authors

Institutions

Publication Details

Journal
Current Issues in Sport Science (CISS)
Published
2024-09-23
DOI
https://doi.org/10.36950/2024.4ciss020
Citations
1
Primary Topic
Winter Sports Injuries and Performance
Type
article
Field-Weighted Citation Impact
0.42
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Force-velocity profiling in elite luge athletes during the simulated luge start

Carson Patterson, Christian Raschner, F. F. Locker, Lukas Demuth
1 citations
Current Issues in Sport Science (CISS)
Winter Sports Injuries and Performance
0.42
article

Force-velocity profiling in elite luge athletes during the simulated luge start

Carson Patterson, Christian Raschner, F. F. Locker, Lukas Demuth
article en
1 citations

Abstract

Introduction & Purpose The idea of some athletes having high strength levels but being too slow for a particular movement while others are fast but not strong enough for the demands of their sport exists among many coaches. Therefore, planning decisions are often based on the measurement results of speed and force capabilities and the interest in the relationships between additional load, force, velocity and power during the required movement is high. The luge start is a ballistic movement that requires both high forces and velocities to reach maximal performance (i.e. fast starting times). Examining the load, force, velocity and power capabilities of elite luge athletes during the luge start could deliver insights for training prescription to improve individual weak points and improve performance. This study investigated the relationship between the calculated maxima of force-velocity (FV), load-velocity (LV) and power-velocity (PV) profiles during a simulated luge start and on-ice start times in elite luge athletes. Methods Eleven Austrian national luge team athletes (1 female, 10 male, 25 [4.4] y, 182.4 [7.3] cm, 86.65 [10.4] kg) participated in this study. Measurements were conducted during a single testing session on a specially designed start simulator. Each athlete performed starts against 5 increasing resistances relative to their bodyweight (0%, 25%, 50%, 75%, 100%). For each pull, relative maximum force (Fmax), maximum power (Pmax) and maximum velocity (Vmax) were calculated using the measurements of a linear position transducer (GymAware PowerTool). An experimental approach was used to create the FV-, LV- and PV-profiles showing the highest R2. On-ice start times [s] were measured by photoelectric sensors (ALGE). A correlation matrix (p < 0.05) was used to analyze the relationships between the theoretical Pmax, Fmax (i.e. theoretical isometric maximum force), theoretically unloaded Vmax (Vmax(F0)), theoretical relative maximum loads (Lmax(V0)) and starting times obtained from the profiles. Results Ten linear FV profiles and 11 exponential LV were created (Figure 1; FV: R2mean = 0.9532 ± 0.06; LV: R2mean = 0.9918 ± 0.01). There were significant negative relationships between Vmax(F0) and on-ice starting times (r(9) = -.74, p < .05; r(9) = -.71, p < .05; r(9) = -.74, p < .05) and significant positive correlations between Lmax(V0) and ice start times (r(9) = .71, p < .05; r(9) = .71, p < .05; r(9) = .72, p < .05). Discussion The FV relationship during a simulated luge start can be represented mostly through a linear regression line, supporting the findings of former studies which showed a quasilinear relationship between force and velocity in multi-joint movements (Bobbert, 2012; Jaric, 2015). In line with earlier approaches, PV relationships could be represented quite accurately by use of second-degree polynomial regressions (Allison et al., 2013; Padulo et al., 2017). Contradictory to the linear LV relationships found in other movements (García-Ramos et al., 2018), the luge start LV profile can be better represented by exponential regression curves which could be due to the minimized friction in the luge start setup. Conclusion The abilities of accelerating high external loads and producing high velocities both affect luge start time. References Allison, S. J., Brooke-Wavell, K., & Folland, J. P. (2013). Multiple joint muscle function with ageing: The force-velocity and power-velocity relationships in young and older men. Aging Clinical and Experimental Research, 25(2), 159-166. https://doi.org/10.1007/s40520-013-0024-y Bobbert, M. F. (2012). Why is the force-velocity relationship in leg press tasks quasi-linear rather than hyperbolic? Journal of Applied Physiology, 112(12), 1975-1983. https://doi.org/10.1152/japplphysiol.00787.2011 García-Ramos, A., Pestaña-Melero, F. L., Pérez-Castilla, A., Rojas, F. J., & Haff, G. G. (2018). Differences in the load–velocity profile between 4 bench-press variants. International Journal of Sports Physiology and Performance, 13(3), 326–331. https://doi.org/10.1123/ijspp.2017-0158 Jaric, S. (2015). Force-velocity relationship of muscles performing multi-joint maximum performance tasks. International Journal of Sports Medicine, 36(9), 699-704. https://doi.org/10.1055/s-0035-1547283 Padulo, J., Migliaccio, G. M., Ardigò, L. P., Leban, B., Cosso, M., & Samozino, P. (2017). Lower limb force, velocity, power capabilities during leg press and squat movements. International Journal of Sports Medicine, 38(14), 1083-1089. https://doi.org/10.1055/s-0043-118341

Current Issues in Sport Science (CISS)Vol. 9(4)
Universität Innsbruck (AT), Tirol Kliniken (AT)
Openalex Percentile: Top 33%
Winter Sports Injuries and Performance
0.42
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.