Probe Vertical Jump

probe_vj simulates the vertical jump, but estimate which parameter brings biggest change. This is done by keeping all parameters at initial value, while changing only one parameter. This is then repeated for all parameters. This way we can answer by changing what parameter for standardize change (change_ratio) yield biggest change in summary metric (e.g. jump height)

probe_vj(
  mass = 75,
  push_off_distance = 0.4,
  max_force = 3000,
  max_velocity = 4,
  time_to_max_activation = 0.3,
  change_ratio = seq(0.9, 1.1, length.out = 3),
  aggregate = "raw",
  ...
)

Arguments

mass	Numeric value. Initial parameter value to be changed using change_ratio.
push_off_distance	Numeric value. Initial parameter value to be changed using change_ratio
max_force	Numeric value. Initial parameter value to be changed using change_ratio
max_velocity	Numeric value. Initial parameter value to be changed using change_ratio
time_to_max_activation	Numeric value. Initial parameter value to be changed using change_ratio
change_ratio	Numeric vector indicating probing change ratios
aggregate	How should vj_simulate output be aggregated? Default is "raw". Other options involve "ratio" and "diff" which use initial output values
...	Extra argument forwarded to vj_simulate

Value

Probing data frame

Examples

require(tidyverse)

vj_probe_data <- probe_vj(
  mass = 75,
  max_force = 3000,
  max_velocity = 3,
  time_to_max_activation = 0.3,
  time_step = 0.001
)

# Invert for mass and time_to_max_activation
vj_probe_data$change_ratio <- ifelse(
  vj_probe_data$probing == "time_to_max_activation",
  1 / vj_probe_data$change_ratio,
  vj_probe_data$change_ratio
)

vj_probe_data$change_ratio <- ifelse(
  vj_probe_data$probing == "mass",
  1 / vj_probe_data$change_ratio,
  vj_probe_data$change_ratio
)


plot_data <- gather(vj_probe_data, key = "variable", value = "value", -(1:9)) %>%
  filter(variable %in% c(
    "height",
    "take_off_time",
    "mean_velocity",
    "peak_velocity",
    "take_off_velocity",
    "mean_GRF_over_distance",
    "mean_GRF_over_time",
    "peak_GRF",
    "peak_power",
    "mean_power",
    "peak_RFD",
    "peak_RPD"
  ))

plot_data$reverse <- plot_data$probing %in% c("mass", "time_to_max_activation")

ggplot(plot_data, aes(x = change_ratio, y = value, color = probing, linetype = reverse)) +
  theme_minimal() +
  geom_line() +
  facet_wrap(~variable, scales = "free_y") +
  xlab("Normalized parameter change") +
  ylab(NULL) +
  scale_color_manual(values = c(
    "mass" = "#4D4D4D",
    "max_force" = "#5DA5DA",
    "max_velocity" =  "#FAA43A",
    "push_off_distance" = "#60BD68",
    "time_to_max_activation" = "#B276B2"
  ))