Model Using Split Times

This function models the sprint split times using mono-exponential equation that estimates maximum sprinting speed (MSS) and relative acceleration (TAU). time is used as target or outcome variable, and distance as predictor.

model_using_split_times(
  distance,
  time,
  time_correction = 0,
  weights = 1,
  na.rm = FALSE,
  ...
)

Arguments

distance	Numeric vector
time	Numeric vector
time_correction	Numeric vector. Used to correct for different starting techniques. This correction is done by adding time_correction to time. Default is 0. See more in Haugen et al. (2018)
weights	Numeric vector. Default is vector of 1 This is used to give more weight to particular observations. For example, use 1\distance to give more weight to observations from shorter distances.
na.rm	Logical. Default is FALSE
...	Forwarded to nls function

Value

List object with the following elements:

parameters

List with the following estimated parameters: MSS, TAU, MAC, and PMAX

model_fit

List with the following components: RSE, R_squared, minErr, maxErr, and RMSE

model

Model returned by the nls function

data

Data frame used to estimate the sprint parameters, consisting of distance, time, time_correction, corrected_time, weights,and pred_time columns

References

Haugen TA, Tønnessen E, Seiler SK. 2012. The Difference Is in the Start: Impact of Timing and Start Procedure on Sprint Running Performance: Journal of Strength and Conditioning Research 26:473–479. DOI: 10.1519/JSC.0b013e318226030b.

Examples

split_times <- data.frame(
distance = c(5, 10, 20, 30, 35),
time = c(1.21, 1.99, 3.38, 4.71, 5.30)
)

sprint_model <- with(
  split_times,
  model_using_split_times(distance, time)
)

sprint_model$parameters
#> $MSS
#> [1] 7.553185
#> 
#> $TAU
#> [1] 0.706663
#> 
#> $MAC
#> [1] 10.68852
#> 
#> $PMAX
#> [1] 20.1831
#>