This function models the sprint split times using mono-exponential equation and non-linear mixed model using nlme to estimate fixed and random maximum sprinting speed (MSS) and relative acceleration (TAU) parameters. In mixed model, fixed and random effects are estimated for MSS and TAU parameters using athlete as levels. time is used as target or outcome variable, and distance as predictor.

mixed_model_using_split_times(
  data,
  distance,
  time,
  athlete,
  time_correction = 0,
  na.rm = FALSE,
  ...
)

Arguments

data

Data frame

distance

Character string. Name of the column in data

time

Character string. Name of the column in data

athlete

Character string. Name of the column in data. Used as levels in the nlme

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)

na.rm

Logical. Default is FALSE

...

Forwarded to nlme function

Value

List object with the following elements:

parameters

List with two data frames: fixed and random containing 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 nlme function

data

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

data("split_times") mixed_model <- mixed_model_using_split_times(split_times, "distance", "time", "athlete") mixed_model$parameters
#> $fixed #> MSS TAU MAC PMAX #> 1 8.350701 0.5092097 16.39934 34.23649 #> #> $random #> athlete MSS TAU MAC PMAX #> 1 John 8.112372 0.6308761 12.858900 26.07905 #> 2 Kimberley 7.229267 0.2924022 24.723707 44.68357 #> 3 Jim 9.222750 1.1688497 7.890449 18.19291 #> 4 James 10.052620 0.2089077 48.119903 120.93278 #> 5 Samantha 7.136494 0.2450125 29.127060 51.96627 #>