This function models the sprint instantaneous velocity using mono-exponential equation that estimates
maximum sprinting speed (MSS
) and relative acceleration (TAU
). velocity
is used as target or outcome
variable, and time
as predictor.
model_using_radar( time, velocity, time_correction = 0, weights = 1, LOOCV = FALSE, na.rm = FALSE, ... ) model_using_radar_with_time_correction( time, velocity, weights = 1, LOOCV = FALSE, na.rm = FALSE, ... )
time | Numeric vector |
---|---|
velocity | Numeric vector |
time_correction | Numeric vector. Used to filter out noisy data from the radar gun. This correction is
done by adding |
weights | Numeric vector. Default is 1 |
LOOCV | Should Leave-one-out cross-validation be used to estimate model fit? Default is |
na.rm | Logical. Default is FALSE |
... | Forwarded to |
List object with the following elements:
List with the following estimated parameters:
MSS
, TAU
, MAC
, and PMAX
List with the following components:
RSE
, R_squared
, minErr
, maxErr
, and RMSE
Model returned by the nls
function
Data frame used to estimate the sprint parameters, consisting of time
,
velocity
, weights
, and pred_velocity
columns
Samozino P. 2018. A Simple Method for Measuring Force, Velocity and Power Capabilities and Mechanical Effectiveness During Sprint Running. In: Morin J-B, Samozino P eds. Biomechanics of Training and Testing. Cham: Springer International Publishing, 237–267. DOI: 10.1007/978-3-319-05633-3_11.
instant_velocity <- data.frame( time = c(0, 1, 2, 3, 4, 5, 6), velocity = c(0.00, 4.99, 6.43, 6.84, 6.95, 6.99, 7.00) ) sprint_model <- with( instant_velocity, model_using_radar(time, velocity) ) print(sprint_model)#> Estimated model parameters #> -------------------------- #> MSS TAU MAC PMAX #> 7.0032304 0.8013733 8.7390359 15.3003704 #> time_correction distance_correction #> 0.0000000 0.0000000 #> #> Model fit estimators #> -------------------- #> RSE R_squared minErr maxErr maxAbsErr RMSE #> 0.003513977 0.999998447 -0.004097950 0.005636913 0.005636913 0.002969852 #> MAE MAPE #> 0.002266110 NaN#> MSS TAU MAC PMAX #> 7.0032304 0.8013733 8.7390359 15.3003704 #> time_correction distance_correction #> 0.0000000 0.0000000sprint_model_correction <- with( instant_velocity, model_using_radar_with_time_correction(time + 0.3, velocity) ) print(sprint_model_correction)#> Estimated model parameters #> -------------------------- #> MSS TAU MAC PMAX #> 7.0032221 0.8013434 8.7393523 15.3009063 #> time_correction distance_correction #> -0.3000396 0.0000000 #> #> Model fit estimators #> -------------------- #> RSE R_squared minErr maxErr maxAbsErr RMSE #> 0.003924907 0.999998447 -0.004080233 0.005635220 0.005635220 0.002966951 #> MAE MAPE #> 0.002312053 Inf#> MSS TAU MAC PMAX #> 7.0032221 0.8013434 8.7393523 15.3009063 #> time_correction distance_correction #> -0.3000396 0.0000000