Type:	Package
Title:	Short Sprints
Version:	3.2.0
Description:	Create short sprint acceleration-velocity (AVP) and force-velocity (FVP) profiles and predict kinematic and kinetic variables using the timing-gate split times, laser or radar gun data, tether devices data, as well as the data provided by the GPS and LPS monitoring systems. The modeling method utilized in this package is based on the works of Furusawa K, Hill AV, Parkinson JL (1927) <doi:10.1098/rspb.1927.0035>, Greene PR. (1986) <doi:10.1016/0025-5564(86)90063-5>, Chelly SM, Denis C. (2001) <doi:10.1097/00005768-200102000-00024>, Clark KP, Rieger RH, Bruno RF, Stearne DJ. (2017) <doi:10.1519/JSC.0000000000002081>, Samozino P. (2018) <doi:10.1007/978-3-319-05633-3_11>, Samozino P. and Peyrot N., et al (2022) <doi:10.1111/sms.14097>, Clavel, P., et al (2023) <doi:10.1016/j.jbiomech.2023.111602>, Jovanovic M. (2023) <doi:10.1080/10255842.2023.2170713>, and Jovanovic M., et al (2024) <doi:10.3390/s24092894>.
URL:	https://mladenjovanovic.github.io/shorts/
BugReports:	https://github.com/mladenjovanovic/shorts/issues
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.2.3
Depends:	R (≥ 2.10)
Imports:	stats, LambertW, tidyr, ggplot2, minpack.lm, purrr
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2024-05-22 10:55:30 UTC; mladenjovanovic
Author:	Mladen Jovanović [aut, cre, cph]
Maintainer:	Mladen Jovanović <coach.mladen.jovanovic@gmail.com>
Repository:	CRAN
Date/Publication:	2024-05-22 11:40:02 UTC

LPS Basketball Session Dataset

Description

LPS Basketball Session Dataset

Usage

data(LPS_session)

Format

Data frame with 5 variables and 91,099 observations:

time

Time in seconds from the start of the session

x

x-coordinate in meters provided by the LPS

y

y-coordinate in meters provided by the LPS

velocity

Velocity provided by LPS in m/s

acceleration

Acceleration provided by LPS in m/s

Details

This dataset represents a sample data provided by Local Positioning System (LPS) on a single individual performing a single basketball practice session (aprox. 90min). Sampling frequency is 20Hz.

S3 method for extracting model parameters from shorts_model object

Description

S3 method for extracting model parameters from shorts_model object

Usage

## S3 method for class 'shorts_model'
coef(object, ...)

Arguments

Examples

split_distances <- c(10, 20, 30, 40, 50)
split_times <- create_timing_gates_splits(
  gates = split_distances,
  MSS = 10,
  MAC = 9,
  FD = 0.25,
  TC = 0
)

# Simple model
simple_model <- model_timing_gates(split_distances, split_times)
coef(simple_model)

S3 method for providing confidence intervals for the shorts_model

Description

S3 method for providing confidence intervals for the shorts_model

Usage

## S3 method for class 'shorts_model'
confint(object, ...)

Arguments

Examples

## Not run: 
split_distances <- c(10, 20, 30, 40, 50)
split_times <- create_timing_gates_splits(
  gates = split_distances,
  MSS = 10,
  MAC = 9,
  FD = 0,
  TC = 0,
  noise = 0.01
)

# Simple model
simple_model <- model_timing_gates(split_distances, split_times)
confint(simple_model)

## End(Not run)

Convert Force-Velocity profile back to Acceleration-Velocity profile

Description

This function converts back the Force-Velocity profile (FVP; F0 and V0 parameters) to Acceleration-Velocity profile (AVP; MSS and MAC parameters)

Usage

convert_FVP(
  F0,
  V0,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  wind_velocity = 0,
  ...
)

Arguments

Value

A list with calculated MSS and MAC parameters

Examples

FVP <- create_FVP(7, 8.3, inertia = 10, resistance = 50)

convert_FVP(FVP$F0, FVP$V0, inertia = 10, resistance = 50)

Create Force-Velocity Profile

Description

Creates Force-Velocity Profile (FVP) modified using ideas by Pierre Samozino and JB-Morin, et al. (2016) and Pierre Samozino and Nicolas Peyror, et al (2021).

Usage

create_FVP(
  MSS,
  MAC,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  wind_velocity = 0,
  ...
)

Arguments

Value

List containing the following elements:

bodymass

Returned bodymass used in FV profiling

F0

Horizontal force when velocity=0

F0_rel

F0 divided by bodymass

V0

Velocity when horizontal force=0

Pmax

Maximal horizontal power

Pmax_rel

Pmax divided by bodymass

FV_slope

Slope of the FV profile. See References for more info

References

Samozino P, Rabita G, Dorel S, Slawinski J, Peyrot N, Saez de Villarreal E, Morin J-B. 2016. A simple method for measuring power, force, velocity properties, and mechanical effectiveness in sprint running: Simple method to compute sprint mechanics. Scandinavian Journal of Medicine & Science in Sports 26:648–658. DOI: 10.1111/sms.12490.

Samozino P, Peyrot N, Edouard P, Nagahara R, Jimenez‐Reyes P, Vanwanseele B, Morin J. 2022. Optimal mechanical force‐velocity profile for sprint acceleration performance. Scandinavian Journal of Medicine & Science in Sports 32:559–575. DOI: 10.1111/sms.14097.

Examples

data("jb_morin")

m1 <- model_radar_gun(time = jb_morin$time, velocity = jb_morin$velocity)

fv_profile <- create_FVP(
  MSS = m1$parameters$MSS,
  MAC = m1$parameters$MAC,
  bodyheight = 1.72,
  bodymass = 120,
)

fv_profile

Create Sprint Trace

Description

This function creates sprint trace either using time or distance vectors

Usage

create_sprint_trace(
  MSS,
  MAC,
  time = NULL,
  distance = NULL,
  TC = 0,
  DC = 0,
  FD = 0,
  remove_leading = FALSE
)

Arguments

Value

Data-frame with following 6 columns

time

Measurement-scale time vector in seconds. Equal to parameter time

distance

Measurement-scale distance vector in meters. Equal to parameter distance

velocity

Velocity vector in m/s

acceleration

Acceleration vector in m/s/s

sprint_time

Sprint scale time vector in seconds. Sprint always start at t=0s

sprint_distance

Sprint scale distance vector in meters. Sprint always start at d=0m

See Also

create_timing_gates_splits

Examples

df <- create_sprint_trace(8, 7, time = seq(0, 6, by = 0.01))
df <- create_sprint_trace(8, 7, distance = seq(0, 40, by = 1))

Create Timing Gates Splits

Description

This function is used to generate timing gates splits with predetermined parameters

Usage

create_timing_gates_splits(
  MSS,
  MAC,
  gates = c(5, 10, 20, 30, 40),
  FD = 0,
  TC = 0,
  noise = 0
)

Arguments

See Also

create_sprint_trace

Examples

create_timing_gates_splits(
  gates = c(10, 20, 30, 40, 50),
  MSS = 10,
  MAC = 9,
  FD = 0.5,
  TC = 0
)

DynaSpeed Single Sprint Data

Description

DynaSpeed(TM) data collected for a single athlete (female, 177cm, 64kg) and a single sprint over 40m. Sampling frequency is 1,000Hz. Additional time and distance shift is added to the dataset to provide a sandbox for potential issues during the analysis

Usage

data(dynaspeed)

Format

Data frame with 4 variables and 7,251 observations:

time

time in seconds

distance

Distance in meters

velocity

Smoothed velocity in meters per second

raw_velocity

Velocity in meters per second

Author(s)

Håkan Andersson
The High-Performance Center
Växjö, Sweden
hakan.andersson@hpcsweden.com

Find functions

Description

Family of functions that serve a purpose of finding maximal value and critical distances and times at which power, acceleration or velocity drops below certain threshold.

find_peak_power_distance finds peak power and distance at which peak power occurs

find_peak_power_time finds peak power and time at which peak power occurs

find_velocity_critical_distance finds critical distance at which percent of MSS is achieved

find_velocity_critical_time finds critical time at which percent of MSS is achieved

find_acceleration_critical_distance finds critical distance at which percent of MAC is reached

find_acceleration_critical_time finds critical time at which percent of MAC is reached

find_power_critical_distance finds critical distances at which peak power over percent is achieved

find_power_critical_time finds critical times at which peak power over percent is achieved

Usage

find_peak_power_distance(MSS, MAC, inertia = 0, resistance = 0, ...)

find_peak_power_time(MSS, MAC, inertia = 0, resistance = 0, ...)

find_velocity_critical_distance(MSS, MAC, percent = 0.9)

find_velocity_critical_time(MSS, MAC, percent = 0.9)

find_acceleration_critical_distance(MSS, MAC, percent = 0.9)

find_acceleration_critical_time(MSS, MAC, percent = 0.9)

find_power_critical_distance(
  MSS,
  MAC,
  inertia = 0,
  resistance = 0,
  percent = 0.9,
  ...
)

find_power_critical_time(
  MSS,
  MAC,
  inertia = 0,
  resistance = 0,
  percent = 0.9,
  ...
)

Arguments

Value

find_peak_power_distance returns list with two elements: peak_power and distance at which peak power occurs

find_peak_power_time returns list with two elements: peak_power and time at which peak power occurs

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.

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.

Examples

dist <- seq(0, 40, length.out = 1000)

velocity <- predict_velocity_at_distance(
  distance = dist,
  MSS = 10,
  MAC = 9
)

acceleration <- predict_acceleration_at_distance(
  distance = dist,
  MSS = 10,
  MAC = 9
)

# Use ... to forward parameters to the shorts::get_air_resistance
pwr <- predict_power_at_distance(
  distance = dist,
  MSS = 10,
  MAC = 9
  # bodyweight = 100,
  # bodyheight = 1.9,
  # barometric_pressure = 760,
  # air_temperature = 25,
  # wind_velocity = 0
)

# Find critical distance when 90% of MSS is reached
plot(x = dist, y = velocity, type = "l")
abline(h = 10 * 0.9, col = "gray")
abline(v = find_velocity_critical_distance(MSS = 10, MAC = 9), col = "red")

# Find critical distance when 20% of MAC is reached
plot(x = dist, y = acceleration, type = "l")
abline(h = 9 * 0.2, col = "gray")
abline(v = find_acceleration_critical_distance(MSS = 10, MAC = 9, percent = 0.2), col = "red")

# Find peak power and location of peak power
plot(x = dist, y = pwr, type = "l")

peak_pwr <- find_peak_power_distance(
  MSS = 10,
  MAC = 9
  # Use ... to forward parameters to the shorts::get_air_resistance
)
abline(h = peak_pwr$peak_power, col = "gray")
abline(v = peak_pwr$distance, col = "red")

# Find distance in which relative power stays over 75% of PMAX'
plot(x = dist, y = pwr, type = "l")
abline(h = peak_pwr$peak_power * 0.75, col = "gray")
pwr_zone <- find_power_critical_distance(MSS = 10, MAC = 9, percent = 0.75)
abline(v = pwr_zone$lower, col = "blue")
abline(v = pwr_zone$upper, col = "blue")

Function that finds the distance at which the sprint, probe, or FV profile is optimal (i.e., equal to 100 perc)

Description

Function that finds the distance at which the sprint, probe, or FV profile is optimal (i.e., equal to 100 perc)

Usage

find_optimal_distance(..., optimal_func = optimal_FV, min = 1, max = 60)

Arguments

Value

Distance

Examples

MSS <- 10
MAC <- 8
bodymass <- 75

fv <- create_FVP(MSS, MAC, bodymass)

find_optimal_distance(
  F0 = fv$F0,
  V0 = fv$V0,
  bodymass = fv$bodymass,
  optimal_func = optimal_FV,
  method = "max"
)

find_optimal_distance(
  MSS = MSS,
  MAC = MAC,
  optimal_func = optimal_MSS_MAC
)

find_optimal_distance(
  MSS = MSS,
  MAC = MAC,
  optimal_func = probe_MSS_MAC
)

S3 method for returning predictions of shorts_model

Description

S3 method for returning predictions of shorts_model

Usage

## S3 method for class 'shorts_model'
fitted(object, ...)

Arguments

Examples

split_distances <- c(10, 20, 30, 40, 50)
split_times <- create_timing_gates_splits(
  gates = split_distances,
  MSS = 10,
  MAC = 9,
  FD = 0.25,
  TC = 0
)

# Simple model
simple_model <- model_timing_gates(split_distances, split_times)
fitted(simple_model)

Format Split Data

Description

Function formats split data and calculates split distances, split times and average split velocity

Usage

format_splits(distance, time)

Arguments

Value

Data frame with the following columns:

split

Split number

split_distance_start

Distance at which split starts

split_distance_stop

Distance at which split ends

split_distance

Split distance

split_time_start

Time at which distance starts

split_time_stop

Time at which distance ends

split_time

Split time

split_mean_velocity

Mean velocity over split distance

split_mean_acceleration

Mean acceleration over split distance

Examples

data("split_times")

john_data <- split_times[split_times$athlete == "John", ]

format_splits(john_data$distance, john_data$time)

Get Air Resistance

Description

get_air_resistance estimates air resistance in Newtons

Usage

get_air_resistance(
  velocity,
  bodymass = 75,
  bodyheight = 1.75,
  barometric_pressure = 760,
  air_temperature = 25,
  wind_velocity = 0
)

Arguments

Value

Air resistance in Newtons (N)

References

Arsac LM, Locatelli E. 2002. Modeling the energetics of 100-m running by using speed curves of world champions. Journal of Applied Physiology 92:1781–1788. DOI: 10.1152/japplphysiol.00754.2001.

Samozino P, Rabita G, Dorel S, Slawinski J, Peyrot N, Saez de Villarreal E, Morin J-B. 2016. A simple method for measuring power, force, velocity properties, and mechanical effectiveness in sprint running: Simple method to compute sprint mechanics. Scandinavian Journal of Medicine & Science in Sports 26:648–658. DOI: 10.1111/sms.12490.

van Ingen Schenau GJ, Jacobs R, de Koning JJ. 1991. Can cycle power predict sprint running performance? European Journal of Applied Physiology and Occupational Physiology 63:255–260. DOI: 10.1007/BF00233857.

Examples

get_air_resistance(
  velocity = 5,
  bodymass = 80,
  bodyheight = 1.90,
  barometric_pressure = 760,
  air_temperature = 16,
  wind_velocity = -0.5
)

JB Morin Sample Dataset

Description

Sample radar gun data provided by Jean-Benoît Morin on his website. See https://jbmorin.net/2017/12/13/a-spreadsheet-for-sprint-acceleration-force-velocity-power-profiling/ for more details.

Usage

data(jb_morin)

Format

Data frame with 2 variables and 232 observations:

time

Time in seconds

velocity

Velocity in m/s

Details

This dataset represents a sample data provided by Jean-Benoît Morin on a single individual running approximately 35m from a stand still position that is measured with the radar gun. Individual's body mass is 75kg, height is 1.72m. Conditions of the run are the following: air temperature 25C, barometric pressure 760mmHg, wind velocity 0m/s.

The purpose of including this dataset in the package is to check the agreement of the model estimates with Jean-Benoît Morin Microsoft Excel spreadsheet.

Author(s)

Jean-Benoît Morin
Inter-university Laboratory of Human Movement Biology
Saint-Étienne, France https://jbmorin.net/

References

Morin JB. 2017.A spreadsheet for Sprint acceleration Force-Velocity-Power profiling. Available at https://jbmorin.net/2017/12/13/a-spreadsheet-for-sprint-acceleration-force-velocity-power-profiling/ (accessed October 27, 2020).

Laser Gun Data

Description

Performance of 35m sprint by a youth basketball player done using standing start. Sample was collected by laser gun (CMP3 Distance Sensor, Noptel Oy, Oulu, Finland) and was sampled at a rate of 2.56 KHz. A polynomial function modeling the relationship between distance and time was employed and subsequently resampled at a frequency of 1,000 Hz using Musclelab™ v10.232.107.5298, a software developed by Ergotest Technology AS located in Langesund, Norway. Data was further modified by calculating raw acceleration using dv/dt (using smoothed velocity provided by the system), and then smoothed out using 4th-order Butterworth filter with a cutoff frequency of 1 Hz.

Usage

data(laser_gun_data)

Format

Data frame with 6 variables and 4805 observations:

time

Time vector in seconds

distance

Distance vector in meters

velocity

Smoothed velocity vector in m/s; this represent step-averaged velocity

raw_velocity

Raw velocity vector in m/s

raw_acceleration

Raw acceleration vector in m/s/s; calculated using difference in smoothed velocity divided by time difference (i.e., dv/dt method of derivation)

butter_acceleration

Smoothed acceleration vector in m/s/s; smoothed out using 4th-order Butterworth filter with a cutoff frequency of 1 Hz

Model functions

Description

Family of functions that serve a purpose of estimating short sprint parameters

model_in_situ estimates short sprint parameters using velocity-acceleration trace, provided by the monitoring systems like GPS or LPS. See references for the information

model_radar_gun estimates short sprint parameters using time-velocity trace, with additional parameter TC serving as intercept

model_laser_gun alias for model_radar_gun

model_tether estimates short sprint parameters using distance-velocity trace (e.g., tether devices).

model_tether_DC estimates short sprint parameters using distance-velocity trace (e.g., tether devices) with additional distance correction DC parameter

model_time_distance estimates short sprint parameters using time distance trace

model_time_distance_FD estimates short sprint parameters using time-distance trace with additional estimated flying distance correction parameter FD

model_time_distance_FD_fixed estimates short sprint parameters using time-distance trace with additional flying distance correction parameter FD which is fixed by the user

model_time_distance estimates short sprint parameters using time distance trace with additional time correction parameter TC

model_time_distance estimates short sprint parameters using time distance trace with additional distance correction parameter DC

model_time_distance estimates short sprint parameters using time distance trace with additional time correction TC and distance correction TC parameters

model_timing_gates estimates short sprint parameters using distance-time trace (e.g., timing gates/photo cells)

model_timing_gates_TC estimates short sprint parameters using distance-time trace (e.g., timing gates/photo cells), with additional time correction parameter TC

model_timing_gates_FD estimates short sprint parameters using distance-time trace (e.g., timing gates/photo cells), with additional estimated flying distance correction parameter FD

model_timing_gates_FD_fixed estimates short sprint parameters using distance-time trace (e.g., timing gates/photo cells), with additional flying distance correction parameter FD which is fixed by the user

model_timing_gates_DC estimates short sprint parameters using distance-time trace (e.g., timing gates/photo cells), with additional distance correction parameter DC

model_timing_gates_TC_DC estimates short sprint parameters using distance-time trace (e.g., timing gates/photo cells), with additional time correction TC and distance correction DC parameters

Usage

model_in_situ(
  velocity,
  acceleration,
  weights = 1,
  velocity_threshold = NULL,
  velocity_step = 0.2,
  n_observations = 2,
  CV = NULL,
  na.rm = FALSE,
  ...
)

model_radar_gun(
  time,
  velocity,
  weights = 1,
  CV = NULL,
  use_observed_MSS = FALSE,
  na.rm = FALSE,
  ...
)

model_laser_gun(
  time,
  velocity,
  weights = 1,
  CV = NULL,
  use_observed_MSS = FALSE,
  na.rm = FALSE,
  ...
)

model_tether(
  distance,
  velocity,
  weights = 1,
  CV = NULL,
  use_observed_MSS = FALSE,
  na.rm = FALSE,
  ...
)

model_tether_DC(
  distance,
  velocity,
  weights = 1,
  CV = NULL,
  use_observed_MSS = FALSE,
  na.rm = FALSE,
  ...
)

model_time_distance(time, distance, weights = 1, CV = NULL, na.rm = FALSE, ...)

model_time_distance_FD(
  time,
  distance,
  weights = 1,
  CV = NULL,
  na.rm = FALSE,
  ...
)

model_time_distance_FD_fixed(
  time,
  distance,
  weights = 1,
  FD = 0,
  CV = NULL,
  na.rm = FALSE,
  ...
)

model_time_distance_TC(
  time,
  distance,
  weights = 1,
  CV = NULL,
  na.rm = FALSE,
  ...
)

model_time_distance_DC(
  time,
  distance,
  weights = 1,
  CV = NULL,
  na.rm = FALSE,
  ...
)

model_time_distance_TC_DC(
  time,
  distance,
  weights = 1,
  CV = NULL,
  na.rm = FALSE,
  ...
)

model_timing_gates(distance, time, weights = 1, CV = NULL, na.rm = FALSE, ...)

model_timing_gates_TC(
  distance,
  time,
  weights = 1,
  CV = NULL,
  na.rm = FALSE,
  ...
)

model_timing_gates_FD(
  distance,
  time,
  weights = 1,
  CV = NULL,
  na.rm = FALSE,
  ...
)

model_timing_gates_FD_fixed(
  distance,
  time,
  weights = 1,
  FD = 0,
  CV = NULL,
  na.rm = FALSE,
  ...
)

model_timing_gates_DC(
  distance,
  time,
  weights = 1,
  CV = NULL,
  na.rm = FALSE,
  ...
)

model_timing_gates_TC_DC(
  distance,
  time,
  weights = 1,
  CV = NULL,
  na.rm = FALSE,
  ...
)

Arguments

Value

List object with the following elements:

data

Data frame used to estimate the sprint parameters

model_info

Extra information regarding model used

model

Model returned by the nlsLM function

parameters

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

correction

List with additional model correcitons

predictions

Data frame with .predictor, .observed, .predicted, and .residual columns

model_fit

List with multiple model fit estimators

CV

If cross-validation is performed, this will included the data as above, but for each fold

References

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.

Clavel, P., Leduc, C., Morin, J.-B., Buchheit, M., & Lacome, M. (2023). Reliability of individual acceleration-speed profile in-situ in elite youth soccer players. Journal of Biomechanics, 153, 111602. https://doi.org/10.1016/j.jbiomech.2023.111602

Morin, J.-B. (2021). The “in-situ” acceleration-speed profile for team sports: testing players without testing them. JB Morin, PhD – Sport Science website. Accessed 31. Dec. 2023. https://jbmorin.net/2021/07/29/the-in-situ-sprint-profile-for-team-sports-testing-players-without-testing-them/

Examples

# Model In-Situ (Embedded profiling)
data("LPS_session")
m1 <- model_in_situ(
  velocity = LPS_session$velocity,
  acceleration = LPS_session$acceleration,
  # Use specific cutoff value
  velocity_threshold = 4)
m1
plot(m1)


# Model Radar Gun (includes Time Correction)
df <- create_sprint_trace(MSS = 8, MAC = 6, time = seq(0, 6, 0.1))

# Add some noise
df$velocity <- df$velocity + rnorm(n = nrow(df), 0, 10^-2)

m1 <- model_radar_gun(time = df$time, velocity = df$velocity)
m1
plot(m1)


# Model Laser Gun (includes Time Correction)
df <- create_sprint_trace(MSS = 8, MAC = 6, time = seq(0, 6, 0.1))

# Add some noise
df$velocity <- df$velocity + rnorm(n = nrow(df), 0, 10^-2)

m1 <- model_laser_gun(time = df$time, velocity = df$velocity)
m1
plot(m1)


# Model Tether
df <- create_sprint_trace(MSS = 8, MAC = 6, time = seq(0, 6, 0.5))
m1 <- model_tether(distance = df$distance, velocity = df$velocity)
m1
plot(m1)


# Model Tether with Distance Correction (DC)
df <- create_sprint_trace(MSS = 8, MAC = 6, time = seq(0.001, 6, 0.5), DC = 5)
m1 <- model_tether_DC(distance = df$distance, velocity = df$velocity)
m1
plot(m1)


# Model Time-Distance trace (simple, without corrections)
df <- create_sprint_trace(MSS = 8, MAC = 6, time = seq(0, 5, by = 0.5))
m1 <- model_time_distance(time = df$time, distance = df$distance)
m1
plot(m1)


# Model Time-Distance trace (with Flying Distance Correction)
df <- create_sprint_trace(MSS = 8, MAC = 6, time = seq(0, 5, by = 0.5), FD = 0.5)
m1 <- model_time_distance_FD(time = df$time, distance = df$distance)
m1
plot(m1)


# Model Time-Distance trace (with Flying Distance Correction fixed)
df <- create_sprint_trace(MSS = 8, MAC = 6, time = seq(0, 5, by = 0.5), FD = 0.5)
m1 <- model_time_distance_FD_fixed(time = df$time, distance = df$distance, FD = 0.5)
m1
plot(m1)


# Model Time-Distance trace (with Time Correction)
df <- create_sprint_trace(MSS = 8, MAC = 6, time = seq(0, 5, by = 0.5), TC = 1.5)
m1 <- model_time_distance_TC(time = df$time, distance = df$distance)
m1
plot(m1)


# Model Time-Distance trace (with Distance Correction)
df <- create_sprint_trace(MSS = 8, MAC = 6, time = seq(0, 5, by = 0.5), DC = -5)
m1 <- model_time_distance_DC(time = df$time, distance = df$distance)
m1
plot(m1)


# Model Time-Distance trace (with Time and Distance Corrections)
df <- create_sprint_trace(MSS = 8, MAC = 6, time = seq(0, 5, by = 0.5), TC = -1.3, DC = 5)
m1 <- model_time_distance_TC_DC(time = df$time, distance = df$distance)
m1
plot(m1)


# Model Timing Gates (simple, without corrections)
df <- create_sprint_trace(MSS = 8, MAC = 6, distance = c(5, 10, 20, 30, 40))
m1 <- model_timing_gates(distance = df$distance, time = df$time)
m1
plot(m1)


# Model Timing Gates (with Time Correction)
df <- create_sprint_trace(MSS = 8, MAC = 6, distance = c(5, 10, 20, 30, 40), TC = 0.2)
m1 <- model_timing_gates_TC(distance = df$distance, time = df$time)
m1
plot(m1)


# Model Timing Gates (with Flying Distance Correction)
df <- create_sprint_trace(MSS = 8, MAC = 6, distance = c(5, 10, 20, 30, 40), FD = 0.5)
m1 <- model_timing_gates_FD(distance = df$distance, time = df$time)
m1
plot(m1)


# Model Timing Gates (with Flying Distance Correction fixed)
df <- create_sprint_trace(MSS = 8, MAC = 6, distance = c(5, 10, 20, 30, 40), FD = 0.5)
m1 <- model_timing_gates_FD_fixed(distance = df$distance, time = df$time)
m1
plot(m1)


# Model Timing Gates (with Distance Correction)
df <- create_sprint_trace(MSS = 8, MAC = 6, distance = c(5, 10, 20, 30, 40), DC = 1.5)
m1 <- model_timing_gates_DC(distance = df$distance, time = df$time)
m1
plot(m1)


# Model Timing Gates (with Time and Distance Corrections)
df <- create_sprint_trace(MSS = 8, MAC = 6, distance = c(5, 10, 20, 30, 40), TC = 0.25, DC = 1.5)
m1 <- model_timing_gates_TC_DC(distance = df$distance, time = df$time)
m1
plot(m1)

Optimal profile functions

Description

Family of functions that serve a purpose of finding optimal sprint or force-velocity profile

optimal_FV finds "optimal" F0 and V0 where time at distance is minimized, while keeping the power the same

optimal_MSS_MAC finds "optimal" MSS and MAS where time at distance is minimized, while keeping the Pmax the same

Usage

optimal_FV(
  distance,
  F0,
  V0,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  method = "max",
  ...
)

optimal_MSS_MAC(distance, MSS, MAC)

Arguments

Value

optimal_FV returns s data frame with the following columns

F0

Original F0

V0

Original F0

bodymass

Bodymass

inertia

Inertia

resistance

Resistance

Pmax

Maximal power estimated using F0 * V0 / 4

Pmax_rel

Relative maximal power

slope

FV profile slope

distance

Distance

time

Time to cover distance

Ppeak

Peak power estimated quantitatively

Ppeak_rel

Relative peak power

Ppeak_dist

Distance at which peak power is manifested

Ppeak_time

Time at which peak power is manifested

F0_optim

Optimal F0

F0_coef

Ratio between F0_optim an F0

V0_optim

Optimal V0

V0_coef

Ratio between V0_optim an V0

Pmax_optim

Optimal maximal power estimated F0_optim * V0_optim / 4

Pmax_rel_optim

Optimal relative maximal power

slope_optim

Optimal FV profile slope

profile_imb

Percent ratio between slope and optimal slope

time_optim

Time to cover distance when profile is optimal

time_gain

Difference in time to cover distance between time_optimal and time

Ppeak_optim

Optimal peak power estimated quantitatively

Ppeak_rel_optim

Optimal relative peak power

Ppeak_dist_optim

Distance at which optimal peak power is manifested

Ppeak_time_optim

Time at which optimal peak power is manifested

optimal_MSS_MAC returns a data frame with the following columns

MSS

Original MSS

MAC

Original MAC

Pmax_rel

Relative maximal power estimated using MSS * MAC / 4

slope

Sprint profile slope

distance

Distance

time

Time to cover distance

MSS_optim

Optimal MSS

MSS_coef

Ratio between MSS_optim an MSS

MAC_optim

Optimal MAC

MAC_coef

Ratio between MAC_optim an MAC

Pmax_rel_optim

Optimal relative maximal power estimated using MSS_optim * MAC_optim / 4

slope_optim

Optimal sprint profile slope

profile_imb

Percent ratio between slope and optimal slope

time_optim

Time to cover distance when profile is optimal

time_gain

Difference in time to cover distance between time_optimal and time

References

Samozino P, Peyrot N, Edouard P, Nagahara R, Jimenez‐Reyes P, Vanwanseele B, Morin J. 2022. Optimal mechanical force-velocity profile for sprint acceleration performance. Scandinavian Journal of Medicine & Science in Sports 32:559–575. DOI: 10.1111/sms.14097.

Examples

MSS <- 10
MAC <- 8
bodymass <- 75

fv <- create_FVP(MSS, MAC, bodymass)

dist <- seq(5, 40, by = 5)

opt_MSS_MAC_profile <- optimal_MSS_MAC(
  distance = dist,
  MSS,
  MAC
)[["profile_imb"]]

opt_FV_profile <- optimal_FV(
  distance = dist,
  fv$F0,
  fv$V0,
  fv$bodymass
)[["profile_imb"]]

opt_FV_profile_peak <- optimal_FV(
  distance = dist,
  fv$F0,
  fv$V0,
  fv$bodymass,
  method = "peak"
)[["profile_imb"]]

plot(x = dist, y = opt_MSS_MAC_profile, type = "l", ylab = "Profile imbalance")
lines(x = dist, y = opt_FV_profile, type = "l", col = "blue")
lines(x = dist, y = opt_FV_profile_peak, type = "l", col = "red")
abline(h = 100, col = "gray", lty = 2)

S3 method for plotting shorts_model object

Description

S3 method for plotting shorts_model object

Usage

## S3 method for class 'shorts_model'
plot(x, type = "model", ...)

Arguments

Value

ggplot object

Examples

# Simple model with radar gun data
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)
)

radar_model <- with(
  instant_velocity,
  model_radar_gun(time, velocity)
)

plot(radar_model)
plot(radar_model, "kinematics-time")
plot(radar_model, "kinematics-distance")
plot(radar_model, "residuals")

S3 method for making predictions using shorts_model

Description

S3 method for making predictions using shorts_model

Usage

## S3 method for class 'shorts_model'
predict(object, ...)

Arguments

Examples

split_distances <- c(10, 20, 30, 40, 50)
split_times <- create_timing_gates_splits(
  gates = split_distances,
  MSS = 10,
  MAC = 9,
  FD = 0.25,
  TC = 0
)

# Simple model
simple_model <- model_timing_gates(split_distances, split_times)
predict(simple_model)

Kinematics prediction functions

Description

Predicts kinematic from known MSS and MAC parameters

Usage

predict_velocity_at_time(time, MSS, MAC)

predict_distance_at_time(time, MSS, MAC)

predict_acceleration_at_time(time, MSS, MAC)

predict_time_at_distance(distance, MSS, MAC)

predict_time_at_distance_FV(
  distance,
  F0,
  V0,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  ...
)

predict_velocity_at_distance(distance, MSS, MAC)

predict_acceleration_at_distance(distance, MSS, MAC)

predict_acceleration_at_velocity(velocity, MSS, MAC)

predict_air_resistance_at_time(time, MSS, MAC, ...)

predict_air_resistance_at_distance(distance, MSS, MAC, ...)

predict_force_at_velocity(
  velocity,
  MSS,
  MAC,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  ...
)

predict_force_at_time(
  time,
  MSS,
  MAC,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  ...
)

predict_force_at_distance(
  distance,
  MSS,
  MAC,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  ...
)

predict_power_at_distance(
  distance,
  MSS,
  MAC,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  ...
)

predict_power_at_time(
  time,
  MSS,
  MAC,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  ...
)

predict_relative_power_at_distance(
  distance,
  MSS,
  MAC,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  ...
)

predict_relative_power_at_time(
  time,
  MSS,
  MAC,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  ...
)

predict_work_till_time(time, ...)

predict_work_till_distance(distance, ...)

predict_kinematics(
  object = NULL,
  MSS,
  MAC,
  max_time = 6,
  frequency = 100,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  add_inertia_to_vertical = TRUE,
  ...
)

Arguments

Value

Numeric vector

Data frame with kinetic and kinematic variables

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.

Jovanović, M., Vescovi, J.D. (2020). shorts: An R Package for Modeling Short Sprints. Preprint available at SportRxiv. https://doi.org/10.31236/osf.io/4jw62

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.

Examples

MSS <- 8
MAC <- 9

time_seq <- seq(0, 6, length.out = 10)

df <- data.frame(
  time = time_seq,
  distance_at_time = predict_distance_at_time(time_seq, MSS, MAC),
  velocity_at_time = predict_velocity_at_time(time_seq, MSS, MAC),
  acceleration_at_time = predict_acceleration_at_time(time_seq, MSS, MAC)
)

df$time_at_distance <- predict_time_at_distance(df$distance_at_time, MSS, MAC)
df$velocity_at_distance <- predict_velocity_at_distance(df$distance_at_time, MSS, MAC)
df$acceleration_at_distance <- predict_acceleration_at_distance(df$distance_at_time, MSS, MAC)
df$acceleration_at_velocity <- predict_acceleration_at_velocity(df$velocity_at_time, MSS, MAC)

# Power calculation uses shorts::get_air_resistance function and its defaults
# values to calculate power. Use the ... to setup your own parameters for power
# calculations
df$power_at_time <- predict_power_at_time(
  time = df$time, MSS = MSS, MAC = MAC,
  # Check shorts::get_air_resistance for available params
  bodymass = 100, bodyheight = 1.85
)

df

# Example for predict_kinematics
split_times <- data.frame(
  distance = c(5, 10, 20, 30, 35),
  time = c(1.20, 1.96, 3.36, 4.71, 5.35)
)

# Simple model
simple_model <- with(
  split_times,
  model_timing_gates(distance, time)
)

predict_kinematics(simple_model)

S3 method for printing shorts_model object

Description

S3 method for printing shorts_model object

Usage

## S3 method for class 'shorts_model'
print(x, ...)

Arguments

Examples

split_distances <- c(10, 20, 30, 40, 50)
split_times <- create_timing_gates_splits(
  gates = split_distances,
  MSS = 10,
  MAC = 9,
  FD = 0.25,
  TC = 0
)

# Simple model
simple_model <- model_timing_gates(split_distances, split_times)
simple_model

Probe profile functions

Description

Family of functions that serve a purpose of probing sprint or force-velocity profile. This is done by increasing individual sprint parameter for a percentage and calculating which parameter improvement yield biggest deduction in sprint tim

probe_FV "probes" F0 and V0 and calculates which one improves sprint time for a defined distance

probe_MSS_MAC "probes" MSS and MAC and calculates which one improves sprint time for a defined distance

Usage

probe_FV(
  distance,
  F0,
  V0,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  perc = 2.5,
  ...
)

probe_MSS_MAC(distance, MSS, MAC, perc = 2.5)

Arguments

Value

probe_FV returns a data frame with the following columns

F0

Original F0

V0

Original F0

bodymass

Bodymass

inertia

Inertia

resistance

Resistance

Pmax

Maximal power estimated using F0 * V0 / 4

Pmax_rel

Relative maximal power

slope

FV profile slope

distance

Distance

time

Time to cover distance

probe_perc

Probe percentage

F0_probe

Probing F0

F0_probe_time

Predicted time for distance when F0 is probed

F0_probe_time_gain

Difference in time to cover distance between time_optimal and time

V0_probe

Probing V0

V0_probe_time

Predicted time for distance when V0 is probed

V0_probe_time_gain

Difference in time to cover distance between time_optimal and time

profile_imb

Percent ratio between V0_probe_time_gain and F0_probe_time_gain

probe_MSS_MAC returns a data frame with the following columns

MSS

Original MSS

MAC

Original MAC

Pmax_rel

Relative maximal power estimated using MSS * MAC / 4

slope

Sprint profile slope

distance

Distance

time

Time to cover distance

probe_perc

Probe percentage

MSS_probe

Probing MSS

MSS_probe_time

Predicted time for distance when MSS is probed

MSS_probe_time_gain

Difference in time to cover distance between probe time and time

MAC_probe

Probing MAC

MAC_probe_time

Predicted time for distance when MAC is probed

MAC_probe_time_gain

Difference in time to cover distance between probing time and time

profile_imb

Percent ratio between MSS_probe_time_gain and MAC_probe_time_gain

Examples

MSS <- 10
MAC <- 8
bodymass <- 75

fv <- create_FVP(MSS, MAC, bodymass)

dist <- seq(5, 40, by = 5)

probe_MSS_MAC_profile <- probe_MSS_MAC(
  distance = dist,
  MSS,
  MAC
)[["profile_imb"]]

probe_FV_profile <- probe_FV(
  distance = dist,
  fv$F0,
  fv$V0,
  fv$bodymass
)[["profile_imb"]]

plot(x = dist, y = probe_MSS_MAC_profile, type = "l", ylab = "Profile imbalance")
lines(x = dist, y = probe_FV_profile, type = "l", col = "blue")
abline(h = 100, col = "gray", lty = 2)

Radar Gun Data

Description

Data generated from known MSS and TAU and measurement error for N=5 athletes using radar gun with sampling frequency of 100Hz over 6 seconds.

Usage

data(radar_gun_data)

Format

Data frame with 4 variables and 3000 observations:

athlete

Character string

bodyweight

Bodyweight in kilograms

time

Time reported by the radar gun in seconds

velocity

Velocity reported by the radar gun in m/s

S3 method for returning residuals of shorts_model

Description

S3 method for returning residuals of shorts_model

Usage

## S3 method for class 'shorts_model'
residuals(object, ...)

Arguments

Examples

split_distances <- c(10, 20, 30, 40, 50)
split_times <- create_timing_gates_splits(
  gates = split_distances,
  MSS = 10,
  MAC = 9,
  FD = 0.25,
  TC = 0
)

# Simple model
simple_model <- model_timing_gates(split_distances, split_times)
residuals(simple_model)

Split Testing Data

Description

Data generated from known MSS and TAU and measurement error for N=5 athletes using 6 timing gates: 5m, 10m, 15m, 20m, 30m, 40m

Usage

data(split_times)

Format

Data frame with 4 variables and 30 observations:

athlete

Character string

bodyweight

Bodyweight in kilograms

distance

Distance of the timing gates from the sprint start in meters

time

Time reported by the timing gate

S3 method for providing summary for the shorts_model object

Description

S3 method for providing summary for the shorts_model object

Usage

## S3 method for class 'shorts_model'
summary(object, ...)

Arguments

Examples

split_distances <- c(10, 20, 30, 40, 50)
split_times <- create_timing_gates_splits(
  gates = split_distances,
  MSS = 10,
  MAC = 9,
  FD = 0.25,
  TC = 0
)

# Simple model
simple_model <- model_timing_gates(split_distances, split_times)
summary(simple_model)

Vescovi Timing Gates Sprint Times

Description

Timing gates sprint times involving 52 female athletes. Timing gates were located at 5m, 10m, 20m, 30m, and 35m. See Details for more information.

Usage

data(vescovi)

Format

Data frame with 17 variables and 52 observations:

Team

Team or sport. Contains the following levels: 'W Soccer' (Women Soccer), 'FH Sr' (Field Hockey Seniors), 'FH U21' (Field Hockey Under 21), and 'FH U17' (Field Hockey Under 17)

Surface

Type of testing surface. Contains the following levels: 'Hard Cours' and 'Natural Grass'

Athlete

Athlete ID

Age

Athlete age in years

Height

Body height in cm

Bodyweight

Body weight in kg

BMI

Body Mass Index

BSA

Body Surface Area. Calculated using Mosteller equation sqrt((height/weight)/3600)

5m

Time in seconds at 5m gate

10m

Time in seconds at 10m gate

20m

Time in seconds at 20m gate

30m

Time in seconds at 30m gate

35m

Time in seconds at 35m gate

10m-5m split

Split time in seconds between 10m and 5m gate

20m-10m split

Split time in seconds between 20m and 10m gate

30m-20m split

Split time in seconds between 30m and 20m gate

35m-30m split

Split time in seconds between 35m and 30m gate

Details

This data-set represents sub-set of data from a total of 220 high-level female athletes (151 soccer players and 69 field hockey players). Using a random number generator, a total of 52 players (35 soccer and 17 field hockey) were selected for this data-set. Soccer players were older (24.6±3.6 vs. 18.9±2.7 yr, p < 0.001), however there were no differences for height (167.3±5.9 vs. 167.0±5.7 cm, p = 0.886), body mass (62.5±5.9 vs. 64.0±9.4 kg, p = 0.500) or any sprint interval time (p > 0.650).

The protocol for assessing linear sprint speed has been described previously (Vescovi 2014, 2016, 2012) and was identical for each cohort. Briefly, all athletes performed a standardized warm-up that included general exercises such as jogging, shuffling, multi-directional movements, and dynamic stretching exercises. Infrared timing gates (Brower Timing, Utah) were positioned at the start line and at 5, 10, 20, and 35 meters at a height of approximately 1.0 meter. Participants stood with their lead foot positioned approximately 5 cm behind the initial infrared beam (i.e., start line). Only forward movement was permitted (no leaning or rocking backwards) and timing started when the laser of the starting gate was triggered. The best 35 m time, and all associated split times were kept for analysis. The assessment of linear sprints using infrared timing gates does not require familiarization (Moir, Button, Glaister, and Stone 2004).

Author(s)

Jason D. Vescovi
University of Toronto
Faculty of Kinesiology and Physical Education
Graduate School of Exercise Science
Toronto, ON Canada
vescovij@gmail.com

References

Moir G, Button C, Glaister M, Stone MH (2004). "Influence of Familiarization on the Reliability of Vertical Jump and Acceleration Sprinting Performance in Physically Active Men." The Journal of Strength and Conditioning Research, 18(2), 276. ISSN 1064-8011, 1533-4287. doi:10.1519/R-13093.1.

Vescovi JD (2012). "Sprint Speed Characteristics of High-Level American Female Soccer Players: Female Athletes in Motion (FAiM) Study." Journal of Science and Medicine in Sport, 15(5), 474-478. ISSN 14402440. doi:10.1016/j.jsams.2012.03.006.

Vescovi JD (2014). "Impact of Maximum Speed on Sprint Performance During High-Level Youth Female Field Hockey Matches: Female Athletes in Motion (FAiM) Study." International Journal of Sports Physiology and Performance, 9(4), 621-626. ISSN 1555-0265, 1555-0273. doi:10.1123/ijspp.2013-0263.

Vescovi JD (2016). "Locomotor, Heart-Rate, and Metabolic Power Characteristics of Youth Women's Field Hockey: Female Athletes in Motion (FAiM) Study." Research Quarterly for Exercise and Sport, 87(1), 68-77. ISSN 0270-1367, 2168-3824. doi:10.1080/02701367.2015.1124972.