Acute Chronic Workload Ratio Calculation

Functions for calculating the acute chronic workload ratio using three different methods: exponentially weighted moving average (EWMA), rolling average coupled (RAC) and rolling averaged uncoupled (RAU). Examples of this methods can be found in Williams et al. (2017) for EWMA and Windt & Gabbet (2018) for RAC and RAU .

ACWR

Overview

The ACWR package have been designed to calculate the the acute chronic workload ratio using three different methods: exponentially weighted moving average (EWMA), rolling average coupled (RAC) and rolling averaged uncoupled (RAU).

Resources

• Application of ACWR (European Journal of Sport Science)
• Rolling average method (British Journal of Sports Medicine)
• Exponentially weighted moving averages method (British Journal of Sports Medicine)

Example

This is a basic example which shows you how to use the ACWR package:

library(devtools)
install_github("JorgeDelro/ACWR")
library(ACWR)

First, we have to load the data stored in the package

data("training_load", package = "ACWR")
# Convert to data.frame
training_load <- data.frame(training_load)

Then, we can calculate the ACWR:

result_ACWR <- ACWR(db = training_load,
                  ID = "ID",
                  TL = "TL",
                  weeks = "Week",
                  training_dates = "Training_Date",
                  ACWR_method = c("EWMA", "RAC", "RAU"))

Additionally, individual plot can be obtained:

ACWR_plot <- plot_ACWR(db = result_ACWR,
                        TL = "TL",
                        ACWR = "RAC_ACWR",
                        day = "Day",
                        ID = "ID")

Functions for each individual method have been implemented too:

# Select the first subject
training_load_1 <- training_load[training_load[["ID"]] == 1,  ]

# EWMA
result_EWMA <- EWMA(TL = training_load_1$TL)

# RAC
result_RAC <- RAC(TL = training_load_1$TL,
                  weeks = training_load_1$Week,
                  training_dates = training_load_1$Training_Date)
                    
# RAU
result_RAU <- RAU(TL = training_load_db_1$TL,
                  weeks = training_load_1$Week,
                  training_dates = training_load_1$Training_Date)