This repository contains a collection of R functions for processing and preparing raw data from two seasons of women’s football (Soccermon). It also includes a Shiny App that enables interactive exploration of both seasons, focusing on the concepts of maximum-demand scenarios and critical speed.

You can access a live example of the Shiny App at the following link: [HERE]
(Note: The last tab of the app is currently non-functional in the online version.)

git clone https://github.com/danielfrenandez/CriticalSpeed_shiny.git cd CriticalSpeed_shiny 

Make sure you have R (version ≥ 4.2.0) and RStudio installed.
Then, install the necessary packages by running the following commands in R:

install.packages(c(   "shiny",   "tidyverse",   "data.table",   "DT",   "ggplot2",   "readr",   "dplyr",   "purrr",   "stringr" )) 

If additional packages are used in your scripts, list them in a requirements.R file or include them above.

Once all dependencies are installed, you can launch the app by running:

shiny::runApp("app.R") 

or simply open app.R in RStudio and click Run App.

Notes:

• Some features (like Session Analysis) only work when running the app locally.

• The app may take several seconds to load due to the size and complexity of the datasets.

If you need to prepare the raw data before using the app, you can run the following functions included in the repository:

download2020data() load_data() create_database_fast() 

These functions will:

• Download and store all raw data files.

• Convert them from .parquet to .RDS format.

• Build a summarized database used by the app for analysis.

Downloads the raw data from the Zenodo online repository and stores all files in a local folder.

Note: This process can take a long time due to the large file sizes. Ensure sufficient local storage before running the function.
These data include numerous raw files from multiple training sessions across two full seasons.

Converts the stored files from PARQUET format to RDS, significantly reducing storage size.

• Only needs to be executed once.

• Takes time to complete, as it processes a large number of files.

Main function to build a summary database that aggregates all training sessions and matches.

• Minimum runtime: approximately 1 hour, depending on system performance.

• Generates one summarized entry per session and player, containing:

-   Session duration

-   Total distance

-   Distance \> 21 km/h

-   Number of sprints \> 21 km/h

-   PlayerLoad

-   Mean heart rate

-   Maximum heart rate

Computes moving averages for multiple time windows (e.g., 3, 5, 10 seconds) across all training sessions for each player, based on speed.

• Produces a dataset identifying the best performance moments for each player across different time frames.

• This is the primary analytical function used to estimate each player’s critical speed.

• Computationally intensive due to the number of sessions processed.

Performs a detailed individual moving-window analysis on the speed variable across numerous time windows (1, 2, 3, 4 seconds, etc.).

• Designed for optimization, but still computationally demanding.

• Returns a dataset providing, for each player and session, the maximum mean values of speed for each analyzed time window.

Utility function that allows filtering and extracting historical maximum values for each player within a user-defined time range of the season.

This file contains the code for a Shiny App designed to visualize and analyze all the data.
The app includes three main tabs for exploring and comparing players’ top speed performances:

Compares a player’s metrics with the rest of the team to identify her relative position within the group.

• Allows filtering by date range to analyze specific periods of the season.

  

Compares each player’s season-long individual records with those from a specific training session.

• Helps visualize how close or far a player was from her best performances during a given session.

• Includes filters to select the desired time range within the season.

• Also provides a summary data table with key metrics.

  

⚠️ Currently, this feature only works when running the app locally (it is not functional on the hosted shinyapps.io version).

This tab allows you to:

• Upload a session file in RDS format.

• Automatically analyze multiple time windows within the uploaded session.

• Assess:

  • The structure and intensity of the training session.

  • How close the player’s efforts were to her historical maximums.

  • The number of repetitions performed within each time window.

The analysis typically takes around 30–40 seconds to complete due to the heavy computational processing involved.