Win a Fantasy Soccer Championship with Free Analytics Data

How to Easily Access Fantasy Soccer Data for Analytics Projects

Fantasy soccer, much like other fantasy sports, relies heavily on data. By mastering the right tools, you can outsmart your competition. In this guide, we’ll show you how to retrieve fantasy soccer data using R packages like soccerR and ffsoccer, which will allow you to create your own projections, analyze player performance, and connect to private leagues to run simulations. We’ll also cover key leagues such as the English Premier League, La Liga, Bundesliga, Champions League, and MLS.

Step 1: Utilizing R Packages for Fantasy Soccer Data

There are several R packages available that will help you access and analyze fantasy soccer data. Here are the most valuable ones:

soccerR: Provides tools to access public soccer data from sources such as Soccerway, FBref, and other API services, making it a valuable tool for analyzing player performance across various leagues like the English Premier League, La Liga, Bundesliga, and more.

Example: Installing soccerR

install.packages("remotes")
remotes::install_github("jthomasmock/soccerR")

Example: Retrieving EPL Data for Fantasy Soccer

library(soccerR)

# Get player data from the English Premier League (EPL) for 2023
epl_player_data <- soccer_data(league = "EPL", season = 2023)
head(epl_player_data)

This retrieves detailed player data for fantasy soccer analysis, such as goals, assists, and minutes played.

ffsoccer: Similar to ffverse for fantasy football, ffsoccer allows you to connect to private fantasy soccer leagues from platforms like the Premier League Fantasy, MLS Fantasy, and La Liga Fantasy.

Example: Connecting to a Private Premier League Fantasy League

library(ffsoccer)

# Connect to Premier League Fantasy soccer league
pl_connection <- pl_connect(
  season = 2023,
  league_code = "123456",  # Replace with your Premier League league code
  token = "YOUR_OAUTH_TOKEN"  # Replace with your OAuth token
)

# Retrieve league standings
standings <- pl_standings(conn = pl_connection)
head(standings)

This connects to your Premier League Fantasy soccer league and retrieves standings, player stats, and more.

ffpros: Fetches expert rankings, projections, and ADP (Average Draft Position) data for fantasy soccer from FantasyPros.

Example: Installing ffpros

install.packages("ffpros")

Example: Fetching FantasyPros ADP Data for Fantasy Soccer

library(ffpros)

# Retrieve FantasyPros ADP data for soccer
adp_data <- ffpros_adp(sport = "soccer")
head(adp_data)

This retrieves ADP data from FantasyPros, which you can use to analyze draft trends and player popularity.

Step 2: How to Connect to Private MLS Fantasy Leagues

To connect to an MLS Fantasy league, you will need your league code and OAuth token.

Finding Your MLS Fantasy League Code

Log in to MLS Fantasy: Open your browser and log in to your MLS Fantasy account.
Navigate to Your League: Once logged in, navigate to your fantasy soccer league. The league code will be part of the URL.
Copy the League Code: Copy the league code and save it for your R connection.

Example: Connecting to MLS Fantasy League with ffsoccer

library(ffsoccer)

# Connect to MLS Fantasy soccer league
mls_connection <- mls_connect(
  season = 2023,
  league_code = "123456",  # Replace with your MLS league code
  token = "YOUR_OAUTH_TOKEN"  # Replace with your OAuth token
)

# Retrieve league standings
standings <- mls_standings(conn = mls_connection)
head(standings)

This connects to your MLS Fantasy soccer league and retrieves standings, player stats, and more.

Step 3: Running League Simulations for Fantasy Soccer

Simulating league outcomes helps you project player performance and potential wins. Using packages like ffsimulator, you can simulate thousands of league outcomes based on player stats and settings.

Example: Running League Simulations with ffsimulator

library(ffsimulator)

# Run 5000 simulations for the connected league
simulation_results <- ff_simulate(conn = pl_connection, n_seasons = 5000)

# Visualize the simulation results
plot(simulation_results)

This simulates your fantasy soccer league’s schedule and projects win probabilities based on historical data.

Step 4: Projecting Player Performance with ffanalytics

The ffanalytics package allows you to project player performance across multiple seasons. It aggregates expert projections from various sources and can help you project player stats for upcoming games or seasons.

Example: Projecting Player Stats with ffanalytics

library(ffanalytics)

# Project player stats for 2023
projections <- run_projections(sport = "soccer", season = 2023, sources = c("FantasyPros", "EPL", "MLS"))
head(projections)

This example projects player stats for the 2023 fantasy soccer season, helping you plan your strategy.

Step 5: Analyze Draft Data with ffpros

Analyzing draft trends is critical to dominating your fantasy soccer league. Using ffpros, you can retrieve ADP data and expert rankings to gain insights into draft strategy.

Example: Fetching ADP Data for Fantasy Soccer

library(ffpros)

# Fetch FantasyPros ADP for fantasy soccer
adp_data <- ffpros_adp(sport = "soccer")
head(adp_data)

This retrieves ADP data for fantasy soccer players, helping you prepare for your draft by understanding where players are typically selected.

Step 6: Explore Public Fantasy Soccer Data Sources

In addition to R packages, several public sources offer valuable fantasy soccer data:

FantasyPros: Provides expert rankings, projections, and ADP data for multiple soccer leagues.
Premier League Fantasy: Provides player stats, projections, and league data for the Premier League.
MLS Fantasy: Offers detailed stats, league data, and projections for MLS Fantasy.
FBref: Provides detailed statistics for European leagues such as La Liga, Bundesliga, and Champions League.
WhoScored: Offers advanced player statistics, match ratings, and player performance metrics across major soccer leagues.

Step 7: Apply Your Data Skills to Fantasy Soccer Analytics Projects

Now that you know how to access and analyze fantasy soccer data, it’s time to apply your skills to real-world projects. Whether you’re analyzing player projections, simulating league schedules, or strategizing for your draft, tools like soccerR and ffsoccer will give you a competitive edge.

Explore our Soccer Analytics Courses to learn more about data analysis techniques, custom models, and statistical approaches that will help you dominate your fantasy league.

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