Functions to Scrape Rugby Data

Provides a set of functions to scrape and analyze rugby data. Supports competitions including the National Rugby League, New South Wales Cup, Queensland Cup, Super League, and various representative and women's competitions. Includes functions to fetch player statistics, match results, ladders, venues, and coaching data. Designed to assist analysts, fans, and researchers in exploring historical and current rugby league data. See Woods et al. (2017) for an example of rugby league performance analysis methodology.

nrlR 📊🏉

A powerful R package to scrape, clean, and analyze publicly available Rugby data

🚀 Overview

nrlR provides a streamlined toolkit for R users to scrape, clean, and analyze rugby data from public sources specifically rugby league. It covers:

• 🏆 NRL & NRLW
• 🏟️ State of Origin
• 🌏 Super League, Championship, League One
• 🏉 NSW Cup, QLD Cup, Women's Super League
• 🔥 Historical seasons back to 1998

With nrlR, you can easily pull:

✅ Match fixtures and results
✅ Ladder standings
✅ Player statistics (runs, tries, tackles, points)
✅ Team statistics (totals, differentials)
✅ Venues, crowds & more

Use it to build dashboards, run predictive models, or simply explore your favourite teams.

💿 Installation

Install the development version directly from GitHub using:

# install.packages("devtools")
devtools::install_github("DanielTomaro13/nrlR")

🔥 Quick Examples

library(nrlR)

📊 Get the NRL ladder for 2025

ladder <- fetch_ladder(season = 2025, league = "nrl", source = "nrl")
head(ladder)
#> # A tibble: 6 × 12
#>   position team        played  wins draws losses points_for points_against
#>      <int> <chr>        <int> <int> <int>  <int>      <int>          <int>
#> 1        1 Storm           8     7     0      1        168             98
#> 2        2 Panthers        8     6     1      1        152            106
#> 3        3 Roosters        8     6     0      2        144            112
#> 4        4 Sharks          8     5     1      2        138            118
#> 5        5 Cowboys         8     5     0      3        142            124
#> 6        6 Eels            8     5     0      3        126            128

🏟️ Fetch fixtures for NRL Round 5, 2025

fixtures <- fetch_fixture(season = 2025, round_number = 5)
print(fixtures)
#> # A tibble: 8 × 10
#>   match_id date       round home_team    away_team    venue      crowd result
#>      <int> <date>     <int> <chr>        <chr>        <chr>      <int> <chr> 
#> 1   401234 2025-04-03     5 Storm        Panthers     AAMI Park  18500 19-16
#> 2   401235 2025-04-04     5 Roosters     Sharks       Allianz    35241 24-10
#> 3   401236 2025-04-05     5 Cowboys      Eels         QCB        22000 28-12
#>   # ... with 5 more rows

📈 Fetch Champion Data team stats

# Get the available competitions first
comps <- fetch_cd_competitions()
print(comps)
#> # A tibble: 15 × 4
#>   comp_id competition_name             season year 
#>     <int> <chr>                         <int> <int>
#> 1   12755 NRL Telstra Premiership       2025  2025
#> 2   12754 NRLW Premiership              2025  2025
#> 3   12753 State of Origin               2025  2025
#>   # ... with 12 more rows

# Then fetch team stats, e.g. comp ID 12755
cd_team_stats <- fetch_team_stats_championdata(comp = 12755, round = 4)
head(cd_team_stats)
#> # A tibble: 6 × 20
#>   team          round tries line_breaks tackle_breaks metres_gained
#>   <chr>         <int> <int>       <int>         <int>         <int>
#> 1 Melbourne Storm   4     4          12            18          1450
#> 2 Penrith Panthers  4     3          10            15          1380
#> 3 Sydney Roosters   4     5          14            20          1520
#>   # ... with 3 more rows and 15 more variables

👟 Fetch Champion Data player stats

# For competition ID 12755 (2025 NRL Telstra Premiership)
cd_player_stats <- fetch_player_stats(
  comp = 111, 
  round = 4, 
  source = "championdata"
)
head(cd_player_stats)
#> # A tibble: 6 × 18
#>   player_name    team     position round tries assists line_breaks
#>   <chr>          <chr>    <chr>    <int> <int>   <int>       <int>
#> 1 Nathan Cleary  Panthers Halfback     4     0       3           2
#> 2 Jahrome Hughes Storm    Halfback     4     1       2           4
#> 3 James Tedesco  Roosters Fullback     4     2       1           6
#>   # ... with 3 more rows and 11 more variables

📊 Data Sources

nrlR aggregates data from multiple reliable sources:

Primary Sources

• NRL.com - Official fixtures, results, ladders
• Rugby League Project - Comprehensive historical statistics
• Champion Data - Advanced statistics and analytics
• Super League - UK competitions data

Supported Competitions

🛠 Core Functions

Fixtures & Results

# Get fixtures for a specific round
fetch_fixture(season = 2025, round_number = 5, league = "nrl")

# Get all results for a season
fetch_results(season = 2024, league = "nrl", source = "nrl")

# Get specific match details
fetch_match_details(match_id = 401234, source = "nrl")

Ladder & Standings

# Current season ladder
fetch_ladder(season = 2025, league = "nrl")

# Historical ladder positions
fetch_historical_ladder(season = 2020, round = 10, league = "nrl")

Player Statistics

# Individual player stats
fetch_player_stats(
  season = 2024, 
  league = "nrl", 
  round = 1:27,
  player_name = "Nathan Cleary"
)

# Top try scorers
fetch_player_leaders(
  season = 2024, 
  stat_type = "tries", 
  league = "nrl"
)

Team Statistics

# Team performance metrics
fetch_team_stats(season = 2024, league = "nrl", source = "rugbyproject")

# Head-to-head records
fetch_h2h_record(team1 = "Storm", team2 = "Panthers", years = 2020:2024)

Advanced Analytics

# Champion Data advanced stats
fetch_cd_player_advanced(comp = 12755, round = 4)

# Team efficiency metrics
fetch_team_efficiency(season = 2024, league = "nrl")

⚙️ Configuration

Rate Limiting

# Set request delays to be respectful to data sources
set_nrl_config(delay_seconds = 1, max_retries = 3)

Caching

# Enable caching to speed up repeated requests
enable_caching(cache_dir = "~/.nrlR_cache", expire_hours = 24)

Data Quality

# Check data completeness
check_data_completeness(season = 2024, league = "nrl")

# Validate scraped data
validate_nrl_data(data = my_nrl_data)

🧪 Testing & Reliability

nrlR includes comprehensive testing to ensure data quality:

# Run package tests
devtools::test()

# Check specific scraper functionality
test_scraper_health("nrl")
test_scraper_health("rugbyproject")
test_scraper_health("championdata")

Error Handling

The package gracefully handles common issues:

• Missing data (returns NA with warnings)
• Network timeouts (automatic retries)
• Structural changes to source websites (informative error messages)
• Invalid inputs (parameter validation)

🤝 Contributing

We welcome contributions! Here's how you can help:

🐛 Bug Reports

Found an issue? Please create an issue with:

• Reproducible example
• Expected vs actual behavior
• Your R session info (sessionInfo())

💡 Feature Requests

Want a new feature? Open an issue describing:

• The use case
• Expected functionality
• Proposed implementation (if any)

🔧 Development

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes with tests
4. Run devtools::check() to ensure quality
5. Submit a pull request

📊 New Data Sources

Adding a new competition or data source:

1. Create scraper functions following existing patterns
2. Add comprehensive tests
3. Update documentation
4. Ensure data format consistency

🚨 Ethical Usage

Please use nrlR responsibly:

• ✅ Respect robots.txt and website terms of service
• ✅ Implement delays between requests (default: 1 second)
• ✅ Cache data to minimize repeated requests
• ✅ Attribute data sources in your analysis
• ❌ Don't overwhelm servers with rapid requests
• ❌ Don't redistribute scraped data commercially without permission

🆘 Getting Help

• 📖 Documentation: ?function_name or help(package = "nrlR")
• 💬 Issues: GitHub Issues
• 📧 Email: [email protected]

📄 Citation

If you use nrlR in academic research, please cite:

Tomaro, D. (2025). nrlR: An R package for rugby league data analysis. 
R package version 0.1.1. https://github.com/DanielTomaro13/nrlR

BibTeX:

@Manual{nrlR,
  title = {nrlR: An R package for rugby league data analysis},
  author = {Daniel Tomaro},
  year = {2025},
  note = {R package version 0.1.1},
  url = {https://github.com/DanielTomaro13/nrlR},
}

📝 License

MIT © Daniel Tomaro

This package is not affiliated with the NRL, Rugby League Project, Champion Data, or any official rugby league organization. All data is sourced from publicly available information.

📢 Build your next footy model, dashboard, or data viz with nrlR.

Happy coding! 🏉📊

Last updated: August 2025