Rugby Analytics with R: Complete Guide to Performance Analysis in Rugby Union and League

Rugby is a sport defined by collisions, structure, and constant tactical adaptation. Unlike many other invasion sports, rugby alternates between highly structured moments—scrums, lineouts, restarts—and extended passages of chaotic open play. Each phase generates rich performance data: tackles, rucks, carries, kicks, meters gained, penalties conceded, turnovers, and spatial changes in territory. Despite this richness, rugby analytics has historically lagged behind other sports, especially in terms of open, reproducible analytical workflows.

This gap presents a clear opportunity. R provides a complete environment for rugby performance analysis: data acquisition, cleaning, modeling, visualization, and automated reporting. For analysts, sports scientists, and coaches, R enables evidence-based decision-making that goes far beyond traditional statistics and subjective video review.

Why rugby analytics requires a different analytical mindset

Rugby is not a possession-by-possession sport in the same way as basketball, nor a continuous-flow game like football. Possession can be short or long, territory often matters more than time on the ball, and a single penalty can flip match momentum. Analytics must therefore respect rugby’s unique structure.

Simple totals—tackles, carries, meters—are insufficient on their own. Analysts must consider game state, field position, opposition quality, and player role. R makes it possible to incorporate this context systematically and consistently across matches and seasons.

Data acquisition in rugby: scraping, APIs, and internal feeds

Public rugby data is fragmented and inconsistent. Analysts often combine multiple sources to build a usable dataset. R is particularly well suited to this challenge because it supports web scraping, API consumption, and database integration within a single workflow.

# Core libraries for rugby data acquisition
library(tidyverse)
library(rvest)
library(httr)
library(jsonlite)

# Example: pulling match data from an API
response <- GET("https://api.example.com/rugby/match/9876")
raw_json <- content(response, "text")
match_data <- fromJSON(raw_json)

Web scraping is often necessary when APIs are unavailable. This requires careful handling of HTML structure, rate limits, and data validation to ensure accuracy and reproducibility.

# Scraping a match statistics table
page <- read_html("https://example-rugby-site.com/match/9876")

team_stats <- page %>%
  html_node("table.match-stats") %>%
  html_table()

team_stats

Data cleaning and validation: a critical but underestimated step

Rugby datasets are rarely analysis-ready. Player substitutions, injury replacements, and data entry inconsistencies introduce errors that can distort results if left unchecked.

# Standardizing and validating team statistics
team_stats_clean <- team_stats %>%
  janitor::clean_names() %>%
  mutate(across(where(is.character), str_trim)) %>%
  mutate(
    possession = as.numeric(possession),
    territory = as.numeric(territory)
  )

# Basic validation check
stopifnot(all(team_stats_clean$possession <= 100))

Validation logic should be embedded directly into the pipeline. This ensures that every new match is processed consistently, reducing human error and analyst workload.

Transforming events into rugby-specific units of analysis

Raw events are only the starting point. Meaningful rugby analysis requires transforming events into units such as phases, possessions, sets, and passages of play.

# Creating phase identifiers from ruck events
events <- events %>%
  arrange(match_id, event_time) %>%
  mutate(
    phase_id = cumsum(event_type == "ruck")
  )

# Summarising phase-level performance
phase_summary <- events %>%
  group_by(match_id, team, phase_id) %>%
  summarise(
    duration = max(event_time) - min(event_time),
    carries = sum(event_type == "carry"),
    meters = sum(meters_gained, na.rm = TRUE),
    turnovers = sum(event_type == "turnover"),
    .groups = "drop"
  )

These structures allow analysts to study momentum, ruck efficiency, and attacking intent in a way that aligns with how coaches understand the game.

Advanced player performance analysis with R

Player evaluation in rugby must be contextual and role-specific. Front-row players, halves, and outside backs contribute in fundamentally different ways.

# Player-level performance profile
player_profile <- events %>%
  group_by(player_id, player_name, position) %>%
  summarise(
    minutes_played = max(event_time) / 60,
    tackles = sum(event_type == "tackle"),
    missed_tackles = sum(event_type == "missed_tackle"),
    carries = sum(event_type == "carry"),
    meters = sum(meters_gained, na.rm = TRUE),
    offloads = sum(event_type == "offload"),
    penalties_conceded = sum(event_type == "penalty_conceded"),
    .groups = "drop"
  ) %>%
  mutate(
    tackles_per_min = tackles / minutes_played,
    meters_per_carry = meters / carries
  )

Rate-based metrics reveal impact more effectively than totals, especially when comparing starters to bench players or evaluating performance across different match contexts.

Defensive systems analysis: beyond individual tackles

Effective defense is systemic. Missed tackles often result from spacing errors, fatigue, or poor decision-making rather than individual incompetence.

# Defensive performance by field channel
defense_analysis <- events %>%
  filter(event_type %in% c("tackle", "missed_tackle")) %>%
  group_by(team, field_channel) %>%
  summarise(
    tackles = sum(event_type == "tackle"),
    misses = sum(event_type == "missed_tackle"),
    success_rate = tackles / (tackles + misses),
    .groups = "drop"
  )

Territory, kicking strategy, and spatial dominance

Territory remains a core determinant of success in rugby. Teams that consistently win the territorial battle reduce defensive workload and increase scoring opportunities.

# Kicking distance and efficiency
kicks <- events %>%
  filter(event_type == "kick") %>%
  mutate(kick_distance = end_x - start_x)

kicking_summary <- kicks %>%
  group_by(team, kick_type) %>%
  summarise(
    avg_distance = mean(kick_distance, na.rm = TRUE),
    kicks = n(),
    .groups = "drop"
  )

Spatial analysis allows analysts to quantify whether a team’s kicking strategy aligns with its stated game model and environmental constraints.

Win probability and decision modeling in rugby

Win probability models convert complex match states into intuitive probabilities. In rugby, these models must account for score, time, territory, possession, and discipline risk.

# Building a basic win probability model
wp_data <- matches %>%
  mutate(
    score_diff = team_score - opponent_score,
    time_remaining = 80 - minute
  )

wp_model <- glm(
  win ~ score_diff + time_remaining + territory,
  data = wp_data,
  family = binomial()
)

summary(wp_model)

Even simple models provide immediate value by framing tactical decisions—such as kicking for touch versus taking the points—in probabilistic terms.

Automated reporting and reproducible workflows

The final step in rugby analytics is communication. R enables analysts to automate reporting, ensuring consistency and freeing time for deeper insight generation.

# Creating a clean match summary table
summary_table <- team_stats_clean %>%
  select(team, possession, territory, tackles, line_breaks, penalties_conceded)

knitr::kable(summary_table)

Automated reports ensure that analysis becomes part of the weekly rhythm rather than an optional extra.

The strategic opportunity in rugby analytics with R

There is clear and growing interest in rugby analytics, but very little comprehensive, R-focused content. Analysts, sports scientists, and coaches are actively searching for practical guidance.

A dedicated, end-to-end approach—covering data acquisition, performance metrics, modeling, and reporting—fills a genuine gap and establishes authority in a niche with minimal competition.