penalties_in_play <- goals %>% filter(goal_type == "penalty") %>% count(player, sort = TRUE)
: Offers 27 distinct datasets covering everything from match results and squad lineups to bookings, penalty kicks, and substitutions.
“It’s July 13, 2014. Mario Götze controls the ball on his chest in the 113th minute of the World Cup final and volleys it past Sergio Romero. Germany wins its fourth title. That moment — every pass, every foul, every substitution — is captured in a single R package.” jfjelstul worldcup r package
The following examples use tidyverse workflows to demonstrate how to query and manipulate the database. Example 1: Historical Scoring Efficiency (Men vs. Women)
# Calculate total goals scored by each team team_goals <- matches %>% group_by(team) %>% summarise(total_goals = sum(score)) Germany wins its fourth title
: Contains over 1.58 million data points , making it one of the most complete open-source resources for World Cup analytics.
End with:
Start with a story:
“Did you know Italy has scored more own goals (6) than Ghana, Japan, and Portugal combined in World Cups? I analyzed 90+ years of matches using the worldcup R package. Here’s what I found about penalties, red cards, and late-game chokes — a thread.” Women) # Calculate total goals scored by each
: Exhaustive records mapping out match-official deployments across tournaments.