Worldcup R Package Jfjelstul Best (2025)

In the world of sports data science, few events are as rich with narrative and statistical depth as the FIFA World Cup. For R users looking to analyze penalty shootout pressure, goal distribution patterns, or player longevity, accessing clean, comprehensive historical data is the first hurdle.

As the next World Cup cycle begins, analysts would do well to revisit Fjelstul’s work—and politely lobby for a 2018/2022 extension. Until then, worldcup remains the gold standard for historical football data in R. worldcup r package jfjelstul

library(worldcup) library(dplyr) goals %>% group_by(match_id) %>% summarise(total_goals = n(), .groups = "drop") %>% arrange(desc(total_goals)) %>% left_join(matches, by = "match_id") %>% select(year, home_team, away_team, total_goals) %>% slice(1:3) In the world of sports data science, few