Here is a story that imagines a data analyst diving into that specific package to uncover a hidden truth.

: Includes data from the inaugural 1930 tournament to the most recent editions.

| Package | Purpose | |---------|---------| | worldfootballR | Scrapes live/current data from FBref, Transfermarkt, etc. | | StatsBombR | Advanced event data (passes, shots, positions) for selected competitions | | engsoccerdata | Historical English league and cup data | | fitzRoy | Australian football (AFL) data |

: Use historical match data to build Poisson regression models to predict future tournament outcomes.

matches %>% filter(tournament_id == "women") %>% mutate(winner = case_when( home_goals > away_goals ~ home_team, away_goals > home_goals ~ away_team, TRUE ~ "Draw" )) %>% left_join(teams, by = c("winner" = "team")) %>% filter(winner != "Draw") %>% count(confederation, sort = TRUE) %>% mutate(win_pct = n / sum(n))

R Package Worldcup Fjelstul Exclusive

Here is a story that imagines a data analyst diving into that specific package to uncover a hidden truth.

: Includes data from the inaugural 1930 tournament to the most recent editions. r package worldcup fjelstul

| Package | Purpose | |---------|---------| | worldfootballR | Scrapes live/current data from FBref, Transfermarkt, etc. | | StatsBombR | Advanced event data (passes, shots, positions) for selected competitions | | engsoccerdata | Historical English league and cup data | | fitzRoy | Australian football (AFL) data | Here is a story that imagines a data

: Use historical match data to build Poisson regression models to predict future tournament outcomes. | | StatsBombR | Advanced event data (passes,

matches %>% filter(tournament_id == "women") %>% mutate(winner = case_when( home_goals > away_goals ~ home_team, away_goals > home_goals ~ away_team, TRUE ~ "Draw" )) %>% left_join(teams, by = c("winner" = "team")) %>% filter(winner != "Draw") %>% count(confederation, sort = TRUE) %>% mutate(win_pct = n / sum(n))