The score of each team in a matchup or scoring period and the match outcome.
tidy_scores(leagueId = ffl_id(), leagueHistory = FALSE, useMatchup = TRUE, ...)Numeric league ID or ESPN fantasy page URL. Defaults to
getOption("fflr.leagueId"). Function fails if no ID is found.
logical; Should the leagueHistory version of the API
be called? If TRUE, a list of results is returned, with one element for
each historical year of the league.
logical; Whether scoring should be summarized by
matchupPeriodId (default) or scoringPeriodId. The later always relates
to a single week of the NFL season, while fantasy matchups might span
several scoring periods, especially in the playoffs.
Additional queries passed to httr::GET(). Arguments are
converted to a named list and passed to query alongside view.
A tidy data frame of scores by team and matchup/scoring period.
expectedWins are calculated by comparing a team score against all other
scores for a given matchup period. This statistic expresses how a team would
fair if the schedule was random The highest scoring team is thus expected to
earn 1 win and the lowest scoring team would expect to win 0 matchups.
Other scoring functions:
live_scoring()
tidy_scores(leagueId = "42654852", useMatchup = FALSE)
#> # A tibble: 8 × 8
#> seasonId scoringPeriodId matchupId teamId abbrev isHome points expectedWins
#> <int> <dbl> <int> <int> <fct> <lgl> <dbl> <dbl>
#> 1 2026 1 1 1 AUS TRUE 131. 0.333
#> 2 2026 1 1 3 CHI FALSE 96.8 0
#> 3 2026 1 2 2 BOS TRUE 162. 1
#> 4 2026 1 2 4 DEN FALSE 146. 0.667
#> 5 2026 2 3 3 CHI TRUE 115. 0.333
#> 6 2026 2 3 4 DEN FALSE 103. 0
#> 7 2026 2 4 1 AUS TRUE 132. 0.667
#> 8 2026 2 4 2 BOS FALSE 168. 1