The backbone of the ffsimulator resampling process is coming up with a population of weekly outcomes for every preseason positional rank. This function creates that dataframe by connecting historical FantasyPros.com rankings to nflfastR-based scoring data, as created by ffscrapr::ff_scoringhistory().
Usage
ffs_adp_outcomes(
scoring_history,
gp_model = "simple",
pos_filter = c("QB", "RB", "WR", "TE")
)Arguments
- scoring_history
a scoring history table as created by
ffscrapr::ff_scoringhistory()- gp_model
either "simple" or "none" - simple uses the average games played per season for each position/adp combination, none assumes every game is played.
- pos_filter
a character vector: filter the positions returned to these specific positions, default: c("QB","RB","WR","TE)
Value
a dataframe with position, rank, probability of games played, and a corresponding nested list per row of all week score outcomes.
See also
fp_rankings_history for the included historical rankings
fp_injury_table for the historical injury table
vignette("custom") for usage details.
Examples
# \donttest{
# cached data
scoring_history <- .ffs_cache("mfl_scoring_history.rds")
ffs_adp_outcomes(scoring_history, gp_model = "simple")
#> # A tidytable: 640 × 6
#> pos rank prob_gp week_outcomes player_name fantasypros_id
#> <chr> <dbl> <dbl> <list> <list> <list>
#> 1 QB 1 0.919 <dbl [88]> <chr [6]> <chr [6]>
#> 2 QB 2 0.919 <dbl [80]> <chr [6]> <chr [6]>
#> 3 QB 3 0.919 <dbl [83]> <chr [6]> <chr [6]>
#> 4 QB 4 0.919 <dbl [84]> <chr [6]> <chr [6]>
#> 5 QB 5 0.919 <dbl [95]> <chr [6]> <chr [6]>
#> 6 QB 6 0.918 <dbl [81]> <chr [6]> <chr [6]>
#> 7 QB 7 0.917 <dbl [82]> <chr [6]> <chr [6]>
#> 8 QB 8 0.915 <dbl [78]> <chr [6]> <chr [6]>
#> 9 QB 9 0.913 <dbl [83]> <chr [6]> <chr [6]>
#> 10 QB 10 0.911 <dbl [83]> <chr [6]> <chr [6]>
#> # … with 630 more rows
#> # ℹ Use `print(n = ...)` to see more rows
ffs_adp_outcomes(scoring_history, gp_model = "none")
#> # A tidytable: 644 × 6
#> pos rank prob_gp week_outcomes player_name fantasypros_id
#> <chr> <dbl> <dbl> <list> <list> <list>
#> 1 QB 1 1 <dbl [88]> <chr [6]> <chr [6]>
#> 2 QB 2 1 <dbl [80]> <chr [6]> <chr [6]>
#> 3 QB 3 1 <dbl [83]> <chr [6]> <chr [6]>
#> 4 QB 4 1 <dbl [84]> <chr [6]> <chr [6]>
#> 5 QB 5 1 <dbl [95]> <chr [6]> <chr [6]>
#> 6 QB 6 1 <dbl [81]> <chr [6]> <chr [6]>
#> 7 QB 7 1 <dbl [82]> <chr [6]> <chr [6]>
#> 8 QB 8 1 <dbl [78]> <chr [6]> <chr [6]>
#> 9 QB 9 1 <dbl [83]> <chr [6]> <chr [6]>
#> 10 QB 10 1 <dbl [83]> <chr [6]> <chr [6]>
#> # … with 634 more rows
#> # ℹ Use `print(n = ...)` to see more rows
# }