Runs the bootstrapped resampling of player week outcomes on the latest rankings and rosters for a given number of seasons and weeks per season.
Arguments
- adp_outcomes
a dataframe of adp-based weekly outcomes, as created by
ffs_adp_outcomes()- latest_rankings
a dataframe of rankings, as created by
ffs_latest_rankings()- n
number of weeks to simulate
- rosters
a dataframe of rosters, as created by
ffs_rosters()- optional, reduces computation to just rostered players
Value
a dataframe of weekly scores for each player in the simulation, approximately of length n_seasons x n_weeks x latest_rankings
Examples
# \donttest{
# cached examples
adp_outcomes_week <- .ffs_cache("adp_outcomes_week.rds")
latest_rankings_week <- .ffs_cache("latest_rankings_week.rds")
ffs_generate_projections_week(adp_outcomes_week, latest_rankings_week)
#> week fantasypros_id player pos team ecr sd rank
#> <int> <char> <char> <char> <char> <num> <num> <num>
#> 1: 1 17233 Lamar Jackson QB BAL 1.79 0.67 1
#> 2: 9 17233 Lamar Jackson QB BAL 1.79 0.67 1
#> 3: 18 17233 Lamar Jackson QB BAL 1.79 0.67 1
#> 4: 22 17233 Lamar Jackson QB BAL 1.79 0.67 1
#> 5: 30 17233 Lamar Jackson QB BAL 1.79 0.67 1
#> ---
#> 276996: 374 19270 Devin Duvernay WR BAL 121.00 15.00 145
#> 276997: 460 19270 Devin Duvernay WR BAL 121.00 15.00 145
#> 276998: 208 19270 Devin Duvernay WR BAL 121.00 15.00 148
#> 276999: 781 19270 Devin Duvernay WR BAL 121.00 15.00 148
#> 277000: 863 18318 Jalen Hurd WR SF 117.67 15.92 150
#> scrape_date projected_score projection gp_model season
#> <Date> <num> <num> <num> <num>
#> 1: 2021-09-06 NA NA 1 1
#> 2: 2021-09-06 NA NA 1 1
#> 3: 2021-09-06 NA NA 1 1
#> 4: 2021-09-06 NA NA 1 1
#> 5: 2021-09-06 NA NA 1 1
#> ---
#> 276996: 2021-09-06 NA NA 1 1
#> 276997: 2021-09-06 NA NA 1 1
#> 276998: 2021-09-06 NA NA 1 1
#> 276999: 2021-09-06 NA NA 1 1
#> 277000: 2021-09-06 0.9 0.9 1 1
# }