Skip to contents

Wins Added

Source: R/0_ff_wins_added.R
ff_wins_added.Rd

(EXPERIMENTAL) This function adds a basic wins-added calculation for each player on every team, presenting the change in wins if that player was removed from the team as the net wins-over-replacement for that player. This can be a bit of a time/compute-expensive calculation.

Usage

ff_wins_added(conn, ...)

Arguments

conn

an connection to a league made with ff_connect() and friends (required)

...

Arguments passed on to ff_simulate

n_seasons

number of seasons to simulate, default = 100

n_weeks

number of weeks per season, default = 14

best_ball

a logical: are weekly wins based on optimal lineups?

seed

an integer to control reproducibility

gp_model

select between "simple", "none" to apply a model for whether a player played in a given game, defaults to "simple"

base_seasons

a numeric vector that selects seasons as base data, earliest available is 2012

actual_schedule

a logical: use actual ff_schedule? default is FALSE

replacement_level

a logical: use best available on waiver as replacement level? defaults to TRUE

pos_filter

a character vector of positions to filter/run, default is c("QB","RB","WR","TE","K")

verbose

a logical: print status messages? default is TRUE, configure with options(ffsimulator.verbose)

return

one of c("default", "all") - what objects to return in the output list

Value

a dataframe summarising the net effect of each player on their team's wins

Details

Runs base simulation once (with the usual parameters available for ff_simulate), then for every player on every team (except replacement level players):

  • remove them from that specific roster

  • reoptimize the lineups just for that roster without the player to calculate what the score ends up being without the player

  • summarise the new simulation

  • return the delta in wins and points

Summarise wins added as the difference between the sim with the player and the sim without them

Examples

# \donttest{
try({ # try block to prevent CRAN-related issues
# n_seasons set so that the example runs more quickly
ff_wins_added(mfl_connect(2021,54040), n_seasons = 5)
})
#> ── BASE SIMULATION ─────────────────────────────────────────────────────────────
#> ── Starting simulation 2024-02-08 13:18:27.924818 ──────────────────────────────
#> ℹ Importing data
#> ✔ Importing data ... done
#> 
#> ℹ Generating Projections
#> ✔ Generating Projections ... done
#> 
#> ℹ Calculating Roster Scores
#> ✔ Calculating Roster Scores ... done
#> 
#> ℹ Optimizing Lineups
#> ✔ Optimizing Lineups ... done
#> 
#> ℹ Building Schedules
#> ✔ Building Schedules ... done
#> 
#> ℹ Summarising Simulation Data
#> ✔ Summarising Simulation Data ... done
#> 
#> ── Simulation complete! 2024-02-08 13:18:45.188658 ─────────────────────────────
#> ── Start WAR calcs 2024-02-08 13:18:45.192931 ──────────────────────────────────
#> ── WAR calcs complete! 2024-02-08 13:19:21.29987 ───────────────────────────────
#> <ff_wins_added: 5 simulated seasons of The Super Smash Bros Dynasty League>
#> List of 15
#>  $ war               :Classes ‘data.table’ and 'data.frame':	449 obs. of  13 variables:
#>  $ summary_simulation:Classes ‘data.table’ and 'data.frame':	14 obs. of  11 variables:
#>  $ summary_season    :Classes ‘data.table’ and 'data.frame':	70 obs. of  12 variables:
#>  $ summary_week      :Classes ‘data.table’ and 'data.frame':	980 obs. of  16 variables:
#>  $ schedules         :Classes ‘data.table’ and 'data.frame':	980 obs. of  5 variables:
#>  $ optimal_scores    :Classes ‘data.table’ and 'data.frame':	980 obs. of  10 variables:
#>  $ roster_scores     :Classes ‘data.table’ and 'data.frame':	35210 obs. of  22 variables:
#>  $ projected_scores  :Classes ‘data.table’ and 'data.frame':	17920 obs. of  14 variables:
#>  $ scoring_history   : tibble [58,570 × 26] (S3: tbl_df/tbl/data.frame)
#>  $ franchises        : tibble [14 × 8] (S3: tbl_df/tbl/data.frame)
#>  $ rosters           :'data.frame':	449 obs. of  13 variables:
#>  $ lineup_constraints: tibble [4 × 7] (S3: tbl_df/tbl/data.frame)
#>  $ latest_rankings   : nflvrs_d [661 × 9] (S3: nflverse_data/tbl_df/tbl/data.table/data.frame)
#>  $ league_info       : tibble [1 × 17] (S3: tbl_df/tbl/data.frame)
#>  $ simulation_params :List of 9
# }