Usage
ffsimulator connects directly to your league (via the ffscrapr package) and can be used at a high level with just a few lines of code:
foureight_conn <- mfl_connect(2021, 22627)
foureight_sim <- ff_simulate(conn = foureight_conn, n_seasons = 25, n_weeks = 14)
foureight_sim#> <ff_simulation: 25 simulated seasons of Four-Eight Dynasty League>
#> List of 7
#> $ summary_simulation: tibble [12 x 11] (S3: tbl_df/tbl/data.frame)
#> $ summary_season : tibble [300 x 12] (S3: tbl_df/tbl/data.frame)
#> $ summary_week : tibble [4,200 x 15] (S3: tbl_df/tbl/data.frame)
#> $ roster_scores : tibble [124,600 x 23] (S3: tbl_df/tbl/data.frame)
#> $ projected_scores : tibble [124,600 x 15] (S3: tbl_df/tbl/data.frame)
#> $ league_info : tibble [1 x 14] (S3: tbl_df/tbl/data.frame)
#> $ simulation_params :List of 6
#> ..$ n_seasons : num 25
#> ..$ n_weeks : num 14
#> ..$ best_ball : logi FALSE
#> ..$ seed : NULL
#> ..$ injury_model: chr "simple"
#> ..$ base_seasons: int [1:9] 2012 2013 2014 2015 2016 2017 2018 2019 2020
#> - attr(*, "class")= chr "ff_simulation"ffsimulator includes a few automatic plots that can be run on this ff_simulation object to get a quick sense of the simulation output:
autoplot(foureight_sim) # defaults to type = "wins"
autoplot(foureight_sim, type = "rank")
autoplot(foureight_sim, type = "points")
#> Picking joint bandwidth of 6.5
You can also access the various component dataframes by name, for further analysis.
The summary_simulation table is a team-level summary across all of the simulated seasons.
foureight_sim$summary_simulation
#> # A tibble: 12 x 11
#> league_id franchise_id franchise_name seasons h2h_wins h2h_winpct
#> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 22627 0012 Indigo Plateau Elite 25 11.2 0.803
#> 2 22627 0001 Mexico City Space Force 25 9.96 0.711
#> 3 22627 0011 TB12 25 7.96 0.569
#> 4 22627 0007 Paw Patrol 25 7.8 0.557
#> 5 22627 0008 Domination Station 25 8.32 0.594
#> 6 22627 0005 Motor City Mutts 25 6.84 0.489
#> 7 22627 0009 Kill Devil Hills Carpetba~ 25 7.16 0.511
#> 8 22627 0003 Alberta Armadillos 25 6.64 0.474
#> 9 22627 0004 Electric Woodies 25 5.8 0.414
#> 10 22627 0010 [Redacted] 25 5.36 0.383
#> 11 22627 0006 Hoth City Tauntauns 25 3.76 0.269
#> 12 22627 0002 Dirty Dgens 25 3.16 0.226
#> # ... with 5 more variables: allplay_wins <dbl>, allplay_winpct <dbl>,
#> # points_for <dbl>, points_against <dbl>, potential_points <dbl>The summary_season table is a season-level summary of all weeks within those seasons - any one of these seasons “could” theoretically be the outcome of the upcoming season!
foureight_sim$summary_season
#> # A tibble: 300 x 12
#> season league_id franchise_id franchise_name h2h_wins h2h_winpct allplay_wins
#> <dbl> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 1 22627 0001 Mexico City S~ 11 0.786 112
#> 2 1 22627 0002 Dirty Dgens 2 0.143 28
#> 3 1 22627 0003 Alberta Armad~ 9 0.643 91
#> 4 1 22627 0004 Electric Wood~ 2 0.143 48
#> 5 1 22627 0005 Motor City Mu~ 8 0.571 86
#> 6 1 22627 0006 Hoth City Tau~ 4 0.286 38
#> 7 1 22627 0007 Paw Patrol 9 0.643 96
#> 8 1 22627 0008 Domination St~ 10 0.714 93
#> 9 1 22627 0009 Kill Devil Hi~ 8 0.571 74
#> 10 1 22627 0010 [Redacted] 2 0.143 44
#> # ... with 290 more rows, and 5 more variables: allplay_games <dbl>,
#> # allplay_winpct <dbl>, points_for <dbl>, points_against <dbl>,
#> # potential_points <dbl>The summary_week table is a week-level summary of each team’s performance, including the optimal lineup that could have been played by the team and the randomly-generated lineup efficiency factor:
foureight_sim$summary_week
#> # A tibble: 4,200 x 15
#> season season_week franchise_name optimal_score lineup_efficien~ team_score
#> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 1 1 Mexico City Spa~ 204. 0.738 151.
#> 2 1 1 Dirty Dgens 109. 0.711 77.4
#> 3 1 1 Alberta Armadil~ 178. 0.866 154.
#> 4 1 1 Electric Woodies 158. 0.814 129.
#> 5 1 1 Motor City Mutts 161 0.79 127.
#> 6 1 1 Hoth City Taunt~ 122. 0.831 101.
#> 7 1 1 Paw Patrol 131. 0.715 93.3
#> 8 1 1 Domination Stat~ 215. 0.716 154.
#> 9 1 1 Kill Devil Hill~ 152. 0.722 110.
#> 10 1 1 [Redacted] 144. 0.656 94.2
#> # ... with 4,190 more rows, and 9 more variables: opponent_score <dbl>,
#> # result <chr>, opponent_name <chr>, allplay_wins <dbl>, allplay_games <dbl>,
#> # allplay_pct <dbl>, league_id <chr>, franchise_id <chr>,
#> # optimal_lineup <list>The roster_scores and projected_scores tables both provide individual player-week level scores, with the difference being that the roster_scores table is attached to franchise rosters (and thus duplicates rows if there are multiple copies of players within the league).
foureight_sim$roster_scores
#> # A tibble: 124,600 x 23
#> franchise_id franchise_name player_id player_name pos team age salary
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 0007 Paw Patrol 13128 Cook, Dalv~ RB MIN 25.9 17.6
#> 2 0009 Kill Devil Hills~ 13132 Kamara, Al~ RB NOS 26 27.8
#> 3 0009 Kill Devil Hills~ 13132 Kamara, Al~ RB NOS 26 27.8
#> 4 0001 Mexico City Spac~ 14797 Swift, D'A~ RB DET 22.5 4.95
#> 5 0008 Domination Stati~ 13604 Barkley, S~ RB NYG 24.4 25
#> 6 0009 Kill Devil Hills~ 12625 Elliott, E~ RB DAL 26 24.9
#> 7 0001 Mexico City Spac~ 14081 Gaskin, My~ RB MIA 24.4 1.1
#> 8 0008 Domination Stati~ 12164 Davis, Mike RB ATL 28.4 0.55
#> 9 0005 Motor City Mutts 12930 Anderson, ~ WR CAR 28.2 5.72
#> 10 0005 Motor City Mutts 13404 Ekeler, Au~ RB LAC 26.2 14.1
#> # ... with 124,590 more rows, and 15 more variables: contract_years <dbl>,
#> # roster_status <chr>, draft_year <chr>, draft_round <chr>,
#> # fantasypros_id <chr>, league_id <chr>, ecr <dbl>, rank <dbl>,
#> # projection <dbl>, injury_model <int>, season <int>, week <int>,
#> # projected_score <dbl>, scrape_date <date>, pos_rank <int>Finally, some basic parameters are included, including league_info which is generated by ffscrapr::ff_league().
foureight_sim$league_info
#> # A tibble: 1 x 14
#> league_id league_name season franchise_count qb_type idp scoring_flags
#> <chr> <chr> <int> <dbl> <chr> <lgl> <chr>
#> 1 22627 Four-Eight Dynas~ 2021 12 2QB/SF TRUE 0.5_ppr, TEP~
#> # ... with 7 more variables: best_ball <lgl>, salary_cap <lgl>,
#> # player_copies <dbl>, years_active <chr>, qb_count <chr>, roster_size <dbl>,
#> # league_depth <dbl>