Usage - Simulating Seasons
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 = 10, n_weeks = 14)
foureight_sim#> <ff_simulation: 10 simulated seasons of Four-Eight Dynasty League>
#> List of 7
#> $ summary_simulation: tibble [12 × 11] (S3: tbl_df/tbl/data.frame)
#> $ summary_season : tibble [120 × 12] (S3: tbl_df/tbl/data.frame)
#> $ summary_week : tibble [1,680 × 16] (S3: tbl_df/tbl/data.frame)
#> $ roster_scores :Classes 'data.table' and 'data.frame': 51450 obs. of 23 variables:
#> $ projected_scores :Classes 'data.table' and 'data.frame': 51450 obs. of 14 variables:
#> $ league_info : tibble [1 × 17] (S3: tbl_df/tbl/data.frame)
#> $ simulation_params : tibble [1 × 9] (S3: tbl_df/tbl/data.frame)ffsimulator includes a few automatic plots that can be run on this ff_simulation object to get a quick sense of the simulation output:
plot(foureight_sim) # defaults to type = "wins"
plot(foureight_sim, type = "rank")
plot(foureight_sim, type = "points")
#> Picking joint bandwidth of 7.68
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$summary_simulation
#> # A tibble: 12 × 11
#> league_id franchise…¹ franc…² seasons h2h_w…³ h2h_w…⁴ allpl…⁵ allpl…⁶ point…⁷
#> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 22627 0012 Indigo… 10 12.1 0.864 134. 0.867 2074.
#> 2 22627 0001 Mexico… 10 10.6 0.757 110 0.714 1842.
#> 3 22627 0007 Paw Pa… 10 7.8 0.557 84.9 0.551 1671.
#> 4 22627 0004 Electr… 10 7.4 0.529 84.2 0.547 1649.
#> 5 22627 0011 TB12 10 7.1 0.507 83.5 0.542 1659.
#> 6 22627 0005 Motor … 10 7.6 0.543 79.2 0.514 1626.
#> 7 22627 0009 Kill D… 10 7.3 0.521 74.8 0.486 1570.
#> 8 22627 0008 Domina… 10 6.3 0.45 73.6 0.478 1571.
#> 9 22627 0003 Albert… 10 6.4 0.457 71.4 0.464 1568.
#> 10 22627 0010 [Redac… 10 5.3 0.379 54.9 0.357 1458.
#> 11 22627 0006 Hoth C… 10 3 0.214 38.3 0.249 1318.
#> 12 22627 0002 Dirty … 10 3.1 0.221 35.7 0.232 1277.
#> # … with 2 more variables: points_against <dbl>, potential_points <dbl>, and
#> # abbreviated variable names ¹franchise_id, ²franchise_name, ³h2h_wins,
#> # ⁴h2h_winpct, ⁵allplay_wins, ⁶allplay_winpct, ⁷points_for
#> # ℹ Use `colnames()` to see all variable namesThe summary_season table is a season-level summary of
all weeks within those seasons - any one of these seasons “could”
theoretically be the outcome of the upcoming season!
foureight_sim$summary_season
#> season league_id franchise_id franchise_name h2h_wins
#> <num> <char> <char> <char> <int>
#> 1: 1 22627 0001 Mexico City Space Force 12
#> 2: 1 22627 0002 Dirty Dgens 0
#> 3: 1 22627 0003 Alberta Armadillos 5
#> 4: 1 22627 0004 Electric Woodies 7
#> 5: 1 22627 0005 Motor City Mutts 10
#> ---
#> 116: 10 22627 0008 Domination Station 5
#> 117: 10 22627 0009 Kill Devil Hills Carpetbaggers 12
#> 118: 10 22627 0010 [Redacted] 7
#> 119: 10 22627 0011 TB12 8
#> 120: 10 22627 0012 Indigo Plateau Elite 11
#> h2h_winpct allplay_wins allplay_games allplay_winpct points_for
#> <num> <num> <num> <num> <num>
#> 1: 0.857 125 154 0.812 2034.086
#> 2: 0.000 27 154 0.175 1187.849
#> 3: 0.357 63 154 0.409 1524.148
#> 4: 0.500 71 154 0.461 1579.101
#> 5: 0.714 80 154 0.519 1635.749
#> ---
#> 116: 0.357 64 154 0.416 1460.263
#> 117: 0.857 88 154 0.571 1629.849
#> 118: 0.500 65 154 0.422 1490.238
#> 119: 0.571 100 154 0.649 1727.765
#> 120: 0.786 133 154 0.864 2066.457
#> points_against potential_points
#> <num> <num>
#> 1: 1622.343 2631.29
#> 2: 1798.732 1502.92
#> 3: 1543.897 1947.87
#> 4: 1694.826 2064.90
#> 5: 1519.509 2138.25
#> ---
#> 116: 1555.725 1901.09
#> 117: 1403.566 2075.04
#> 118: 1469.356 1896.33
#> 119: 1601.649 2215.87
#> 120: 1485.318 2732.65The 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
#> season season_week franchise_name optimal_score
#> <num> <num> <char> <num>
#> 1: 1 1 Mexico City Space Force 166.65
#> 2: 1 1 Dirty Dgens 145.65
#> 3: 1 1 Alberta Armadillos 108.13
#> 4: 1 1 Electric Woodies 166.51
#> 5: 1 1 Motor City Mutts 148.63
#> ---
#> 1676: 10 14 Domination Station 182.70
#> 1677: 10 14 Kill Devil Hills Carpetbaggers 175.64
#> 1678: 10 14 [Redacted] 134.50
#> 1679: 10 14 TB12 64.17
#> 1680: 10 14 Indigo Plateau Elite 205.99
#> lineup_efficiency team_score opponent_score result
#> <num> <num> <num> <char>
#> 1: 0.691 115.169 76.084 W
#> 2: 0.737 107.286 132.011 L
#> 3: 0.776 83.912 179.982 L
#> 4: 0.793 132.011 107.286 W
#> 5: 0.682 101.357 143.900 L
#> ---
#> 1676: 0.740 135.286 113.904 W
#> 1677: 0.795 139.638 100.899 W
#> 1678: 0.847 113.904 135.286 L
#> 1679: 0.778 49.902 131.653 L
#> 1680: 0.760 156.605 79.371 W
#> opponent_name allplay_wins allplay_games allplay_pct
#> <char> <num> <num> <num>
#> 1: Hoth City Tauntauns 5 11 0.455
#> 2: Electric Woodies 3 11 0.273
#> 3: Indigo Plateau Elite 1 11 0.091
#> 4: Dirty Dgens 7 11 0.636
#> 5: Kill Devil Hills Carpetbaggers 2 11 0.182
#> ---
#> 1676: [Redacted] 8 11 0.727
#> 1677: Mexico City Space Force 9 11 0.818
#> 1678: Domination Station 5 11 0.455
#> 1679: Electric Woodies 0 11 0.000
#> 1680: Hoth City Tauntauns 11 11 1.000
#> league_id franchise_id optimal_player_id
#> <char> <char> <list>
#> 1: 22627 0001 13592,14777,14081,15265,14805,13672,...
#> 2: 22627 0002 12610,11747,13138,14803,15268,12212,...
#> 3: 22627 0003 10700,11644,14075,14087,14806,15261,...
#> 4: 22627 0004 10703,13589,13139,13319,13236,13880,...
#> 5: 22627 0005 13593,9431,12171,13404,14072,12677,...
#> ---
#> 1676: 22627 0008 11760,15240,12164,13604,13607,14798,...
#> 1677: 22627 0009 14059,7836,11657,13108,13133,15505,...
#> 1678: 22627 0010 12140,13116,14223,15254,13680,11671,...
#> 1679: 22627 0011 15239,13131,13850,14828,13192,11232,...
#> 1680: 22627 0012 14056,8062,11193,12626,13130,14852,...
#> optimal_player_score
#> <list>
#> 1: 17.76,18.54, 8.70,23.70,12.10,17.10,...
#> 2: 8.8, 9.7,17.6, 7.7, 8.8,26.2,...
#> 3: 10.42, 7.96,10.10, 8.60,17.50,22.30,...
#> 4: 24.24,25.12, 0.00,21.40,15.25,13.40,...
#> 5: 16.92,14.76,12.10,15.30,11.90,11.85,...
#> ---
#> 1676: 13.78,12.92, 9.40,22.30,20.30, 9.80,...
#> 1677: 29.60,23.64,20.70,11.30,11.70,16.20,...
#> 1678: 20.65,38.60, 4.00, 4.40, 7.85, 8.20,...
#> 1679: 6.12, 8.20,14.70, 2.40, 2.75, 2.40,...
#> 1680: 27.82,25.52, 7.20,18.30,20.70,33.00,...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
#> Key: <fantasypros_id>
#> fantasypros_id franchise_id franchise_name player_id player_name
#> <char> <char> <char> <char> <char>
#> 1: 10007 0007 Paw Patrol 10413 Taylor, Tyrod
#> 2: 10007 0007 Paw Patrol 10413 Taylor, Tyrod
#> 3: 10007 0007 Paw Patrol 10413 Taylor, Tyrod
#> 4: 10007 0007 Paw Patrol 10413 Taylor, Tyrod
#> 5: 10007 0007 Paw Patrol 10413 Taylor, Tyrod
#> ---
#> 51446: 9907 0007 Paw Patrol 10313 Dalton, Andy
#> 51447: 9907 0007 Paw Patrol 10313 Dalton, Andy
#> 51448: 9907 0007 Paw Patrol 10313 Dalton, Andy
#> 51449: 9907 0007 Paw Patrol 10313 Dalton, Andy
#> 51450: 9907 0007 Paw Patrol 10313 Dalton, Andy
#> pos team age salary contract_years roster_status draft_year
#> <char> <char> <num> <num> <num> <char> <char>
#> 1: QB HOU 32.1 0.61 1 ROSTER 2011
#> 2: QB HOU 32.1 0.61 1 ROSTER 2011
#> 3: QB HOU 32.1 0.61 1 ROSTER 2011
#> 4: QB HOU 32.1 0.61 1 ROSTER 2011
#> 5: QB HOU 32.1 0.61 1 ROSTER 2011
#> ---
#> 51446: QB CHI 33.9 2.07 4 ROSTER 2011
#> 51447: QB CHI 33.9 2.07 4 ROSTER 2011
#> 51448: QB CHI 33.9 2.07 4 ROSTER 2011
#> 51449: QB CHI 33.9 2.07 4 ROSTER 2011
#> 51450: QB CHI 33.9 2.07 4 ROSTER 2011
#> draft_round league_id ecr rank projection gp_model season week
#> <char> <char> <num> <num> <num> <int> <int> <int>
#> 1: 6 22627 32.93 31 16.04 1 1 1
#> 2: 6 22627 32.93 31 16.90 1 1 2
#> 3: 6 22627 32.93 31 4.84 0 1 3
#> 4: 6 22627 32.93 31 12.68 0 1 4
#> 5: 6 22627 32.93 31 21.62 0 1 5
#> ---
#> 51446: 2 22627 35.81 37 18.36 1 10 10
#> 51447: 2 22627 35.81 37 -1.08 0 10 11
#> 51448: 2 22627 35.81 37 0.18 0 10 12
#> 51449: 2 22627 35.81 37 9.92 0 10 13
#> 51450: 2 22627 35.81 37 45.28 1 10 14
#> projected_score scrape_date pos_rank
#> <num> <Date> <int>
#> 1: 16.04 2021-09-03 3
#> 2: 16.90 2021-09-03 2
#> 3: 0.00 2021-09-03 2
#> 4: 0.00 2021-09-03 4
#> 5: 0.00 2021-09-03 4
#> ---
#> 51446: 0.00 2021-09-03 6
#> 51447: 0.00 2021-09-03 6
#> 51448: 0.00 2021-09-03 6
#> 51449: 0.00 2021-09-03 6
#> 51450: 45.28 2021-09-03 1Finally, some basic parameters are included, including
league_info which is generated by
ffscrapr::ff_league().
foureight_sim$league_info
#> # A tibble: 1 × 17
#> league_id leagu…¹ season leagu…² franc…³ qb_type idp scori…⁴ best_…⁵ salar…⁶
#> <chr> <chr> <int> <chr> <dbl> <chr> <lgl> <chr> <lgl> <lgl>
#> 1 22627 Four-E… 2021 dynasty 12 2QB/SF TRUE 0.5_pp… FALSE TRUE
#> # … with 7 more variables: player_copies <dbl>, years_active <chr>,
#> # qb_count <chr>, roster_size <dbl>, league_depth <dbl>, draft_type <chr>,
#> # draft_player_pool <chr>, and abbreviated variable names ¹league_name,
#> # ²league_type, ³franchise_count, ⁴scoring_flags, ⁵best_ball, ⁶salary_cap
#> # ℹ Use `colnames()` to see all variable names
