Preface

nflseedR is designed to efficiently take over the sophisticated and complex rule set of the NFL regarding division ranks, postseason seeding and draft order. It is intended to be used for NFL season simulations to help modelers focus on their models rather than the tie-breaking procedures. The NFL’s official procedures for breaking ties for postseason playoffs can be found here and this site explains the assigning of draft picks.

However, it must be mentioned that nflseedR does not support all levels of tie-breakers at the moment. The deepest tie-breaker possible at the moment is the strength of schedule. After that, the decision is made at random. It should be noted, however, that the need for additional levels is extremely unlikely in reality.

Using In-Simulation Functions

You can get NFL game data from this function:

And if you prefer, you can take or generate any set of game outcomes and let nflseedR handle all of the NFL seeding and tiebreaker math for you with three in-simulation functions (each can handle thousands of seasons at once):

The following sections will demonstrate how to use them and what input is required.

Loading the package is obligatory, so it is done first (along with dplyr for data wrangling and the pipe):

library(nflseedR)
library(dplyr, warn.conflicts = FALSE)
options(digits = 3)
options(warn = -1)

Load Sharpe Games

games <- nflseedR::load_sharpe_games()
games %>% utils::tail(20) %>% knitr::kable()
game_id season game_type week gameday weekday gametime away_team away_score home_team home_score location result total overtime old_game_id espn away_rest home_rest away_moneyline home_moneyline spread_line away_spread_odds home_spread_odds total_line under_odds over_odds div_game roof surface temp wind away_qb_id home_qb_id away_qb_name home_qb_name away_coach home_coach referee stadium_id stadium
2021_17_LA_BAL 2021 REG 17 2022-01-02 Sunday 16:25 LA NA BAL NA Home NA NA NA 2022010212 401326581 7 7 NA NA NA NA NA NA NA NA 0 outdoors grass NA NA NA NA NA NA Sean McVay John Harbaugh NA BAL00 M&T Bank Stadium
2021_17_DET_SEA 2021 REG 17 2022-01-02 Sunday 16:25 DET NA SEA NA Home NA NA NA 2022010213 401326582 7 7 NA NA NA NA NA NA NA NA 0 outdoors fieldturf NA NA NA NA NA NA Dan Campbell Pete Carroll NA SEA00 Lumen Field
2021_17_MIN_GB 2021 REG 17 2022-01-02 Sunday 20:20 MIN NA GB NA Home NA NA NA 2022010214 401326583 7 8 NA NA NA NA NA NA NA NA 1 outdoors grass NA NA NA NA NA NA Mike Zimmer Matt LaFleur NA GNB00 Lambeau Field
2021_17_CLE_PIT 2021 REG 17 2022-01-03 Monday 20:15 CLE NA PIT NA Home NA NA NA 2022010300 401326584 9 8 NA NA NA NA NA NA NA NA 1 outdoors grass NA NA NA NA NA NA Kevin Stefanski Mike Tomlin NA PIT00 Heinz Field
2021_18_NO_ATL 2021 REG 18 2022-01-09 Sunday 13:00 NO NA ATL NA Home NA NA NA 2022010907 401326585 7 7 NA NA NA NA NA NA NA NA 1 NA fieldturf NA NA NA NA NA NA Sean Payton Arthur Smith NA ATL97 Mercedes-Benz Stadium
2021_18_PIT_BAL 2021 REG 18 2022-01-09 Sunday 13:00 PIT NA BAL NA Home NA NA NA 2022010909 401326586 6 7 NA NA NA NA NA NA NA NA 1 outdoors grass NA NA NA NA NA NA Mike Tomlin John Harbaugh NA BAL00 M&T Bank Stadium
2021_18_NYJ_BUF 2021 REG 18 2022-01-09 Sunday 13:00 NYJ NA BUF NA Home NA NA NA 2022010910 401326587 7 7 NA NA NA NA NA NA NA NA 1 outdoors astroturf NA NA NA NA NA NA Robert Saleh Sean McDermott NA BUF00 New Era Field
2021_18_CIN_CLE 2021 REG 18 2022-01-09 Sunday 13:00 CIN NA CLE NA Home NA NA NA 2022010911 401326588 7 6 NA NA NA NA NA NA NA NA 1 outdoors grass NA NA NA NA NA NA Zac Taylor Kevin Stefanski NA CLE00 FirstEnergy Stadium
2021_18_GB_DET 2021 REG 18 2022-01-09 Sunday 13:00 GB NA DET NA Home NA NA NA 2022010901 401326589 7 7 NA NA NA NA NA NA NA NA 1 dome fieldturf NA NA NA NA NA NA Matt LaFleur Dan Campbell NA DET00 Ford Field
2021_18_TEN_HOU 2021 REG 18 2022-01-09 Sunday 13:00 TEN NA HOU NA Home NA NA NA 2022010908 401326590 7 7 NA NA NA NA NA NA NA NA 1 NA grass NA NA NA NA NA NA Mike Vrabel David Culley NA HOU00 NRG Stadium
2021_18_IND_JAX 2021 REG 18 2022-01-09 Sunday 13:00 IND NA JAX NA Home NA NA NA 2022010902 401326591 7 7 NA NA NA NA NA NA NA NA 1 outdoors grass NA NA NA NA NA NA Frank Reich Urban Meyer NA JAX00 TIAA Bank Stadium
2021_18_NE_MIA 2021 REG 18 2022-01-09 Sunday 13:00 NE NA MIA NA Home NA NA NA 2022010906 401326592 7 7 NA NA NA NA NA NA NA NA 1 outdoors grass NA NA NA NA NA NA Bill Belichick Brian Flores NA MIA00 Hard Rock Stadium
2021_18_CHI_MIN 2021 REG 18 2022-01-09 Sunday 13:00 CHI NA MIN NA Home NA NA NA 2022010904 401326593 7 7 NA NA NA NA NA NA NA NA 1 dome sportturf NA NA NA NA NA NA Matt Nagy Mike Zimmer NA MIN01 U.S. Bank Stadium
2021_18_WAS_NYG 2021 REG 18 2022-01-09 Sunday 13:00 WAS NA NYG NA Home NA NA NA 2022010903 401326594 7 7 NA NA NA NA NA NA NA NA 1 outdoors fieldturf NA NA NA NA NA NA Ron Rivera Joe Judge NA NYC01 MetLife Stadium
2021_18_DAL_PHI 2021 REG 18 2022-01-09 Sunday 13:00 DAL NA PHI NA Home NA NA NA 2022010900 401326595 7 7 NA NA NA NA NA NA NA NA 1 outdoors grass NA NA NA NA NA NA Mike McCarthy Nick Sirianni NA PHI00 Lincoln Financial Field
2021_18_CAR_TB 2021 REG 18 2022-01-09 Sunday 13:00 CAR NA TB NA Home NA NA NA 2022010905 401326596 7 7 NA NA NA NA NA NA NA NA 1 outdoors grass NA NA NA NA NA NA Matt Rhule Bruce Arians NA TAM00 Raymond James Stadium
2021_18_SEA_ARI 2021 REG 18 2022-01-09 Sunday 16:25 SEA NA ARI NA Home NA NA NA 2022010915 401326597 7 7 NA NA NA NA NA NA NA NA 1 closed grass NA NA NA NA NA NA Pete Carroll Kliff Kingsbury NA PHO00 State Farm Stadium
2021_18_KC_DEN 2021 REG 18 2022-01-09 Sunday 16:25 KC NA DEN NA Home NA NA NA 2022010914 401326598 7 7 NA NA NA NA NA NA NA NA 1 outdoors grass NA NA NA NA NA NA Andy Reid Vic Fangio NA DEN00 Empower Field at Mile High
2021_18_SF_LA 2021 REG 18 2022-01-09 Sunday 16:25 SF NA LA NA Home NA NA NA 2022010913 401326599 7 7 NA NA NA NA NA NA NA NA 1 dome matrixturf NA NA NA NA NA NA Kyle Shanahan Sean McVay NA LAX01 SoFi Stadium
2021_18_LAC_LV 2021 REG 18 2022-01-09 Sunday 16:25 LAC NA LV NA Home NA NA NA 2022010912 401326600 7 7 NA NA NA NA NA NA NA NA 1 dome grass NA NA NA NA NA NA Brandon Staley Jon Gruden NA VEG00 Allegiant Stadium

This pulls game information from the games.rds file (equivalent to the games.csv file) from Lee Sharpe’s NFL Data Github

Find Division Ranks

This functions computes division ranks based on a data frame containing game results of one or more NFL seasons. So let’s load some game data first (this example uses the game data of the 2012 and 2019 seasons):

games <- nflseedR::load_sharpe_games() %>%
  dplyr::filter(season %in% c(2012, 2019)) %>%
  dplyr::select(sim = season, game_type, week, away_team, home_team, result)

dplyr::glimpse(games)
#> Rows: 534
#> Columns: 6
#> $ sim       <int> 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, …
#> $ game_type <chr> "REG", "REG", "REG", "REG", "REG", "REG", "REG", "REG", "REG…
#> $ week      <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, …
#> $ away_team <chr> "DAL", "IND", "PHI", "STL", "MIA", "ATL", "JAX", "WAS", "BUF…
#> $ home_team <chr> "NYG", "CHI", "CLE", "DET", "HOU", "KC", "MIN", "NO", "NYJ",…
#> $ result    <int> -7, 20, -1, 4, 20, -16, 3, -8, 20, -21, 4, -8, 6, 12, 31, -8…

Please note the required column names:

  • sim : A simulation ID. Normally 1 - n simulated seasons or (like in this case) just the year.

  • game_type : One of ‘REG’, ‘WC’, ‘DIV’, ‘CON’, ‘SB’ indicating if a game was a regular season game or one of the playoff rounds.

  • week : The week of the corresponding NFL season.

  • away_team : Team abbreviation of the away team.

  • home_team : Team abbreviation of the home team.

  • result : Equals home score - away score.

Now the games data frame can be used to compute the division ranks (the parameter .debug is set to TRUE to show what the function is doing).

div_standings <- nflseedR::compute_division_ranks(games, .debug = TRUE)
#> • 2021-07-08 17:35:10: Calculating team data
#> • 2021-07-08 17:35:10: Calculating head to head
#> • 2021-07-08 17:35:10: Calculating division rank #1
#> • 2021-07-08 17:35:10: DIV (2): Head-to-head
#> • 2021-07-08 17:35:10: DIV (2): Division Record
#> • 2021-07-08 17:35:10: Calculating division rank #2
#> • 2021-07-08 17:35:10: DIV (3): Head-to-head
#> • 2021-07-08 17:35:10: DIV (3): Division Record
#> • 2021-07-08 17:35:10: DIV (3): Common Record
#> • 2021-07-08 17:35:11: DIV (2): Head-to-head
#> • 2021-07-08 17:35:11: DIV (2): Division Record
#> • 2021-07-08 17:35:11: DIV (2): Common Record
#> • 2021-07-08 17:35:11: DIV (2): Conference Record
#> • 2021-07-08 17:35:11: Calculating division rank #3
#> • 2021-07-08 17:35:11: DIV (2): Head-to-head
#> • 2021-07-08 17:35:11: DIV (2): Division Record
#> • 2021-07-08 17:35:11: DIV (2): Common Record
#> • 2021-07-08 17:35:11: Calculating division rank #4
dplyr::glimpse(div_standings)
#> List of 2
#>  $ standings: tibble [64 × 14] (S3: tbl_df/tbl/data.frame)
#>   ..$ sim         : int [1:64] 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 ...
#>   ..$ conf        : chr [1:64] "AFC" "AFC" "AFC" "AFC" ...
#>   ..$ division    : chr [1:64] "AFC East" "AFC East" "AFC East" "AFC East" ...
#>   ..$ team        : chr [1:64] "BUF" "MIA" "NE" "NYJ" ...
#>   ..$ games       : int [1:64] 16 16 16 16 16 16 16 16 16 16 ...
#>   ..$ wins        : num [1:64] 6 7 12 6 10 10 5 8 12 11 ...
#>   ..$ true_wins   : int [1:64] 6 7 12 6 10 10 5 8 12 11 ...
#>   ..$ win_pct     : num [1:64] 0.375 0.438 0.75 0.375 0.625 ...
#>   ..$ div_pct     : num [1:64] 0.333 0.333 1 0.333 0.667 ...
#>   ..$ conf_pct    : num [1:64] 0.417 0.417 0.917 0.333 0.667 ...
#>   ..$ sov         : num [1:64] 0.281 0.415 0.466 0.401 0.438 ...
#>   ..$ sos         : num [1:64] 0.48 0.5 0.496 0.512 0.496 ...
#>   ..$ div_rank    : num [1:64] 4 2 1 3 1 2 4 3 1 2 ...
#>   ..$ max_reg_week: int [1:64] 17 17 17 17 17 17 17 17 17 17 ...
#>  $ h2h      : tibble [2,048 × 6] (S3: tbl_df/tbl/data.frame)
#>   ..$ sim       : int [1:2048] 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 ...
#>   ..$ team      : chr [1:2048] "ARI" "ARI" "ARI" "ARI" ...
#>   ..$ opp       : chr [1:2048] "ARI" "ATL" "BAL" "BUF" ...
#>   ..$ h2h_games : int [1:2048] 0 1 0 1 0 1 0 0 0 0 ...
#>   ..$ h2h_wins  : num [1:2048] 0 0 0 0 0 0 0 0 0 0 ...
#>   ..$ h2h_played: num [1:2048] 0 1 0 1 0 1 0 0 0 0 ...

Please note that the function outputs a list of data frames, the actual division standings as well as a data frame named h2h. The latter is an important input in the other functions (as it is used to break head-to-head ties) and can only be computed with compute_division_ranks().

So here is the resulting division standings data frame for the 2012 season

div_standings %>% 
  purrr::pluck("standings") %>% 
  dplyr::filter(sim == 2012) %>% 
  dplyr::select(division:div_rank) %>% 
  knitr::kable()
division team games wins true_wins win_pct div_pct conf_pct sov sos div_rank
AFC East BUF 16 6.0 6 0.375 0.333 0.417 0.281 0.480 4
AFC East MIA 16 7.0 7 0.438 0.333 0.417 0.415 0.500 2
AFC East NE 16 12.0 12 0.750 1.000 0.917 0.466 0.496 1
AFC East NYJ 16 6.0 6 0.375 0.333 0.333 0.401 0.512 3
AFC North BAL 16 10.0 10 0.625 0.667 0.667 0.438 0.496 1
AFC North CIN 16 10.0 10 0.625 0.500 0.583 0.381 0.438 2
AFC North CLE 16 5.0 5 0.312 0.333 0.417 0.388 0.508 4
AFC North PIT 16 8.0 8 0.500 0.500 0.417 0.438 0.465 3
AFC South HOU 16 12.0 12 0.750 0.833 0.833 0.432 0.496 1
AFC South IND 16 11.0 11 0.688 0.667 0.667 0.403 0.441 2
AFC South JAX 16 2.0 2 0.125 0.333 0.167 0.531 0.539 4
AFC South TEN 16 6.0 6 0.375 0.167 0.417 0.344 0.512 3
AFC West DEN 16 13.0 13 0.812 1.000 0.833 0.385 0.457 1
AFC West KC 16 2.0 2 0.125 0.000 0.000 0.438 0.516 4
AFC West OAK 16 4.0 4 0.250 0.333 0.333 0.219 0.469 3
AFC West SD 16 7.0 7 0.438 0.667 0.583 0.286 0.457 2
NFC East DAL 16 8.0 8 0.500 0.500 0.417 0.422 0.523 3
NFC East NYG 16 9.0 9 0.562 0.500 0.667 0.490 0.521 2
NFC East PHI 16 4.0 4 0.250 0.167 0.167 0.484 0.508 4
NFC East WAS 16 10.0 10 0.625 0.833 0.667 0.450 0.494 1
NFC North CHI 16 10.0 10 0.625 0.500 0.583 0.403 0.512 3
NFC North DET 16 4.0 4 0.250 0.000 0.250 0.383 0.566 4
NFC North GB 16 11.0 11 0.688 0.833 0.667 0.440 0.508 1
NFC North MIN 16 10.0 10 0.625 0.667 0.583 0.456 0.520 2
NFC South ATL 16 13.0 13 0.812 0.500 0.750 0.418 0.422 1
NFC South CAR 16 7.0 7 0.438 0.500 0.417 0.464 0.516 2
NFC South NO 16 7.0 7 0.438 0.500 0.417 0.446 0.521 3
NFC South TB 16 7.0 7 0.438 0.500 0.333 0.446 0.502 4
NFC West ARI 16 5.0 5 0.312 0.167 0.250 0.475 0.559 4
NFC West SEA 16 11.0 11 0.688 0.500 0.667 0.534 0.504 2
NFC West SF 16 11.5 11 0.719 0.583 0.625 0.477 0.504 1
NFC West STL 16 7.5 7 0.469 0.750 0.542 0.496 0.539 3

In that season the seconds division rank of the NFC South required a three way tie-breaker between the Panthers, Saints and Bucs. It was broken with the three-way Conference Record. This can be seen in the above given console output: ...DIV (3): Common Record for the division rank number 2. The Bucs lost this tie-breaker with a 0.333 win percentage in the conference and the tie-breaking procedure goes on with a 2-way head-to-head comparison.

Find Conference Seedings

This functions computes conference seedings based on the above computed division standings data frame. For efficiency reasons the above computed h2h data frame has to be passed to the function. The easiest way is to pass the list of data frames that is computed in the first step so we can do this (please note the number of playoff seeds):

seeds <- div_standings %>% 
  nflseedR::compute_conference_seeds(h2h = .$h2h, playoff_seeds = 6, .debug = TRUE)
#> • 2021-07-08 17:35:11: Calculating seed #1
#> • 2021-07-08 17:35:11: CONF (3): Head-to-head Sweep
#> • 2021-07-08 17:35:11: Calculating seed #2
#> • 2021-07-08 17:35:11: CONF (2): Head-to-head Sweep
#> • 2021-07-08 17:35:11: CONF (2): Conference Record
#> • 2021-07-08 17:35:11: Calculating seed #3
#> • 2021-07-08 17:35:11: Calculating seed #4
#> • 2021-07-08 17:35:12: Calculating seed #5
#> • 2021-07-08 17:35:12: Calculating seed #6
#> • 2021-07-08 17:35:12: CONF (2): Best-in-division reduction
dplyr::glimpse(seeds)
#> List of 2
#>  $ standings: tibble [64 × 15] (S3: tbl_df/tbl/data.frame)
#>   ..$ sim      : int [1:64] 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 ...
#>   ..$ conf     : chr [1:64] "AFC" "AFC" "AFC" "AFC" ...
#>   ..$ division : chr [1:64] "AFC East" "AFC East" "AFC East" "AFC East" ...
#>   ..$ team     : chr [1:64] "BUF" "MIA" "NE" "NYJ" ...
#>   ..$ games    : int [1:64] 16 16 16 16 16 16 16 16 16 16 ...
#>   ..$ wins     : num [1:64] 6 7 12 6 10 10 5 8 12 11 ...
#>   ..$ true_wins: int [1:64] 6 7 12 6 10 10 5 8 12 11 ...
#>   ..$ win_pct  : num [1:64] 0.375 0.438 0.75 0.375 0.625 ...
#>   ..$ div_pct  : num [1:64] 0.333 0.333 1 0.333 0.667 ...
#>   ..$ conf_pct : num [1:64] 0.417 0.417 0.917 0.333 0.667 ...
#>   ..$ sov      : num [1:64] 0.281 0.415 0.466 0.401 0.438 ...
#>   ..$ sos      : num [1:64] 0.48 0.5 0.496 0.512 0.496 ...
#>   ..$ div_rank : num [1:64] 4 2 1 3 1 2 4 3 1 2 ...
#>   ..$ seed     : num [1:64] NA NA 2 NA 4 6 NA NA 3 5 ...
#>   ..$ exit     : num [1:64] 17 17 NA 17 NA NA 17 17 NA NA ...
#>  $ h2h      : tibble [2,048 × 6] (S3: tbl_df/tbl/data.frame)
#>   ..$ sim       : int [1:2048] 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 ...
#>   ..$ team      : chr [1:2048] "ARI" "ARI" "ARI" "ARI" ...
#>   ..$ opp       : chr [1:2048] "ARI" "ATL" "BAL" "BUF" ...
#>   ..$ h2h_games : int [1:2048] 0 1 0 1 0 1 0 0 0 0 ...
#>   ..$ h2h_wins  : num [1:2048] 0 0 0 0 0 0 0 0 0 0 ...
#>   ..$ h2h_played: num [1:2048] 0 1 0 1 0 1 0 0 0 0 ...

Just like compute_division_ranks(), this function returns a list of two data frames so we can use it within a pipe. The resulting seeds for the 2012 season are given below.

seeds %>% 
  purrr::pluck("standings") %>% 
  dplyr::filter(sim == 2012) %>% 
  dplyr::select(division:seed) %>% 
  knitr::kable()
division team games wins true_wins win_pct div_pct conf_pct sov sos div_rank seed
AFC East BUF 16 6.0 6 0.375 0.333 0.417 0.281 0.480 4 NA
AFC East MIA 16 7.0 7 0.438 0.333 0.417 0.415 0.500 2 NA
AFC East NE 16 12.0 12 0.750 1.000 0.917 0.466 0.496 1 2
AFC East NYJ 16 6.0 6 0.375 0.333 0.333 0.401 0.512 3 NA
AFC North BAL 16 10.0 10 0.625 0.667 0.667 0.438 0.496 1 4
AFC North CIN 16 10.0 10 0.625 0.500 0.583 0.381 0.438 2 6
AFC North CLE 16 5.0 5 0.312 0.333 0.417 0.388 0.508 4 NA
AFC North PIT 16 8.0 8 0.500 0.500 0.417 0.438 0.465 3 NA
AFC South HOU 16 12.0 12 0.750 0.833 0.833 0.432 0.496 1 3
AFC South IND 16 11.0 11 0.688 0.667 0.667 0.403 0.441 2 5
AFC South JAX 16 2.0 2 0.125 0.333 0.167 0.531 0.539 4 NA
AFC South TEN 16 6.0 6 0.375 0.167 0.417 0.344 0.512 3 NA
AFC West DEN 16 13.0 13 0.812 1.000 0.833 0.385 0.457 1 1
AFC West KC 16 2.0 2 0.125 0.000 0.000 0.438 0.516 4 NA
AFC West OAK 16 4.0 4 0.250 0.333 0.333 0.219 0.469 3 NA
AFC West SD 16 7.0 7 0.438 0.667 0.583 0.286 0.457 2 NA
NFC East DAL 16 8.0 8 0.500 0.500 0.417 0.422 0.523 3 NA
NFC East NYG 16 9.0 9 0.562 0.500 0.667 0.490 0.521 2 NA
NFC East PHI 16 4.0 4 0.250 0.167 0.167 0.484 0.508 4 NA
NFC East WAS 16 10.0 10 0.625 0.833 0.667 0.450 0.494 1 4
NFC North CHI 16 10.0 10 0.625 0.500 0.583 0.403 0.512 3 NA
NFC North DET 16 4.0 4 0.250 0.000 0.250 0.383 0.566 4 NA
NFC North GB 16 11.0 11 0.688 0.833 0.667 0.440 0.508 1 3
NFC North MIN 16 10.0 10 0.625 0.667 0.583 0.456 0.520 2 6
NFC South ATL 16 13.0 13 0.812 0.500 0.750 0.418 0.422 1 1
NFC South CAR 16 7.0 7 0.438 0.500 0.417 0.464 0.516 2 NA
NFC South NO 16 7.0 7 0.438 0.500 0.417 0.446 0.521 3 NA
NFC South TB 16 7.0 7 0.438 0.500 0.333 0.446 0.502 4 NA
NFC West ARI 16 5.0 5 0.312 0.167 0.250 0.475 0.559 4 NA
NFC West SEA 16 11.0 11 0.688 0.500 0.667 0.534 0.504 2 5
NFC West SF 16 11.5 11 0.719 0.583 0.625 0.477 0.504 1 2
NFC West STL 16 7.5 7 0.469 0.750 0.542 0.496 0.539 3 NA

Find Draft Order

This function computes the draft order based on the playoff outcome and the regular season games. It requires all playoff results in the games data frame and the game_type of the Super Bowl has to be "SB". For efficiency reasons the above computed h2h data frame has to be passed to the function as well. The easiest way is to pass the list of data frames that is computed in the above steps:

draft <- seeds %>% 
  nflseedR::compute_draft_order(games = games, h2h = .$h2h, .debug = TRUE)
#> • 2021-07-08 17:35:12: Calculating draft order #32
#> • 2021-07-08 17:35:12: Calculating draft order #31
#> • 2021-07-08 17:35:12: Calculating draft order #30
#> • 2021-07-08 17:35:12: Calculating draft order #29
#> • 2021-07-08 17:35:12: Calculating draft order #28
#> • 2021-07-08 17:35:12: Calculating draft order #27
#> • 2021-07-08 17:35:12: Calculating draft order #26
#> • 2021-07-08 17:35:12: Calculating draft order #25
#> • 2021-07-08 17:35:12: Calculating draft order #24
#> • 2021-07-08 17:35:12: Calculating draft order #23
#> • 2021-07-08 17:35:12: Calculating draft order #22
#> • 2021-07-08 17:35:12: Calculating draft order #21
#> • 2021-07-08 17:35:12: Calculating draft order #20
#> • 2021-07-08 17:35:12: Calculating draft order #19
#> • 2021-07-08 17:35:12: Calculating draft order #18
#> • 2021-07-08 17:35:13: Calculating draft order #17
#> • 2021-07-08 17:35:13: Calculating draft order #16
#> • 2021-07-08 17:35:13: Calculating draft order #15
#> • 2021-07-08 17:35:13: Calculating draft order #14
#> • 2021-07-08 17:35:13: Calculating draft order #13
#> • 2021-07-08 17:35:13: Calculating draft order #12
#> • 2021-07-08 17:35:13: Calculating draft order #11
#> • 2021-07-08 17:35:13: Calculating draft order #10
#> • 2021-07-08 17:35:13: DRAFT: Divisional Rank
#> • 2021-07-08 17:35:13: DRAFT: Conference Rank
#> • 2021-07-08 17:35:13: CONF (2): Best-in-division reduction
#> • 2021-07-08 17:35:13: CONF (2): Head-to-head Sweep
#> • 2021-07-08 17:35:13: Calculating draft order #9
#> • 2021-07-08 17:35:13: Calculating draft order #8
#> • 2021-07-08 17:35:13: Calculating draft order #7
#> • 2021-07-08 17:35:13: Calculating draft order #6
#> • 2021-07-08 17:35:13: Calculating draft order #5
#> • 2021-07-08 17:35:13: Calculating draft order #4
#> • 2021-07-08 17:35:13: Calculating draft order #3
#> • 2021-07-08 17:35:13: Calculating draft order #2
#> • 2021-07-08 17:35:13: Calculating draft order #1
dplyr::glimpse(draft)
#> Rows: 64
#> Columns: 16
#> $ sim         <int> 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012…
#> $ team        <chr> "BUF", "MIA", "NE", "NYJ", "BAL", "CIN", "CLE", "PIT", "HO…
#> $ conf        <chr> "AFC", "AFC", "AFC", "AFC", "AFC", "AFC", "AFC", "AFC", "A…
#> $ division    <chr> "AFC East", "AFC East", "AFC East", "AFC East", "AFC North…
#> $ games       <int> 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16…
#> $ wins        <dbl> 6, 7, 12, 6, 10, 10, 5, 8, 12, 11, 2, 6, 13, 2, 4, 7, 8, 9…
#> $ true_wins   <int> 6, 7, 12, 6, 10, 10, 5, 8, 12, 11, 2, 6, 13, 2, 4, 7, 8, 9…
#> $ win_pct     <dbl> 0.375, 0.438, 0.750, 0.375, 0.625, 0.625, 0.312, 0.500, 0.…
#> $ div_pct     <dbl> 0.333, 0.333, 1.000, 0.333, 0.667, 0.500, 0.333, 0.500, 0.…
#> $ conf_pct    <dbl> 0.417, 0.417, 0.917, 0.333, 0.667, 0.583, 0.417, 0.417, 0.…
#> $ sov         <dbl> 0.281, 0.415, 0.466, 0.401, 0.438, 0.381, 0.388, 0.438, 0.…
#> $ sos         <dbl> 0.480, 0.500, 0.496, 0.512, 0.496, 0.438, 0.508, 0.465, 0.…
#> $ div_rank    <dbl> 4, 2, 1, 3, 1, 2, 4, 3, 1, 2, 4, 3, 1, 4, 3, 2, 3, 2, 4, 1…
#> $ seed        <dbl> NA, NA, 2, NA, 4, 6, NA, NA, 3, 5, NA, NA, 1, NA, NA, NA, …
#> $ exit        <dbl> 17, 17, 20, 17, 22, 18, 17, 17, 19, 18, 17, 17, 19, 17, 17…
#> $ draft_order <dbl> 8, 12, 29, 9, 32, 21, 6, 17, 27, 24, 2, 10, 28, 1, 3, 11, …

As this is the final step, the function compute_draft_order does not output h2h again. Instead it directly outputs the final standings including the draft order and the variable exit which indicates the week number of each team’s final game (the Super Bowl Winner’s exit equals 22):

draft %>% 
  dplyr::filter(sim == 2012) %>% 
  dplyr::select(division:draft_order) %>% 
  knitr::kable()
division games wins true_wins win_pct div_pct conf_pct sov sos div_rank seed exit draft_order
AFC East 16 6.0 6 0.375 0.333 0.417 0.281 0.480 4 NA 17 8
AFC East 16 7.0 7 0.438 0.333 0.417 0.415 0.500 2 NA 17 12
AFC East 16 12.0 12 0.750 1.000 0.917 0.466 0.496 1 2 20 29
AFC East 16 6.0 6 0.375 0.333 0.333 0.401 0.512 3 NA 17 9
AFC North 16 10.0 10 0.625 0.667 0.667 0.438 0.496 1 4 22 32
AFC North 16 10.0 10 0.625 0.500 0.583 0.381 0.438 2 6 18 21
AFC North 16 5.0 5 0.312 0.333 0.417 0.388 0.508 4 NA 17 6
AFC North 16 8.0 8 0.500 0.500 0.417 0.438 0.465 3 NA 17 17
AFC South 16 12.0 12 0.750 0.833 0.833 0.432 0.496 1 3 19 27
AFC South 16 11.0 11 0.688 0.667 0.667 0.403 0.441 2 5 18 24
AFC South 16 2.0 2 0.125 0.333 0.167 0.531 0.539 4 NA 17 2
AFC South 16 6.0 6 0.375 0.167 0.417 0.344 0.512 3 NA 17 10
AFC West 16 13.0 13 0.812 1.000 0.833 0.385 0.457 1 1 19 28
AFC West 16 2.0 2 0.125 0.000 0.000 0.438 0.516 4 NA 17 1
AFC West 16 4.0 4 0.250 0.333 0.333 0.219 0.469 3 NA 17 3
AFC West 16 7.0 7 0.438 0.667 0.583 0.286 0.457 2 NA 17 11
NFC East 16 8.0 8 0.500 0.500 0.417 0.422 0.523 3 NA 17 18
NFC East 16 9.0 9 0.562 0.500 0.667 0.490 0.521 2 NA 17 19
NFC East 16 4.0 4 0.250 0.167 0.167 0.484 0.508 4 NA 17 4
NFC East 16 10.0 10 0.625 0.833 0.667 0.450 0.494 1 4 18 22
NFC North 16 10.0 10 0.625 0.500 0.583 0.403 0.512 3 NA 17 20
NFC North 16 4.0 4 0.250 0.000 0.250 0.383 0.566 4 NA 17 5
NFC North 16 11.0 11 0.688 0.833 0.667 0.440 0.508 1 3 19 26
NFC North 16 10.0 10 0.625 0.667 0.583 0.456 0.520 2 6 18 23
NFC South 16 13.0 13 0.812 0.500 0.750 0.418 0.422 1 1 20 30
NFC South 16 7.0 7 0.438 0.500 0.417 0.464 0.516 2 NA 17 14
NFC South 16 7.0 7 0.438 0.500 0.417 0.446 0.521 3 NA 17 15
NFC South 16 7.0 7 0.438 0.500 0.333 0.446 0.502 4 NA 17 13
NFC West 16 5.0 5 0.312 0.167 0.250 0.475 0.559 4 NA 17 7
NFC West 16 11.0 11 0.688 0.500 0.667 0.534 0.504 2 5 19 25
NFC West 16 11.5 11 0.719 0.583 0.625 0.477 0.504 1 2 21 31
NFC West 16 7.5 7 0.469 0.750 0.542 0.496 0.539 3 NA 17 16