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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 gsis nfl_detail_id pfr pff 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
2022_17_NYJ_SEA 2022 REG 17 2023-01-01 Sunday 16:05 NYJ NA SEA NA Home NA NA NA 2023010111 NA NA 202301010sea NA 401437944 10 8 NA NA NA NA NA NA NA NA 0 outdoors fieldturf NA NA NA NA NA NA Robert Saleh Pete Carroll NA SEA00 Lumen Field
2022_17_MIN_GB 2022 REG 17 2023-01-01 Sunday 16:25 MIN NA GB NA Home NA NA NA 2023010112 NA NA 202301010gnb NA 401437945 8 7 NA NA NA NA NA NA NA NA 1 outdoors grass NA NA NA NA NA NA Kevin O’Connell Matt LaFleur NA GNB00 Lambeau Field
2022_17_LA_LAC 2022 REG 17 2023-01-01 Sunday 20:20 LA NA LAC NA Home NA NA NA 2023010113 NA NA 202301010sdg NA 401437946 7 6 NA NA NA NA NA NA NA NA 0 dome matrixturf NA NA NA NA NA NA Sean McVay Brandon Staley NA LAX01 SoFi Stadium
2022_17_BUF_CIN 2022 REG 17 2023-01-02 Monday 20:30 BUF NA CIN NA Home NA NA NA 2023010200 NA NA 202301020cin NA 401437947 9 9 NA NA NA NA NA NA NA NA 0 outdoors grass NA NA NA NA NA NA Sean McDermott Zac Taylor NA CIN00 Paycor Stadium
2022_18_TB_ATL 2022 REG 18 2023-01-08 Sunday 13:00 TB NA ATL NA Home NA NA NA 2023010800 NA NA 202301080atl NA 401437948 7 7 NA NA NA NA NA NA NA NA 1 NA fieldturf NA NA NA NA NA NA Todd Bowles Arthur Smith NA ATL97 Mercedes-Benz Stadium
2022_18_NE_BUF 2022 REG 18 2023-01-08 Sunday 13:00 NE NA BUF NA Home NA NA NA 2023010801 NA NA 202301080buf NA 401437949 7 6 NA NA NA NA NA NA NA NA 1 outdoors astroturf NA NA NA NA NA NA Bill Belichick Sean McDermott NA BUF00 New Era Field
2022_18_MIN_CHI 2022 REG 18 2023-01-08 Sunday 13:00 MIN NA CHI NA Home NA NA NA 2023010802 NA NA 202301080chi NA 401437950 7 7 NA NA NA NA NA NA NA NA 1 outdoors grass NA NA NA NA NA NA Kevin O’Connell Matt Eberflus NA CHI98 Soldier Field
2022_18_BAL_CIN 2022 REG 18 2023-01-08 Sunday 13:00 BAL NA CIN NA Home NA NA NA 2023010803 NA NA 202301080cin NA 401437951 7 6 NA NA NA NA NA NA NA NA 1 outdoors grass NA NA NA NA NA NA John Harbaugh Zac Taylor NA CIN00 Paycor Stadium
2022_18_LAC_DEN 2022 REG 18 2023-01-08 Sunday 13:00 LAC NA DEN NA Home NA NA NA 2023010812 NA NA 202301080den NA 401437960 7 7 NA NA NA NA NA NA NA NA 1 outdoors grass NA NA NA NA NA NA Brandon Staley Nathaniel Hackett NA DEN00 Empower Field at Mile High
2022_18_DET_GB 2022 REG 18 2023-01-08 Sunday 13:00 DET NA GB NA Home NA NA NA 2023010804 NA NA 202301080gnb NA 401437952 7 7 NA NA NA NA NA NA NA NA 1 outdoors grass NA NA NA NA NA NA Dan Campbell Matt LaFleur NA GNB00 Lambeau Field
2022_18_HOU_IND 2022 REG 18 2023-01-08 Sunday 13:00 HOU NA IND NA Home NA NA NA 2023010805 NA NA 202301080clt NA 401437953 7 7 NA NA NA NA NA NA NA NA 1 NA fieldturf NA NA NA NA NA NA Lovie Smith Frank Reich NA IND00 Lucas Oil Stadium
2022_18_TEN_JAX 2022 REG 18 2023-01-08 Sunday 13:00 TEN NA JAX NA Home NA NA NA 2023010806 NA NA 202301080jax NA 401437954 10 7 NA NA NA NA NA NA NA NA 1 outdoors grass NA NA NA NA NA NA Mike Vrabel Doug Pederson NA JAX00 TIAA Bank Stadium
2022_18_KC_LV 2022 REG 18 2023-01-08 Sunday 13:00 KC NA LV NA Home NA NA NA 2023010813 NA NA 202301080rai NA 401437961 7 7 NA NA NA NA NA NA NA NA 1 dome grass NA NA NA NA NA NA Andy Reid Josh McDaniels NA VEG00 Allegiant Stadium
2022_18_NYJ_MIA 2022 REG 18 2023-01-08 Sunday 13:00 NYJ NA MIA NA Home NA NA NA 2023010807 NA NA 202301080mia NA 401437955 7 7 NA NA NA NA NA NA NA NA 1 outdoors grass NA NA NA NA NA NA Robert Saleh Mike McDaniel NA MIA00 Hard Rock Stadium
2022_18_CAR_NO 2022 REG 18 2023-01-08 Sunday 13:00 CAR NA NO NA Home NA NA NA 2023010808 NA NA 202301080nor NA 401437956 7 7 NA NA NA NA NA NA NA NA 1 dome astroturf NA NA NA NA NA NA Matt Rhule Dennis Allen NA NOR00 Mercedes-Benz Superdome
2022_18_NYG_PHI 2022 REG 18 2023-01-08 Sunday 13:00 NYG NA PHI NA Home NA NA NA 2023010809 NA NA 202301080phi NA 401437957 7 7 NA NA NA NA NA NA NA NA 1 outdoors grass NA NA NA NA NA NA Brian Daboll Nick Sirianni NA PHI00 Lincoln Financial Field
2022_18_CLE_PIT 2022 REG 18 2023-01-08 Sunday 13:00 CLE NA PIT NA Home NA NA NA 2023010810 NA NA 202301080pit NA 401437958 7 7 NA NA NA NA NA NA NA NA 1 outdoors grass NA NA NA NA NA NA Kevin Stefanski Mike Tomlin NA PIT00 Acrisure Stadium
2022_18_LA_SEA 2022 REG 18 2023-01-08 Sunday 13:00 LA NA SEA NA Home NA NA NA 2023010814 NA NA 202301080sea NA 401437963 7 7 NA NA NA NA NA NA NA NA 1 outdoors fieldturf NA NA NA NA NA NA Sean McVay Pete Carroll NA SEA00 Lumen Field
2022_18_ARI_SF 2022 REG 18 2023-01-08 Sunday 13:00 ARI NA SF NA Home NA NA NA 2023010815 NA NA 202301080sfo NA 401437962 7 7 NA NA NA NA NA NA NA NA 1 outdoors grass NA NA NA NA NA NA Kliff Kingsbury Kyle Shanahan NA SFO01 Levi’s Stadium
2022_18_DAL_WAS 2022 REG 18 2023-01-08 Sunday 13:00 DAL NA WAS NA Home NA NA NA 2023010811 NA NA 202301080was NA 401437959 10 7 NA NA NA NA NA NA NA NA 1 outdoors grass NA NA NA NA NA NA Mike McCarthy Ron Rivera NA WAS00 FedExField

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)
#>  2022-09-29 02:24:11: Calculating team data
#>  2022-09-29 02:24:11: Calculating head to head
#>  2022-09-29 02:24:11: Calculating division rank #1
#>  2022-09-29 02:24:11: DIV (2): Head-to-head
#>  2022-09-29 02:24:11: DIV (2): Division Record
#>  2022-09-29 02:24:11: Calculating division rank #2
#>  2022-09-29 02:24:11: DIV (3): Head-to-head
#>  2022-09-29 02:24:11: DIV (3): Division Record
#>  2022-09-29 02:24:11: DIV (3): Common Record
#>  2022-09-29 02:24:11: DIV (2): Head-to-head
#>  2022-09-29 02:24:11: DIV (2): Division Record
#>  2022-09-29 02:24:11: DIV (2): Common Record
#>  2022-09-29 02:24:11: DIV (2): Conference Record
#>  2022-09-29 02:24:12: Calculating division rank #3
#>  2022-09-29 02:24:12: DIV (2): Head-to-head
#>  2022-09-29 02:24:12: DIV (2): Division Record
#>  2022-09-29 02:24:12: DIV (2): Common Record
#>  2022-09-29 02:24:12: Calculating division rank #4
dplyr::glimpse(div_standings)
#> List of 2
#>  $ standings: tibble [64 × 16] (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 ...
#>   ..$ losses      : int [1:64] 10 9 4 10 6 6 11 8 4 5 ...
#>   ..$ ties        : int [1:64] 0 0 0 0 0 0 0 0 0 0 ...
#>   ..$ 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 losses ties win_pct div_pct conf_pct sov sos div_rank
AFC East BUF 16 6.0 6 10 0 0.375 0.333 0.417 0.281 0.480 4
AFC East MIA 16 7.0 7 9 0 0.438 0.333 0.417 0.415 0.500 2
AFC East NE 16 12.0 12 4 0 0.750 1.000 0.917 0.466 0.496 1
AFC East NYJ 16 6.0 6 10 0 0.375 0.333 0.333 0.401 0.512 3
AFC North BAL 16 10.0 10 6 0 0.625 0.667 0.667 0.438 0.496 1
AFC North CIN 16 10.0 10 6 0 0.625 0.500 0.583 0.381 0.438 2
AFC North CLE 16 5.0 5 11 0 0.312 0.333 0.417 0.388 0.508 4
AFC North PIT 16 8.0 8 8 0 0.500 0.500 0.417 0.438 0.465 3
AFC South HOU 16 12.0 12 4 0 0.750 0.833 0.833 0.432 0.496 1
AFC South IND 16 11.0 11 5 0 0.688 0.667 0.667 0.403 0.441 2
AFC South JAX 16 2.0 2 14 0 0.125 0.333 0.167 0.531 0.539 4
AFC South TEN 16 6.0 6 10 0 0.375 0.167 0.417 0.344 0.512 3
AFC West DEN 16 13.0 13 3 0 0.812 1.000 0.833 0.385 0.457 1
AFC West KC 16 2.0 2 14 0 0.125 0.000 0.000 0.438 0.516 4
AFC West OAK 16 4.0 4 12 0 0.250 0.333 0.333 0.219 0.469 3
AFC West SD 16 7.0 7 9 0 0.438 0.667 0.583 0.286 0.457 2
NFC East DAL 16 8.0 8 8 0 0.500 0.500 0.417 0.422 0.523 3
NFC East NYG 16 9.0 9 7 0 0.562 0.500 0.667 0.490 0.521 2
NFC East PHI 16 4.0 4 12 0 0.250 0.167 0.167 0.484 0.508 4
NFC East WAS 16 10.0 10 6 0 0.625 0.833 0.667 0.450 0.494 1
NFC North CHI 16 10.0 10 6 0 0.625 0.500 0.583 0.403 0.512 3
NFC North DET 16 4.0 4 12 0 0.250 0.000 0.250 0.383 0.566 4
NFC North GB 16 11.0 11 5 0 0.688 0.833 0.667 0.440 0.508 1
NFC North MIN 16 10.0 10 6 0 0.625 0.667 0.583 0.456 0.520 2
NFC South ATL 16 13.0 13 3 0 0.812 0.500 0.750 0.418 0.422 1
NFC South CAR 16 7.0 7 9 0 0.438 0.500 0.417 0.464 0.516 2
NFC South NO 16 7.0 7 9 0 0.438 0.500 0.417 0.446 0.521 3
NFC South TB 16 7.0 7 9 0 0.438 0.500 0.333 0.446 0.502 4
NFC West ARI 16 5.0 5 11 0 0.312 0.167 0.250 0.475 0.559 4
NFC West SEA 16 11.0 11 5 0 0.688 0.500 0.667 0.534 0.504 2
NFC West SF 16 11.5 11 4 1 0.719 0.583 0.625 0.477 0.504 1
NFC West STL 16 7.5 7 8 1 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)
#>  2022-09-29 02:24:12: Calculating seed #1
#>  2022-09-29 02:24:12: CONF (3): Head-to-head Sweep
#>  2022-09-29 02:24:12: Calculating seed #2
#>  2022-09-29 02:24:12: CONF (2): Head-to-head Sweep
#>  2022-09-29 02:24:12: CONF (2): Conference Record
#>  2022-09-29 02:24:12: Calculating seed #3
#>  2022-09-29 02:24:12: Calculating seed #4
#>  2022-09-29 02:24:12: Calculating seed #5
#>  2022-09-29 02:24:12: Calculating seed #6
#>  2022-09-29 02:24:13: CONF (2): Best-in-division reduction
dplyr::glimpse(seeds)
#> List of 2
#>  $ standings: tibble [64 × 17] (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 ...
#>   ..$ losses   : int [1:64] 10 9 4 10 6 6 11 8 4 5 ...
#>   ..$ ties     : int [1:64] 0 0 0 0 0 0 0 0 0 0 ...
#>   ..$ 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 losses ties win_pct div_pct conf_pct sov sos div_rank seed
AFC East BUF 16 6.0 6 10 0 0.375 0.333 0.417 0.281 0.480 4 NA
AFC East MIA 16 7.0 7 9 0 0.438 0.333 0.417 0.415 0.500 2 NA
AFC East NE 16 12.0 12 4 0 0.750 1.000 0.917 0.466 0.496 1 2
AFC East NYJ 16 6.0 6 10 0 0.375 0.333 0.333 0.401 0.512 3 NA
AFC North BAL 16 10.0 10 6 0 0.625 0.667 0.667 0.438 0.496 1 4
AFC North CIN 16 10.0 10 6 0 0.625 0.500 0.583 0.381 0.438 2 6
AFC North CLE 16 5.0 5 11 0 0.312 0.333 0.417 0.388 0.508 4 NA
AFC North PIT 16 8.0 8 8 0 0.500 0.500 0.417 0.438 0.465 3 NA
AFC South HOU 16 12.0 12 4 0 0.750 0.833 0.833 0.432 0.496 1 3
AFC South IND 16 11.0 11 5 0 0.688 0.667 0.667 0.403 0.441 2 5
AFC South JAX 16 2.0 2 14 0 0.125 0.333 0.167 0.531 0.539 4 NA
AFC South TEN 16 6.0 6 10 0 0.375 0.167 0.417 0.344 0.512 3 NA
AFC West DEN 16 13.0 13 3 0 0.812 1.000 0.833 0.385 0.457 1 1
AFC West KC 16 2.0 2 14 0 0.125 0.000 0.000 0.438 0.516 4 NA
AFC West OAK 16 4.0 4 12 0 0.250 0.333 0.333 0.219 0.469 3 NA
AFC West SD 16 7.0 7 9 0 0.438 0.667 0.583 0.286 0.457 2 NA
NFC East DAL 16 8.0 8 8 0 0.500 0.500 0.417 0.422 0.523 3 NA
NFC East NYG 16 9.0 9 7 0 0.562 0.500 0.667 0.490 0.521 2 NA
NFC East PHI 16 4.0 4 12 0 0.250 0.167 0.167 0.484 0.508 4 NA
NFC East WAS 16 10.0 10 6 0 0.625 0.833 0.667 0.450 0.494 1 4
NFC North CHI 16 10.0 10 6 0 0.625 0.500 0.583 0.403 0.512 3 NA
NFC North DET 16 4.0 4 12 0 0.250 0.000 0.250 0.383 0.566 4 NA
NFC North GB 16 11.0 11 5 0 0.688 0.833 0.667 0.440 0.508 1 3
NFC North MIN 16 10.0 10 6 0 0.625 0.667 0.583 0.456 0.520 2 6
NFC South ATL 16 13.0 13 3 0 0.812 0.500 0.750 0.418 0.422 1 1
NFC South CAR 16 7.0 7 9 0 0.438 0.500 0.417 0.464 0.516 2 NA
NFC South NO 16 7.0 7 9 0 0.438 0.500 0.417 0.446 0.521 3 NA
NFC South TB 16 7.0 7 9 0 0.438 0.500 0.333 0.446 0.502 4 NA
NFC West ARI 16 5.0 5 11 0 0.312 0.167 0.250 0.475 0.559 4 NA
NFC West SEA 16 11.0 11 5 0 0.688 0.500 0.667 0.534 0.504 2 5
NFC West SF 16 11.5 11 4 1 0.719 0.583 0.625 0.477 0.504 1 2
NFC West STL 16 7.5 7 8 1 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)
#>  2022-09-29 02:24:13: Calculating draft order #32
#>  2022-09-29 02:24:13: Calculating draft order #31
#>  2022-09-29 02:24:13: Calculating draft order #30
#>  2022-09-29 02:24:13: Calculating draft order #29
#>  2022-09-29 02:24:13: Calculating draft order #28
#>  2022-09-29 02:24:13: Calculating draft order #27
#>  2022-09-29 02:24:13: Calculating draft order #26
#>  2022-09-29 02:24:13: Calculating draft order #25
#>  2022-09-29 02:24:13: Calculating draft order #24
#>  2022-09-29 02:24:13: Calculating draft order #23
#>  2022-09-29 02:24:14: Calculating draft order #22
#>  2022-09-29 02:24:14: Calculating draft order #21
#>  2022-09-29 02:24:14: Calculating draft order #20
#>  2022-09-29 02:24:14: Calculating draft order #19
#>  2022-09-29 02:24:14: Calculating draft order #18
#>  2022-09-29 02:24:14: Calculating draft order #17
#>  2022-09-29 02:24:14: Calculating draft order #16
#>  2022-09-29 02:24:14: Calculating draft order #15
#>  2022-09-29 02:24:14: Calculating draft order #14
#>  2022-09-29 02:24:14: Calculating draft order #13
#>  2022-09-29 02:24:14: Calculating draft order #12
#>  2022-09-29 02:24:14: Calculating draft order #11
#>  2022-09-29 02:24:14: Calculating draft order #10
#>  2022-09-29 02:24:14: DRAFT: Divisional Rank
#>  2022-09-29 02:24:14: DRAFT: Conference Rank
#>  2022-09-29 02:24:14: CONF (2): Best-in-division reduction
#>  2022-09-29 02:24:14: CONF (2): Head-to-head Sweep
#>  2022-09-29 02:24:14: Calculating draft order #9
#>  2022-09-29 02:24:14: Calculating draft order #8
#>  2022-09-29 02:24:14: Calculating draft order #7
#>  2022-09-29 02:24:14: Calculating draft order #6
#>  2022-09-29 02:24:14: Calculating draft order #5
#>  2022-09-29 02:24:15: Calculating draft order #4
#>  2022-09-29 02:24:15: Calculating draft order #3
#>  2022-09-29 02:24:15: Calculating draft order #2
#>  2022-09-29 02:24:15: Calculating draft order #1
dplyr::glimpse(draft)
#> Rows: 64
#> Columns: 18
#> $ 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…
#> $ losses      <int> 10, 9, 4, 10, 6, 6, 11, 8, 4, 5, 14, 10, 3, 14, 12, 9, 8, …
#> $ ties        <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ 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 losses ties win_pct div_pct conf_pct sov sos div_rank seed exit draft_order
AFC East 16 6.0 6 10 0 0.375 0.333 0.417 0.281 0.480 4 NA 17 8
AFC East 16 7.0 7 9 0 0.438 0.333 0.417 0.415 0.500 2 NA 17 12
AFC East 16 12.0 12 4 0 0.750 1.000 0.917 0.466 0.496 1 2 20 29
AFC East 16 6.0 6 10 0 0.375 0.333 0.333 0.401 0.512 3 NA 17 9
AFC North 16 10.0 10 6 0 0.625 0.667 0.667 0.438 0.496 1 4 22 32
AFC North 16 10.0 10 6 0 0.625 0.500 0.583 0.381 0.438 2 6 18 21
AFC North 16 5.0 5 11 0 0.312 0.333 0.417 0.388 0.508 4 NA 17 6
AFC North 16 8.0 8 8 0 0.500 0.500 0.417 0.438 0.465 3 NA 17 17
AFC South 16 12.0 12 4 0 0.750 0.833 0.833 0.432 0.496 1 3 19 27
AFC South 16 11.0 11 5 0 0.688 0.667 0.667 0.403 0.441 2 5 18 24
AFC South 16 2.0 2 14 0 0.125 0.333 0.167 0.531 0.539 4 NA 17 2
AFC South 16 6.0 6 10 0 0.375 0.167 0.417 0.344 0.512 3 NA 17 10
AFC West 16 13.0 13 3 0 0.812 1.000 0.833 0.385 0.457 1 1 19 28
AFC West 16 2.0 2 14 0 0.125 0.000 0.000 0.438 0.516 4 NA 17 1
AFC West 16 4.0 4 12 0 0.250 0.333 0.333 0.219 0.469 3 NA 17 3
AFC West 16 7.0 7 9 0 0.438 0.667 0.583 0.286 0.457 2 NA 17 11
NFC East 16 8.0 8 8 0 0.500 0.500 0.417 0.422 0.523 3 NA 17 18
NFC East 16 9.0 9 7 0 0.562 0.500 0.667 0.490 0.521 2 NA 17 19
NFC East 16 4.0 4 12 0 0.250 0.167 0.167 0.484 0.508 4 NA 17 4
NFC East 16 10.0 10 6 0 0.625 0.833 0.667 0.450 0.494 1 4 18 22
NFC North 16 10.0 10 6 0 0.625 0.500 0.583 0.403 0.512 3 NA 17 20
NFC North 16 4.0 4 12 0 0.250 0.000 0.250 0.383 0.566 4 NA 17 5
NFC North 16 11.0 11 5 0 0.688 0.833 0.667 0.440 0.508 1 3 19 26
NFC North 16 10.0 10 6 0 0.625 0.667 0.583 0.456 0.520 2 6 18 23
NFC South 16 13.0 13 3 0 0.812 0.500 0.750 0.418 0.422 1 1 20 30
NFC South 16 7.0 7 9 0 0.438 0.500 0.417 0.464 0.516 2 NA 17 14
NFC South 16 7.0 7 9 0 0.438 0.500 0.417 0.446 0.521 3 NA 17 15
NFC South 16 7.0 7 9 0 0.438 0.500 0.333 0.446 0.502 4 NA 17 13
NFC West 16 5.0 5 11 0 0.312 0.167 0.250 0.475 0.559 4 NA 17 7
NFC West 16 11.0 11 5 0 0.688 0.500 0.667 0.534 0.504 2 5 19 25
NFC West 16 11.5 11 4 1 0.719 0.583 0.625 0.477 0.504 1 2 21 31
NFC West 16 7.5 7 8 1 0.469 0.750 0.542 0.496 0.539 3 NA 17 16