Simulating NFL seasons using nflseedR

Running a Simulation

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)

Note: For this guide, we’ll set an initial random seed of 4 at the beginning and simulations = 100 for the purposes of this document so you can follow along by entering the same code and get the same results shown here. We’ll also set fresh_season = TRUE to blank out the existing results from the 2020 season, but normally when simulating an incomplete season, you wouldn’t do these things.

set.seed(4)
sims <- simulate_nfl(
  nfl_season = 2020,
  fresh_season = TRUE,
  simulations = 100
)
#> ℹ 19:16:11 | Loading games data
#> ℹ 19:16:12 | Beginning simulation of 100 seasons in 1 round
#> ℹ 19:16:26 | Combining simulation data
#> ℹ 19:16:26 | Aggregating across simulations
#> ℹ 19:16:26 | DONE!

The output contains a lot of pre-aggregated information, as well as the individual results from each game of each simulation. For example, let’s look at the overall results for the Bears:

sims$overall %>% dplyr::filter(team == "CHI") %>% knitr::kable()

conf	division	team	wins	playoff	div1	seed1	won_conf	won_sb	draft1	draft5
NFC	NFC North	CHI	10.8	0.87	0.39	0.1	0.15	0.04	0	0.01

We can see the Bears got 10.8 wins on average. They made the playoffs 87% of simulations, won the division in 39%, won the Super Bowl in 4%, and only in 1% did they receive a top five draft pick. The teams section of the output will show how a team did in each simulated season.

sims$teams %>%
  dplyr::filter(team == "CHI") %>%
  dplyr::select(sim, team, wins, seed, draft_order) %>% 
  utils::head(6) %>%
  knitr::kable()

sim	team	wins	seed	draft_order
1	CHI	10	6	32
2	CHI	11	3	21
3	CHI	9	6	20
4	CHI	10	6	26
5	CHI	10	3	20
6	CHI	6	NA	11

Let’s check out the playoff games from the first simulation, where the Bears went 10-6 and got the 6th seed.

sims$games %>% dplyr::filter(sim == 1, game_type != "REG") %>% knitr::kable()

sim	game_type	week	away_team	home_team	away_rest	home_rest	location	result
1	WC	18	MIA	CLE	7	7	Home	-8
1	WC	18	TEN	DEN	7	7	Home	24
1	WC	18	NYJ	IND	7	7	Home	14
1	WC	18	PHI	TB	7	7	Home	-6
1	WC	18	CHI	LA	7	7	Home	-26
1	WC	18	DET	MIN	7	7	Home	2
1	DIV	19	DEN	IND	7	7	Home	3
1	DIV	19	MIA	NE	7	14	Home	3
1	DIV	19	PHI	MIN	7	7	Home	-12
1	DIV	19	CHI	DAL	7	14	Home	-11
1	CON	20	IND	NE	7	7	Home	10
1	CON	20	CHI	PHI	7	7	Home	-3
1	SB	21	CHI	NE	14	14	Neutral	-6

In this simulation, the Bears beat the Rams in a wildcard game by 26 points, then beat the Cowboys in the divisional round by 11 points, took the Eagles by a field goal in the NFC Championship Game, and finally defeated the Patriots by 6 in the Super Bowl.

As you may have gathered at this point, the default simulation code picks a random Elo for every team, and uses those as the starting Elo ratings for all 32 teams. However, the default code Elo will adjust independently within each simulation as each week is simulated. (The Elo model used is loosely based off of that of FiveThirtyEight.)

Use Your Own Model

But of course the real value of nflseedR is putting in your own model into the simulator. To accomplish this, you can write your own function which will determine the output of games instead. As an example, here’s a very stupid model that makes the team earlier alphabetically win by 3 points 90% of the time, and lose by 3 points the other 10% of the time.

stupid_games_model <- function(teams, games, week_num, ...) {
  # make the earlier alphabetical team win 90% of the time
  games <- games %>%
    dplyr::mutate(
      result = dplyr::case_when(
        !is.na(result) | week != week_num ~ result,
        away_team < home_team ~ sample(c(-3, 3), n(), prob = c(0.9, 0.1), replace = TRUE),
        away_team > home_team ~ sample(c(-3, 3), n(), prob = c(0.1, 0.9), replace = TRUE),
        TRUE ~ 0
      )
    )
  
  # return values
  return(list(teams = teams, games = games))
}

When you create this function, the first two inputs are data on the teams (one row per team per sim), and data on the games (one row per game per sim). The third argument is the week number currently being simulated, as only one week is processed at a time.

Your function’s job - by whatever means you choose - is to update the result column for that week’s games in each of the sims with the number of points the home team won by (or lost by if negative, or 0 if the game ended in a tie).

It returns both the teams and the games data. It does both because this way you can store information in new columns by team or by game to use in the next call. Make sure your code both accepts and returns the appropriate information, or the simulator will break!

For example, the default function updates a team’s Elo after the game, and stores it in the teams data. When the simulator processes the next week, it uses the updated Elo rating to inform the team’s next game.

!! Also, make sure you aren’t overriding completed games or games that aren’t in the current week of w. The simulator will not stop you from setting past, present, or future game results in your function, whether you meant to do so or not. !!

Let’s run a simulation with stupid_games_model and see what happens:

sims2 <- simulate_nfl(
  nfl_season = 2020,
  process_games = stupid_games_model,
  fresh_season = TRUE,
  simulations = 100
)
#> ℹ 19:16:26 | Loading games data
#> ℹ 19:16:26 | Beginning simulation of 100 seasons in 1 round
#> ℹ 19:16:38 | Combining simulation data
#> ℹ 19:16:38 | Aggregating across simulations
#> ℹ 19:16:38 | DONE!

sims2$overall %>% dplyr::arrange(team) %>% utils::head() %>% knitr::kable()

conf	division	team	wins	playoff	div1	seed1	won_conf	won_sb
NFC	NFC West	ARI	14.3	1.00	0.97	0.40	0.64	0.59
NFC	NFC South	ATL	14.4	1.00	0.91	0.47	0.29	0.26
AFC	AFC North	BAL	14.4	1.00	0.82	0.60	0.79	0.09
AFC	AFC East	BUF	13.7	1.00	1.00	0.29	0.16	0.04
NFC	NFC South	CAR	12.0	0.96	0.09	0.03	0.03	0.01
NFC	NFC North	CHI	12.9	0.98	0.88	0.10	0.02	0.00

sims2$overall %>% dplyr::arrange(team) %>% utils::tail() %>% knitr::kable()

conf	division	team	wins	draft1	draft5
AFC	AFC North	PIT	3.09	0.01	0.40
NFC	NFC West	SEA	3.94	0.02	0.33
NFC	NFC West	SF	2.39	0.10	0.86
NFC	NFC South	TB	1.63	0.24	0.85
AFC	AFC South	TEN	1.60	0.21	0.82
NFC	NFC East	WAS	1.68	0.39	0.85

As you might expect, the earliest alphabetical teams win a lot. The Cardinals won the Super Bowl in 59% of seasons! Meanwhile, the teams at the bottom alphabetically are virtually certain to be at the top of the draft order.

Adding In Your Own Data

This is all well and good, you might be thinking, but your model works off of other data not in the simulator! How can that work? This is where we utilize R’s ability to have generic arguments.

The ... at the end of the function definition means that the function can be called with any number of additional arguments. You can name these whatever you want, as long as they’re not already the name of other defined arguments.

When you call the simulate_nfl() function, it too uses the ... syntax, which allows you to pass in any number of additional arguments to the function. The simulator will in turn pass these on to your function that processes games.

For example, let’s slightly modify our last example:

biased_games_model <- function(teams, games, week_num, ...) {
  
  # arguments
  args <- list(...)
  best <- ""
  worst <- ""
  
  # best team?
  if ("best" %in% names(args)) {
    best <- args$best
  }
  
  # worst team?
  if ("worst" %in% names(args)) {
    worst <- args$worst
  }

  # make the best team always win and the worst team always lose
  # otherwise, make the earlier alphabetical team win 90% of the time
  games <- games %>%
    dplyr::mutate(
      result = dplyr::case_when(
        !is.na(result) | week != week_num ~ result,
        away_team == best | home_team == worst ~ -3,
        away_team == worst | home_team == best ~ 3,
        away_team < home_team ~ sample(c(-3, 3), n(), prob = c(0.9, 0.1), replace = TRUE),
        away_team > home_team ~ sample(c(-3, 3), n(), prob = c(0.1, 0.9), replace = TRUE),
        TRUE ~ 0
      )
    )
  
  # return values
  return(list(teams = teams, games = games))
}

This allows us to define best and worst, and use that information to determine a result (in this case, have the best team always win and the worst team always lose). While best and worst are in this example single-length character vectors, they can be data frames or any other R data type.

Let’s simulate using this:

sims3 <- simulate_nfl(
  nfl_season = 2020,
  process_games = biased_games_model, 
  fresh_season = TRUE, 
  simulations = 100,
  best = "CHI", 
  worst = "GB"
)
#> ℹ 19:16:38 | Loading games data
#> ℹ 19:16:38 | Beginning simulation of 100 seasons in 1 round
#> ℹ 19:16:50 | Combining simulation data
#> ℹ 19:16:50 | Aggregating across simulations
#> ℹ 19:16:50 | DONE!

Now let nflseedR summarize the simulation for you by using summary() with the nflseedR simulation object. This will print a gt table.

summary(sims3)
#> Warning: Since gt v0.9.0, the `colors` argument has been deprecated.
#> • Please use the `fn` argument instead.
#> This warning is displayed once every 8 hours.


Simulating the 2020 NFL Season
Summary of 100 Simulations using nflseedR
	AVG. WINS	Make POST	Win DIV	No.1 Seed	Win Conf	Win SB	No.1 Pick	Top-5 Pick	AVG. WINS	Make POST	Win DIV	No.1 Seed	Win Conf	Win SB	No.1 Pick	Top-5 Pick
E A S T
	13.4	100%	100%	22%	22%	0%	0%	0%	10.5	95%	94%	0%	0%	0%	0%	0%
	6.5	3%	0%	0%	0%	0%	0%	1%	7.9	15%	4%	0%	0%	0%	0%	0%
	4.8	0%	0%	0%	0%	0%	0%	9%	6.7	4%	2%	0%	0%	0%	0%	1%
	3.4	0%	0%	0%	0%	0%	0%	46%	1.5	0%	0%	0%	0%	0%	0%	86%
N O R T H
	14.3	99%	83%	63%	74%	0%	0%	0%	16.0	100%	100%	99%	100%	100%	0%	0%
	12.6	99%	15%	6%	2%	0%	0%	0%	10.4	95%	0%	0%	0%	0%	0%	0%
	11.1	94%	2%	1%	2%	0%	0%	0%	6.5	4%	0%	0%	0%	0%	0%	0%
	3.3	0%	0%	0%	0%	0%	0%	33%	0.0	0%	0%	0%	0%	0%	100%	100%
S O U T H
	10.5	93%	86%	0%	0%	0%	0%	0%	13.6	100%	94%	0%	0%	0%	0%	0%
	8.7	40%	11%	0%	0%	0%	0%	0%	11.1	96%	6%	0%	0%	0%	0%	0%
	7.2	6%	3%	0%	0%	0%	0%	0%	5.6	0%	0%	0%	0%	0%	0%	2%
	2.4	0%	0%	0%	0%	0%	0%	63%	2.5	0%	0%	0%	0%	0%	0%	66%
W E S T
	11.9	97%	92%	8%	0%	0%	0%	0%	14.6	100%	98%	1%	0%	0%	0%	0%
	8.9	60%	7%	0%	0%	0%	0%	0%	10.1	91%	2%	0%	0%	0%	0%	0%
	7.2	8%	1%	0%	0%	0%	0%	0%	3.8	0%	0%	0%	0%	0%	0%	35%
	5.6	1%	0%	0%	0%	0%	0%	1%	3.4	0%	0%	0%	0%	0%	0%	57%
nflseedR

And this shows exactly what we expect. By defining the Bears as the best team, they always go 16-0, win the division, and win the Super Bowl. Interestingly, they do not always get the #1 seed. This makes sense, however, as in games without the Bears or the Packers, the alphabetically earlier teams still wins 90% of the time. The Cardinals would therefore be expected to go 16-0 in some of the simulations, and in some of those have thee tiebreakers over the Bears. However, even in these simulations, they’ll still lose to Bears in the end when they meet in the playoffs.

Similarly, the Packers always go 0-16, and never make the playoffs. While in these simulated seasons they got the #1 draft pick every time, they aren’t guaranteed to do so. Using the same logic as above, sometimes the Washington Commanders will go 0-16 too, and may beat the Packers out for the #1 pick through tiebreakers.

Passing Data in from One Week to the Next

Sometimes though you want your data to keep updating as the simulation progresses. For example, an Elo-based model that updates each team’s Elo after each game. You can pass in the starting Elo values per team, and as games are simulated, update the Elo values for each team and store them in the teams data. This column will be part of the teams data passed into your function when the following week is simulated and your function is called.

Read the comments in the code below for specific tips on doing this but here are good ones:

You can add columns to teams and/or games if you want.
When doing joins to do the above, do left joins to make sure no rows are removed.
Remove any “helper” columns you generate along the way you don’t actually need before returning.
Make sure any column doesn’t get blindly joined on so it has .x and .y versions in Week 2 and R throws an error because an expected column name doesn’t exist.
Make sure you only update games with is.na(result) & week == week_num! You don’t want to override completed games, or games from a week other than the current week being simulated.

elo_model <- function(teams, games, week_num, ...) {

  # round out (away from zero)
  # this way the simulator never simulates a tie
  # the simulator will still allow ties to be simulated if you want
  # ... but not on playoff games
  round_out <- function(x) {
    x[!is.na(x) & x < 0] <- floor(x[!is.na(x) & x < 0])
    x[!is.na(x) & x > 0] <- ceiling(x[!is.na(x) & x > 0])
    return(x)
  }

  # we're going to store elo as a new columns in the teams data
  # it won't start off there of course, so we need to determine it
  # from our arguments
  if (!("elo" %in% colnames(teams))) {
    args <- list(...)
    if ("elo" %in% names(args)) {
      # pull the elo info from custom arguments
      teams <- teams %>%
        dplyr::inner_join(args$elo %>% dplyr::select(team, elo), by = c("team" = "team"))
    } else {
      # error with a friendly error message if no elo data is passed in
      stop("Pass in a tibble `elo` as an argument to `simulate_nfl()`")
    }
  }

  # isolate the ratings data by sim and by team only
  # we will want to join to the games data later and don't want excess columns
  ratings <- teams %>% dplyr::select(sim, team, elo)

  # simulate game outcomes
  games <- games %>%
    # add in the away team's elo to the game data
    # note we join on both `sim` and the team
    # always join on `sim` to make sure each sim cares about only its data
    dplyr::inner_join(ratings, by = c("sim" = "sim", "away_team" = "team")) %>%
    dplyr::rename(away_elo = elo) %>%
    # repeat for the home team as well
    dplyr::inner_join(ratings, by = c("sim" = "sim", "home_team" = "team")) %>%
    dplyr::rename(home_elo = elo) %>%
    dplyr::mutate(
      # calculate the elo difference
      elo_diff = home_elo - away_elo,
      # add in a small HFA amount if played at home
      elo_diff = elo_diff + ifelse(location == "Home", 20, 0),
      # make an adjustment for rest
      elo_diff = elo_diff + (home_rest - away_rest) / 7 * 25,
      # playoff games swing elo more
      elo_diff = elo_diff * ifelse(game_type == "REG", 1, 1.2),
      # from elo, we calculate the home team's win percentage
      wp = 1 / (10^(-elo_diff / 400) + 1),
      # we also can calculate the estimate (mean points home team wins by)
      estimate = elo_diff / 25,
      result = dplyr::case_when(
        # !!! ALWAYS DO THIS NEXT LINE IN YOUR `result` CHANGES !!!
        # you have to make sure you're only changing unfinished games in current week
        # if you don't do this, it will usually error out on a friendly error message
        is.na(result) & week == week_num ~ 
          as.integer(round_out(rnorm(n(), estimate, 13))),
        # if not this week or known result, leave as-is
        TRUE ~ as.integer(result)
      ),
      # simplify to 1 = win, 0 = loss, 0.5 = tie to help calculate elo shift
      outcome = dplyr::case_when(
        is.na(result) ~ NA_real_,
        result > 0 ~ 1,
        result < 0 ~ 0,
        TRUE ~ 0.5
      ),
      # calculate the amount to adjust home team's elo by
      elo_input = dplyr::case_when(
        is.na(result) ~ NA_real_,
        result > 0 ~ elo_diff * 0.001 + 2.2,
        result < 0 ~ -elo_diff * 0.001 + 2.2,
        TRUE ~ 1.0,
      ),
      elo_mult = log(pmax(abs(result), 1) + 1.0) * 2.2 / elo_input,
      elo_shift = 20 * elo_mult * (outcome - wp)
    ) %>%
    # we don't want these columns in `games` any more
    # remove any columns you don't need when you're done
    # otherwise the next week they'll get joined as `col.x` and `col.y`
    # which will almost certainly break your script
    dplyr::select(
      -away_elo, -home_elo, -elo_diff, -wp, -estimate,
      -outcome, -elo_input, -elo_mult
    )

  # apply elo shifts
  teams <- teams %>%
    # join games results from this week to away teams (within same sim!)
    # note this is a LEFT join, we don't want to remove any teams rows
    dplyr::left_join(games %>%
        dplyr::filter(week == week_num) %>%
        dplyr::select(sim, away_team, elo_shift),
      by = c("sim" = "sim", "team" = "away_team")
    ) %>%
    # away team's elo gets subtracted by elo amount
    # if the team wasn't an away team, do nothing
    dplyr::mutate(elo = elo - ifelse(!is.na(elo_shift), elo_shift, 0)) %>%
    # we don't want to keep `elo_shift` in `teams` either, remove it
    dplyr::select(-elo_shift) %>%
    # repeat the above except now do it for the home team
    dplyr::left_join(games %>%
        dplyr::filter(week == week_num) %>%
        dplyr::select(sim, home_team, elo_shift),
      by = c("sim" = "sim", "team" = "home_team")
    ) %>%
    # note that a team on a bye will have `elo_shift` as NA for both joins
    # this means it won't change, which is what we want
    dplyr::mutate(elo = elo + ifelse(!is.na(elo_shift), elo_shift, 0)) %>%
    dplyr::select(-elo_shift)

  # we need to keep `elo_shift` out of `games` too and we're done with it
  games <- games %>%
    dplyr::select(-elo_shift)

  # return the updated teams and games information
  # note that `teams` will now have an updated `elo` column which will
  # be used for the next week's games
  # note that starting `elo` values are the same per-team... 
  # ... but after that will differ per sim depending on that sim's results
  return(list(teams = teams, games = games))
}

Let’s generate initial random Elo values for each team. To see how this works, we’ll supply an test_week = 3 as an argument into simulate_nfl() which will abort after simulating Week 3, and instead return the result of our elo_model() function.

initial_elo <- tibble::tibble(
  team = unique(nflseedR::divisions$team),
  elo = rnorm(length(unique(nflseedR::divisions$team)), 1500, 150)
)
test <- simulate_nfl(
  nfl_season = 2020,
  process_games = elo_model,
  elo = initial_elo,
  fresh_season = TRUE,
  test_week = 3
)
#> ℹ 19:16:51 | Loading games data
#> ℹ 19:16:51 | Beginning simulation of 1000 seasons in 1 round
#> ℹ 19:16:52 | Aborting and returning your `process_games` function's results
#> from Week 3

Let’s look at the Bears’ Elo after Week 3 in the top handful of simulations:

test$teams %>%
  dplyr::filter(team == "CHI") %>%
  utils::head() %>%
  knitr::kable()

sim	team	conf	division	sdiv	elo
1	CHI	NFC	NFC North	NFCN	1498
2	CHI	NFC	NFC North	NFCN	1498
3	CHI	NFC	NFC North	NFCN	1482
4	CHI	NFC	NFC North	NFCN	1464
5	CHI	NFC	NFC North	NFCN	1447
6	CHI	NFC	NFC North	NFCN	1496

You can see that different simulations have different Elo results for the Bears, as the simulated seasons had different results for the games, and the Elos were adjusted accordingly.

Let’s examine the Bears’ games in that first simulation:

test$games %>%
  filter(sim == 1) %>%
  filter(away_team == "CHI" | home_team == "CHI")
#> ── nflverse games and schedules ────────────────────────────────────────────────
#> ℹ Data updated: 2024-01-20 19:16:51 UTC
#> # A tibble: 16 × 9
#>      sim game_type  week away_team home_team away_rest home_rest location result
#>    <dbl> <chr>     <int> <chr>     <chr>         <int>     <int> <chr>     <int>
#>  1     1 REG           1 CHI       DET               7         7 Home         13
#>  2     1 REG           2 NYG       CHI               6         7 Home         18
#>  3     1 REG           3 CHI       ATL               7         7 Home         -1
#>  4     1 REG           4 IND       CHI               7         7 Home         NA
#>  5     1 REG           5 TB        CHI               4         4 Home         NA
#>  6     1 REG           6 CHI       CAR              10         7 Home         NA
#>  7     1 REG           7 CHI       LA                8         8 Home         NA
#>  8     1 REG           8 NO        CHI               7         6 Home         NA
#>  9     1 REG           9 CHI       TEN               7         7 Home         NA
#> 10     1 REG          10 MIN       CHI               8         8 Home         NA
#> 11     1 REG          12 CHI       GB               13         7 Home         NA
#> 12     1 REG          13 DET       CHI              10         7 Home         NA
#> 13     1 REG          14 HOU       CHI               7         7 Home         NA
#> 14     1 REG          15 CHI       MIN               7         7 Home         NA
#> 15     1 REG          16 CHI       JAX               7         7 Home         NA
#> 16     1 REG          17 GB        CHI               7         7 Home         NA

Note that only the first three weeks have the result filled in, while the others are NA, indicating that game hasn’t yet occurred or been simulated. This is because the test_week = 3 input aborted the simulation after Week 3, which was useful for seeing the Elo above.

Simulation Configuration

There is a lot of flexibility in how you choose to run the simulation. These are the parameters and how to configure them when you run the simulate_nfl() function.

nfl_season - Which NFL season are you simulating? By default, it simulates the most recent season for which the regular season schedule is available through Lee Sharpe’s NFL game data. The earliest season you can simulate is 2002.
Note: Before the schedule for a new season is released, nflseedR may support simulating using a fake schedule for the upcoming season. It will notify you if it is doing this. The opponents will be correct, but the weeks in which games occur will not match the actual NFL schedule. The actual schedule will be utilized instead after it is released by the NFL.
process_games - This is where you supply a function you’ve written to encompass your model used to determine simulated games results, like the examples above. By default, this will generate a random Elo for every team per round of simulations, then use that to determine game data.
playoff_seeds - How many playoff seeds per conference are used? By default, this is 7 for seasons 2020 and later, and 6 for earlier seasons.
if_ended_today - This should only be used when running in the middle of the regular season. It will take all completed games as done, but remove the rest of the regular season games from the schedule, and begin the playoffs as if everything was locked in based on the regular season data that exists so far.
fresh_season - You’ll see this was set to TRUE in all of our examples above. This setting deletes any playoff games and clears out the results for all regular season games, so everything is generated fresh. The default is FALSE where all games that have been completed in real life are treated as locked in, and instead remaining games are simulated.
fresh_playoffs - Similar to fresh_season, except instead when set to TRUE, regular season results remain and only playoff games are deleted and then simulated. The default is FALSE in which case playoff games that are completed are accepted as they occurred,
tiebreaker_depth - How far do you want tiebreakers to be utilized? Usually leaving it at the default below (3) is fine, but before the season starts, you may wish to have less tie-breaking in order to
- 1: All teams with the same record have any ties broken randomly.
- 2: Instead of evaluating common games if that step is reached, break any ties randomly. But all earlier tiebreakers are handled correctly.
- 3: The default. All tiebreakers are handled through strength of schedule are processed (or through strength of victory for draft pick order). In the unlikely event of a further tie, it will be broken randomly.
test_week - This will abort after simulating this week number in the simulator. simulate_nfl() instead will return the output of your process_games() function. This is a useful input for debugging your code, but should be left as NULL (the default) for actual simulations. This also means only the first round will be simulated.
simulations - How many simulations should be run? Defaults to 1000.
sims_per_round - The simulator can break things up into chunks of simulated seasons, process each chunk on its own (called a round), and then aggregate everything together at the end. The default value determines the number of locally available cores and calculates the number of simulations per round to be equal to half of the available cores (various benchmarks showed this results in optimal performance in parallel processes). If your computer is hanging and forces a restart while running this simulation, it is recommended that you lower this number.

Simulation Output

The output of simulate_nfl(), assuming you don’t put in a test_week to debug your function, is a list of class "nflseedR_simulation" that holds four data frames with simulation results as well as a list of parameters used in the simulation. Here are the contents of each: