nfl_simulations: Simulate an NFL Season

In nflseedR: Functions to Efficiently Simulate and Evaluate NFL Seasons

Simulate an NFL Season

Description

Simulate NFL games based on a user provided games/schedule object that holds matchups with and without results. Missing results are computed using the argument compute_results and possible further arguments to compute_results in ... (please see simulations_verify_fct for further information.).

It is possible to let the function calculate playoff participants and simulate the post-season. The code is also developed for maximum performance and allows parallel computation by splitting the number of simulations into chunks and calling the appropriate future::plan. Progress updates can be activated by calling progressr::handlers before the start of the simulations. Please see the below given section "Details" for further information.

Usage

nfl_simulations(
  games,
  compute_results = nflseedR_compute_results,
  ...,
  playoff_seeds = 7L,
  simulations = 10000L,
  chunks = 8L,
  byes_per_conf = 1L,
  tiebreaker_depth = c("SOS", "PRE-SOV", "RANDOM"),
  sim_include = c("DRAFT", "REG", "POST"),
  verbosity = c("MIN", "MAX", "NONE")
)

Arguments

games	A data frame containing real or simulated game scores. Outside of simulations, this is simply the output of nflreadr::load_schedules. The following variables are required as a minimum: sim or season A season or simulation ID. Normally 1 - n simulated seasons. 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 (please see divisions for valid team abbreviations). home_team Team abbreviation of the home team (please see divisions for valid team abbreviations). result Equals home score - away score. If tiebreakers beyond SOS are to be used, then the actual scores of the home (home_score) and away (away_score) teams must also be available.
compute_results	Defaults to the nflseedR function nflseedR_compute_results. A function to compute results of games. Uses team, schedule, and week number as arguments. Please see simulations_verify_fct for further information.
...	Additional parameters passed on to the function compute_results.
playoff_seeds	If NULL (the default), will compute all 16 conference ranks. This means, the function applies conference tiebreakers to all conference ranks. For better performance, it is possible to set this to a value < 16 to make the function skip tiebreakers of those conference ranks.
simulations	Equals the number of times the given NFL season shall be simulated
chunks	The number of chunks simulations should be split into and potentially be processed parallel. This parameter controls the number of simulations per chunk. There is no obvious way to determine the ideal number of chunks in advance because there are too many dependencies on the hardware. Too many chunks can be just as slow as too few. It is therefore up to the user to determine the optimum number themselves.
byes_per_conf	The number of teams with a playoff bye week per conference. This number influences the number of wildcard games that are simulated.
tiebreaker_depth	One of "SOS", "PRE-SOV", "POINTS" or "RANDOM". Controls which tiebreakers are to be applied. The implemented tiebreakers are documented here https://nflseedr.com/articles/tiebreaker.html. The values mean: "SOS" (default): Apply all tiebreakers through Strength of Schedule. If there are still remaining ties, break them through coin toss. "PRE-SOV": Apply all tiebreakers before Strength of Victory. If there are still remaining ties, break them through coin toss. Why Pre SOV? It's the first tiebreaker that requires knowledge of how OTHER teams played. "POINTS": Apply all tiebreakers through point differential. If there are still remaining ties, break them through coin toss. This will go beyond SOS and requires knowledge of points scored and points allowed. As this is not usually part of season simulations, caution is advised in this case. These tiebreakers should only be used if the scores are real or are deliberately simulated. "RANDOM": Breaks all tiebreakers with a coin toss. I don't really know, why I allow this...
sim_include	One of "REG", "POST", "DRAFT" (the default). Simulation will behave as follows: "REG": Simulate the regular season and compute standings, division ranks, and playoff seeds "POST": Do "REG" + simulate the postseason "DRAFT" (default): Do "POST" + compute draft order
verbosity	One of "MIN", "MAX", or "NONE" allowing the user to set the grade of verbosity of status reports. They mean: "MIN" (default): Prints main steps of the process. "MAX": Prints all steps of the complete tiebreaking process. "NONE": No status reports at all. Do this to maximize the performance.

Details

More Speed Using Parallel Processing

We recommend choosing a default parallel processing method and saving it as an environment variable in the R user profile to make sure all futures will be resolved with the chosen method by default. This can be done by following the below given steps.

First, run the below line and the user profile should be opened automatically. If you haven't saved any environment variables yet, this will be an empty file.

usethis::edit_r_environ()

In the opened file add the next line, then save the file and restart your R session. Please note that this example sets "multisession" as default. For most users this should be the appropriate plan but please make sure it truly is.

R_FUTURE_PLAN="multisession"

After the session is freshly restarted please check if the above method worked by running the next line. If the output is FALSE you successfully set up a default non-sequential future::plan(). If the output is TRUE all functions will behave like they were called with purrr::map() and NOT in multisession.

inherits(future::plan(), "sequential")

For more information on possible plans please see the future package Readme.

Get Progress Updates while Functions are Running

nflseedR is able to show progress updates using progressr::progressor() if they are turned on before the function is called. There are at least two basic ways to do this by either activating progress updates globally (for the current session) with

progressr::handlers(global = TRUE)

or by piping the function call into progressr::with_progress():

nflseedR::nfl_simulations(
  games = nflseedR::sims_games_example,
  simulations = 4,
  chunks = 2
) |>
  progressr::with_progress()

For more information how to work with progress handlers please see progressr::progressr.

Reproducible Random Number Generation (RNG)

It is to be expected that some form of random number generation is required in the function in argument compute_results. For better performance, nflseedR uses the furrr package to parallelize chunks. furrr functions are guaranteed to generate the exact same sequence of random numbers given the same initial seed if, and only if, the initial seed is of the type "L'Ecuyer-CMRG". So if you want a consistent seed to be used across all chunks, you must ensure that the correct type is specified in set.seed, e.g. with the following code

set.seed(5, "L'Ecuyer-CMRG")

It is sufficient to set the seed before nfl_simulations is called. To check that the type has been set correctly, you can use the following code.

RNGkind()
"L'Ecuyer-CMRG" "Inversion"     "Rejection"

# Should be a integer vector of length 7
.Random.seed
10407  1157214768 -1674567567 -1532971138 -1249749529  1302496508  -253670963

For more information, please see the section "Reproducible random number generation (RNG)" in furrr::furrr_options.

Value

An nflseedR_simulation object containing a list of 6 data frames with the results of all simulated games, the final standings in each simulated season, summary statistics across all simulated seasons, and the simulation parameters. For a full list, please see the package website.

See Also

The examples on the package website

The method summary.nflseedR_simulation() that creates a pretty html summary table.

Examples

library(nflseedR)

# Activate progress updates
# progressr::handlers(global = TRUE)

# Parallel processing can be activated via the following line
# future::plan("multisession")

sim <- nflseedR::nfl_simulations(
  games = nflseedR::sims_games_example,
  simulations = 4,
  chunks = 2
)

# Overview output
str(sim, max.level = 3)

nflseedR documentation built on April 4, 2025, 2:08 a.m.