Package 'ffsimulator'

Title: Simulate Fantasy Football Seasons
Description: Uses bootstrap resampling to run fantasy football season simulations supported by historical rankings and 'nflfastR' data, calculating optimal lineups, and returning aggregated results.
Authors: Tan Ho [aut, cre, cph]
Maintainer: Tan Ho <[email protected]>
License: MIT + file LICENSE
Version: 1.2.3.01
Built: 2024-07-07 04:58:21 UTC
Source: https://github.com/ffverse/ffsimulator

Help Index


Automatically Plot ff_simulation Object

Description

Creates automatic plots for wins, ranks, or points for an ff_simulation object as created by ff_simulate().

Usage

autoplot.ff_simulation(object, type = c("wins", "rank", "points"), ...)

## S3 method for class 'ff_simulation'
plot(x, ..., type = c("wins", "rank", "points"), y)

Arguments

object

a ff_simulation object as created by ff_simulate()

type

one of "wins", "rank", "points"

...

unused, required by autoplot generic

x

A ff_simulation object.

y

Ignored, required for compatibility with the plot() generic.

Details

Usage of this function/method requires the ggplot2 package and (for wins and points plots) the ggridges package.

Value

a ggplot object

See Also

vignette("basic") for example usage

Examples

simulation <- .ffs_cache("foureight_sim.rds")

ggplot2::autoplot(simulation) # default is type = "wins"
ggplot2::autoplot(simulation, type = "rank")
ggplot2::autoplot(simulation, type = "points")

Automatically Plot ff_simulation Object

Description

Creates automatic plots for wins, ranks, or points for an ff_simulation object as created by ff_simulate().

Usage

autoplot.ff_simulation_week(object, type = c("luck", "points"), ...)

## S3 method for class 'ff_simulation_week'
plot(x, ..., type = c("luck", "points"), y)

Arguments

object

a ff_simulation object as created by ff_simulate()

type

one of "luck" or "points"

...

unused, required by autoplot generic

x

A ff_simulation_week object.

y

Ignored, required for compatibility with the plot() generic.

Details

Usage of this function/method requires the ggplot2 package and (for wins and points plots) the ggridges package.

Value

a ggplot object

See Also

vignette("basic") for example usage

Examples

simulation <- .ffs_cache("foureight_sim_week.rds")

ggplot2::autoplot(simulation) # default is type = "luck"
ggplot2::autoplot(simulation, type = "points")

Connect to a league

Description

See ffscrapr::espn_connect() for details.

Value

a connection object to be used with ⁠ff_*⁠ functions

See Also

Other ffscrapr-imports: ff_connect(), ff_scoringhistory(), ff_starter_positions(), fleaflicker_connect(), mfl_connect(), sleeper_connect()


Connect to a league

Description

See ffscrapr::ff_connect() for details.

Value

a connection object to be used with ⁠ff_*⁠ functions

See Also

Other ffscrapr-imports: espn_connect(), ff_scoringhistory(), ff_starter_positions(), fleaflicker_connect(), mfl_connect(), sleeper_connect()


Get league scoring history

Description

See ffscrapr::ff_scoringhistory for details.

Value

A tidy dataframe of weekly fantasy scoring data, one row per player per week

See Also

Other ffscrapr-imports: espn_connect(), ff_connect(), ff_starter_positions(), fleaflicker_connect(), mfl_connect(), sleeper_connect()


Simulate Fantasy Seasons

Description

The main function of the package - uses bootstrap resampling to run fantasy football season simulations supported by historical rankings and nflfastR data, calculating optimal lineups, and returns aggregated results.

Usage

ff_simulate(
  conn,
  n_seasons = 100,
  n_weeks = 14,
  best_ball = FALSE,
  seed = NULL,
  gp_model = c("simple", "none"),
  base_seasons = 2012:2022,
  actual_schedule = FALSE,
  replacement_level = TRUE,
  pos_filter = c("QB", "RB", "WR", "TE", "K"),
  verbose = NULL,
  return = c("default", "all")
)

Arguments

conn

an connection to a league made with ff_connect() and friends (required)

n_seasons

number of seasons to simulate, default = 100

n_weeks

number of weeks per season, default = 14

best_ball

a logical: are weekly wins based on optimal lineups?

seed

an integer to control reproducibility

gp_model

select between "simple", "none" to apply a model for whether a player played in a given game, defaults to "simple"

base_seasons

a numeric vector that selects seasons as base data, earliest available is 2012

actual_schedule

a logical: use actual ff_schedule? default is FALSE

replacement_level

a logical: use best available on waiver as replacement level? defaults to TRUE

pos_filter

a character vector of positions to filter/run, default is c("QB","RB","WR","TE","K")

verbose

a logical: print status messages? default is TRUE, configure with options(ffsimulator.verbose)

return

one of c("default", "all") - what objects to return in the output list

Value

an ff_simulation object which can be passed to plot() and contains the output data from the simulation.

See Also

vignette("basic") for example usage

vignette("custom") for examples on using the subfunctions for your own processes.

Examples

try({ # try block to prevent CRAN-related issues
conn <- mfl_connect(2021, 22627)
ff_simulate(conn, n_seasons = 25)
})

Simulate Fantasy Week

Description

This function simulates a single upcoming week using the same methodology as in the season-long simulation, ff_simulate().

Usage

ff_simulate_week(
  conn,
  n = 1000,
  best_ball = FALSE,
  seed = NULL,
  base_seasons = 2012:2022,
  actual_schedule = TRUE,
  replacement_level = FALSE,
  pos_filter = c("QB", "RB", "WR", "TE", "K"),
  verbose = NULL,
  return = c("default", "all")
)

Arguments

conn

an connection to a league made with ff_connect() and friends (required)

n

number of times to simulate the upcoming week, default is 1000

best_ball

a logical: are weekly wins based on optimal lineups?

seed

an integer to control reproducibility

base_seasons

a numeric vector that selects seasons as base data, earliest available is 2012

actual_schedule

a logical: use actual ff_schedule? default is TRUE

replacement_level

a logical: use best available on waiver as replacement level? defaults to FALSE for upcoming week simulations

pos_filter

a character vector of positions to filter/run, default is c("QB","RB","WR","TE","K")

verbose

a logical: print status messages? default is TRUE, configure with options(ffsimulator.verbose)

return

one of c("default", "all") - what objects to return in the output list

Value

an ff_simulation object which can be passed to plot() and contains the output data from the simulation.

See Also

vignette("basic") for example usage

vignette("custom") for examples on using the subfunctions for your own processes.

Examples

try({ # try block to prevent CRAN-related issues
conn <- mfl_connect(2021, 22627)
ff_simulate_week(conn, n = 1000, actual_schedule = TRUE)
})

Get league starter positions

Description

See ffscrapr::ff_starter_positions for details.

Value

A tidy dataframe of positional lineup rules, one row per position with minimum and maximum starters as well as total starter calculations.

See Also

Other ffscrapr-imports: espn_connect(), ff_connect(), ff_scoringhistory(), fleaflicker_connect(), mfl_connect(), sleeper_connect()


Wins Added

Description

(EXPERIMENTAL) This function adds a basic wins-added calculation for each player on every team, presenting the change in wins if that player was removed from the team as the net wins-over-replacement for that player. This can be a bit of a time/compute-expensive calculation.

Usage

ff_wins_added(conn, ...)

Arguments

conn

an connection to a league made with ff_connect() and friends (required)

...

Arguments passed on to ff_simulate

n_seasons

number of seasons to simulate, default = 100

n_weeks

number of weeks per season, default = 14

best_ball

a logical: are weekly wins based on optimal lineups?

seed

an integer to control reproducibility

gp_model

select between "simple", "none" to apply a model for whether a player played in a given game, defaults to "simple"

base_seasons

a numeric vector that selects seasons as base data, earliest available is 2012

actual_schedule

a logical: use actual ff_schedule? default is FALSE

replacement_level

a logical: use best available on waiver as replacement level? defaults to TRUE

pos_filter

a character vector of positions to filter/run, default is c("QB","RB","WR","TE","K")

verbose

a logical: print status messages? default is TRUE, configure with options(ffsimulator.verbose)

return

one of c("default", "all") - what objects to return in the output list

Details

Runs base simulation once (with the usual parameters available for ff_simulate), then for every player on every team (except replacement level players):

  • remove them from that specific roster

  • reoptimize the lineups just for that roster without the player to calculate what the score ends up being without the player

  • summarise the new simulation

  • return the delta in wins and points

Summarise wins added as the difference between the sim with the player and the sim without them

Value

a dataframe summarising the net effect of each player on their team's wins

Examples

try({ # try block to prevent CRAN-related issues
# n_seasons set so that the example runs more quickly
ff_wins_added(mfl_connect(2021,54040), n_seasons = 5)
})

Add replacement level players to each roster

Description

Add replacement level players to each roster

Usage

ffs_add_replacement_level(
  rosters,
  latest_rankings,
  franchises,
  lineup_constraints,
  pos_filter = c("QB", "RB", "WR", "TE")
)

Arguments

rosters

a dataframe of rosters as created by ffs_rosters()

latest_rankings

a dataframe of latest rankings as created by ff_latest_rankings()

franchises

a dataframe of franchises as created by ffs_franchises()

lineup_constraints

a dataframe of lineup constraints as created by ffs_starter_positions

pos_filter

a character vector of positions to filter to, defaults to c("QB","RB","WR","TE","K")

Value

a dataframe of rosters with replacements


Connects ff_scoringhistory to past ADP rankings

Description

The backbone of the ffsimulator resampling process is coming up with a population of weekly outcomes for every preseason positional rank. This function creates that dataframe by connecting historical FantasyPros.com rankings to nflfastR-based scoring data, as created by ffscrapr::ff_scoringhistory().

Usage

ffs_adp_outcomes(
  scoring_history,
  gp_model = "simple",
  pos_filter = c("QB", "RB", "WR", "TE")
)

Arguments

scoring_history

a scoring history table as created by ffscrapr::ff_scoringhistory()

gp_model

either "simple" or "none" - simple uses the average games played per season for each position/adp combination, none assumes every game is played.

pos_filter

a character vector: filter the positions returned to these specific positions, default: c("QB","RB","WR","TE)

Value

a dataframe with position, rank, probability of games played, and a corresponding nested list per row of all week score outcomes.

See Also

fp_rankings_history for the included historical rankings

fp_injury_table for the historical injury table

vignette("custom") for usage details.

Examples

# cached data
scoring_history <- .ffs_cache("mfl_scoring_history.rds")

ffs_adp_outcomes(scoring_history, gp_model = "simple")
ffs_adp_outcomes(scoring_history, gp_model = "none")

Connects ff_scoringhistory to past ADP rankings

Description

The backbone of the ffsimulator resampling process is coming up with a population of weekly outcomes for every inseason weekly rank. This function creates that dataframe by connecting historical FantasyPros.com rankings to nflfastR-based scoring data, as created by ffscrapr::ff_scoringhistory().

Usage

ffs_adp_outcomes_week(scoring_history, pos_filter = c("QB", "RB", "WR", "TE"))

Arguments

scoring_history

a scoring history table as created by ffscrapr::ff_scoringhistory()

pos_filter

a character vector: filter the positions returned to these specific positions, default: c("QB","RB","WR","TE)

Value

a dataframe with position, rank, probability of games played, and a corresponding nested list per row of all week score outcomes.

See Also

fp_rankings_history_week for the included historical rankings

Examples

# cached data
scoring_history <- .ffs_cache("mfl_scoring_history.rds")
ffs_adp_outcomes_week(scoring_history, pos_filter = c("QB","RB","WR","TE"))

Generate fantasy schedules

Description

This function generates random head to head schedules for a given number of seasons, teams, and weeks.

Usage

ffs_build_schedules(
  n_teams = NULL,
  n_seasons = 100,
  n_weeks = 14,
  franchises = NULL,
  seed = NULL
)

Arguments

n_teams

number of teams in simulation

n_seasons

number of seasons to simulate, default = 100

n_weeks

number of weeks per season, default = 14

franchises

optional: a dataframe of franchises as created by ffs_franchises() - overrides the n_teams argument and will attach actual franchise IDs to the schedule output.

seed

an integer to control reproducibility

Details

It starts with the circle method for round robin scheduling, grows or shrinks the schedule to match the required number of weeks, and then shuffles both the order that teams are assigned in and the order that weeks are generated. This doesn't "guarantee" unique schedules, but there are n_teams! x n_weeks! permutations of the schedule so it's very very likely that the schedules are unique (3x10^18 possible schedules for a 12 team league playing 13 weeks).

Value

a dataframe of schedules

See Also

vignette("custom") for example usage

Examples

ffs_build_schedules(n_teams = 12, n_seasons = 1, n_weeks = 14)

Copy simulation template to filename

Description

Creates a simulation template file with all of the components of ff_simulate, ready for tinkering!

Usage

ffs_copy_template(
  filename = "ff_simulation.R",
  template = c("season", "week"),
  overwrite = NULL
)

Arguments

filename

New file name, defaults to putting "ff_simulation.R" into your current directory

template

choice of template: one of "season" or "week"

overwrite

a logical (or NULL) - overwrite if existing file found?

Value

a success message signalling success/failure.

Examples

tmp <- tempfile()
ffs_copy_template(tmp)

Get Franchises

Description

This function lightly wraps ffscrapr::ff_franchises() and adds league_id, which is a required column for ffsimulator.

Usage

ffs_franchises(conn)

Arguments

conn

a connection object as created by ffscrapr::ff_connect() and friends.

Value

a dataframe of franchises that includes the league_id column

See Also

vignette("Custom Simulations") for more detailed example usage

Examples

# cached examples
conn <- .ffs_cache("mfl_conn.rds")

try({ # prevents CRAN connectivity issues, not actually required in normal usage
ffs_franchises(conn)
})

Generate Projections

Description

Runs the bootstrapped resampling of player week outcomes on the latest rankings and rosters for a given number of seasons and weeks per season.

Usage

ffs_generate_projections(
  adp_outcomes,
  latest_rankings,
  n_seasons = 100,
  weeks = 1:14,
  rosters = NULL
)

Arguments

adp_outcomes

a dataframe of adp-based weekly outcomes, as created by ffs_adp_outcomes()

latest_rankings

a dataframe of rankings, as created by ffs_latest_rankings()

n_seasons

number of seasons, default is 100

weeks

a numeric vector of weeks to simulate, defaults to 1:14

rosters

a dataframe of rosters, as created by ffs_rosters() - optional, reduces computation to just rostered players

Value

a dataframe of weekly scores for each player in the simulation, approximately of length n_seasons x n_weeks x latest_rankings

See Also

vignette("custom") for example usage

Examples

# cached examples
adp_outcomes <- .ffs_cache("adp_outcomes.rds")
latest_rankings <- .ffs_cache("latest_rankings.rds")

ffs_generate_projections(adp_outcomes, latest_rankings)

Generate Projections

Description

Runs the bootstrapped resampling of player week outcomes on the latest rankings and rosters for a given number of seasons and weeks per season.

Usage

ffs_generate_projections_week(
  adp_outcomes,
  latest_rankings,
  n = 1000,
  rosters = NULL
)

Arguments

adp_outcomes

a dataframe of adp-based weekly outcomes, as created by ffs_adp_outcomes()

latest_rankings

a dataframe of rankings, as created by ffs_latest_rankings()

n

number of weeks to simulate

rosters

a dataframe of rosters, as created by ffs_rosters() - optional, reduces computation to just rostered players

Value

a dataframe of weekly scores for each player in the simulation, approximately of length n_seasons x n_weeks x latest_rankings

See Also

vignette("custom") for example usage

Examples

# cached examples
adp_outcomes_week <- .ffs_cache("adp_outcomes_week.rds")
latest_rankings_week <- .ffs_cache("latest_rankings_week.rds")

ffs_generate_projections_week(adp_outcomes_week, latest_rankings_week)

Download latest rankings from DynastyProcess GitHub

Description

Fetches a copy of the latest FantasyPros redraft positional rankings data from DynastyProcess.com's data repository.

Usage

ffs_latest_rankings(type = c("draft", "week"))

Arguments

type

one of "draft" or "week" - controls whether to pull preseason or inseason rankings.

Details

If you have any issues with the output of this data, please open an issue in the DynastyProcess data repository.

Value

a dataframe with a copy of the latest FP rankings from DynastyProcess's data repository

See Also

https://github.com/dynastyprocess/data

vignette("custom") for example usage

Examples

try({ # try block to prevent CRAN-related issues
ffs_latest_rankings()
})

Optimise Lineups

Description

Calculates optimal lineups for all franchises in the dataframe based on a table of lineup constraints.

Usage

ffs_optimise_lineups(
  roster_scores,
  lineup_constraints,
  lineup_efficiency_mean = 0.775,
  lineup_efficiency_sd = 0.05,
  best_ball = FALSE,
  pos_filter = c("QB", "RB", "WR", "TE")
)

ffs_optimize_lineups(
  roster_scores,
  lineup_constraints,
  lineup_efficiency_mean = 0.775,
  lineup_efficiency_sd = 0.05,
  best_ball = FALSE,
  pos_filter = c("QB", "RB", "WR", "TE")
)

Arguments

roster_scores

a dataframe as generated by ffs_score_rosters() - should contain columns like: projected_score, pos, and player_id

lineup_constraints

a dataframe as generated by ffscrapr::ff_starter_positions() - should contain columns pos, min, max, and offense_starters

lineup_efficiency_mean

the average lineup efficiency to use, defaults to 0.775

lineup_efficiency_sd

the standard deviation of lineup efficiency, defaults to 0.05

best_ball

a logical: FALSE will apply a lineup efficiency factor and TRUE uses optimal scores as actual scores, default = FALSE

pos_filter

a character vector specifying which positions are eligible - defaults to ⁠c("QB","RB","WR","TE)⁠

Details

Lineup efficiency is the percentage of optimal/best-ball score that is used as the actual score - by default, the lineup efficiency for a team in non-best-ball settings is normally distributed around a mean of 77.5% and a standard deviation of 5%.

Value

a dataframe of what each team scored for each week

See Also

vignette("custom") for example usage

Examples

# cached examples
roster_scores <- .ffs_cache("roster_scores.rds")
lineup_constraints <- .ffs_cache("mfl_lineup_constraints.rds")

ffs_optimise_lineups(roster_scores, lineup_constraints)

Repeat fantasy schedules

Description

This function repeats an actual ffs_schedule() by the appropriate number of seasons.

Usage

ffs_repeat_schedules(actual_schedule, n_seasons)

Arguments

actual_schedule

a schedule retrieved by ffs_schedule()

n_seasons

number of seasons to simulate, default = 100

Value

a dataframe of schedules for the simulation

See Also

vignette("Custom Simulations") for example usage

Examples

try({ # try block to prevent CRAN-related issues
conn <- .ffs_cache("mfl_conn.rds") # cached connection
actual_schedule <- ffs_schedule(conn)

ffs_repeat_schedules(actual_schedule = actual_schedule, n_seasons = 10)
})

Get Rosters

Description

This function lightly wraps ffscrapr::ff_rosters() and adds fantasypros_id, which is a required column for ffsimulator.

Usage

ffs_rosters(conn)

## S3 method for class 'mfl_conn'
ffs_rosters(conn)

## S3 method for class 'sleeper_conn'
ffs_rosters(conn)

## S3 method for class 'flea_conn'
ffs_rosters(conn)

## S3 method for class 'espn_conn'
ffs_rosters(conn)

Arguments

conn

a connection object as created by ffscrapr::ff_connect() and friends.

Value

a dataframe of rosters that includes a fantasypros_id column

See Also

vignette("custom") for more detailed example usage

Examples

# cached examples
conn <- .ffs_cache("mfl_conn.rds")

try({ # prevents CRAN connectivity issues, not actually required in normal usage
  ffs_rosters(conn)
})

Get Schedule

Description

This function lightly wraps ffscrapr::ff_schedule() and adds league_id, which is a required column for ffsimulator, casts IDs to character, and drops actual games played so as to only simulate unplayed games.

Usage

ffs_schedule(conn)

Arguments

conn

a connection object as created by ffscrapr::ff_connect() and friends.

Value

a dataframe of schedule that includes the league_id column

See Also

vignette("Custom Simulations") for more detailed example usage

Examples

# cached examples
try({ # try block to prevent CRAN-related issues
conn <- .ffs_cache("mfl_conn.rds")
ffs_schedule(conn)
})

Join Rosters to Projected Scores

Description

Attaches projected scores to rosters (via an inner-join) and creates a positional ranking column.

Usage

ffs_score_rosters(projected_scores, rosters)

Arguments

projected_scores

a dataframe of projected scores, as created by ffs_generate_projections()

rosters

a dataframe of rosters, as created by ffs_rosters()

Value

A dataframe of roster-level projected scores

See Also

vignette("custom") for example usage

Examples

# cached examples
projected_scores <- .ffs_cache("projected_scores.rds")
rosters <- .ffs_cache("mfl_rosters.rds")

ffs_score_rosters(projected_scores, rosters)

Get league starter positions

Description

This function lightly wraps ffscrapr::ff_starter_positions() and cleans up some abbreviations (PK -> K)

Usage

ffs_starter_positions(conn)

Arguments

conn

a connection object as created by ffscrapr::ff_connect() and friends.

Value

A tidy dataframe of positional lineup rules, one row per position with minimum and maximum starters as well as total starter calculations.

Examples

# cached examples
try({ # try block to prevent CRAN-related issues
conn <- .ffs_cache("mfl_conn.rds")
ffs_starter_positions(conn)
})

Summarise simulation outputs

Description

These functions are used to summarise the simulation outputs, typically by joining the optimal scores with a matching schedule.

Usage

ffs_summarise_week(optimal_scores, schedules)

ffs_summarise_season(summary_week)

ffs_summarise_simulation(summary_season)

ffs_summarise_inseason(summary_week, n)

ffs_summarize_week(optimal_scores, schedules)

ffs_summarize_season(summary_week)

ffs_summarize_simulation(summary_season)

Arguments

optimal_scores

a dataframe of optimized lineups as created by ffs_optimize_lineups()

schedules

a dataframe of schedules as created by ffs_build_schedules() or ffs_actual_schedules()

summary_week

a dataframe as created by ffs_summarise_week()

summary_season

a dataframe as created by ffs_summarise_season()

n

number of weeks

Value

ffs_summarise_week: a dataframe summarising team results by simulation week

ffs_summarise_season: a dataframe summarising franchise results across each simulation season

ffs_summarise_simulation: a dataframe summarising franchise results across the simulation

ffs_summarise_inseason: a dataframe summarising franchise results for the inseason simulation

See Also

vignette("custom") for example usage

Examples

# cached examples
optimal_scores <- .ffs_cache("optimal_scores.rds")
schedules <- .ffs_cache("schedules.rds")

summary_week <- ffs_summarise_week(optimal_scores, schedules)
summary_week
summary_season <- ffs_summarise_season(summary_week)
summary_season
summary_simulation <- ffs_summarise_simulation(summary_season)
summary_simulation

Connect to a league

Description

See ffscrapr::fleaflicker_connect() for details.

Value

a connection object to be used with ⁠ff_*⁠ functions

See Also

Other ffscrapr-imports: espn_connect(), ff_connect(), ff_scoringhistory(), ff_starter_positions(), mfl_connect(), sleeper_connect()


FP injury table

Description

This dataframe contains a column (prob_gp) for each positional ranking that describes the probability of a player with that preseason ADP playing in a given game. It is modelled from historical rankings data and the number of games played per season for a given positional rank.

Usage

fp_injury_table

Format

An object of class tbl_df (inherits from tbl, data.frame) with 692 rows and 3 columns.


Historical draft position ranks

Description

This dataframe has historical positional draft rankings for 2012-2020 QB/RB/WR/TE/PK and 2015-2020 DL/LB/DB, as gathered by the ffpros package.

Usage

fp_rankings_history

Format

An object of class tbl_df (inherits from tbl, data.frame) with 11336 rows and 10 columns.


Historical position ranks

Description

This dataframe has historical positional in-season rankings for 2012-2020 QB/RB/WR/TE/PK and 2015-2020 DL/LB/DB, as gathered by the ffpros package.

Usage

fp_rankings_history_week

Format

An object of class tbl_df (inherits from tbl, data.frame) with 94257 rows and 11 columns.


Connect to a league

Description

See ffscrapr::mfl_connect() for details.

Value

a connection object to be used with ⁠ff_*⁠ functions

See Also

Other ffscrapr-imports: espn_connect(), ff_connect(), ff_scoringhistory(), ff_starter_positions(), fleaflicker_connect(), sleeper_connect()


Connect to a league

Description

See ffscrapr::sleeper_connect() for details.

Value

a connection object to be used with ⁠ff_*⁠ functions

See Also

Other ffscrapr-imports: espn_connect(), ff_connect(), ff_scoringhistory(), ff_starter_positions(), fleaflicker_connect(), mfl_connect()