Package 'puntr'

Title: Analysis of Punting
Description: A package for importing, manipulating, analyzing, and visualizing data related to football punting. Uses play-by-play dataframes in the format used by nflscrapR and nflfastR.
Authors: Dennis Brookner [aut, cre], Raphael LadenGuindon [aut]
Maintainer: Dennis Brookner <[email protected]>
License: MIT
Version: 1.4
Built: 2026-09-19 01:47:11 UTC
Source: https://github.com/Puntalytics/puntr

Help Index

Summarize data for player-games

Description

This function is essentially a convenient wrapper for dplyr::summarise which includes all of the relevant columns from a puntr-style data frame. This function differs from puntr::create_mini and puntr::create_miniY in that it groups by punter_player_name, season, and week (and adds a convenient weekid column to uniquely identify each row). It is unfortunately not customizable beyond the setting of a minimum number of punts to be included; if you have additional parameters you'd like to be summarised, the easiest thing would be to call summarise yourself.

Usage

by_punter_games(punts, ..., threshold = 1)

Arguments

punts

The play-by-play punting data to be summarized
threshold

The minimum number of punts for a week to be included, defaults to 1

Value

A tibble miniG where each row is a punter-week and each column is a stat

Examples

## Not run: 
create_miniG(punts)

## End(Not run)

Summarize data for player-seasons

Description

This function is essentially a convenient wrapper for dplyr::summarise which includes all of the relevant columns from a puntr-style data frame. This function differs from puntr::create_mini in that it groups by both punter_player_name and season (and adds a convenient seasonid column to uniquely identify each row). It is unfortunately not customizable beyond the setting of a minimum number of punts to be included; if you have additional parameters you'd like to be summarised, the easiest thing would be to call summarise yourself.

Usage

by_punter_seasons(punts, ..., threshold = 32)

Arguments

punts

The play-by-play punting data to be summarized
threshold

The minimum number of punts for a season to be included, defaults to 32

Value

A tibble miniY where each row is a punter-season and each column is a stat

Examples

## Not run: 
create_miniY(punts)

## End(Not run)

Summarize data for player careers

Description

This function is essentially a convenient wrapper for dplyr::summarise which provides a dataframe with the following columns: NumPunts, pEPA, Gross, Net, RERUN, SHARP_RERUN_OF, SHARP_RERUN_PD, first_year, last_year, team_logo_espn, team_color, team_color2. Additional columns may be added via valid calls to dplyr::summarise as additional arguments. For comparison of punter seasons, see puntr::by_punter_seasons; for comparison of punter games, see puntr::by_punter_games.

Usage

by_punters(punts, ..., threshold = 64)

Arguments

punts

The play-by-play punting data to be summarized
...

Any additional arguments will be passed through to dplyr::summarise
threshold

The minimum number of career punts needed to be included, defaults to 64

Value

A tibble punters where each row is a punter and each column is a stat

Examples

## Not run: 
create_mini(punts)

## End(Not run)

Calculate metrics

Description

This function adds all Puntalytics metrics to your punting data: RERUN, SHARP, SHARPnet and SHARP_RERUN (plus open-field and pin-deep versions of the 3 SHARPs) as well as average and cumulative EPA (our custom EPA metric.)

Usage

calculate_all(punts)

Arguments

punts

The already-processed play-by-play punting data to which metrics will be added. Must contain at least three seasons and 1000 punts.

Value

A tibble punts with the calculated metrics added

Examples

## Not run: 
calculate_all(punts)

## End(Not run)

Convert college data to puntr format

Description

Rename columns and process data such that the output can be plugged directly into puntr::calculate_all, and the output of that can be plugged directly into puntr::create_mini (or puntr::create_miniY).

Usage

college_to_pro(punts, power_five = TRUE)

Arguments

punts

A data frame containing punts in the cfbfastR format
power_five

Logical, defaults to TRUE to include only punters from Power 5 teams

Value

A tibble punts in a format usable for puntr::calculate_all

Examples

## Not run: 
college_to_pro(punts)

## End(Not run)

Summarize data for players

Description

This function is essentially a convenient wrapper for dplyr::summarise which includes all of the relevant columns from a puntr-style data frame. NOTE: puntr::create_mini, puntr::create_miniY, and puntr::create_miniG are being phased out in favor of puntr::by_punters, puntr::by_punter_seasons, and puntr::by_punter_games

Usage

create_mini(punts, ..., threshold = 64)

Arguments

punts

The play-by-play punting data to be summarized
...

Any additional arguments will be passed through to dplyr::summarise
threshold

The minimum number of career punts needed to be included, defaults to 64

Value

A tibble mini where each row is a punter and each column is a stat

Examples

## Not run: 
create_mini(punts)

## End(Not run)

Summarize data for player-games

Description

This function is essentially a convenient wrapper for dplyr::summarise which includes all of the relevant columns from a puntr-style data frame. This function differs from puntr::create_mini and puntr::create_miniY in that it groups by punter_player_name, season, and week (and adds a convenient weekid column to uniquely identify each row). NOTE: puntr::create_mini, puntr::create_miniY, and puntr::create_miniG are being phased out in favor of puntr::by_punters, puntr::by_punter_seasons, and puntr::by_punter_games

Usage

create_miniG(punts, ..., threshold = 1)

Arguments

punts

The play-by-play punting data to be summarized
...

Any additional arguments will be passed through to dplyr::summarise
threshold

The minimum number of punts for a week to be included, defaults to 1

Value

A tibble miniG where each row is a punter-week and each column is a stat

Examples

## Not run: 
create_miniG(punts)

## End(Not run)

Summarize data for player-seasons

Description

This function is essentially a convenient wrapper for dplyr::summarise which includes all of the relevant columns from a puntr-style data frame. This function differs from puntr::create_mini in that it groups by both punter_player_name and season (and adds a convenient seasonid column to uniquely identify each row). NOTE: puntr::create_mini, puntr::create_miniY, and puntr::create_miniG are being phased out in favor of puntr::by_punters, puntr::by_punter_seasons, and puntr::by_punter_games

Usage

create_miniY(punts, ..., threshold = 32)

Arguments

punts

The play-by-play punting data to be summarized
...

Any additional arguments will be passed through to dplyr::summarise
threshold

The minimum number of punts for a season to be included, defaults to 32

Value

A tibble miniY where each row is a punter-season and each column is a stat

Examples

## Not run: 
create_miniY(punts)

## End(Not run)

Import college punting data

Description

Import college punting data for seasons in the scope of cfbfastR (back to 2014). This function is a wrapper around cfbfastR::load_cfb_pbp.

Usage

import_college_punts(years)

Arguments

years

A year or range of years to be scraped

Value

A tibble punts of punts in the cfbfastR format

Examples

## Not run: 
import_college_punts(2018:2021)

## End(Not run)

Import punting data

Description

Easily import punting data for completed seasons. This data has been pre-scraped and compiled here. The only cleaning performed on this data before storing is filter(play_type == 'punt').

Usage

import_punts(years, local = FALSE, path = NULL)

Arguments

years

A year or range of years between 1999 and 2020, inclusive
local

Defaults to 'FALSE'. If 'TRUE', must also specify the local path to the 'puntr-data' folder. Importing a local copy of the data is much faster (~3 seconds, vs ~15 minutes)
path

The local path to the 'puntr-data' folder. Required when 'local = TRUE'. Omit the trailing '/', e.g. '/Users/yourname/footballfolder'

Value

A tibble punts containing play-by-play punting data for the specified years

Examples

## Not run: 
import_punts(1999:2020)
import_punts(1999:2020, local=TRUE, path='/my/local/path')

## End(Not run)

Import play-by-play data

Description

Grab all play-by-play data, not just punts. This function pulls data directly from the nflfastR-data repo, and is purely a wrapper for ease of use.

Usage

import_seasons(years)

Arguments

years

A year or range of years between 1999 and 2020, inclusive

Value

A tibble pbp containing play-by-play data for the specified years

Examples

## Not run: 
import_seasons(1999:2019)

## End(Not run)

Filter columns relevant to punting

Description

This function is exported in case you would like include custom columns; if you would NOT, you can just use [puntr::trust_the_process]. If you do use puntr::punt_trim yourself, be sure to include the trim=FALSE flag in puntr::trust_the_process.

Usage

punt_trim(punts, columns = NULL)

Arguments

punts

The play-by-play punting data
columns

Defaults to NULL indicating trimming down to the default columns. If columns is a list of other columns, those will be additionally included

Value

A tibble punts containing only the specified columns

Examples

## Not run: 
punt_trim(punts)
punt_trim(punts, columns=c("solo_tackle", "assist_tackle"))

## End(Not run)

Clean and process play-by-play punting data

Description

This function handles all of the standard data cleaning necessary/preferable for puntalytics.

Usage

trust_the_process(punts, seasontype = "REG", trim = TRUE)

Arguments

punts

The play-by-play punting data to be cleaned and processed
seasontype

One of "REG" (default) or "POST" to filter data, or NULL to include all data
trim

Specify trim=FALSE if you would like to use puntr::punt_trim to create custom columns (or not use puntr::punt_trim at all and include all columns)

Value

A tibble punts with cleaned and processed play-by-play punting data

Examples

## Not run: 
trust_the_process(punts)
trust_the_process(punts_custom_trimmed_already, trim=FALSE)

## End(Not run)