In Fackz/baseball: Functions for acquiring and analyzing baseball data

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baseballr

baseballr 0.3.4

(latest version released 2018-05-29)

baseballr is a package written for R focused on baseball analysis. It includes functions for scraping various data from websites, such as FanGraphs.com, Baseball-Reference.com, and baseballsavant.com. It also includes functions for calculating metrics, such as wOBA, FIP, and team-level consistency over custom time frames.

You can read more about some of the functions and how to use them at its official site as well as this Hardball Times article.

Installation

You can install baseballr from github with:

# install.packages("devtools")
devtools::install_github("BillPetti/baseballr")

Functionality

The package consists of two main sets of functions: data acquisition and metric calculation.

For example, if you want to see the standings for a specific MLB division on a given date, you can use the standings_on_date_bref() function. Just pass the year, month, day, and division you want:

library(baseballr)
standings_on_date_bref("2015-08-01", "NL East", from = FALSE)

Right now the function works as far as back as 1994, which is when both leagues split into three divisions.

You can also pull data for all hitters over a specific date range. Here are the results for all hitters from August 1st through October 3rd during the 2015 season:

library(dplyr)
data <- daily_batter_bref("2015-08-01", "2015-10-03") %>%
  head()

In terms of metric calculation, the package allows the user to calculate the consistency of team scoring and run prevention for any year using team_consistency():

team_consistency(2015)

You can also calculate wOBA per plate appearance and wOBA on contact for any set of data over any date range, provided you have the data available.

Simply pass the proper data frame to woba_plus:

data %>%
  filter(PA > 200) %>%
  woba_plus %>%
  arrange(desc(wOBA)) %>%
  select(Name, Team, season, PA, wOBA, wOBA_CON) %>%
  head()

You can also generate these wOBA-based stats, as well as FIP, for pitchers using the fip_plus() function:

daily_pitcher_bref("2015-04-05", "2015-04-30") %>% 
  fip_plus() %>% 
  select(season, Name, IP, ERA, SO, uBB, HBP, HR, FIP, wOBA_against, wOBA_CON_against) %>%
  arrange(desc(IP)) %>% 
  head(10)

The edge_scrape() function allows the user to scrape PITCHf/x data from the GameDay application using Carson Sievert's pitchRx package and to calculate metrics associated with Edge%. The function returns a dataframe grouped by either pitchers or batters and the percentge of pitches in each of the various Edge zones.

Example (pitchers):

edge_scrape("2015-04-06", "2015-04-07", "pitcher") %>% 
  select(-6:-4, -13) %>% 
  head(10)

Example (batters):

edge_scrape("2015-04-06", "2015-04-07", "batter") %>% 
  select(-6:-4, -13) %>% 
  head(10)

More functionality will be added soon. Please leave any suggestions or bugs in the Issues section.

Fackz/baseball documentation built on June 25, 2019, 12:05 a.m.