(latest version released 2021-06-25)
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.
You can install the released version of
baseballr from
GitHub with:
# You can install using the pacman package using the following code:
if (!requireNamespace('pacman', quietly = TRUE)){
install.packages('pacman')
}
pacman::p_load_current_gh("BillPetti/baseballr")# if you would prefer devtools installation
if (!requireNamespace('devtools', quietly = TRUE)){
install.packages('devtools')
}
# Alternatively, using the devtools package:
devtools::install_github(repo = "BillPetti/baseballr")For experimental functions in development, you can install the development branch:
# install.packages("devtools")
devtools::install_github("BillPetti/baseballr", ref = "development_branch")Pull request are welcome, but I cannot guarantee that they will be accepted or accepted quickly. Please make all pull requests to the development branch for review.
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)## Data courtesy of Baseball-Reference.com. Please consider supporting Baseball-Reference by signing up for a Stathead account: https://stathead.com
## # A tibble: 5 x 8
## Tm W L `W-L%` GB RS RA `pythW-L%`
## <chr> <int> <int> <dbl> <chr> <int> <int> <dbl>
## 1 WSN 54 48 0.529 -- 422 391 0.535
## 2 NYM 54 50 0.519 1.0 368 373 0.494
## 3 ATL 46 58 0.442 9.0 379 449 0.423
## 4 MIA 42 62 0.404 13.0 370 408 0.455
## 5 PHI 41 64 0.39 14.5 386 511 0.374
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)##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
data <- daily_batter_bref("2015-08-01", "2015-10-03") %>%
head()## Data courtesy of Baseball-Reference.com. Please consider supporting Baseball-Reference by signing up for a Statehead account: https://stathead.com
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)## Data courtesy of Baseball-Reference.com. Please consider supporting Baseball-Reference by signing up for a Stathead account: https://stathead.com
## # A tibble: 30 x 5
## Team Con_R Con_RA Con_R_Ptile Con_RA_Ptile
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 ARI 0.37 0.36 17 15
## 2 ATL 0.41 0.4 88 63
## 3 BAL 0.4 0.38 70 42
## 4 BOS 0.39 0.4 52 63
## 5 CHC 0.38 0.41 30 85
## 6 CHW 0.39 0.4 52 63
## 7 CIN 0.41 0.36 88 15
## 8 CLE 0.41 0.4 88 63
## 9 COL 0.35 0.34 7 3
## 10 DET 0.39 0.38 52 42
## # ... with 20 more rows
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()## Name Team season PA wOBA wOBA_CON
## 1 Shin-Soo Choo Texas 2015 260 0.430 0.495
## 2 Francisco Lindor Cleveland 2015 259 0.404 0.468
## 3 Jose Altuve Houston 2015 262 0.372 0.406
## 4 Adam Eaton Chicago 2015 262 0.367 0.436
## 5 Manny Machado Baltimore 2015 266 0.362 0.396
## 6 Matt Duffy San Francisco 2015 264 0.312 0.338
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)## Data courtesy of Baseball-Reference.com. Please consider supporting Baseball-Reference by signing up for a Statehead account: https://stathead.com
## season Name IP ERA SO uBB HBP HR FIP wOBA_against
## 1 2015 Johnny Cueto 37.0 1.95 38 4 2 3 2.62 0.210
## 2 2015 Dallas Keuchel 37.0 0.73 22 11 0 0 2.84 0.169
## 3 2015 Sonny Gray 36.1 1.98 25 6 1 1 2.69 0.218
## 4 2015 Mike Leake 35.2 3.03 25 7 0 5 4.16 0.240
## 5 2015 Felix Hernandez 34.2 1.82 36 6 3 1 2.20 0.225
## 6 2015 Corey Kluber 34.0 4.24 36 5 2 2 2.40 0.295
## 7 2015 Jake Odorizzi 33.2 2.41 26 8 1 0 2.38 0.213
## 8 2015 Josh Collmenter 32.2 2.76 16 3 0 1 2.82 0.290
## 9 2015 Bartolo Colon 32.2 3.31 25 1 0 4 3.29 0.280
## 10 2015 Zack Greinke 32.2 1.93 27 7 1 2 3.01 0.240
## wOBA_CON_against
## 1 0.276
## 2 0.151
## 3 0.239
## 4 0.281
## 5 0.272
## 6 0.391
## 7 0.228
## 8 0.330
## 9 0.357
## 10 0.274
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):
library(dplyr)
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.
