baseballr
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.mlb.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 the CRAN version of
baseballr with:
install.packages("baseballr")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")# Alternatively, using the devtools package:
if (!requireNamespace('devtools', quietly = TRUE)){
install.packages('devtools')
}
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")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 bref_standings_on_date()
function. Just pass the year, month, day, and division you want:
library(baseballr)
library(dplyr)
bref_standings_on_date("2015-08-01", "NL East", from = FALSE)## ── MLB Standings on Date data from baseball-reference.com ─── baseballr 1.5.0 ──
## ℹ Data updated: 2023-12-25 02:24:44 EST
## # A tibble: 5 × 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 0.374Right 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:
data <- bref_daily_batter("2015-08-01", "2015-10-03")
data %>%
dplyr::glimpse()## Rows: 764
## Columns: 30
## $ bbref_id <chr> "machama01", "duffyma01", "altuvjo01", "eatonad02", "choosh01…
## $ season <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2…
## $ Name <chr> "Manny Machado", "Matt Duffy", "José Altuve", "Adam Eaton", "…
## $ Age <dbl> 22, 24, 25, 26, 32, 21, 27, 28, 36, 28, 29, 29, 27, 29, 27, 2…
## $ Level <chr> "Maj-AL", "Maj-NL", "Maj-AL", "Maj-AL", "Maj-AL", "Maj-AL", "…
## $ Team <chr> "Baltimore", "San Francisco", "Houston", "Chicago", "Texas", …
## $ G <dbl> 59, 59, 57, 58, 58, 58, 59, 58, 59, 57, 55, 57, 57, 58, 56, 5…
## $ PA <dbl> 266, 264, 262, 262, 260, 259, 259, 258, 257, 257, 255, 255, 2…
## $ AB <dbl> 237, 248, 244, 230, 211, 224, 239, 235, 231, 233, 213, 218, 2…
## $ R <dbl> 36, 33, 30, 37, 48, 35, 32, 29, 37, 27, 50, 37, 36, 25, 38, 4…
## $ H <dbl> 66, 71, 81, 74, 71, 79, 54, 66, 75, 48, 65, 56, 61, 51, 78, 5…
## $ X1B <dbl> 43, 54, 53, 56, 47, 51, 34, 37, 48, 30, 34, 32, 35, 33, 66, 2…
## $ X2B <dbl> 10, 12, 19, 12, 14, 17, 6, 17, 16, 11, 13, 13, 15, 10, 7, 13,…
## $ X3B <dbl> 0, 2, 3, 1, 1, 4, 1, 0, 2, 1, 2, 4, 0, 1, 3, 0, 4, 0, 1, 1, 0…
## $ HR <dbl> 13, 3, 6, 5, 9, 7, 13, 12, 9, 6, 16, 7, 11, 7, 2, 20, 9, 8, 8…
## $ RBI <dbl> 32, 30, 18, 31, 34, 32, 27, 40, 53, 21, 50, 19, 31, 39, 23, 4…
## $ BB <dbl> 26, 15, 10, 23, 39, 18, 16, 17, 21, 21, 34, 33, 21, 39, 12, 3…
## $ IBB <dbl> 1, 0, 1, 1, 1, 0, 0, 6, 1, 1, 0, 1, 1, 5, 0, 4, 3, 3, 7, 2, 2…
## $ uBB <dbl> 25, 15, 9, 22, 38, 18, 16, 11, 20, 20, 34, 32, 20, 34, 12, 35…
## $ SO <dbl> 42, 35, 28, 55, 51, 38, 68, 56, 29, 53, 46, 62, 41, 48, 27, 7…
## $ HBP <dbl> 2, 0, 4, 5, 8, 1, 3, 5, 1, 1, 2, 3, 3, 1, 1, 6, 1, 3, 4, 1, 0…
## $ SH <dbl> 0, 0, 1, 2, 1, 11, 0, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, …
## $ SF <dbl> 1, 1, 3, 2, 1, 5, 1, 1, 4, 2, 5, 1, 2, 2, 3, 0, 3, 2, 3, 4, 3…
## $ GDP <dbl> 5, 9, 6, 1, 1, 4, 2, 2, 9, 7, 5, 1, 4, 8, 1, 2, 3, 10, 5, 4, …
## $ SB <dbl> 6, 8, 11, 9, 2, 10, 0, 0, 0, 3, 3, 4, 5, 4, 24, 2, 1, 0, 6, 0…
## $ CS <dbl> 4, 0, 4, 4, 0, 2, 0, 0, 0, 1, 0, 1, 3, 2, 7, 2, 3, 0, 2, 0, 0…
## $ BA <dbl> 0.279, 0.286, 0.332, 0.322, 0.337, 0.353, 0.226, 0.281, 0.325…
## $ OBP <dbl> 0.353, 0.326, 0.364, 0.392, 0.456, 0.395, 0.282, 0.341, 0.377…
## $ SLG <dbl> 0.485, 0.387, 0.508, 0.448, 0.540, 0.558, 0.423, 0.506, 0.528…
## $ OPS <dbl> 0.839, 0.713, 0.872, 0.840, 0.996, 0.953, 0.705, 0.848, 0.906…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)## # A tibble: 30 × 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
## # ℹ 20 more rowsYou 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 %>%
dplyr::filter(PA > 200) %>%
woba_plus %>%
dplyr::arrange(desc(wOBA)) %>%
dplyr::select(Name, Team, season, PA, wOBA, wOBA_CON) %>%
dplyr::glimpse()## Rows: 117
## Columns: 6
## $ Name <chr> "Edwin Encarnación", "Bryce Harper", "David Ortiz", "Joey Vot…
## $ Team <chr> "Toronto", "Washington", "Boston", "Cincinnati", "Baltimore",…
## $ season <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2…
## $ PA <dbl> 216, 248, 213, 251, 253, 260, 245, 255, 223, 241, 223, 259, 2…
## $ wOBA <dbl> 0.490, 0.450, 0.449, 0.445, 0.434, 0.430, 0.430, 0.422, 0.410…
## $ wOBA_CON <dbl> 0.555, 0.529, 0.541, 0.543, 0.617, 0.495, 0.481, 0.494, 0.459…You can also generate these wOBA-based stats, as well as FIP, for
pitchers using the fip_plus() function:
bref_daily_pitcher("2015-04-05", "2015-04-30") %>%
fip_plus() %>%
dplyr::select(season, Name, IP, ERA, SO, uBB, HBP, HR, FIP, wOBA_against, wOBA_CON_against) %>%
dplyr::arrange(dplyr::desc(IP)) %>%
head(10)## ── MLB Daily Pitcher data from baseball-reference.com ─────── baseballr 1.5.0 ──
## ℹ Data updated: 2023-12-25 02:27:52 EST
## # A tibble: 10 × 11
## season Name IP ERA SO uBB HBP HR FIP wOBA_against
## <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 Johnny Cueto 37 1.95 38 4 2 3 2.62 0.21
## 2 2015 Dallas Keuchel 37 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 0.24
## 5 2015 Félix Hernández 34.2 1.82 36 6 3 1 2.2 0.225
## 6 2015 Corey Kluber 34 4.24 36 5 2 2 2.4 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 0.29
## 9 2015 Bartolo Colón 32.2 3.31 25 1 0 4 3.29 0.28
## 10 2015 Zack Greinke 32.2 1.93 27 7 1 2 3.01 0.24
## # ℹ 1 more variable: wOBA_CON_against <dbl>Issues
Please leave any suggestions or bugs in the Issues section.
Pull Requests
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.
Breaking Changes
Follow the SportsDataverse (@SportsDataverse) on Twitter and star this repo
Our Authors
Bill Petti (@BillPetti)
Saiem Gilani (@saiemgilani)
Our Contributors (they’re awesome)
Ben Baumer (@BaumerBen)
Ben Dilday (@BenDilday)
Robert Frey (@RobertFrey40)
Camden Kay (@k_camden)
Citations
To cite the baseballr R
package in publications, use:
BibTex Citation
@misc{petti_gilani_2021,
author = {Bill Petti and Saiem Gilani},
title = {baseballr: The SportsDataverse's R Package for Baseball Data.},
url = {https://billpetti.github.io/baseballr/},
year = {2021}
}