baseballr
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.
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")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 1.3.0 ──
## ℹ Data updated: 2022-09-08 20:13:31 EDT
## # 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> "547989", "554429", "542436", "571431", "501303", "346793", "…
## $ season <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2…
## $ Name <chr> "Manny Machado", "Matt Duffy", "Jose 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
## # … with 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 Encarnacion", "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 1.3.0 ──
## ℹ Data updated: 2022-09-08 20:13:44 EDT
## # A tibble: 10 × 11
## season Name IP ERA SO uBB HBP HR FIP wOBA_…¹ wOBA_…²
## <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 Johnny Cueto 37 1.95 38 4 2 3 2.62 0.21 0.276
## 2 2015 Dallas Keuc… 37 0.73 22 11 0 0 2.84 0.169 0.151
## 3 2015 Sonny Gray 36.1 1.98 25 6 1 1 2.69 0.218 0.239
## 4 2015 Mike Leake 35.2 3.03 25 7 0 5 4.16 0.24 0.281
## 5 2015 Felix Herna… 34.2 1.82 36 6 3 1 2.2 0.225 0.272
## 6 2015 Corey Kluber 34 4.24 36 5 2 2 2.4 0.295 0.391
## 7 2015 Jake Odoriz… 33.2 2.41 26 8 1 0 2.38 0.213 0.228
## 8 2015 Josh Collme… 32.2 2.76 16 3 0 1 2.82 0.29 0.33
## 9 2015 Bartolo Col… 32.2 3.31 25 1 0 4 3.29 0.28 0.357
## 10 2015 Zack Greinke 32.2 1.93 27 7 1 2 3.01 0.24 0.274
## # … with abbreviated variable names ¹wOBA_against, ²wOBA_CON_againstPlease leave any suggestions or bugs in the Issues section.
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.
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}
}