(latest version released 2018-04-09)
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.
It can be installed by using devtools:
require(devtools)
install_github("BillPetti/baseballr")
require(baseballr)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:
> standings_on_date_bref("2015-08-01", "NL East", from = FALSE)
$`NL East`
Tm W L W-L% GB RS RA pythW-L%
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 0.390 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:
> head(daily_batter_bref("2015-08-01", "2015-10-03"))
season Name Age Level Team G PA AB R H X1B X2B X3B HR RBI BB IBB uBB SO HBP SH SF GDP SB CS BA OBP
1 2015 Manny Machado 22 MLB-AL Baltimore 59 266 237 36 66 43 10 0 13 32 26 1 25 42 2 0 1 5 6 4 0.278 0.353
2 2015 Matt Duffy 24 MLB-NL San Francisco 59 264 248 33 71 54 12 2 3 30 15 0 15 35 0 0 1 9 8 0 0.286 0.326
3 2015 Jose Altuve 25 MLB-AL Houston 57 262 244 30 81 53 19 3 6 18 10 1 9 28 4 1 3 6 11 4 0.332 0.364
4 2015 Adam Eaton 26 MLB-AL Chicago 58 262 230 37 74 56 12 1 5 31 23 1 22 55 5 2 2 1 9 4 0.322 0.392
5 2015 Shin-Soo Choo 32 MLB-AL Texas 58 260 211 48 71 47 14 1 9 34 39 1 38 51 8 1 1 1 2 0 0.336 0.456
6 2015 Francisco Lindor 21 MLB-AL Cleveland 58 259 224 35 79 51 17 4 7 32 18 0 18 38 1 11 5 4 10 2 0.353 0.395
SLG OPS
1 0.485 0.839
2 0.387 0.713
3 0.508 0.872
4 0.448 0.840
5 0.540 0.996
6 0.558 0.953In 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)
Source: local data frame [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 22 15
2 ATL 0.41 0.40 87 67
3 BAL 0.40 0.38 70 42
4 BOS 0.39 0.40 52 67
5 CHC 0.38 0.41 33 88
6 CHW 0.39 0.40 52 67
7 CIN 0.41 0.36 87 15
8 CLE 0.41 0.40 87 67
9 COL 0.35 0.34 7 3
10 DET 0.39 0.38 52 42
.. ... ... ... ... ...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:
> x <- woba_plus(df)
> head(x)[,c(1,2,24,26,27)]
Name Team season wOBA wOBA_CON
1 Bryce Harper Nationals 2015 0.464 0.554
2 Joey Votto Reds 2015 0.428 0.485
3 Paul Goldschmidt Diamondbacks 2015 0.422 0.517
4 Mike Trout Angels 2015 0.418 0.519
5 Miguel Cabrera Tigers 2015 0.415 0.462
6 Josh Donaldson Blue Jays 2015 0.404 0.467You 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)
season Name IP ERA SO uBB HBP HR FIP wOBA_against wOBA_CON_against
1 2015 Johnny Cueto 37.0 1.95 38 4 2 3 2.62 0.210 0.276
2 2015 Dallas Keuchel 37.0 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 0.240 0.281
5 2015 Felix Hernandez 34.2 1.82 36 6 3 1 2.20 0.225 0.272
6 2015 Corey Kluber 34.0 4.24 36 5 2 2 2.40 0.295 0.391
7 2015 Jake Odorizzi 33.2 2.41 26 8 1 0 2.38 0.213 0.228
8 2015 Josh Collmenter 32.2 2.76 16 3 0 1 2.82 0.290 0.330
9 2015 Bartolo Colon 32.2 3.31 25 1 0 4 3.29 0.280 0.357
10 2015 Zack Greinke 32.2 1.93 27 7 1 2 3.01 0.240 0.274The 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") %>% .[, c(1:3,7:12)] %>% head(10)
pitcher_name pitcher All_pitches Upper_Edge Lower_Edge Inside_Edge Outside_Edge Heart Out_of_Zone
(chr) (dbl) (int) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl)
1 Bartolo Colon 112526 86 0.035 0.081 0.058 0.151 0.209 0.465
2 LaTroy Hawkins 115629 12 0.000 0.333 0.000 0.000 0.083 0.583
3 Joe Nathan 150274 4 0.000 0.000 0.000 0.000 0.000 1.000
4 Buddy Carlyle 234194 9 0.000 0.222 0.000 0.000 0.333 0.444
5 Jason Grilli 276351 14 0.000 0.000 0.214 0.000 0.286 0.500
6 Kevin Gregg 276514 17 0.000 0.000 0.118 0.176 0.235 0.471
7 Joaquin Benoit 276542 19 0.053 0.053 0.105 0.000 0.158 0.632
8 Ryan Vogelsong 285064 99 0.010 0.051 0.141 0.061 0.182 0.556
9 Jeremy Affeldt 346793 5 0.000 0.000 0.200 0.000 0.000 0.800
10 Grant Balfour 346797 21 0.095 0.000 0.000 0.048 0.333 0.524Example (batters):
> edge_scrape("2015-04-06", "2015-04-07", "batter") %>% .[, c(1:3,7:12)] %>% head(10)
batter_name batter All_pitches Upper_Edge Lower_Edge Inside_Edge Outside_Edge Heart Out_of_Zone
(chr) (dbl) (int) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl)
1 Bartolo Colon 112526 7 0.000 0.000 0.429 0.000 0.143 0.429
2 Torii Hunter 116338 19 0.000 0.105 0.105 0.105 0.000 0.684
3 David Ortiz 120074 18 0.056 0.000 0.111 0.056 0.222 0.556
4 Alex Rodriguez 121347 17 0.000 0.000 0.353 0.000 0.118 0.529
5 Aramis Ramirez 133380 23 0.000 0.087 0.261 0.000 0.261 0.391
6 Adrian Beltre 134181 26 0.000 0.038 0.154 0.115 0.231 0.462
7 Carlos Beltran 136860 22 0.136 0.045 0.136 0.000 0.136 0.545
8 Michael Cuddyer 150212 14 0.000 0.214 0.214 0.000 0.214 0.357
9 Jimmy Rollins 276519 41 0.024 0.122 0.049 0.049 0.220 0.537
10 Ryan Vogelsong 285064 10 0.000 0.200 0.300 0.000 0.200 0.300More functionality will be added soon. Please leave any suggestions or bugs in the Issues section.