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:
## ── 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.
See Also
Statcast Search CSV DocumentationAn Intro to Pybaseball’s StatcastHow to Build a Statcast Database from BaseballSavant, v2.0baseballr package - RDocumentationYou 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:
## 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.
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>