Fielding table

Usage

data(Fielding)

Format

A data frame with 151507 observations on the following 18 variables.

playerID: Player ID code
yearID: Year
stint: player's stint (order of appearances within a season)
teamID: Team; a factor
lgID: League; a factor with levels AA AL FL NL PL UA
POS: Position
G: Games
GS: Games Started
InnOuts: Time played in the field expressed as outs
PO: Putouts
A: Assists
E: Errors
DP: Double Plays
PB: Passed Balls (by catchers)
WP: Wild Pitches (by catchers)
SB: Opponent Stolen Bases (by catchers)
CS: Opponents Caught Stealing (by catchers)
ZR: Zone Rating

Source

Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/

Examples

data(Fielding)
# Basic fielding data

require("dplyr")


# Roberto Clemente's fielding profile
# pitching and catching related data removed
# subset(Fielding, playerID == "clemero01")[, 1:13]
Fielding %>% 
   filter(playerID == "clemero01") %>%
   select(1:13)
#>     playerID yearID stint teamID lgID POS   G  GS InnOuts  PO  A  E DP
#> 1  clemero01   1955     1    PIT   NL  OF 118 107    2892 253 18  6  5
#> 2  clemero01   1956     1    PIT   NL  2B   2   0      18   1  1  1  0
#> 3  clemero01   1956     1    PIT   NL  3B   1   0       6   0  2  1  0
#> 4  clemero01   1956     1    PIT   NL  OF 139 130    3499 274 17 13  2
#> 5  clemero01   1957     1    PIT   NL  OF 109 107    2836 272  9  6  1
#> 6  clemero01   1958     1    PIT   NL  OF 135 129    3452 312 22  6  3
#> 7  clemero01   1959     1    PIT   NL  OF 104 102    2700 229 10 13  1
#> 8  clemero01   1960     1    PIT   NL  OF 142 140    3767 246 19  8  2
#> 9  clemero01   1961     1    PIT   NL  OF 144 142    3723 256 27  9  5
#> 10 clemero01   1962     1    PIT   NL  OF 142 138    3686 269 19  8  1
#> 11 clemero01   1963     1    PIT   NL  OF 151 148    3943 239 11 11  2
#> 12 clemero01   1964     1    PIT   NL  OF 154 154    4085 289 13 10  2
#> 13 clemero01   1965     1    PIT   NL  OF 145 143    3810 288 16 10  1
#> 14 clemero01   1966     1    PIT   NL  OF 154 153    4147 318 17 12  3
#> 15 clemero01   1967     1    PIT   NL  OF 145 141    3751 273 17  9  4
#> 16 clemero01   1968     1    PIT   NL  OF 131 128    3477 297  9  5  1
#> 17 clemero01   1969     1    PIT   NL  OF 135 128    3372 226 14  5  1
#> 18 clemero01   1970     1    PIT   NL  OF 104 102    2703 189 12  7  2
#> 19 clemero01   1971     1    PIT   NL  OF 124 120    3243 267 11  2  4
#> 20 clemero01   1972     1    PIT   NL  OF  94  88    2436 199  5  0  2
   
# Yadier Molina's fielding profile
# PB, WP, SP and CS apply to catchers
Fielding %>% 
  subset(playerID == "molinya01") %>%
  select(-WP, -ZR)
#>        playerID yearID stint teamID lgID POS   G  GS InnOuts   PO  A  E DP PB
#> 93856 molinya01   2004     1    SLN   NL   C  51  39    1032  256 16  2  1  4
#> 93857 molinya01   2005     1    SLN   NL  1B   1   0       3    0  0  0  0 NA
#> 93858 molinya01   2005     1    SLN   NL   C 114 111    2878  684 66  7  4  8
#> 93859 molinya01   2006     1    SLN   NL  1B   4   0      15    5  0  1  1 NA
#> 93860 molinya01   2006     1    SLN   NL   C 127 118    3112  734 79  4  6  7
#> 93861 molinya01   2007     1    SLN   NL  1B   1   0       3    1  0  0  0 NA
#> 93862 molinya01   2007     1    SLN   NL   C 107 101    2584  582 63  6  8  7
#> 93863 molinya01   2008     1    SLN   NL  1B   2   1      33   13  3  0  1 NA
#> 93864 molinya01   2008     1    SLN   NL   C 119 114    3006  653 70 10  7  5
#> 93865 molinya01   2009     1    SLN   NL  1B   6   0      30   10  1  0  2 NA
#> 93866 molinya01   2009     1    SLN   NL   C 138 136    3530  884 82  5  6  4
#> 93867 molinya01   2010     1    SLN   NL  1B   7   0      21    7  0  0  2 NA
#> 93868 molinya01   2010     1    SLN   NL   C 135 130    3414  895 79  5 10  7
#> 93869 molinya01   2011     1    SLN   NL  1B   2   0       6    3  0  1  0 NA
#> 93870 molinya01   2011     1    SLN   NL   C 137 132    3450  857 67  5  5  6
#> 93871 molinya01   2012     1    SLN   NL  1B   3   0      27   11  2  0  0 NA
#> 93872 molinya01   2012     1    SLN   NL   C 136 133    3484  962 88  3 12  6
#> 93873 molinya01   2013     1    SLN   NL  1B   5   2      54   17  1  0  1 NA
#> 93874 molinya01   2013     1    SLN   NL   C 131 129    3346  976 63  4 11  3
#> 93875 molinya01   2014     1    SLN   NL  1B   1   1      27   10  1  0  0 NA
#> 93876 molinya01   2014     1    SLN   NL   C 107 106    2795  810 56  2 10  3
#> 93877 molinya01   2015     1    SLN   NL   C 134 131    3449 1064 56  7  9  4
#> 93878 molinya01   2016     1    SLN   NL  1B   2   1      21   10  1  0  0 NA
#> 93879 molinya01   2016     1    SLN   NL   C 146 142    3655 1113 60  2  5  8
#> 93880 molinya01   2017     1    SLN   NL  1B   1   0       6    4  0  0  0 NA
#> 93881 molinya01   2017     1    SLN   NL   C 133 133    3377 1082 55  7 13  6
#> 93882 molinya01   2018     1    SLN   NL  1B   5   0      36    9  2  1  3 NA
#> 93883 molinya01   2018     1    SLN   NL   C 121 120    3053  966 42  2  8  4
#> 93884 molinya01   2019     1    SLN   NL  1B   4   0      24    6  0  0  1 NA
#> 93885 molinya01   2019     1    SLN   NL  3B   1   0       3    0  0  0  0 NA
#> 93886 molinya01   2019     1    SLN   NL   C 111 108    2818  916 30  1  5  4
#> 93887 molinya01   2020     1    SLN   NL  1B   2   0      15    3  0  0  0 NA
#> 93888 molinya01   2020     1    SLN   NL   C  42  42    1012  307 16  5  1  3
#> 93889 molinya01   2021     1    SLN   NL  1B   1   0       9    1  1  0  0 NA
#> 93890 molinya01   2021     1    SLN   NL   C 118 118    3004  869 49  3 10  6
#> 93891 molinya01   2022     1    SLN   NL  1B   1   0       3    0  0  0  0 NA
#> 93892 molinya01   2022     1    SLN   NL   C  77  71    1885  512 25  3  6  1
#> 93893 molinya01   2022     1    SLN   NL   P   2   0       6    0  0  0  0 NA
#>       SB CS
#> 93856  9  8
#> 93857 NA NA
#> 93858 14 25
#> 93859 NA NA
#> 93860 37 29
#> 93861 NA NA
#> 93862 23 27
#> 93863 NA NA
#> 93864 34 18
#> 93865 NA NA
#> 93866 32 22
#> 93867 NA NA
#> 93868 35 33
#> 93869 NA NA
#> 93870 46 19
#> 93871 NA NA
#> 93872 38 35
#> 93873 NA NA
#> 93874 26 20
#> 93875 NA NA
#> 93876 23 21
#> 93877 37 26
#> 93878 NA NA
#> 93879 67 18
#> 93880 NA NA
#> 93881 43 24
#> 93882 NA NA
#> 93883 27 12
#> 93884 NA NA
#> 93885 NA NA
#> 93886 22  8
#> 93887 NA NA
#> 93888  6  5
#> 93889 NA NA
#> 93890 26 18
#> 93891 NA NA
#> 93892 20 13
#> 93893 NA NA

# Pedro Martinez's fielding profile
Fielding %>% subset(playerID == "martipe02")
#>        playerID yearID stint teamID lgID POS  G GS InnOuts PO  A E DP PB WP SB
#> 85473 martipe02   1992     1    LAN   NL   P  2  1      24  0  0 0  0 NA NA NA
#> 85474 martipe02   1993     1    LAN   NL   P 65  2     321  4  4 0  1 NA NA NA
#> 85475 martipe02   1994     1    MON   NL   P 24 23     434  9 15 4  0 NA NA NA
#> 85476 martipe02   1995     1    MON   NL   P 30 30     584 14 23 2  0 NA NA NA
#> 85477 martipe02   1996     1    MON   NL   P 33 33     650 11 17 3  1 NA NA NA
#> 85478 martipe02   1997     1    MON   NL   P 31 31     724  7 31 1  1 NA NA NA
#> 85479 martipe02   1998     1    BOS   AL   P 33 33     701 11 18 4  3 NA NA NA
#> 85480 martipe02   1999     1    BOS   AL   P 31 29     640 13 15 1  0 NA NA NA
#> 85481 martipe02   2000     1    BOS   AL   P 29 29     651 14 28 0  2 NA NA NA
#> 85482 martipe02   2001     1    BOS   AL   P 18 18     350  5  5 0  0 NA NA NA
#> 85483 martipe02   2002     1    BOS   AL   P 30 30     598 16 16 2  1 NA NA NA
#> 85484 martipe02   2003     1    BOS   AL   P 29 29     560 14 20 0  0 NA NA NA
#> 85485 martipe02   2004     1    BOS   AL   P 33 33     651 16 16 1  0 NA NA NA
#> 85486 martipe02   2005     1    NYN   NL   P 31 31     651  6 19 0  0 NA NA NA
#> 85487 martipe02   2006     1    NYN   NL   P 23 23     398  7 13 5  0 NA NA NA
#> 85488 martipe02   2007     1    NYN   NL   P  5  5      84  1  0 0  0 NA NA NA
#> 85489 martipe02   2008     1    NYN   NL   P 20 20     327  6 17 0  1 NA NA NA
#> 85490 martipe02   2009     1    PHI   NL   P  9  9     134  1  6 0  1 NA NA NA
#>       CS ZR
#> 85473 NA NA
#> 85474 NA NA
#> 85475 NA NA
#> 85476 NA NA
#> 85477 NA NA
#> 85478 NA NA
#> 85479 NA NA
#> 85480 NA NA
#> 85481 NA NA
#> 85482 NA NA
#> 85483 NA NA
#> 85484 NA NA
#> 85485 NA NA
#> 85486 NA NA
#> 85487 NA NA
#> 85488 NA NA
#> 85489 NA NA
#> 85490 NA NA

# Table of games played by Pete Rose at different positions
with(subset(Fielding, playerID == "rosepe01"), xtabs(G ~ POS))
#> POS
#>   1B   2B   3B   OF 
#>  939  628  634 1327 

# Career total G/PO/A/E/DP for Luis Aparicio
Fielding %>%
    filter(playerID == "aparilu01") %>% 
    select(G, PO, A, E, DP) %>%
    summarise_each(funs(sum))
#> Warning: `summarise_each()` was deprecated in dplyr 0.7.0.
#> ℹ Please use `across()` instead.
#> Warning: `funs()` was deprecated in dplyr 0.8.0.
#> ℹ Please use a list of either functions or lambdas:
#> 
#> # Simple named list: list(mean = mean, median = median)
#> 
#> # Auto named with `tibble::lst()`: tibble::lst(mean, median)
#> 
#> # Using lambdas list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
#>      G   PO    A   E   DP
#> 1 2583 4548 8016 366 1553


# Top ten 2B/SS in turning DPs
Fielding %>%
    subset(POS %in% c("2B", "SS")) %>%
    group_by(playerID) %>%
    summarise(TDP = sum(DP, na.rm = TRUE)) %>%
    arrange(desc(TDP)) %>%
    head(., 10)
#> # A tibble: 10 × 2
#>    playerID    TDP
#>    <chr>     <int>
#>  1 vizquom01  1775
#>  2 mazerbi01  1706
#>  3 foxne01    1619
#>  4 smithoz01  1590
#>  5 ripkeca01  1565
#>  6 aparilu01  1553
#>  7 randowi01  1547
#>  8 whitalo01  1527
#>  9 doerrbo01  1507
#> 10 morgajo02  1505

# League average fielding statistics, 1961-present
Fielding %>% 
   filter(yearID >= 1961 & POS != "DH") %>%
   select(yearID, lgID, POS, InnOuts, PO, A, E) %>%
   group_by(yearID, lgID) %>%
   summarise_at(vars(InnOuts, PO, A, E), funs(sum), na.rm = TRUE) %>%
   mutate(fpct = round( (PO + A)/(PO + A + E), 3), 
          OPE = round(InnOuts/E, 3))
#> Warning: `funs()` was deprecated in dplyr 0.8.0.
#> ℹ Please use a list of either functions or lambdas:
#> 
#> # Simple named list: list(mean = mean, median = median)
#> 
#> # Auto named with `tibble::lst()`: tibble::lst(mean, median)
#> 
#> # Using lambdas list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
#> # A tibble: 126 × 8
#> # Groups:   yearID [63]
#>    yearID lgID  InnOuts    PO     A     E  fpct   OPE
#>     <int> <fct>   <int> <int> <int> <int> <dbl> <dbl>
#>  1   1961 AL     389523 43213 17237  1506 0.976  259.
#>  2   1961 NL     297720 33080 13728  1167 0.976  255.
#>  3   1962 AL     391878 43449 17138  1364 0.978  287.
#>  4   1962 NL     391206 43470 17832  1555 0.975  252.
#>  5   1963 AL     392877 43589 16998  1347 0.978  292.
#>  6   1963 NL     392946 43661 17681  1577 0.975  249.
#>  7   1964 AL     394593 43774 17130  1261 0.98   313.
#>  8   1964 NL     392094 43566 17933  1586 0.975  247.
#>  9   1965 AL     392562 43546 17231  1359 0.978  289.
#> 10   1965 NL     395384 43932 18008  1486 0.977  266.
#> # ℹ 116 more rows