Pitching {Lahman}

Pitching table

Description

Pitching table

Usage

data(Pitching)

Format

A data frame with 50402 observations on the following 30 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

W

Wins

L

Losses

G

Games

GS

Games Started

CG

Complete Games

SHO

Shutouts

SV

Saves

IPouts

Outs Pitched (innings pitched x 3)

H

Hits

ER

Earned Runs

HR

Homeruns

BB

Walks

SO

Strikeouts

BAOpp

Opponent's Batting Average

ERA

Earned Run Average

IBB

Intentional Walks

WP

Wild Pitches

HBP

Batters Hit By Pitch

BK

Balks

BFP

Batters faced by Pitcher

GF

Games Finished

R

Runs Allowed

SH

Sacrifices by opposing batters

SF

Sacrifice flies by opposing batters

GIDP

Grounded into double plays by opposing batter

Source

Lahman, S. (2023) Lahman's Baseball Database, 1871-2022, 2022 version, https://www.seanlahman.com/baseball-archive/statistics/

Examples

# Pitching data

require("dplyr")

###################################
# cleanup, and add some other stats
###################################

# Restrict to AL and NL data, 1901+
# All data re SH, SF and GIDP are missing, so remove
# Intentional walks (IBB) not recorded until 1955
pitching <- Pitching %>%
               filter(yearID >= 1901 & lgID %in% c("AL", "NL")) %>%
               select(-(28:30)) %>%  # remove SH, SF, GIDP
               mutate(BAOpp = round(H/(H + IPouts), 3),  # loose def'n
                      WHIP = round((H + BB) * 3/IPouts, 2),
                      KperBB = round(ifelse(yearID >= 1955, 
                                            SO/(BB - IBB), SO/BB), 2))
                                            

#####################
# some simple queries
#####################

# Team pitching statistics, Toronto Blue Jays, 1993
tor93 <- pitching %>%
           filter(yearID == 1993 & teamID == "TOR") %>%
           arrange(ERA)

# Career pitching statistics, Greg Maddux
subset(pitching, playerID == "maddugr01")

# Best ERAs for starting pitchers post WWII
pitching %>% 
    filter(yearID >= 1946 & IPouts >= 600) %>%
    group_by(lgID) %>%
    arrange(ERA) %>%
    do(head(., 5))


# Best K/BB ratios post-1955 among starters (excludes intentional walks)
pitching %>% 
    filter(yearID >= 1955 & IPouts >= 600) %>%
    mutate(KperBB = SO/(BB - IBB)) %>%
    arrange(desc(KperBB)) %>%
    head(., 10)
    
# Best K/BB ratios among relievers post-1950 (min. 20 saves)
pitching %>% 
    filter(yearID >= 1950 & SV >= 20) %>%
    arrange(desc(KperBB)) %>%
    head(., 10)

###############################################
# Winningest pitchers in each league each year:
###############################################

# Add name & throws information:
peopleInfo <- People %>%
                select(playerID, nameLast, nameFirst, throws)
                
# Merge peopleInfo into the pitching data
pitching1 <- right_join(peopleInfo, pitching, by = "playerID")

# Extract the pitcher with the maximum number of wins 
# each year, by league
winp <- pitching1 %>%
         group_by(yearID, lgID) %>%
         filter(W == max(W)) %>% 
         select(nameLast, nameFirst, teamID, W, throws)

# A simple ANCOVA model of wins vs. year, league and hand (L/R)
anova(lm(formula = W ~ yearID + I(yearID^2) + lgID + throws, data = winp))

# Nature of managing pitching staffs has altered importance of
# wins over time
## Not run: 
require("ggplot2") 

# compare loess smooth with quadratic fit
ggplot(winp, aes(x = yearID, y = W)) +
    geom_point(aes(colour = throws, shape=lgID), size = 2) +
    geom_smooth(method="loess", size=1.5, color="blue") +
    geom_smooth(method = "lm", se=FALSE, color="black", 
                 formula = y ~ poly(x,2)) +
    ylab("League maximum Wins") + xlab("Year") +
    ggtitle("Maximum pitcher wins by year")
    
## To reinforce this, plot the mean IPouts by year and league,
## which gives some idea of pitcher usage. Restrict pitcher
## pool to those who pitched at least 100 innings in a year.

pitching %>% filter(IPouts >= 300) %>%  # >= 100 IP

ggplot(., aes(x = yearID, y = IPouts, color = lgID)) +
  geom_smooth(method="loess") +
  labs(x = "Year", y = "IPouts")

## Another indicator: total number of complete games pitched
## (Mirrors the trend from the preceding plot.)
pitching %>% 
   group_by(yearID, lgID) %>%
   summarise(totalCG = sum(CG, na.rm = TRUE)) %>%
   ggplot(., aes(x = yearID, y = totalCG, color = lgID)) +
      geom_point() +
      geom_path() +
      labs(x = "Year", y = "Number of complete games")

## End(Not run)