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Chap8.trajectories.R
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Chap8.trajectories.R
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####################################################
# Chapter 8 - Career Trajectories
#
# uses .csv files from the Lahman's database
# (placed in the "lahman" subfolder)
# uses R packages car, plyr, ggplot2
####################################################
# Section 8.2 Mickey Mantle's Career Trajectory
Batting <- read.csv("lahman/Batting.csv")
Master <- read.csv("lahman/Master.csv")
mantle.info <- subset(Master, nameFirst=="Mickey" & nameLast=="Mantle")
mantle.id <- as.character(mantle.info$playerID)
library(car)
Batting$SF <- recode(Batting$SF, "NA = 0")
Batting$HBP <- recode(Batting$HBP, "NA = 0")
get.birthyear <- function(player.id){
playerline <- subset(Master, playerID == player.id)
birthyear <- playerline$birthYear
birthmonth <- playerline$birthMonth
ifelse(birthmonth >= 7, birthyear, birthyear + 1)
}
get.birthyear(mantle.id)
get.stats <- function(player.id){
d <- subset(Batting, playerID==player.id)
byear <- get.birthyear(player.id)
d$Age <- d$yearID - byear
d$SLG <- with(d, (H - X2B - X3B - HR +
2 * X2B + 3 * X3B + 4 * HR) / AB)
d$OBP <- with(d, (H + BB) / (H + AB + BB + SF))
d$OPS <- with(d, SLG + OBP)
d
}
Mantle <- get.stats(mantle.id)
with(Mantle, plot(Age, OPS, cex=1.5, pch=19))
fit.model <- function(d){
fit <- lm(OPS ~ I(Age - 30) + I((Age - 30)^2), data = d)
b <- coef(fit)
Age.max <- 30 - b[2] / b[3] / 2
Max <- b[1] - b[2] ^ 2 / b[3] / 4
list(fit = fit,
Age.max = Age.max, Max = Max)
}
F2 <- fit.model(Mantle)
coef(F2$fit)
c(F2$Age.max, F2$Max)
lines(Mantle$Age, predict(F2$fit, Age=Mantle$Age), lwd=3)
abline(v = F2$Age.max, lwd=3, lty=2, col="grey")
abline(h = F2$Max, lwd=3, lty=2, col="grey")
text(29, .72, "Peak.age" , cex=2)
text(20, 1, "Max", cex=2)
summary(F2$fit)
# Section 8.3 - Comparing Trajectories
Fielding <- read.csv("lahman/Fielding.csv")
library(plyr)
AB.totals <- ddply(Batting, .(playerID),
summarize,
Career.AB = sum(AB, na.rm = TRUE))
Batting <- merge(Batting, AB.totals)
Batting.2000 <- subset(Batting, Career.AB >=2000)
find.position <- function(p){
positions <- c("OF", "1B", "2B", "SS", "3B", "C", "P", "DH")
d <- subset(Fielding, playerID == p)
count.games <- function(po)
sum(subset(d, POS == po)$G)
FLD <- sapply(positions, count.games)
positions[FLD == max(FLD)][1]
}
PLAYER <- as.character(unique(Batting.2000$playerID))
POSITIONS <- sapply(PLAYER, find.position) #takes long time to complete!
Fielding.2000 <- data.frame(playerID = names(POSITIONS),
POS = POSITIONS)
Batting.2000 <- merge(Batting.2000, Fielding.2000)
# comparing career statistics
library(plyr)
C.totals <- ddply(Batting, .(playerID),
summarize,
C.G = sum(G, na.rm = TRUE),
C.AB = sum(AB, na.rm = TRUE),
C.R = sum(R, na.rm = TRUE),
C.H = sum(H, na.rm = TRUE),
C.2B = sum(X2B, na.rm = TRUE),
C.3B = sum(X3B, na.rm = TRUE),
C.HR = sum(HR, na.rm = TRUE),
C.RBI = sum(RBI, na.rm = TRUE),
C.BB = sum(BB, na.rm = TRUE),
C.SO = sum(SO, na.rm = TRUE),
C.SB = sum(SB, na.rm = TRUE))
C.totals$C.AVG <- with(C.totals, C.H / C.AB)
C.totals$C.SLG <- with(C.totals,
(C.H - C.2B - C.3B - C.HR + 2 * C.2B +
3 * C.3B + 4 * C.HR) / C.AB)
C.totals <- merge(C.totals, Fielding.2000)
C.totals$Value.POS <- with(C.totals,
ifelse(POS=="C", 240,
ifelse(POS=="SS", 168,
ifelse(POS=="2B", 132,
ifelse(POS=="3B", 84,
ifelse(POS=="OF", 48,
ifelse(POS=="1B", 12, 0)))))))
# computing similarity scores
similar <- function(p, number=10){
P <- subset(C.totals, playerID == p)
C.totals$SS <- with(C.totals,
1000 -
floor(abs(C.G - P$C.G) / 20) -
floor(abs(C.AB - P$C.AB) / 75) -
floor(abs(C.R - P$C.R) / 10) -
floor(abs(C.H - P$C.H) / 15) -
floor(abs(C.2B - P$C.2B) / 5) -
floor(abs(C.3B - P$C.3B) / 4) -
floor(abs(C.HR - P$C.HR) / 2) -
floor(abs(C.RBI - P$C.RBI) / 10) -
floor(abs(C.BB - P$C.BB) / 25) -
floor(abs(C.SO - P$C.SO) / 150) -
floor(abs(C.SB - P$C.SB) / 20) -
floor(abs(C.AVG - P$C.AVG) / 0.001) -
floor(abs(C.SLG - P$C.SLG) / 0.002) -
abs(Value.POS - P$Value.POS))
C.totals <- C.totals[order(C.totals$SS, decreasing = TRUE), ]
C.totals[1:number, ]
}
similar(mantle.id, 6)
# defining age, OBP, SLG, and OPS variables
collapse.stint <- function(d){
G <- sum(d$G); AB <- sum(d$AB); R <- sum(d$R)
H <- sum(d$H); X2B <- sum(d$X2B); X3B <- sum(d$X3B)
HR <- sum(d$HR); RBI <- sum(d$RBI); SB <- sum(d$SB)
CS <- sum(d$CS); BB <- sum(d$BB); SH <- sum(d$SH)
SF <- sum(d$SF); HBP <- sum(d$HBP)
SLG <- (H - X2B - X3B - HR + 2 * X2B +
3 * X3B + 4 * HR) / AB
OBP <- (H + BB + HBP) / (AB + BB + HBP + SF)
OPS <- SLG + OBP
data.frame(G = G, AB = AB, R = R, H = H, X2B = X2B,
X3B = X3B, HR = HR, RBI = RBI, SB = SB,
CS = CS, BB = BB, HBP = HBP, SH = SH, SF = SF,
SLG = SLG, OBP = OBP, OPS = OPS,
Career.AB = d$Career.AB[1], POS = d$POS[1])
}
Batting.2000 <- ddply(Batting.2000,
.(playerID, yearID), collapse.stint) #requires a long time to complete
player.list <- as.character(unique(Batting.2000$playerID))
birthyears <- sapply(player.list, get.birthyear) #requires a long time to complete
Batting.2000 <- merge(Batting.2000,
data.frame(playerID=player.list,
Birthyear=birthyears))
Batting.2000$Age <- with(Batting.2000, yearID - Birthyear)
Batting.2000 <- Batting.2000[complete.cases(Batting.2000$Age), ]
# fitting and plotting trajectories
fit.traj <- function(d){
fit <- lm(OPS ~ I(Age - 30) + I((Age - 30)^2), data = d)
data.frame(Age = d$Age, Fit = predict(fit, Age = d$Age))
}
plot.traj2 <- function(first, last, n.similar = 5, ncol){
require(plyr)
require(ggplot2)
get.name <- function(playerid){
d1 <- subset(Master, playerID == playerid)
with(d1, paste(nameFirst, nameLast))
}
player.id <- subset(Master,
nameFirst == first & nameLast == last)$playerID
player.id <- as.character(player.id)
player.list <- as.character(similar(player.id, n.similar)$playerID)
Batting.new <- subset(Batting.2000, playerID %in% player.list)
F2 <- ddply(Batting.new, .(playerID), fit.traj)
F2 <- merge(F2,
data.frame(playerID = player.list,
Name = sapply(as.character(player.list), get.name)))
print(ggplot(F2, aes(Age, Fit)) + geom_line(size=1.5) +
facet_wrap(~ Name, ncol=ncol) + theme_bw())
return(Batting.new)
}
d <- plot.traj2("Mickey", "Mantle", 6, 2)
d <- plot.traj2("Derek", "Jeter", 9, 3)
fit.traj2 <- function(d){
f <- lm(OPS ~ I(Age - 30) + I((Age - 30)^2), data = d)
b <- coef(f)
Age.max <- round(30 - b[2] / b[3] / 2, 1)
Max <- round(b[1] - b[2] ^ 2 / b[3] / 4, 3)
data.frame(Age.max = Age.max, Max = Max,
Curve = round(b[3], 5))
}
d <- plot.traj2("Derek", "Jeter", 9, 3)
S <- ddply(d, .(playerID), fit.traj2)
S
with(S, plot(Age.max, Curve, pch=19, cex=1.5,
xlab="Peak Age", ylab="Curvature",
xlim=c(27, 36), ylim=c(-0.0035, 0)))
S$lastNames <- as.character(subset(Master,
playerID %in% S$playerID)$nameLast)
with(S, text(Age.max, Curve, lastNames, pos=3))
# Section 8.4 General Patterns of Peak Age
library(plyr)
midcareers <- ddply(Batting.2000, .(playerID),
summarize,
Midyear = (min(yearID) + max(yearID))/2)
Batting.2000 <- merge(Batting.2000, midcareers)
fit.traj3 <- function(d){
b <- coef(lm(OPS ~ I(Age - 30) + I((Age - 30)^2), data = d))
data.frame(A = b[1], B = b[2], C = b[3],
Midyear = d$Midyear[1], Career.AB = d$Career.AB[1])
}
Beta.coef <- ddply(Batting.2000, .(playerID), fit.traj3)
Beta.coef$Peak.age <- with(Beta.coef, 30 - B / 2 / C)
with(Beta.coef,
plot(Midyear, Peak.age, ylim=c(20, 40),
xlab = "Mid Career", ylab = "Peak Age"))
i <- is.finite(Beta.coef$Peak.age)
with(Beta.coef,
lines(lowess(Midyear[i], Peak.age[i]), lwd=3))
with(Beta.coef,
plot(log2(Career.AB[i]), Peak.age[i], ylim=c(20, 40),
xlab = "log2 Career AB", ylab = "Peak Age"))
with(Beta.coef,
lines(lowess(log2(Career.AB[i]), Peak.age[i]), lwd=3))
# Section 8.5 Trajectories and Fielding Position
Batting.2000a <- subset(Batting.2000, Midyear >= 1985 & Midyear <= 1995)
fit.traj4 <- function(d){
b <- coef(lm(OPS ~ I(Age - 30) + I((Age - 30)^2), data = d))
data.frame(A = b[1], B = b[2], C = b[3],
Peak.Age = 30 - b[2] / 2 / b[3],
Midyear = d$Midyear[1], Career.AB = d$Career.AB[1],
Position = d$POS[1])
}
Beta.estimates <- ddply(Batting.2000a, .(playerID), fit.traj4)
Beta.estimates1 <- subset(Beta.estimates, Position %in%
c("1B", "2B", "3B", "SS", "C", "OF"))
Beta.estimates1$Position <- Beta.estimates1$Position[ , drop=TRUE]
Beta.estimates1 <- merge(Beta.estimates1, Master)
stripchart(Peak.Age ~ Position, data=Beta.estimates1,
xlim=c(20, 40), method="jitter", pch=1)
special <- with(Beta.estimates1, identify(Peak.Age, Position,
n=5, labels=nameLast))
dnew <- subset(Batting.2000,
playerID %in% Beta.estimates1$playerID[special])
dnew <- merge(dnew, Master)
ggplot(dnew, aes(Age, OPS)) + geom_point(size=4) +
facet_wrap(~ nameLast, ncol=2) + ylim(0.4, 1.05) +
stat_smooth(method="lm", se=FALSE, size=1.5,
formula=y ~ poly(x, 2, raw=TRUE)) + theme_bw()
################################################################