MLB team runs per game

gistfile1.r

# INDIVIDUAL TEAM HISTORY
#
# discussion and output can be found at
# http://bayesball.blogspot.ca/XXXXXXXXXXXXXXXXXXXXX
#
# Select the team you want from the franchID variable in the Teams.merge data frame
# and create a new data frame called "Team1"
# For this exercise we will use the Seattle Mariners, which are coded as SEA
# Note the use of double "=" to define the team!
Team1 <- as.data.frame(subset (Teams.merge, franchID == "SEA"))
#
# Create what will be the chart title from the contents of Team1
# Note that teams sprang into existence in different years, thus the requirement to define both the start and end dates
firstyear <- Team1$yearID[1]
lastyear <- tail(Team1$yearID, 1)
team.name <- Team1$name[1]
# alternate way to define "team.name"
team.name <- tail(Team1$name, 1)
# 
# Combine all of the component into a single variable, "title.text"
# "paste" works like "concatenate" in other languages
title.text <- paste(team.name, firstyear, "-", lastyear, 
   ": Runs scored relative to the league average")
#
#
# TREND LINE -- using LOESS modeling
# references: 
#  http://princeofslides.blogspot.ca/2011/05/sab-r-metrics-basics-of-loess.html 
#  http://research.stowers-institute.org/efg/R/Statistics/loess.htm
#
# Model Version 1, using default settings
# Creates a new object Runs.LO for loess model
RunIndex.LO <- loess(Team1$R_index ~ Team1$yearID)  			
RunIndex.LO.predict <- predict(RunIndex.LO)
#
# Model Version 2 (with more nuance/zig-zag to the trend line)
# create new object RunIndex.LO for loess model, span=0.25
RunIndex.LO.25 <- loess(Team1$R_index ~ Team1$yearID, span=0.25)				
RunIndex.LO.25.predict <- predict(RunIndex.LO.25)
#
RunIndex.LO.5 <- loess(Team1$R_index ~ Team1$yearID, span=0.5)				
RunIndex.LO.5.predict <- predict(RunIndex.LO.5)
#
# plot the data, add loess curve
ylim <- c(60,140)
plot(Team1$R_index ~ Team1$yearID, 
  ylim = ylim, 
  main = title.text, 
  xlab = "Year", ylab = "Index (league average = 100)")
# loess predicted value line
  lines(Team1$yearID, RunIndex.LO.predict, lty="solid", col="black", lwd=1.5)
  lines(Team1$yearID, RunIndex.LO.25.predict, lty="dashed", col="red", lwd=2)
  lines(Team1$yearID, RunIndex.LO.5.predict, lty="dotdash", col="blue", lwd=1.5)
# chart legend and grid lines
  legend(firstyear+2, 140, 
    c("default", "span=0.25", "span=0.50"), 
    lty=c("solid", "dashed", "dotdash"), 
    col=c("black", "red", "blue"), 
    lwd=c(1.5, 2, 1.5))
  grid()
# straight line at 100
  abline(h = 100, lty="dotdash") 
#
# And save the data fram as a csv file
write.csv(Team1, file="Team1.csv")
#