Ichiro: not just hits

As we count down to Ichiro's 4,000th hit (combined Nippon Professional Baseball and Major League Baseball), it's worth remembering his outstanding abilities in the field.  The best representation for my dollar is this award-winning photo captured by Scott Eklund:

Ichiro makes a leaping catch (photo: Scott Eklund; click to link)

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Annotating select points on an X-Y plot using ggplot2

or, Is the Seattle Mariners outfield a disaster?

The Backstory

Earlier this week (2013-06-10), a blog post by Dave Cameron appeared at USS Mariner under the title “Maybe It's Time For Dustin Ackley To Play Some Outfield”. In the first paragraph, Cameron describes to the Seattle Mariners outfield this season as “a complete disaster” and Raul Ibanez as “nothing short of a total disaster”.

To back up the Ibanez assertion, the article included a link to a Fangraphs table showing the defensive metrics for all MLB outfielders with a minimum of 200 innings played to date, sorted in ascending order of UZR.150 (UZR is generally recognized as the best defensive metric). And there, at the top (or bottom) of the list, Raul Ibanez.

But surely, I thought, Ibanez's offensive production – starting with the 11 home runs he had hit at the time, now up to 13 – off-sets to some degree the lack of defense. So I took a look at a variety of offensive measures, to see how Ibanez stacks up. It quickly struck me that wRAA (Weighted Runs Above Average), the offensive component of WAR (Wins Above Replacement, the best comprehensive measure of a player's overall contribution, which also includes a base running not examined here), would make an interesting scatterplot against UZR. And a great opportunity to use ggplot2.

Manipulating the data

Using this table from Fangraphs (advanced batting stats of all MLB players so far this season), I created a new table “outfield” that appended the advanced hitting stats to the defensive stats in the original table, and then set about creating the plot using the ggplot2 package in R.

Note: once I had downloaded the two Fangraphs tables as csv files (with results through 2013-06-15), I edited the file names slightly.

# load the ggplot2 and grid packages
library(ggplot2)
library(grid)
# read data (note csv files are renamed)
tbl1 = read.csv("FanGraphs_Leaderboard_h.csv")
tbl2 = read.csv("FanGraphs_Leaderboard_d.csv")
# create new table with data from both tbl1 and tbl2 by link on variable
# 'playerid'
outfield = data.frame(merge(tbl1, tbl2, by = "playerid"))
# clean up the variable names of the two Name fields
names(outfield)[2] = paste("Name")
names(outfield)[21] = paste("Name.y")
#

A quick plot

With the two data sets now merged, we can start plotting the results. First of all, a quick plot using ggplot2's “qplot” needs only one line of code, and three specifications (X axis data, Y axis data, and the name of the source table):

  qplot(UZR.150, wRAA, data = outfield)

So that must be Raul Ibanez over there on the far left. It's clear from this plot that his hitting (represented on the Y axis) is just above the 0 line, and a long way below the outfielders who are hitting up a storm. It's worth keeping in mind that Ibanez's hitting contribution is helped to some degree by the fact that just over one-third of his plate appearances so far this year (126 of 187) have been as a designated hitter or pinch hitter.

In looking at this plot, you might ask the same thing I did: Where are the rest of the Mariners outfielders, and who are the stars of the X and Y axes?

Code to set up the tables for plotting

The next chunk of code takes three approaches to identifying groups and individuals on the chart. We don't want to plot the names of all 110 players, that would be utterly illegible. Instead, we'll focus on three groups: the Seattle Mariners, the top UZR.150 players, and the top wRAA players. The Mariners player points and names will be navy blue, and others in black. The code will label the Mariners players and the top performers on the wRAA axis automatically, and a manual approach will be adopted to create the code necessary to identify the top UZR players.

But before plotting the results, new variables in the “outfield” table are created that have the names of the Mariners players, the UZR stars, and the wRAA stars.

# create new MarinerNames field that contains only the name of Mariners
# players (plagarized from Winston Chang's R Graphics Cookbook Recipe 5.11)
outfield$MarinerNames = outfield$Name
idx = (outfield$Team.x == "Mariners")
outfield$MarinerNames[!idx] = NA
# create a new table, taking a subset that has only the Mariners players
Mariners = subset(outfield, Team.x == "Mariners")
# add the names of the UZR stars to outfield$Table2 sort the table by
# wRAA, then add the names of the top 4 wRAA stars
outfield$wRAAstars = outfield$Name
outfield = outfield[order(-outfield$wRAA), ]
outfield$wRAAstars[5:110] = NA
# sort the table by UZR.150, then copy the first 3 names
outfield$UZRstars = outfield$Name
outfield = outfield[order(-outfield$UZR.150), ]
outfield$UZRstars[4:110] = NA
#

The final plot code

# the full ggplot verion, creating an object called "WARcht"
WARcht = ggplot(outfield, aes(x=UZR.150, y=wRAA)) + #
   geom_point(colour="gray60", size=2.0) + # set the colour and size of the points
   theme_bw() + # and use the "background white" theme
   ggtitle("Everyday Outfielders, 2013 [to 2013-06-15]") # and put a title on the plot
#
# start with WARcht, add geom_text() [for auto labels] and annotate() [for manual labels and arrows]
#
#
WARcht + # print the chart object
   geom_text(aes(label=MarinerNames), size=4, fontface="bold", colour="navyblue",
      vjust=0, hjust=-0.1) + # add the names of the Mariners players
   geom_text(aes(label=wRAAstars), size=3, fontface="bold",
      vjust=0, hjust=-0.1) + # add the names of the top wRAA players
   annotate("text", label="Shane Victorino", x=40, y=3, size=3, 
      fontface="bold.italic") + # manually place the label for Shane Victorino
   annotate("segment", x=50, y=2, xend=51.7, yend=-0.4, size=0.5,
      arrow=arrow(length=unit(.2, "cm"))) + # manually place the Victorino arrow
   annotate("text", label="Craig Gentry", x=40, y=-7.0, size=3,
      fontface="bold.italic") +
   annotate("segment", x=42, y=-6.6, xend=40.9, yend=-4.0, size=0.5, 
      arrow=arrow(length=unit(.2, "cm"))) +
   annotate("text", label="A.J. Pollock", x=49, y=-2.5, size=3,    
      fontface="bold.italic") +
   geom_point(data=Mariners, aes(x=UZR.150, y=wRAA), colour="navyblue", size=4) # over-plot the points for the Mariners players

The final analysis

In addition to Raul Ibanez, there are four other Mariners outfielders who have logged more than 200 innings. The only one on the plus side of the UZR.150 ledger is Jason Bay, at 5.5. And along with Ibanez, only Michael Morse has a positive wRAA. Put it another way, all five are more or less in the lower right-hand quadrant of the chart. So yes, it's a fair assessment that the Mariners outfield is a disaster.

The Major League outfielders who are the top hitters (the Y axis on the chart) are led by the Rockies' Carlos Gonzalez (at 28.1), ahead of Shin-Soo Choo (21.2) and Mike Trout (19.8). And defensively (the X axis), Shane Victorino leads with 51.9, followed by Craig Gentry (40.9) and A.J. Pollock (39.1).

The only outfielder who shines on both dimensions is the Brewers' Carlos Gomez, who stands in fourth place on both UZR.150 and wRAA. As the chart shows, so far this season he's in a class by himself.

Note: the code above can be found in a gist at github.

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MLB runs allowed by team

Or, How good were the Maddux/Glavine-era Braves?

In this on-going series of posts about run scoring in Major League Baseball, for this installment I'll turn the equation around and look at runs allowed.  In order to account for the changing run scoring environments, the runs allowed by individual teams is compared to the league average for that season, creating an index where 100 is the league average. In this formulation, a score below 100 is a good thing; a team with an index score of 95 allowed runs at a rate 5 percentage points below the league average.

Having written the original code in R, it's now a very simple process to change a few variable names and create the equivalent of the earlier runs scored analysis, but looking at runs allowed. This is one of the most important benefits of a code/syntax environment, an option that doesn't exist if  you are using a point-and-click GUI interface.

Run production, one team at a time

In a previous post, I used R to process data from the Lahman database to calculate index values that compare a team's run production to the league average for that year.  For the purpose of that exercise, I started the sequence at 1947, but for what follows I re-ran the code with the time period 1901-2012.

The R code I used can be found at this Github gist. Instead of boring you here with the ins and outs of what the code is doing, I've embedded that as documentation in the gist. The R code assumes that you've got a data frame called "Teams.merge" already in your workspace.  This can be achieved by running the previous code, or if you've done that before, you'll have created a csv file with the name "Teams.merge.csv", and now have the option to read that file as a data frame "Teams.merge".

The first step is to choose one of the current teams, and create a data frame that contains just that club's history.  Once this has been done, the code then creates trend lines (using the LOESS method, as I did with the leagues in previous posts), and then plot them.

Comparing individual team run production

Or, The 2010 Mariners: How Bad Were They?

In earlier posts, I used the statistical software R to plot the trends in league average run scoring since 1901. This was the first step to answering other questions I had on my mind:

1. How poor was the offensive performance of the 2010 Seattle Mariners?
2. Are they showing any signs of improvement?
3. And how can I use R to tabulate the data to answer these questions?

So, to answer Question #1.  It is well-established that the 2010 Mariners were not very good, at least offensively. (For fans of the team the well-deserved Cy Young award won by Felix Hernandez is surely the highlight of the season.) But I wanted a form of relative measure that would be comparable across time, to accommodate the various fluctuations in run scoring that were the subject of that earlier post.

As I started into this, the first decision was to draw a line in the historical record. I opted to use the eras described in Bill James' "Dividing Baseball History into Eras" article (behind a pay wall – but chances are if you're reading my blog, you already a Bill James subscriber):

• Era 1 (The Pioneer Era), 1871-1892 
• Era 2 (The Spitball Era), 1893-1919 
• Era 3 (The Landis Era), 1920-1946 
• Era 4 (The Baby Boomers Era), 1947-1968 
• Era 5 (The Artifical Turf Era), 1969-1992 
• Era 6 (The Camden Yards Era), 1993-2012

Based on these groupings, I opted to use the range of seasons 1947-2012 inclusive. This yields 1,580 team seasons of National League and American League baseball.

The second step was to calculate a runs per game (RPG) for each team, by year. This corrects for the longer regular season in the post-expansion period, the strike-shortened seasons, and will give us a common denominator to compare the results so far in 2012.

To do this, I accessed the 2012 edition of the Lahman database. Once I had downloaded and extracted the comma-delimted version of the files, I read the "teams" file into R.

Ballparks via Google Maps

Mike Fast, an analyst who used to write for Baseball Prospectus but who is now employed by the Astros, put together a Google Map of the Astros organization -- the location of the team's minor league affiliates.

I liked the idea so much I spent a few minutes plotting the same thing for the Seattle Mariners and their minor league affiliates.

For a more complete view of the Mariners affiliates, visit the wikipedia entry on the topic (which includes a historic timeline of the changing affiliations) or the team's affiliates news page.

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Kicking at the darkness

Last night (2011-04-11) the Seattle Mariners pulled off a preposterous comeback, defeating the Blue Jays 8-7 after trailing 0-7 heading into the seventh inning. Other teams have had comebacks from being down by 7 runs, and pulled off comebacks in bigger games. But as Rob Neyer has pointed out, what made this so unexpected and so special was that the Mariners have been, in a word, hapless. The early part of this game was the best/worst example of their struggles.

The FanGraphs plot (chart below) follows what has become a disturbing Mariner trend this year -- the line quickly plummets to the sub-10% win expectancy range in the early innings, and slowly drifts towards zero from there. (Check out the games vs. Cleveland the day before and the home opener on 2011-04-08 for recent examples).  This time, after bottoming out at 0.3% when Luis Rodrigez (the game's eventual hero) struck out to lead off the Mariner half of the seventh inning, the WE line zigzagged its way to the other end of the scale.

Blue Jays @ Mariners, 2011-04-11 (source: FanGraphs)

For the Mariners, a second consecutive 100 loss season (which would be the third in four seasons) is not at all out of the question. But for the fans who stuck with it last night, this was one for the ages.  Or the U.S.S. Mariner game summary, quoted here in its entirety: "That was horrible, then awesome. Baseball is fun."

The title I used for this entry is a reference to Bruce Cockburn's song "Lovers in a Dangerous Time". In the article linked above, Neyer wrote "It was somebody smart, or maybe an episode of Scrubs, that said nothing worth having comes easy." The song contains the line "Nothing worth having comes without some kind of fight/Got to kick at the darkness until it bleeds daylight". In the late innings of last night's game, the Mariners showed some kick.

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