Archive for December, 2010

Visualizing Major League Baseball During the Aughts

2010 marks the end of the “aught” decade for Major League Baseball.  I thought I would take the opportunity to analyze the last 10 years by visualizing team data.  I used Tableau Public to create the visualization and pulled team data from (on-field statistics) and USA Today (team payroll).

The data is visualized through three dashboards.  The first visualizes the relationship between run differential (RunDiff) and OPS differential (OPSDiff) as well as the cost per win for teams.  The second visualization is in table form and can be sorted and filtered along a number of dimensions.  The final visualization looks at expected wins and actual wins.

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Why I Can’t Ignore Stats

If some of you have been active in following your Hall of Fame voters, you probably read this post on Jon Heyman discussing his ballot. He spent the majority of this piece stating why he didn’t vote for Bert Blyleven, and then he explained why he voted for Jack Morris instead. I promise this is not intended to be a “Vote Blyleven, not Morris!” post, because I’m more interested in something else. Heyman claims that Morris had a bigger impact in his games than Blyleven. Well then, what happens if I never experienced this impact?

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A Guaranteed HOF Snub

On January 5, the Hall of Fame class of 2011 will be announced. It appears that Bert Blyleven will finally get the call after 14 years on the ballot. Roberto Alomar is likely to receive the necessary votes as well. There will be a long list of deserving candidates left out this year. After the announcement, there will be no shortage of analysis of the snubbery. But we know for a fact that, even before the votes are tallied, one deserving candidate will not be inducted.

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A Pitch F/X Look at Cliff Lee

Lee has a tremendous variety of movement in his pitches. He has three pitches that tail away from righties (fourseam, twoseam, changeup) and a nasty curveball with a ton of movement. For most pitchers this would be plenty; but Cliff Lee is not like most pitchers. He also packs a cutter with as much horizontal movement as some sliders.

We can see this with the following graph, which is from the catcher’s perspective (same with all following graphs):


CU=curveball, FC=cutter, FF=fourseam, FT=twoseam, CH=changeup. The black box represents the strikezone and has the average pitch locations for each pitch.

Looking at a pitcher’s entire repertoire like this is useful, but it can be more interesting to look at pitches individually when it comes to pitchers like Lee.



Against righties his location is pretty varied with the fourseam. He mainly locates the pitch middle-away, but often goes up and in too. Against lefties, he consistently pounds the outer half.

Pitch Type Count Selection% Swing% Swing-Miss% HR% GB% LD% FB%
FF 352 13.8 50.9 12.3 0.9 28.9 25.0 46.1
vs LHB
FF 305 36.4 47.2 13.2 0.7 45.6 12.3 42.1

FTdensRHw_strikezone FTdensLHw_strikezone

Against righties he primarily throws the twoseam pitch up and away, which explains why he has a high flyball rate on a pitch typically associated with groundballs. Against lefties the pitch is pretty much thrown low and over the middle of the plate.

Pitch Type Count Selection% Swing% Swing-Miss% HR% GB% LD% FB%
FT 1174 46.2 48.2 14.5 0.5 31.0 21.0 48.1
FT 241 28.8 46.1 11.7 0.0 59.6 27.7 12.8



Against righties the pitch is a real weapon; the cutter results in many whiffs and a solid amount of groundballs. Against lefties the pitch isn’t as remarkable, but still solid. His location against lefties with the cutter is very similar to his location with his fourseamer against lefties.

Pitch Type Count Selection% Swing% Swing-Miss% HR% GB% LD% FB%
FC 510 20.1 54.9 20.4 0.6 47.0 23.0 30.0
FC 185 22.1 49.7 17.4 1.1 41.9 18.6 39.5



His location against righties and lefties is pretty much the same, though he does backdoor the pitch occasionally to righties. He pretty much only throws his curve late in counts for strikeouts.

Pitch Type Count Selection% Swing% Swing-Miss% HR% GB% LD% FB%
CU 170 6.7 44.1 37.3 0.0 76.0 12.0 12.0
CU 49 5.8 36.7 38.9 0.0 20.0 20.0 60.0


Pitch Type Count Selection% Swing% Swing-Miss% HR% GB% LD% FB%
CH 293 11.5 58.7 29.7 0.3 42.5 17.5 40.0

Only one graph here because he only threw 20 changeups to lefties the entire year, so I’m just going to ignore those. According to Fangraphs pitch run values, his changeup was his most effective pitch this year. And you can see why; he was great and locating the pitch down and away.

*all data and tables are from Joe Lefkowitz’ site.

*This article was originally posted on

Graphical wOBA by Count

I am a big fan of graphs and baseball. Fangraphs made me excited because putting complex data into reasonably easy to understand graphs helps open up sabermetrics to more fans. I’m a big fan of statistical analysis, but after a while, a table full of numbers just starts running together and stops making sense. That’s what makes graphs such an effective tool.

I’ve dabbled in graphs myself. When people were creating the WAR graphs to compare hall of famers, I made a sample graph showing cumulative WAR by age on Tom Tango’s Book Blog:

(click for a larger image)

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