Defensive metrics, specifically UZR, does not like think Colorado center fielders (CF) are good defenders. From 2002 to 2012, they are ranked second to last in total UZR. I am going to take a brief look to see if UZR under rates the defense of Rockies’ center fielders.

The easy solution to see if a bias exists would be to compare the home and away UZR values, but those numbers don’t exist. Instead, I will use my own defensive metric, Out% to create home and away defense values. Out% just looks at the number of outs a position/player makes taking nothing else into account. Here are some baseline values for the situation:

Criteria (2002 to 2012): Out%
All CF from 2002 to 2012: 9.4%
COL CF (home and away): 8.7% (ranked last)
COL CF (away):9.1%
COL CF at home:8.4%
Other teams at COL: 8.9%

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I have written a couple articles recently at FanGraphs on wBABIP and Edge%. I have added the two metrics to the pitcher application menu.

xBABIP

From the Rotographs article:

The idea behind using wBABIP is to see if a pitcher gave up an abnormally high number of extra base hits. The the cause of the extra base hits could have been from the pitcher throwing meatballs or slow footed outfielders. The goal is to see if the damage done from batted balls was evenly distributed or were there some extreme cases.

As a whole, I didn’t find the enhancement on BABIP was really needed. Not enough extra information can be extracted from wBABIP to use it in addition to BABIP. Initially, I though I would find a little extra information on some pitchers. In the end, I found wBABIP was just overkill.

Edge%

From the FanGraphs article:

Pitchers can be productive with a Zone% around 45%. The key is to limit the amount of pitches right down the middle of the plate. A pitcher needs to throw at the inside and outside edges of the strike zone. Pitches near the edge of the plate are hard to hit solidly.

With R.A. Dickey throwing in a dome for 1/2 of his games in a dome. Here are the results of pitchers in domes instead of in open air for comparison. It looks like knuckleball pitchers throw a bit better in domes.

While not perfect, I liked the results

1. Batted Ball Leaderboards (link - under Applications – Batters – Batted Ball Leaderboards)

Two leaderboards were created for each season. The first if the average home run and flyball distance for the top 300 flyball hitters in the league. The data is divided up by handedness. The second list, is the angle of the ground balls and line drives a player hits by handedness. This list shows the most likely hitters to pull a ball and therefore are more likely to see some form of shift against them.

2. In the previously created batter applications (example), a hitters handedness data can now be selected

In Nate Silver’s new book, The Signal and the Noise, he had a chapter on climate projection. The chapter showed projections had a sweet spot at sometime in the future where they were the most accurate. Three uncertainty factors were at work affecting the projection and the sweet spot.

• The first is is initial variability. With climate, a location may experience a very cold winter in the first year of the model. The extra cold and hot winters will hopefully even out at some point. This error starts out high and eventually goes to zero.
• The second error is long term unknowns. With climate, maybe a new scientific invention is created which removes CO2 from the air or a couple of volcanoes go off at once cooling the earth. This value starts at 0 and grows steadily over time.
• The third is an underlying unpredictability. This value is the most steady of the three. Say the climatologist want create a 40 year prediction. From the beginning they will have a certain level of uncertainty. The further they want to predict into the future, the higher level of underlying uncertainty exists.

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I have always wanted to create an defensive metric that doesn’t adjust to the league average. Using Retrosheet data, I have made my first stab at it. I plan on making some small improvements and eventually having data available on all players. Here is an exert from my first article at Royals Review explaining in more detail the new metric:

For years, my one issue with the current defensive metrics is they attempt to show how a player is doing compared to the league average at a position. This method helps to give run values to the defense for inclusion into complete value metrics like WAR. The problem is the pool of full seasons of playing time at each position is limited to 30. If a couple of excellent defensive players get hurt or a player is moved to another position, a player’s value may change quite a bit even though they may be creating the same number of outs (example from a few years ago when I looked at this issue with some shortstops).

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In a Fanshot at RoyalsReview, some asked to have explained what is wrong with his swing. I created a few .gifs of a typical swing of his and here are the problems:

Full swing for reference

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I was planning on compiling the Retrosheet data over the Christmas holidays, but I found myself with time. So for the baseball fan in your life get him more mysql data then he has time to analyze. I have imported the data and giving you a few more options in your downloads.

Full MySQL Retrosheet
Last 10 Years MySQL Retrosheet
2012 CSV Retrosheet
2012 MySQL Database
2010s MySQL Database