Garfinkel told the crowd he saw a heat map, which highlights the locations a pitcher has targeted in the past, and that it showed over the last three years Greinke had not thrown a single pitch on a 3-2 count to right-handed hitters on the inner half of the plate.

 

Since 2007 until his last start, here are the locations off every pitch Zack Greinke has thrown on a 3-2 count against right-handed hitters:

Zack definitely comes in side about 1/3 of the time (36.7%). If the definition of inside is pushed to the inner quarter of the plate, the percentage drops to 13.5%. I would love to see the heat map Mr. Garkinkel was examining.

Hitters don’t hit the ball as far in colder temperatures, but when the temperature is under 50 degrees, pitchers have issues throwing strikes.

I decided to the Batted Ball Location application to see how often he pulls the ball which would cause the Rays to put on such a shift. Dividing the field into fifths, here is how often he hit ground balls into each area:

Location: GB%
LF: 29%
LF-CF: 45%
CF: 15%
RF-CF: 9%
RF: 2%

89% of Youkilis’s batted balls went to the center to left side of the field. The extreme shift, especially against a right-henaded hitter, makes sense considering the heavy pull nature and the lack of speed from Youkilis.

First, I plugged Wainwrights contact into my salary calculator (contract calculator tab) with some aggressive salary growth values of 10% salary inflation and the WAR/$$ amount of $6M/WAR. With those values, Adam would need to produce 13.6 WAR.  With a little more conservative numbers of 5% inflation and a 5.5 WAR/$$, the total ends up at 16.1 WAR

Next, I used the Marcel pitcher similarity tool here at BBHM to find pitcher who had similar age-32 projections from 1990 to 2007. After finding nine such pitchers, I looked at how they performed in their age 33 to 37 seasons and here are the results:

Using aggressive price increases, the salary looks fairly valued. The Cardinals are banking on the same level of production they got from Carpenter over the same seasons. Using a conservative price increase, the Cardinals looked to over pay. Only time will be able to tell.

Notes:
• Primarily pitched outside with nothing inside. I was too far away to get good readings on types, but he seemed to fed a nice mix of pitches.
• Displayed good line drive power, but no elite power during the game. I missed batting practice, but I heard he showed good power then.
• His swing is good, good base, keeps hands in tight, shoulder up, compact.

• Adequate at 3B defense. Only a handful of balls were hit his way including knocking down a screaming line drive.

• 3.65 time to 1B.

In my limited viewing of college players this season, he is by far the best I have seen.

Other notes:
New Mexico’s freshman shortstop, Jared Holley, made some great plays.

All of the teams are after the jump with the teams’ ranking next to their name.

Link to combined BA top 100 prospect list.

http://www.baseballheatmaps.com/marcel-database-download/

Also, the data has been added to the Marcel comparison tool (looks for similar players and how they produced in the year of the projection – a way good to find % chance of under or over performed).

http://www.baseballheatmaps.com/graph/batter_marcel.php
http://www.baseballheatmaps.com/graph/pitcher_marcel.php

With the default values, Trout had no comparable 22-year-olds. By spreading out the criteria, I got 3 comps since 1952, Hank Aaron, Frank Robinson and Miguel Cabrera. Not a shabby list.

• It is slow, almost painfully slow at times. With everyone possibly using it right after this article is released, it may be even slower. Right now, it takes over 1 minute to process one pitcher for just one year. If it is too slow over the next day or so, try it again when less people may be using it.

• Predicting possible injures is an imprecise science. Two pitchers could have almost identical values, but one may need Tommy John surgery and the other one wouldn’t. All I have done is make data available on traits which have been previously known to lead to injuries.

• Directions: Select a starting pitcher (tool only really works with starters because a minimum number of pitches needs to be thrown) and date range. Press submit. Next will come up a most common pitches chart for the pitcher. Pick the most common fastball (to use FA if going back to 2009). Press Submit and have a drink or take a nap because this may take a while. The results will eventually appear.

• The first graph is pretty simple. An average velocity graph for the selected pitch with a 5 game average curve.

• The second graph measures late game consistency. A 100 value is an inconsistent pitcher and 0 value is a consistent pitcher. I got the values by looking at pitchers with major arm issues in a season and pitchers without arm issues in a season. Then, I compared one group of pitchers to the others over the last 10 fastballs thrown in each game. Velocity, release points and break were examined using logistic regression. In the end, I got a formula which detects inconsistencies. The exact cause of the inconsistent is not outputted. The user will need to go look at the game data to find the pitcher’s exact issue. Note: Pitchers, like Bruce Chen, who have two distinct release points, will have all their values near 100. I have not been able to work out this problem yet.

• The final graph is the pitcher’s Zone%. A value under 47% (baseline value on graph) means the pitcher had issues throwing strikes and is more likely to be injured.

• In the near future, I will be going back and looking at how the values can be applied to other pitchers and situations.

Let me know of any issue or ideas for improvement.

Second, some great people have been doing some work with the batted ball data I collect.

Here is some work from Clint Hulsey on home and away data:

• The Mariners Rotation Batted Ball Rates: Home/Road Splits
• Batted Ball Differences In Colorado And Texas-Off The Radar
• Can We Quantify the “Marine Air Effect” at Safeco?

Additionally, Mike Podhorzer and Chad Young have been writing a series on  the Quest to Predict HR/FB Rate (5 parts so far):

• Part 1
• Part 2
• Part 3
• Part 4
• Part 5