Mike Schmidt 1988 Score Highlights

post from 30-Year Old Cardboard on 01 February 2013 09:12:16 AM. © 30-Year Old Cardboard

Mike Schmidt 1988 Score Highlights Score really tried to do it all with their debut set in 1988. And I applaud them for all of their efforts! Score offered us great rookie cards, a Reggie Jackson commemorative set, and a … Continue reading →

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Is "High Heat" A Myth

post from Beyond the Box Score - All Posts on 01 February 2013 09:03:30 AM. © Beyond the Box Score - All Posts

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I'm a big fan of strikeouts. Unlike Steve McCatty, I think they are vastly important to being a successful major league pitcher. I probably value strikeouts as much if not more than anyone reading this piece.

A pitcher's ability to get a batter to swing and miss ("whiff") has been shown to be pretty important in helping pitchers to rack up strikeouts.

By simple logic and the transitive property, If strikeouts are important to me and whiffs are important to strikeouts then, whiffs should be important to me as well. This conclusion then brought to me what I think is a very logical question.

If whiffs explain strikeouts then what explains whiffs?

I decided it may be a worthwhile venture to go pitch-by-pitch across baseball and try to find what characteristics may differentiate pitchers in terms of inducing a swing and miss from batters.

The best place to go when attempting to answer this sort of question is PITCHf/x data.

I started my journey by looking at Dan Brooks' PITCHf/x Leaderboards over at Baseball Prospectus and focused only on four-seam fastball.

Study #1

My hypothesis was that pitchers who had more "stuff" would induce more whiffs on average.

Luckily, the leaderboards provide an easy why to go about testing that hypothesis, as metrics that measure "stuff" are positioned right by whiff per swing rate.

I took this information and ran a multiple regression with every pitcher who threw at least 200 four-seam fastballs in 2012 (n=180). Horizontal movement, vertical movement and velocity were the three separate predictors and whiff per swing rate was the dependent variable.

The result was a fairly weak, but not horrible, correlation (r=.35). Velocity and vertical movement were both very significant and horizontal movement was trending towards significance at a 95 percent confidence level (p=.079), with this resulting equation:

Whiff/Swing = -.358 + .0048*Velo + .0026*H-Mov + .0067*V-Mov

This result supported my hypothesis, but in no way should it set the world on fire.

If you walked up to a random Joe on the street who knew anything about baseball and told him that pitchers who throw harder with more movement (have better "stuff") on average get batters to swing and miss more often, I think his response would be something along the lines of "Obviously?!".

"Pure stuff" only explained 12 percent of the variation in whiff rate, which isn't a whole a lot. However, there's obviously a lot of factors that go into a swing that are not being considered in this regression, such as the count, the hitter's ability, the location of the pitch, the situation in the game, the pitcher's deception, the sequence of pitches and many more.

Some of those can be factored in, others can't, but the more predictors you add into this model the more complex it would become which could cause some issues.

I personally think that based on the hypothesis and the results of the test, it's fairly safe to conclude that pitchers who have better fastballs (harder and move more) induce more whiffs on average.

Like I said earlier though, that should not shock anyone; thus, I decided to delve deeper.

Study #2

My next idea was to test this conclusion at the individual level. The first study compared pitchers against each other based on their average velocity, average horizontal movement, average vertical movement and average whiff per swing rate. The obvious theme there is average, my major issue with that is pitchers don't throw their average fastball on every pitch, there's variation.

I decided to test whether an individual pitcher could dial it up or throw a nastier fastball to induce a whiff on a per pitch basis. The only way to test this was to dust off my handy PITCHf/x database.

I pulled data on 50 randomly selected pitchers who threw at least 200 four seam fastballs in 2012. I only looked at pitches that were four seam fastballs and resulted in a swing. All swings that did not result in a whiff were classified as a 0 and in turn all swings that resulted in a whiff were classified as 1.

A separate multiple regression was run on each pitcher in an attempt to predict whether or not the result would be a 0 (non-whiff) or 1 (whiff). Based on the original study, I expected to find that pitchers were throwing their fastballs harder and with more movement when a whiff occurred.

Well, it turns out that I was wrong.

For the majority of pitchers, whiffs were not occurring on fastballs with more movement (as those predictors did not come back as significant) and it seemed that only a few pitchers were throwing harder when a whiff occurred.

Before I bowed my head and disappointedly returned to the drawing board, I decided to include both horizontal and vertical location as predictors.

This actually brought about a pretty interesting result.

In most cases horizontal location (if the pitch was on the edge or not) was not significant; however, the vertical location of fastballs were statistically significant.

I broke down the results into three separate columns in a table below.

The first column shows the correlation between vertical location/velocity and whiffs,
The second column shows whether or not vertical location was statistically significant at a 95 percent confidence level
The third column shows whether or not velocity was statistically significant at a 95 percent confidence level

Pitcher

Vert. Location

Velocity

Zach McAllister

0.33

Yes

Chris Sale

0.32

Yes

No*

Danny Duffy

0.31

Yes

Drew Hutchison

0.3

Yes

Jordan Lyles

0.28

Yes

Roy Oswalt

0.28

Yes

Liam Hendriks

0.27

Yes

Franklin Morales

0.27

Yes

Jason Hammel

0.25

Yes

Wei-Yen Chen

0.25

Yes

Bud Norris

0.25

Yes

Hiroki Kuroda

0.25

Yes

Matt Harvey

0.25

Yes

Neftali Feliz

0.24

Yes

No*

C.J. Wilson

0.24

Yes

Yovani Gallardo

0.24

Yes

Justin Masterson

0.24

Yes

Brian Duensing

0.24

Yes

Chad Billingsley

0.23

Yes

Felix Hernandez

0.22

Yes

Juan Niascio

0.22

Yes

Francisco Liriano

0.22

No*

Jordan Zimmermann

0.21

Yes

Mike Minor

0.21

Yes

James McDonald

0.21

Yes

Gavin Floyd

0.2

Yes

Justin Verlander

0.19

Yes

Daniel Bard

0.17

Yes

No*

Mark Rogers

0.16

No*

Brad Lincoln

0.16

Yes

Johnny Cueto

0.16

Yes

Christian Friedrich

0.14

Yes

Stephen Strasburg

0.14

Yes

Jarrod Parker

0.14

Yes

Miguel Gonzalez

0.14

Yes

Jaime Garcia

0.12

Ivan Nova

0.11

No*

A.J. Burnett

0.11

Yes

No*

Henderson Alvarez

0.1

Yes

Zack Greinke

0.1

Yes

James Shields

0.09

Casey Kelly

0.09

Edwin Jackson

0.08

Yes

0.08

0.07

0.05

0.05

0.05

0.02

0.02

*-Indicates that the predictor was significant at a 90 percent confidence level

All of these correlations are pretty weak, but that should be expected when attempting to predict a result on a pitch-by-pitch basis.

For the majority of pitchers (70%), vertical location was a statistically significant predictor of whiffs at the 95 percent level and that percentage was even higher (76%) at the 90 percent confidence interval.

It's also pretty clear from this table the velocity of the pitches that resulted in whiffs, in most cases, were not different from other pitches where a swing took place, as velocity was only a significant predictor of whiffs for 20% of the pitchers (28% at 90 percent confidence).

Also, I must note that for a few of the pitchers were velocity came back significant the relationship was actually negative, meaning that on the whiffs their pitches were not actually thrown harder.

Despite its weak predictive ability velocity was left in the model for a very specific reason. Velocity was used as an attempt to control for just how much vertical location was explaining whiffs regardless of velocity.

It seems to me, based on the results of this sample, that on average individual pitchers generate more whiffs on fastballs that are higher in the zone relative to their other fastballs.

This may seem like an obvious conclusion.

The idea that batters have to speed up their bats to catch up to higher fastballs is something that is generally accepted; the notion of "high heat".

At the same time, just because something is generally accepted across baseball does not always mean that statistical evidence will end backing that assumption. Many sabermetricians have done great work to debunk what was held for years as "generally accepted".

While I think that is all well and good, I also find it just as fascinating and fulfilling when the results actually back preconceived notions.

The PITCHf/x data used in the first regression came courtesy of Baseball Prospectus.

The PITCHf/x database used for the second regression came courtesy of our friend Jeff Zimmerman.

You can follow me on twitter @Glenn_DuPaul

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earch-baseball-fastballs-pitchfx

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Getting Ready for Spring Training

post from Love My Team on 01 February 2013 08:43:38 AM. © Love My Team

Teams all around the country are packing up trucks and heading to their spring homes. The Rays are[...]

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Sabersphere 2/1: Nick Johnson, Martin Prado,
A-Rod

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Zachary Levine of Baseball Prospectus looks at Nick Johnson:Farewell to Nick Johnson

"I see him more as Jim Thome without the fashion statement." – BP Annual, 1999

"Johnson is the best prospect in baseball, a ranking very difficult for a first baseman to achieve." – BP Annual, 2000

"He'll likely end up as a cross between John Olerud and Barry Bonds. I think most Yankees fans can live with that, even if it takes him a few years to get there." – BP Annual, 2002

Rob Neyer at Baseball Nation looks at Martin Prado's extension: Does Martin Prado's new deal justify trading Justin Upton?

So now we have another big piece of the puzzle.

When the Diamondbacks traded Justin Upton to the Braves for Martín Prado and a squad of marginal prospects, there was something that just didn't add up: Upton was signed through 2015, Prado just 2013. Could it possibly make sense to trade three years of Upton for one year of Prado?

Alex Remington of Fangraphs looks at if the MLB should suspend A-Rod: Should MLB Punish A-Rod Based on News Reports?

As Dave Cameron wrote two days ago, multiple reports have emerged about numerous baseball players connected to a clinic in South Florida that dispensed performance-enhancing drugs and has been nicknamed "BALCO East."

Dan Lependorf of The Hardball Times shares a telling graphic about the Hall of Fame voting: Visualization: Hall of Fame ballot results from 1968 to the present

With Hall of Fame voting shoved back into the closet for another 11 months, I find it's always useful to step back and try to get a sense of relativity and context for the 2013 set of Hall of Fame ballots still fresh in our minds. In that spirit, I've put together a graphic charting BBWAA voting over the last half-century.

If you would like to submit something for Sabersphere, email me at SpencerSchneier22@gmail.com.

Today's BtB Retro is not all that retro. Recently on the podcast, Bryan Grosnick brought this article up so I thought I'd link it: An alternative method for determining defensive WAR (12/7/12)

According to FanGraphs, Darwin Barney was an excellent defensive player in 2012. Barney, the everyday second baseman for the Chicago Cubs, was worth about 13 runs, or 1.3 wins above replacement, due to his defensive contributions at the pivot. This is a very good number.

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rtin-prado-a-rod

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Previewing 2013 Marlin Maniac Top 15 Prospect
List

I have always been intrigued with prospects and how they are ranked. Last off-season, I wanted to do a top-15 prospect list for the Miami Marlins, but when I tried to get started, I quickly realized how time exhausting this task really is. Putting together a list takes a lot of research as well as [...]

Previewing 2013 Marlin Maniac Top 15 Prospect List - Marlin Maniac - Marlin Maniac - A Miami Marlins Fan Site - News, Blogs, Opinion and More

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list/

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Justin Uptons Thumb

I?m sure the majority of you are aware that Justin Upton played through a thumb injury in 2012. A lot of baseball writers and analysts across the industry have pegged it as a reason for his disappointing season. After going through and researching a little about the timeline of Upton?s season, I found an interesting [...]

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Dave’s Free Play, Friday 2/1/13

Okay night for me on Thursday with a 3-2 personal play ledger, and the free opinion on Troy paid off. That said, I missed a couple of spots that I simply should have been on, and as usually seems to be the case when that happens, they won. Then again, I backed off George Mason at the last minute and they managed to blow a huge lead in a loss at home to Drexel. Bottom line is the ink is again black on the daily ledger, and that’s never a bad thing.

Friday’s college slate is the ultimate coin flip for me, which means I’m going to be sitting that portion of the schedule out in all likelihood. But there’s an NBA game I’ll be on, and some possibilities on the ice as well.

Last chance for the guaranteed February special is today, so if you’re interested in the details, fire off an email to cokin@cox.net.

Here’s the comp for Friday in college.

02/01 04:00 PM CB (831) COLUMBIA VS (832) PENNSYLVANIA

Take: (831) COLUMBIA

Good news for beleaguered Penn as the Quakers should finally have Fran Dougherty back in the lineup following a lengthy absence due to mononucleosis. But this is still a bad basketball team and on paper, it’s a pretty good matchup for Columbia. The Lions take good care of the basketball, Penn does not. Columbia is about automatic at the stripe, knocking down 77% of its free throws. The Lions almost never get any offensive boards, but neither do the Quakers. At least Columbia hits the defensive glass pretty well, and that should be the case here as they own a size advantage. Plus, the road team can knock down treys at a decent clip. In other words, Columbia seems to do just about everything a little better than does Pennsylvania. That’s enough to warrant a shaky, but it’s the best I’ve got on this razor thin card, call on Columbia to sneak home the win and cover.

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The Number Two Team in LA

post from LADodgerTalk.com - Matt Kemp, Clayton Kershaw, Vin Scully, Andre Ethier and the Dodgers on 01 February 2013 12:02:38 AM. © LADodgerTalk.com - Matt Kemp, Clayton Kershaw, Vin Scully, Andre Ethier and the Dodgers

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1994 O-Pee-Chee Florida Marlins Team Set

1994 O-Pee-Chee Florida Marlins Team Set I don’t think that the O-Pee-Chee baseball card sets get their just due from collectors. In the 80′s I can see why, as there was just a single logo change on the front of the … Continue reading →

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et/

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