By now, there is not much that Justin Verlander does that should surprise us. The Tigers ace has thrown not one, but two no-hitters and regularly displays the hardest fastball, in terms of average velocity, in the league. Since 2009, only Ubaldo Jimenez has an average four-seam fastball equal to Verlander’s in terms of velocity (95.4), and given Jimenez’s recently struggles Verlander essentially stands alone.

In his last start against the Kansas City Royals, Verlander entered the ninth inning having thrown 104 pitches. Up to that point, the righthander had not thrown more than 18 pitches in a single inning. He would go on to close the game out by throwing 27 more pitches, bringing his pitch total for the night to 131. What was more impressive than the fact that he threw 131 pitches was the fact that, in the 9th inning, he threw four fastballs that topped 100 mph. (Now, the gun in Kansas City that night may have been a little hot, but we are still talking about 98+ mph fastballs.)

It has been said that Verlander is one of those pitchers who generally gains velocity as the game goes on, and that such a trait is quite stable. I was curious about how Verlander compared to other hard-throwing starters who pitched deep into games. To be clear up front, there are not many pitchers who not only throw extremely hard but also pitch as deep into games as Verlander. I came up with two such pitchers: Felix Hernandez and CC Sabathia. Since 2009, Verlander, Hernandez, and Sabathia all averaged over 107 pitches per game, seven innings per start, and ~94 mph on their four-seam fastballs.

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I have been toying around with an idea for pitcher-hitter match-ups based not on prior head-to-head performance or platoon splits, but rather pitch type linear weights.

For those that are unfamiliar, pitch type linear weights basically takes a batter or pitcher’s performance on each type of pitch they throw or face during the year (e.g. four-seam fastball, slider, etc.) and converts that performance into runs created or runs saved relative to average. At FanGraphs, we show both the total runs created or saved for each pitch (e.g. wFB) and a normalized version for the value per 100 pitches thrown (e.g. wFB/C).

I thought it would be interesting to compare the starting pitcher’s pitch type linear weight performance against the lineup he is facing. To do this, I calculated the difference in run value between each pitch type for each starting pitcher and the hitters they might face. The difference is shown in the tables below. Green coding denotes an advantage to the pitcher, while red indicates an advantage for the hitter. I used the normalized version of each pitch type (i.e. run value per 100 pitches thrown/faced) to control for playing time, pitches seen, etc.

The tables below show the match-ups for tonight’s game between the Marlins and Phillies (7:05pm EST) for both Josh Johnson and Roy Halladay:

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This past Tuesday on MLB Network’s Clubhouse Confidential, I predicted that Mets first basemen Ike Davis would be the breakout player in MLB this coming season. Yes, it was a bit of a homer pick, but I had solid reasoning to back it up. In his first 754 plate appearances in the big leagues, Davis put up an OBP of .355, a SLG of .457, and a wOBA of .352 all while playing in the pitcher-friendly Citi Field. That translates to a 121 wRC+, not bad considering only six other players 24 years old or younger have ever matched or exceeded that total over their first 800 plate appearances.

While watching Davis go 0-for-4 with two strike outs in yesterday’s opener I noticed something interesting: the Braves only threw Davis one fastball out of 18 total pitches. Not only that, but 41% of those pitches where thrown low and away, with Davis striking out twice on pitches in that area.

We can’t read too much into performance metrics in the early part of the season, especially after the first game, but the strategy executed by the Braves yesterday is consistent with the book on Ike, and may have provided a preview of what the young slugger will see throughout the year.

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ESPN’s Christina Kahrl wrote a fascinating piece over at the SweetSpot that focused on the rise of the strikeout during the past few decades. In her article, she notes that some teams can get away with lesser defensive players if their pitching staffs have a higher strikeout rate. The logic is that if a team’s pitching staff allows fewer balls in play due to striking out more batters, fewer runners will reach base despite the team’s higher batting average on balls in play (BABIP).

It’s a great point. I wanted to get a more concrete sense of just how much strikeout rates and team defense/park might impact hits allowed in 2012. To do this, I took the ZiPS projections for all pitchers who are currently on the active roster of major-league clubs. The projections were then aggregated by team. Since the active rosters vary at this point, I calculated rates such as projected hits per inning, strikeouts per inning, etc. I then conducted two comparisons: the number of hits allowed relative to league average when BABIP is held constant; and the number of hits allowed, relative to league average, when the number of balls in play per inning is constant. The former gives us a sense of how strikeout rates impact hits; the latter tells us how defense/park impacts hits*. All calculations of hits saved/allowed used an innings pitched constant of 1450 for the year.

Here are the results:

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Most readers understand that the phrase Three True Outcomes (TTO) refers to walks, strike outs and home runs. In an August 2000 article at Baseball Prospectus, Rany Jazayerli noted that these outcomes are “true” in the sense that they are largely independent of all things — outside that mano-a-mano moment between the batter and the pitcher:

Together, the Three True Outcomes distill the game to its essence, the battle of pitcher against hitter, free from the distractions of the defense, the distortion of foot speed or the corruption of managerial tactics like the bunt and his wicked brother, the hit-and-run.

None of the three true outcomes are significantly impacted by what happens outside the batters box*. Therefore, players with a higher percentage of plate appearances that end in TTOs have their fate largely decided at the plate. The poster child for the TTO? Rob Deer. His career TTO percentage ((HR+SO+BB)/(PA)) was 49.7%.

I thought it’d be interesting to look at how TTO players have evolved over time and what accounts for their successes and failures.

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Part two of a two-part series.

In Part I of this series, I dove into some Pitch F/X data to try and tease out how Adrian Gonzalez changed his approach after leaving San Diego in 2011. Overall, pitchers did not appear to adjust their approach to Gonzalez, evidenced by the fact that the distribution of pitches by location in 2011 was almost identical  to 2010. Gonzalez, however, did seem to alter his approach by altering what pitches he offered at, most notably swinging at more balls away and up in the zone. The change in his swing distribution combined with the change in his performance seemed consistent with the theory that the slugger was purposefully being more aggressive on pitches outside of the strike zone in an effort to take advantage of the Green Monster at Fenway Park. But to get a firmer handle on this we needed to split out Gonzalez’s data by home versus away.

That is the focus of this article.

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This is the first of a two-part series on Adrian Gonzalez’s evolution as a hitter.

Writing for ESPN.com last year, Dave Cameron suggested that then-newly-acquired slugger Adrian Gonzalez was displaying a new approach at the plate in Boston. Cameron pointed out that, as of May 14, Gonzalez had significantly reduced his walk percentage (BB%) and increased contact on pitches outside the strike zone (O-Contact %). Like 2010, Gonzalez was back to swinging at more pitches, but was making contact on roughly 85% of his swings. That was a huge jump from his carreer average at the time.

Since we’re on the cusp of a new season, I thought it’d be interesting to revisit Gonzalez’s 2011 to see if those early season trends held for the entire year. Overall, Gonzalez put up a .406 wOBA in 2011, versus a .378 with the Padres in 2010. The smooth-swinging first basemen got on base more often and also slugged for a higher percentage —though his ISO numbers were nearly identical. So the question is this: Was his 2011 success due to a different approach?

To figure that out, I created a series of heat maps based on Pitch F/X data that compare Gonzalez’s performances in 2011 and 2010. For the first part of this series, I’m only focusing on his overall performance. In the second part, I’ll take a deeper look and mine his home-road splits.

The first thing we need to check on is whether opposing pitchers took a different approach with Gonzalez after his move to Boston. As the data suggest, pitchers didn’t appear to approach Gonzalez all that differently:

There are some small changes in certain zones, but pitchers were pretty consistent with Gonzalez. Even if you break out fastballs from off-speed pitches, the percentages are basically the same by zone between 2010 and 2011.

Despite the season-to-season similarities, though, we see striking differences in the slugger’s approach in different zones.

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(Special thanks to Tom Tango for working through the conceptual and analytical issues on this article with me)

After seven outstanding seasons as one of the National League’s premier hitters, Prince Fielder signed a nine-year $214 million deal to play first base for the Detroit Tigers. During his years in Milwaukee, Fielder averaged a .391 wOBA, 32 home runs (.0546 HR/PA) and posted a .257 ISO. Certainly, no one could argue about his productivity. But with a change to a new team —and more importantly, a new park — there are questions about whether Fielder’s offense will be impacted.

If Park Factors are to be believed, he should be in for a decline. By just about any model, Detroit is roughly even offensively overall, but a much tougher hitting environment for left-handed hitters than Milwaukee. That means we should expect Fielder’s offensive performance to decline more than basic aging and regression would predict. Since the Park Factor change only impacts half of a player’s games each year, the theoretical ratio between change in factors and change in performance is 2:1. Essentially, we’d expect a wOBA to decrease by 1.5% and home runs to decrease by 15%. There are a number of different Park Factor formulas, but the general pattern looks similar regardless of the factors you look at.

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One of the many insights to come from Bill James was the fact that a team’s winning percentage could very easily be estimated based simply on the difference between the runs they scored and the runs they allowed. And while James’ Pythagorean Expectation cannot account for all variation in team performance, it does a fantastic job.

One possibility that is not accounted for is that teams may distribute their runs differently, game to game, than others throughout the season. It’s possible that two teams with identical run differentials could have significantly different records. Here’s a short example:

Assume two teams, A and B, both with a run differential of 0 (both score and allow 29 runs) over the course of a 10-game series against each other. The Pythagorean Expectation tells us that both teams should have a record of 5-5. However, in this scenario, team B wins 6 out of 10.
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When we last left the question on Park Factors’ effect on ERA estimators we found that the estimators performed the best in hitters’ parks when looking at starting pitchers. FIP and xFIP performed better than tERA or SIERA when predicting the next year’s ERA for this group of pitchers. For the other park types, the pattern looked similar to what we generally see — SIERA generally performs best, while all estimators provide better leverage over a pitcher’s YR2_ERA.

But what if we want to predict how pitchers with certain batted-ball profiles (fly ball vs. ground ball) will perform in different parks? If we’re trying to predict how C.J. Wilson (lifetime 1.68 GB/FB ratio) will perform moving from Texas to Anaheim — or Michael Pineda’s (0.81 GB/FB ratio) move from pitcher-friendly Safeco to hitter-friendly Yankee Stadium will turn out — in which estimator(s) should we have more faith? That is the focus of Part III.

I used the same methodology as Part II to determine park type. I then coded each pitcher as ground ball or fly ball based on their GB/FB ratio. A pitcher’s GB/FB is one of the most consistent metrics (for starter pitchers, the year-over-year correlation is 0.87, which is highest for all outcome metrics), so there was little concern about a pitcher changing their batted-ball profile between seasons. A GB/FB greater than 1 was coded as ground ball; less than 1 was coded as fly ball. In the end, 1,387 season pairs were included in the analysis:
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