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We have room to appreciate only so many surprises at once. We’re all getting our heads around the Blue Jays leading the AL East, with the Rays in last and the Red Sox lousy. The Brewers continue to lead the NL Central, with the Cardinals fighting to stay over .500 and the Pirates fighting to get there. The Dodgers might hurt their necks from looking up at the Giants, and over in the AL West, the Astros might be in the basement, but for several weeks they’ve played like one of baseball’s best teams. There’s a lot going on, and the Marlins have never been particularly visible, but if you glance over at the NL East standings, you’ll notice it’s not just about the Nationals and Braves.

These Marlins would be interesting if they were hanging around without having suffered the nightmare scenario. It would be remarkable if the Marlins were 34-31 with a healthy Jose Fernandez. But, when Fernandez got hurt, the consensus was that the Marlins were finished. They were already considered a long shot with one of the best pitchers in the world. As soon as Fernandez went down, the Marlins were forgotten, having lost 50% of their superstars. Yet still they haven’t gone away, and as close as they are to the division lead, they’re also presently in a playoff position. If the playoffs started tomorrow, the Marlins would take on the Braves for the right to face the Giants.

Read the rest at FoxSports.com.

Back in March, Major League Baseball Advanced Media announced the formation of a new product that looked like the data-capturing system of our dreams. It was more concept than product, however, and 2014 was essentially going to be a year long beta test, with just three stadiums outfitted with the technology. While the system looks amazing, it hadn’t even yet been named.

That changed today, with this tweet from MLB.

The new Statcast player tracking clips will blow you away. @YasielPuig: http://t.co/jQJJQ6wGwl @TheCUTCH22: http://t.co/XJQ2qO2Aet

— MLB (@MLB) June 13, 2014

We welcome our new StatCast overlords.

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Here on FanGraphs, a lot of the data we present is built around future projections. We host both pre-season and in-season forecasts from multiple systems, and these forecasts form the basis for many of our tools, including our Playoff Odds model. It’s safe to say that we like forecasts.

But I know not everyone reading this post trusts the projection systems, especially when they are forecasting things that we don’t want to be true. It’s natural to want to discard prior information when it doesn’t align with what we want to believe, and often, the forecasts serve as a wet blanket alternative to enthusiasm and excitement. What’s to like about a system that takes the fun out of breakout performances, or tells us that we need to be more patient with the guy who just looks terrible every time we watch him?

And it’s not like projection systems are infallible. Those forecasts never saw Cliff Lee coming, and they certainly didn’t anticipate Jose Bautista was going to go from career scrub to superstar at the age of 29. There are plenty of examples where players made dramatic, unexpected turns in their career arc, and using their track record to project their future would have been wrong. The fact that there are players who have radically changed their performance base allows us to extrapolate that possibility to every player who is performing in a way inconsistent with their projection.

But we just shouldn’t do it. Projections might be fun-sucking, soulless algorithms that are incapable of picking up on adjustments that players can and do make, but the reality is that these forecasts do a pretty good job of predicting the future, even for players whose current performances and projections don’t line up at all.

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In the research for the piece I just posted on Johnny Cueto, I ended up with a treasure trove of data on pitch type outcomes during the PITCHF/x era. It is really fun information, and so I figured I’d share a few tidbits here that don’t necessarily lend themselves to an entire post. This post is basically just a list of interesting numbers without any commentary. Enjoy!

First, here are the 10 lowest wOBAs allowed for all pitch types for starting pitchers in 2014, setting a minimum of 100 pitches thrown for each pitch type.

Keep in mind that there’s a huge selection bias here, in that breaking balls and off-speed pitches get thrown primarily in pitcher’s counts, when the expected wOBA is much lower. So, you don’t want to say that Colin McHugh’s curveball is the second best pitch in baseball. It’s not. But it’s been a pretty fantastic out-pitch so far this year.

And now, the laggards, though we’ll only do bottom five in order to limit our the amount of public shaming.

Maybe time to mix it up or just try something else entirely, boys.

Last night, Johnny Cueto dominated the Dodgers, punching out 12 batters in just six shutout innings. This wasn’t anything new, though; Cueto has been destroying opposing hitters all season long. Hitters are batting just .158/.218/.261 against him this year, good for a pitiful .217 wOBA, and he’s the easy early frontrunner for the NL Cy Young Award.

Cueto has been very good before, but this year, he’s taking things to another level. His 28% strikeout rate is nine percentage points higher than his career average, and seven percentage points better than his career-best, posted last year. Last night was his fourth start of the season in which he punched out 10 or more batters; he’d only done that three times in his entire career prior to 2014. Cueto has always been a strike-thrower with a roughly average strikeout rate who succeeded by limiting hits on balls in play, never walking anyone, and completely shutting down the running game with the game’s best pickoff move.

Cueto is still doing all those things, only now, he’s also posting the fourth highest K% of any starting pitcher in baseball; the only guys ahead of him are Strasburg, Darvish, and Tanaka. Combine an elite strikeout rate with everything else Cueto does well, and you have something close to perfection.

But this isn’t the amazing part. The amazing part is how he’s doing it.

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I’m going to begin this column with an unremarkable looking fact.

When a Major League hitter has swung at the first pitch of an at-bat in 2014, the average OPS in that at-bat — not just OPS on first pitch swings, but the OPS for all at-bats in which there was a first pitch swing — is .710. The average OPS for an at-bat in which the batter does not swing is .708. For all intents and purposes, that is a statistical tie, and suggests that there has been no obvious advantage to pursuing either approach this year.

Here’s why that unremarkable looking fact actually is remarkable; if this lasts, it would mark the first season ever recorded — as far back as Baseball-Reference’s data for that split goes anyway, which for this specific number is 1988 — where the OPS on at-bats with a first pitch swing was higher than the OPS on at-bats with a first pitch take. For most of the last 25 years, it hasn’t even been close.

From 1988 to 2011, the advantage of the first-pitch take was consistent and constant. Sure, there were years where the advantage wasn’t enormous — in 2001, the gap in OPS was only seven points, and it was just nine points in 2004 — but the blue line and red line never really came that close to intersecting until 2012. That year, the gap fell to just two points, which still stands as the lowest recorded advantage for the first-pitch take over a full season in the last 25 years. Last year, the advantage jumped back up to six points, but that was still lower than any season prior to 2012. And now this year, the gap has not only disappeared, but it’s reversed course for the first time.

Read the rest on FoxSports.com.

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This morning, I was playing around with some of our plate discipline calculations. I really like the metrics we have for measuring a player’s approach at the plate, but because they are broad categories that combine numerous variables into one number, there are some questions that none of them answer all that well on their own. For instance, if you just wanted to know who the most unrepentant hacker in 2014 has been, you could simply sort the leaderboards by Swing%, and you’d see Chris Johnson at the very top, swinging at a higher rate of pitches (57.1%) than anyone else in baseball.

But knowing that Johnson has swung at the highest percentage of pitches doesn’t really make him the game’s most aggressive hitter, as part of his high swing rate is that he’s been thrown an above average number of strikes. Pablo Sandoval is right behind Johnson in overall swing rate, but his Zone% is 11 percentage points lower than Johnson’s, so he’s getting far fewer pitches to hit but still chasing the same amount. By any reasonable measure of hackiness, one would have to conclude that Sandoval has been more aggressive than Johnson.

Of course, we have a measure of swings at out-of-zone pitches, which is a pretty good way to measure which players fit the hacker mold. However, by O-Swing%, Matt Adams ranks #2 in baseball, but his overall swing rate only ranks 17th. Is he one of the game’s most deliberate free swingers, or does he just struggle to recognize balls from strikes?

When I think about the hacker label for a hitter, I think of guys who swing as if they had made up their mind before the pitcher even started his delivery. Location and pitch type are secondary to the desire to just put the bat on the ball, and they won’t be deterred from swinging no matter what the pitcher does. So, I thought that perhaps we could better identify those types of hitters by looking at a ratio of two of our plate discipline stats, and so out of curiosity, I divided Swing% by Zone% to get a ratio of swings-per-balls-in-the-zone. Others have created far more complex and mathematically sound formulas to get at the same idea, but there’s value in ease of calculation, so I was curious how Swings Per In-Zone Pitch would do.

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After another late-game meltdown, Grant Balfour is officially out as closer for the Tampa Bay Rays, at least for now, and the Rays will go closer-by-committee for a little while. While no one player can doom an entire team, Balfour’s problems are one of the primary reasons the Rays have the worst record in baseball, especially given that Balfour’s $6 million salary is a larger share of Tampa’s budget than it would be for many of their competitors. But interestingly, Balfour isn’t really an outlier here. This year, nearly every team who spent resources to acquire a “proven closer” would have been better off lighting their money on fire instead.

While the definition of a “proven closer” is up for interpretation, I would suggest that seven relief pitchers changed teams last winter and were paid something of a premium due to their ninth inning experience. They are effective relievers and would have been valuable even without ninth inning experience, but their end-of-game history likely earned them a little more money than they would have had they been career setup men. Those seven, and their contracts, are listed below.

Joe Nathan: 2 years, $20 million
Brian Wilson: 2 years, $18.5 million
Joaquin Benoit: 2 years, $15.5 million
Fernando Rodney: 2 years, $14 million
Grant Balfour: 2 years, $12 million
Jim Johnson: 1 year, $10 million
Edward Mujica: 2 years, $9.5 million

Six of them changed teams as free agents, while Johnson was traded in a salary dump by the Orioles, and was effectively available to any team who wanted to pay the amount he would get in his final trip through arbitration. Here’s how those seven pitchers have done this year, with their 2013 performance listed below for reference.

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