There’s an endless game of cat and mouse to be played among pitchers and batters. Ted Williams famously said that hitting a baseball was the hardest thing to do in all of sports, but what makes it so hard? Sure, hitting something that small traveling that fast with a blunt instrument takes Olympian levels of reaction time, hand-eye coordination, and raw strength, but put even a decent minor-league hitter up against a pitching machine that is “throwing” 95 mph and eventually, he’ll start squaring it up every single time.
The challenges of hitting a baseball are many and difficult. Depending on the speed of the pitch, a batter may have something like half a second to 1) locate the ball as it leaves the pitcher’s hand, 2) predict its movement based on the kind of pitch it is (fastball, slider, curve, etc.), 3) decide whether to swing, and potentially 4) adjust mid-swing to the path of the ball or check his swing. All of which is to say, hitting a baseball in MLB may actually be the hardest thing in the galaxy (I’ve never done it, myself).
Arguably the most demanding part of this battle is purely mental (as Hank Aaron noted). Because of how little time there is for a hitter to perform all of the above-mentioned tasks, it is helpful to have some notion ahead of time of what, where, and how the pitcher is going to throw. Conversely, the more uncertainty and confusion a pitcher can create in the hitter, the more chance he has of catching him off guard.
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A first foray into one of baseball's most murky subjects.
Pitch sequencing has long lurked as a sort of terra incognita in sabermetric analysis. It’s something that all baseball folks agree is important, but it’s proved mostly impenetrable to strictly quantitative approaches. There’s an intuitive sense that sequencing must be one of the crucial determinants of pitcher success, and although we can seemingly identify a good sequence when we see one, any attempt to apply a universal criterion of good sequencing across all pitches (or pitchers) is much more challenging. The rest of this article will be devoted to applying just such a criterion, and determining whether it is of any practical utility in understanding pitching generally.
There are at least two schools of thought about pitch sequencing. On the one hand, there seems to be an appreciation for sequences that mix up locations, speeds, and breaks in unpredictable ways, on the grounds that those kinds of sequences ought to be the most challenging for a hitter. On the other hand, Mitchel Lichtman (aka MGL) has argued forcefully on the basis of game theory that the ideal sequencing would be something like weighted randomness (weighted, that is, by the quality of each pitch). MGL’s argument says that if a pitcher tried too hard to mix things up, for instance by purposefully not throwing two of the same pitch in a row, he would end up tipping the next pitch to the batter, resulting in a powerful disadvantage.
Inside the batter-pitcher matchup: Can the pitches a pitcher just threw help us predict which ones he's about to throw?
Last Friday, I had the pleasure of devouring Dan Brooks’ and Daniel Mack’s introduction of Brooks Baseball’s newest toy, pitch sequence visualization. To me, this is a major step forward in deciphering how every aspect of one pitch—be it type, velocity, or location—affects the strategy of the next. I’m not even so concerned with the results of the sequences—ultimately, well-executed pitches get results irrespective of other factors—but the massive insight we now have into a pitcher’s plan of attack is exciting whether you’re an amateur sabermetrician or a young player looking for a strategic edge.
Pitch sequencing is one of the great white sabermetric whales; we’ve been trying to get into pitchers’ heads for years, but in a game where all hurlers aspire on some level to complete randomness, that’s a very difficult thing to do. One sentence from the piece struck me as particularly insightful, however, and I think it bears repeating. It’s exactly how we’re going to refine this holy grail of baseball into as useful, practical, and applicable a tool as it could ever be. Write Brooks and Mack,
Want to know not just what pitches a pitcher throws, but where, when, and in what order he throws them? Now you can.
At Brooks Baseball, we’ve built a repository where you can access almost any information about any pitcher’s pitches and be confident that the pitch types were identified correctly. For example, you can ask how many times batters swung and missed at a Stephen Strasburg changeup, how often batters hit Chris Sale’s slider for a groundball, or what the overall called-strike rate is for Felix Hernandez’s fastball.
But PITCHf/x databasing is still in its infancy. Pitching is not the sum of individual statistics about individual pitches any more than a piece of music is the sum of an individual set of notes. Pitching is a sequence of events—the previous pitch’s execution may be as germane to the outcome of the at-bat as the current pitch’s execution. We often hear about how a pitcher might go up in the zone with a high fastball to raise a batter’s eye level and then down in the zone with a curveball. None of that was captured in the maze of tables and charts already available.
Earlier this week, Zack Greinke opposed Rays starter Jeremy Hellickson in his Angels debut. Doug reviews each player's approach to pitching.
The Angels showed off their new prize last Sunday, as deadline acquisition Zack Greinke made his Anaheim debut in a face-off with 2011 Rookie of the Year Jeremy Hellickson. Greinke returns to the American League after a year and a half spent taking swings in the senior circuit, arriving via a July 27th exchange for three Angels minor leaguers, including Futures Game LVPAriel Pena. Hellickson made a comeback of his own on Sunday with a return from his first trip to the disabled list, a 15-day hiatus to rest from shoulder fatigue. Greinke and Hellickson are advanced students of the game, and Sunday's throwdown between the two pitch-sequence savants did not disappoint.
Sunday was a clinic for pitcher target practice. The two starters yielded just a single free pass between them, with excellent mechanics contributing to masterful pitch execution on both sides, though each player employed his own unique gameplan. The young Helix posted the superior stat line, allowing just a pair of singles while showing off his penchant for inducing weak contact. Hellickson was also the beneficiary of an opposing lineup lacking Mike Trout, who gave way to out-machine Vernon Wells in a swap that cost about 900 points of OPS.
Can you tell which pitches will leads to hits and which will lead to outs without seeing the results?
If we want to evaluate a pitch, there are few things we can focus on. We can look at the qualities of the pitch itself as it moves toward home plate, including movement, pitch type, and location. We can look at the catcher's glove, to see how much it moves from its target. We can look at the batter, to see how balanced he is as he swings at it. And we can look at the result: hit, out, stung, dribbled. I have a theory, which is that we (non-scouts) are mostly unable to make much of the first, second and third ways. That, mostly, we only remember the fourth.
So what follows is an experiment. I don't know what the point of this experiment is or what it will show. I don't know the best way to conduct this experiment. This might be an experiment I revisit in a better form someday in the future. But the experiment is simple, and I think it will be interesting, and I can't wait.
Looking at how pitching to the situation can keep runs off the board.
All right, so we’ve been looking at how to put a value on the performance of position players—in terms of hitting and fielding. Now let’s move on to pitching.
Evaluating offense is, at least comparatively to everything else, pretty simple—both in principle and in practice. Evaluating fielding is trickier in practice, but I don’t feel it’s any trickier in principle—everyone agrees what a fielder’s job is and what we ought to be measuring.
Taking an in-depth look at a two-inning stint by Francisco Rodriguez in order to understand why he threw certain pitches.
What follows is a story of a pitcher who lost command of his fastball, and a hitter who approached him as if he could throw it to a teacup. The Mets were clinging to a 3-1 lead over the Giants on July 18 as their game entered the late innings at AT&T Park. After another eight-frame master class from Johan Santana, Mets manager Jerry Manuel called on Francisco Rodriguez to lock down a victory. It was a game the Mets desperately needed; they opened the second half of the season by scoring just four runs in their first three games, and if the week following this game is any indication, they aren’t good enough to waste Santana’s brilliance and still make a run at the postseason.
Now that we’ve set the scene, let’s think along with its principal players, and observe how Rodriguez and his opponents adapt—or fail to adapt—to the Mets closer’s uncharacteristic lack of a reliable fastball. We’ll follow K-Rod’s two innings in hopes of learning a thing or two about the mysterious art of pitch sequencing, and see how the information Rodriguez sends with each pitch of this outing may be more predictive of what he’ll throw next than simply relying on his overall tendencies.
The challenge of changing speeds while integrating perceived velocity into the mix.
When a pitch begins its flight towards home plate, the radar gun registers a specific velocity-one that correlates quite strongly to the start speed component of PITCHf/x-which unfortunately becomes the gospel as to how hard the pitch was thrown. Various factors, like the natural loss of velocity as the pitch reaches home plate, the true distance of the release, the actual flight time, the location, when the batter picks the ball up, and what pitch the batter initially anticipated all work together to alter a hitter's perception of velocity.
The shape of the blistering-hot performance of the Dodgers shortstop.
Past experience can generate expectations. Certainly, when they signed Rafael Furcal to a three-year, $39 million deal following the 2005 season, the Dodgers believed that the former Braves leadoff batter would provide a significant spark to their offense. He did not disappoint in 2006, finishing 14th in NL MVP voting. Last year, however, a nagging ankle injury suffered in spring training kept him out of action for the first couple of weeks as well as the final weeks of the season; it also hindered his production level during the 138 games in which he played. A player whose modus operandi involves speed playing with an ankle injury is not a good combination.
Similarly, when Joe Torre signed on to manage the team this offseason, he was fresh off of managing a shortstop that happened to be the longtime face of the most prominent franchise in sports. He may have known his new shortstop could produce at an all-star level even, after that rough 2007. Suffice it to say he could not possibly have had any idea Furcal would be this productive.