Archive for Player Analysis

It’s no secret that a vast majority of Mets fans want Wilmer Flores to be playing shortstop every day. It’s also no secret that manager Terry Collins has some strange infatuation with Ruben Tejada, opting again and again to give him starts at shortstop.

Although Collins hasn’t given the media any clear reasoning as to why this is, there are a few reasons we can speculate. The biggest one is defense — Ruben Tejada has made major strides at shortstop this season, posting the highest DRS of his career. Flores, on the other hand, is a second baseman, and even his defense at second is questionable — he really profiles more as a corner infielder. However, with the other three infield positions being blocked by Daniel Murphy, David Wright, and the new-and-improved Lucas Duda, Ruben Tejada is the odd man out.

The other side of the coin is the one I’m going to be focusing on: offense. When Tejada started getting regular playing time as a 21-year-old in 2011, he showed some legitimate offensive potential, hitting line drives at an extremely impressive 28.1% rate (would have ranked 2nd among qualified batters,) good for .287/.345/.345 in 877 PAs between 2011 and 2012. Then, in 2013, he came to spring training out of shape, hit .202, got sent down, got hurt a couple times, and basically threw yet another monkey wrench into the Mets’ rebuild. The job became his to lose in 2014, and he’s hit a measly .228/.348/.280, the OBP even being inflated by the amount of intentional walks he received in the 8 hole. His 0.4 fWAR this season cancels out his -0.4 last season, making him a perfect replacement-level player.

Meanwhile, Wilmer Flores has been a top offensive prospect in the Mets system since he was signed out of Venezuela as a 16-year-old in 2007.  His numbers finally started to reflect his talent in 2012, when he hit .300/.349/.479 between high A and AA. In 2013, he exploded in AAA, and the past two seasons has hit .321/.360/.543 with 28 home runs and 47 doubles in exactly 162 games. Sure, he plays in Vegas, one of the most hitter friendly parks in AAA, but these are still numbers that demand attention — attention that he hasn’t yet seemed to receive from Terry Collins. Despite Tejada’s offensive struggles, he has still started 86 games at short this season, as opposed to Flores’ 20. One of the reasons a few Mets fans have been pointing to is the fact that Flores has yet to actually produce at the major league level, hitting only .220/.254/.304 in his 201 big league plate appearances. But is that slash-line an accurate reflection of his talent? And, for that matter, is Tejada’s?

For this mini-evaluation, we’ll use slash12’s xBABIP formula. It’s never a perfect system, but it will give us a good estimation of what these players slash-lines should look like (or at least their average and OBP.)

After inserting Ruben Tejada’s batted ball profile, we get that his xBABIP for 2014 is .329 — much higher than his actual BABIP of .288. We can then plug that backwards into the BABIP formula to determine how many hits he theoretically should have. Since the formula is (H-HR)/(AB-HR-K+SF), we can plug in everything except for hits to get (H-2)/(289-2-65+0)=.329, simplify that to (H-2)/(222)=.329, multiply both sides by 222 to get H-2=73, and we can come to the conclusion that Ruben Tejada should have 73 hits on the year, instead of the 66 he has. This would make his batting average .253 and his OBP .364 (although, keep in mind that that’s being inflated by the 10 intentional walks he’s had while hitting 8th in the order. If we decided to remove those, his OBP would drop to .345).

Now, doing the same to Wilmer Flores is slightly tricky, as we don’t have nearly as large a sample size worth of batted ball data to use. In the interest of accuracy, we’ll use his career profile, so we can at least get a sample of 201 PAs instead of his 100 this year. Plugging his batted ball profile into the xBABIP calculator, we get a result of .333, compared to his actual career BABIP of .268. Doing the same backwards math we did with Tejada, this brings his expected career batting average up to .272, and his career OBP up to .304.

Now, these are only two stats, and they only tell us so much — Flores seems to be a better hitter, but his career 4.5% BB rate is clearly overmatched by Tejada. There isn’t a formula out there for expected slugging percentage — at least, as far as I know — so we can’t really determine what that would be (and subsequently, what their OPS would be). We could assume the same ISO, which would not be entirely accurate, but it would give us a .305/.669 for Tejada and a .355/.659 for Flores. Still, I think it’s clear, both from my biased perspective as a Mets fan and my objective perspective as a baseball fan, that Flores has the brighter future offensively — but it’s up to the Mets to decide how to capitalize on it.

Examining the foundations of batting analysis began in Part 1 with an historical examination of the earliest statistics designed to examine the performance of batters. In Part 2, I presented a new method for calculating basic averages reflecting the “real and indisputable” rate at which batters reached base. In Part 3, I examined the development of run estimation techniques over the last century, culminating with the linear weights system. I will employ that system now as I reconstruct run estimation from the bottom up.

We use statistics in baseball to tell stories. Statistics describe the action of the game or the performance of players over a period of time. Statistics inform us of how much value a player provided or how much skill a player showed in comparison to other players. To tell such stories successfully, we must understand how the statistics we use are constructed and what they actually represent.

A single, for instance, seems simple enough at first glance. However, there are details in its definition that we sometimes gloss over. In general, a single is any event in which the batter puts the ball into play without causing an out, while showing an accepted form of batting effectiveness (reaching on a hit), and ultimately advancing to first base due to the primary action of the event (before any secondary fielding errors or advancement on throws to other bases). Though this is specific in many regards, it is still quite a broad definition for a batting event. The event could occur in any inning, following any number of outs, and with any number of runners on the bases. The ball could be hit in any direction, with any speed and trajectory, and result in any number of baserunners advancing any number of bases.

These kinds of details form the contextual backdrop that characterizes all batting events. When we construct a statistic to evaluate these events, we choose what level of contextual detail we want to consider. These choices define our analysis and are critical in developing the story we want to tell. For instance, most statistics built to measure batting effectiveness—from the simple counting statistics like hits and walks, to advanced run estimators like Batter Runs or weighted On Base Average (wOBA)—are constructed to be independent of the “situational context” in which the events occur. That is, it doesn’t matter when during the game a hit is made or if there are any outs or any runners on the bases at the time it happens. As George Lindsey noted in 1963, “the measure of the batting effectiveness of an individual should not depend on the situations that faced him when he came to the plate.”

Situational context is the most commonly cited form of contextual detail. When a statistic is described as “context neutral,” the context being removed is very often the one describing the out/base state before and after the event and the inning in which it occurred. However, there are other contextual details that characterize the circumstances and conditions in which batting events occur that also tend to be removed from consideration when analyzing their value. Historically, where the ball was hit, as well as the speed and trajectory which it took to reach that location, have also not been considered when judging the effectiveness of batters. This has partly been due to the complexity of tracking such things, especially in the century of baseball recordkeeping before the advent of computers. Also, most historical batting analyses focus exclusively on the outcome for the batter, independent of the effect on other baserunners. If the batter hits the ball four feet or 400 feet but still only reaches first base, there is no difference in the personal outcome that he achieved.

If the value of a hit was limited to only how far the batter advances, then there would be no need to consider the “batted-ball context,” but as F.C. Lane observed in 1916, part of the value of making a hit is in the effect on the “runner who may already be upon the bases.” By removing the batted-ball context when considering types of events in which the ball is put into play, we’re assuming that a four-foot single and 400-foot single have the same general effect on other baserunners. For some analyses, this level of contextual detail describing an event may be irrelevant or insignificant, but for others—particularly when estimating run production—such a level of detail is paramount.

Let’s employ the linear weights method for estimating run production, but allow the estimation to vary from one completely independent of any contextual detail to one as detailed as we can make it. In this way, we’ll be able to observe how various details impact our valuation of events. Also, in situations where we are only given a limited amount of information about batting events, it will allow us to make cursory estimations of how much they caused their team’s run expectancy to change.

To begin, let’s define the run-scoring environment for 2013.[i] While we have focused on context concerning how events transpired on the field, the run scoring environment is another kind of contextual detail that characterizes how we evaluate those events. The exact same event in 2013 may not have caused the same change in run expectancy as it would have in 2000 when runs were scored at a different rate. We will define the run scoring environment for 2013 as the average number of runs that scored in an inning following a plate appearance in each of the 24 out/base states – a 2013-specific form of George Lindsey’s run expectancy matrix:

While we will focus on examining various levels of contextual detail concerning the events themselves, the run-scoring environment can also be varied based on contextual details concerning the scoring of runs. The matrix we will employ, as defined by Lindsey, reflects the average number of runs scored across the entire league. If we wanted, we could differentiate environments by league or park, among other things, to try and reflect a more specific estimate of the number of runs produced. As the work I’m going to present is meant to provide a general framework for run estimation, and these adjustments are not trivial, I’m going to stick with the basic model provided by Lindsey.

With Lindsey’s tool, we can define a pair of statistics for general analysis of run production. Expected Runs (xR) reflect the estimated change in a team’s run expectancy caused by a batter’s plate appearances independent of the situational context in which they occur. A batter’s expected Run Average (xRA) is the rate per plate appearance at which he produces xR.

xRA = Expected Runs / Plate Appearances = xR / PA

xR and xRA create a framework for estimating situation-neutral run production. Based on the contextual specificity that is used to describe the action of a plate appearance, xR and xRA will yield various estimations. The base case for calculating expected runs, xR₀, is calculated independently of any contextual detail, considering only that a plate appearance occurred. By definition, an average plate appearance will cause no change in a team’s run expectancy. Consequently, no matter a player’s total number of plate appearances, his xR₀ and, by extension, his xRA₀, will be 0.0.

This is completely uninformative of course, as base cases often are. So let’s add our first layer of contextual specificity by noting whether an out occurred due to the action of the plate appearance. This is the most significant contextual detail that we consider when evaluating batting events – it is the only factor that determines whether a plate appearance increases or decreases a team’s run expectancy. In 2013, 67.5 percent of all plate appearances resulted in at least one out occurring. On average, those events caused a team’s run expectancy to decrease by .252 runs. The 32.5 percent of plate appearances in which an out did not occur caused a team’s run expectancy to increase by .524 runs on average. We’ll define xR₁ as the estimated change in run expectancy based exclusively on whether the batter reached base without causing an out; xRA₁ is the rate at which a batter produced xR₁ per plate appearance.

You’ll notice that the components that construct xRA₁ can only take on two values—.524 and -.252—in the same way that the components that construct effective On Base Average (eOBA) (as defined in Part 2) can only take on two values—1 and 0. These statistics—xRA₁ and eOBA—have a direct linear correlation:

In effect, xRA₁ is a weighted version of eOBA, incorporating the same contextual details but on a different scale. This estimation provides us with an association between reaching base safely and producing runs. However, the lack of detail would suggest that all players that reach base at the same rate produce the same value, which is over simplified. It’s why you wouldn’t just use eOBA, or eBA, or any other basic statistic that reflects the rate which a batter reaches base, when judging the performance of a batter. Let’s add another layer of contextual detail to account for the different kinds of value a batter provides when he reaches base.

xR₂ will represent the estimated change in run expectancy based on whether the batter safely reached base and the number of bases to which he advanced due to the action of the plate appearance; xRA₂ will be the rate at which a batter produces xR₂ per plate appearance. While xR₁ and xRA₁ were built with just two components to estimate run production, xR₂ and xRA₂ require five components: one to define the value of an out, and four to define the value of safely reaching each base.

In 2013, a batter safely reaching first base during a plate appearance caused an average increase of .389 runs to his team’s run expectancy. Reaching second base was worth .748 runs, third base was worth 1.026 runs, and reaching home was worth 1.377 runs on average. Where xRA₁ provided a run estimation analog to eOBA, xRA₂ is built with very similar components to effective Total Bases Average (eTBA), though it’s not quite a direct linear correlation:

The reason xRA₂ and eTBA do not correlate with each other perfectly, like xRA₁ and eOBA, is because the way in which a batter advances bases is significant in determining how valuable his plate appearances were. Consider two players that each had two plate appearances: Player A hit a home run and made an out, Player B reached second base twice. Their eTBA would be identical—2.000—as they each reached four bases in two plate appearances. However, from the run values associated with reaching those bases, Player A would record 1.125 xR₂ from his home run and out, while Player B would record 1.496 xR₂ from the two plate appearances leaving him on second base. Consequently, Player A would have produced a lower xRA₂ (.5625) than Player B (.7480), despite their having the same eTBA. These effects tend to average out over a large enough sample of plate appearances, but they will still cause variations in xRA₂ among players with the same eTBA.

As stated in Part 2, the two main objectives of batters are to not cause an out and to advance as many bases as possible. If the only value that batters produced came from accomplishing these objectives, then we would be done – xR₂ and xRA₂ would reflect the perfect estimations of situation-neutral run production. As I hope is clear, though, the value of a batting event is dependent not only on the outcome for the batter but on the impact the event had on all other runners on base at the time it occurred. Different types of events that result in the batter reaching the same base can have different average effects on other baserunners. For instance, a single and a walk both leave the batter on first base, but the former creates the opportunity for baserunners to advance further on average than the latter. To address this, the next layer of contextual detail will bring the official scorer into the fray. xR₃ will represent the estimated change in run expectancy produced during a batter’s plate appearance based on:

(1)    whether the batter safely reached base,

(2)    the number of bases, if any, to which the batter advanced due to the action of the plate appearance, and

(3)    the type of event, as defined by the official scorer, that caused him to reach base or cause an out.

xRA₃ will, as always, be the rate at which a batter produces xR₃ per plate appearance.

Each of the run estimators that were examined in Part 3, from F.C. Lane’s methods through wOBA, are subsets of this level of xR. Expected runs incorporate estimations of the value produced during every event in which the batter was involved, including those which may be considered “unskilled.” The run estimators examined in Part 3 consider only those events that reflected a batter’s “effectiveness,” and either disregard the “ineffective” events or treat them as failures. xR₃ provides the total value produced by a batter, independent of the effectiveness he showed while producing it, based solely on how the official scorer defines the events. Consequently, some events, like strikeouts, sacrifice bunts, reaches on catcher’s interference, and failed fielder’s choices, among other more obscure occurrences, are examined independently in xR₃. From the two components of xR₂ and the five of xR₃, we build xR₄ with 18 components: five types of outs and 13 types of reaches.

To help illustrate how xR has progressed from level to level, here is a chart reflecting the run values for 2013 as estimated by xR based on the contextual detail provided thus far.

xR Progression

Beyond any consideration of skilled or unskilled production, xR₃ is the level at which most run estimators are constructed. It incorporates events that are well defined in the Official Rules of the game, and have been for at least the last few decades, and in some cases for over a century. While we still define most of a batter’s production by his accomplishing these events, we live in an era where we can differentiate between events on the field in more specific ways. Not all singles are identical events. We weaken our estimation of run production if we don’t account for the different kinds of singles, among other events, that can occur. xR₃ brought the official scorer into action; xR₄ will do the same with the stat stringer.

While the scorer is concerned with the result of an event, a stringer pays attention to the action in between the results. They chart the type, speed, and location of every pitch, and note the batted ball type (bunt, groundball, line drive, flyball, pop up) [ii] and the location to which the ball travels when put into play.While we don’t have this data as far back in time as we have result data, we do have decades worth of information concerning these details. By differentiating events based on these details, we will begin to unravel the “batted-ball context.” Ideally, we would know every detail of the flight of the ball, and use this to group together the most similar possible type of events for comparison.[iii] At present, we’re limited to what the scorers and stringers provide, but that’s still quite a lot of information.

xR₄ will represent the estimated change in run expectancy produced during a batter’s plate appearance based on:

(1)    whether the batter safely reached base,

(2)    the number of bases, if any, to which the batter advanced due to the action of the plate appearance,

(3)    the type of event, as defined by the official scorer, that caused him to reach base or make an out,

(4)    the type of batted ball, if there was one, as defined by the stat stringer, that resulted from the plate appearance,

(5)    the direction in which the ball travelled, and

(6)    whether the ball was fielded in the infield or outfield.

xRA₄ will be the rate at which a batter produces xR₄ per plate appearance.

There are 18 components in xR₃ which describe the assorted types of general events a batter can create.  When you add in these details concerning the batted-ball context, the number of components increases to 145 for xR₄. With such specific details being considered, we can no longer rely on a single season of data to accurately inform us on the average situation in which each type of event occurs; the sample sizes for some events are just too small. To address this, there are two steps required in evaluating events for xR₄. The first is to build a large sample of each event to build an accurate picture of their relative frequency in each out/base state. I’ve done this by using a sample covering the previous ten seasons to the one in which the estimations are being made. Once this step is completed, the run-scoring environment in the season being analyzed is applied to these frequencies, in the same way it is when looking at single season frequencies for basic events.

For instance, the single, which is traditionally treated as just one type of event, is broken into 24 parts based on the contextual details listed above. By observing the rate at which each of these 24 variations of singles occurred in each out/base state from 2004 through 2013, and applying the 2013 run-scoring environment, we get the following breakdown for the estimated value of singles in 2013:

This process is repeated for every type of batting event in which the ball is put into play. One of the ways we can use this information is to consider the run value based not on the result of the event, but on the batted-ball context that describes the event. Here are those values in the 2013 run-scoring environment:

Similarly, we can break down each player’s xR₄ by the value produced on each type of batted ball. Here are graphs for xR₄ produced on each of the four types of batted balls resulting from a swing, with respect to the number of batted balls of that type hit by the player. For simplicity, from this point on, when I drop the subscript when describing a batter’s expected run total, I’m referring to xR₄.

Line drives are the most optimal result for a batter. The first objective of batters is to reach base safely, and they did that on 67.0 percent of line drives last season. No batter who hit at least eight line drives in 2013 caused a net decrease in his team’s run expectancy during those events. For most batters, hitting the ball into the outfield in the air is the ideal way to produce value, as fly ball production tends to create a positive change in a team’s run expectancy. However, fly balls have the most variance of any of the batted ball types, and there are certainly batters who hurt their teams more when hitting the ball at a high launch angle than a low one. Here are the players to produce the lowest xRA on fly balls last season (minimum 50 fly balls):

For each of these batters, hitting the ball on the ground or on a line drive were far better results on average.

While groundballs may be a preferable result for some batters when compared to fly balls, they are still effectively batting failures for the team. There were 840 batters in 2013 to hit at least one groundball and only 44 produced a net positive change in their team’s run expectancy. Of those 44 players, only 11 hit more than 10 groundballs, and only two (Mike Trout and Juan Francisco) hit at least 100 groundballs. Here are the players with the highest xRA on groundballs in 2013 who hit at least 100 groundballs:

xR₄ allows us to tell the most detailed story concerning the type of value a batter produced, independent of the situational context at the time the plate appearance occurred. Because we gradually added layers of detail to our estimation, we can compare how each level of expected runs correlates to this most detailed level. In this way, we can judge how much information each level provides with respect to our most detailed estimation. Here is a graph that charts a batter’s xR₄ with respect to his xR₁, xR₂, and xR₃ estimations:

The line that cuts through the data reflects the xR₄ values charted against themselves. For each xR_n, we can calculate how well it correlates with xR₄ and, consequently, how much of xR₄ it can explain. Remember that we have already shown that xR₁ has a direct linear correlation with eOBA and xR₂ has a very high, though not quite direct, correlation with eTBA. For the xR₁ values, we observe a correlation, r, with xR₄ of .912, and an r² of .832, meaning that knowing the rate at which a batter reaches base explains over four-fifths of our estimation of xR₄. For the xR₂ values, r² increases to .986; for the xR₃ values, r² increases slightly higher to .990.[iv]

The takeaway from this is that when considering the whole population of players, there is little difference in a run estimator that considers the batted-ball context and one that does not; you can still explain 99 percent of the value estimated by xR₄ by stopping at xR₃. In fact, if all you know is the rate at which a batter accomplishes his two main objectives—reaching base and advancing as far as possible—you can explain well over 90 percent of the value estimated by xR₄. However, on an individual level, there is enough variation that observing the batted-ball context can be beneficial. Here are the five players with the largest positive and negative differences between their xR₃ and xR₄ estimations:

These changes are not massive, and these are the extreme cases for 2013, but they are certainly large enough that ignoring them will weaken specific analyses of batting production. Incorporating batted ball details into our analysis adds a significant layer of complexity to our calculation, but it must be considered if we want to tell the most accurate story of the value a batter produced.

If this work seems at all familiar, you may have read this article that I wrote last year on a statistic that I called Offensive Value Added (OVA). For all intents and purposes, OVA and xR are identical. I decided that the name change to xR would help me differentiate estimations more simply, as I could avoid naming four separate statistics for each level of contextual detail, but there was also a secondary reason for changing the presentation of the data. OVAr was the rate statistic associated with OVA, and it was scaled to look like a batting average, much in the same way that wOBA is scaled to look like an on base average. At the time, I choose to do this to make it easier to appreciate how a batter performed, since many baseball enthusiasts are comfortable interpreting the relative significant of a batting average.

After thinking on the subject, though, I came to decide that I prefer statistics that actually “mean” something to those that give a general, unit-less rating. For instance, try to explain what wOBA actually reflects. It starts as a run estimator, but then it’s transformed into a number that looks like a statistic with specific units (OBA), while not actually using those units. Once that transformation occurs, it no longer reflects anything specific and only serves as a way to rate batters. The same principle applies to other statistics as well, most notably OPS, which is arguably the most meaningless of all baseball statistics, perhaps all statistics ever (don’t get me started).

xR and xRA estimate the change in a team’s run expectancy caused by a batter’s plate appearances. They are measured in runs and runs per plate appearance, respectively. xRA may not look like a number you’ve seen before, and generally needs to be written out to four decimal places instead of three, unlike basic averages, but it’s linguistically very simple to use and understand. I’d rather sacrifice the comfort of having a statistic merely look familiar and instead have it actually reflect something tangible. This doesn’t take away from the value of a statistic like wOBA, which is a great run estimator no matter what scale it is on; a lack of meaning certainly does not imply a lack of value. Introducing an unscaled run average, xRA, will hopefully create a different perspective on how to talk about batting production.

There is one final expected run estimation that I want to consider that could easily cover an entire new part on its own, but I’ll limit myself to just a few paragraphs. The xR estimations we have built have been constructed independent of the situational context at the time of the batter’s plate appearance. Since we want to cover the entire spectrum of context-neutral run estimation to context-specific run estimation, we will conclude by considering xR_s, which is an estimate of the change in a team’s run expectancy based on the out/base state before and after the action of the plate appearance. This is very nearly the same thing as RE24 but it only considers runs produced due to the primary action of plate appearances and not baserunning events.

In many respects, xR_s is the simplest run estimator to construct of all that we have built thus far. There are only three pieces of information you need to know in a given plate appearance to construct xR_s: the run-scoring environment, the out/base state at the start of the action of the plate appearance, and the out/base state at the end of the action of the plate appearance. Next time you go to a baseball game, bring along a copy of a run expectancy matrix, like the one provided earlier. On a scorecard, at the start of every plate appearance, take note of the value assigned to the out/base state, making adjustments if any runners move while the batter is still in the batter’s box. Once the plate appearance is over, note the value of the new out/base state, separating out any advancement on secondary fielding errors or throws to other bases. Subtract the first value from the second value, and add in any RBIs on the play, and write the number in the box associated with the batter’s plate appearance; you just calculated xR_s. Do this for a whole game, and you will have a picture of the total value produced by every batter based on the out/base state context in which they performed.

The effective averages and expected run estimations provide a foundation on which batting analysis can be performed. They combine both “real and indisputable facts” with detailed estimations of the run produced in every event in which a batter participates. Any story that aims to describe the value that a batter provides to his team must consider these statistics, as they are the only ones which account for all value produced. 147 years ago, Henry Chadwick suggested that batters should be judged on whether they passed a “test of skill.” I think they should be judged on whether they passed a “test of value.”

Thanks to Benjamin H Byron for editorial assistance, as well as the staff at the Library of Congress for assistance in locating original copies of the 19^th century newspaper articles included in Part 1.

Here is data on eOBA, eTBA, and each level of xR and xRA estimation, for each batter in 2013.

Bibliography

[i] I’ll be focusing on 2013 because the full season is complete. All the work described here could easily be applied to 2014, or any other season, I just don’t want to use incomplete information.

[ii] While these terms are used a lot, there aren’t any specific definitions commonly accepted that differentiate each type of batted ball. For terms used so commonly, it doesn’t make much sense to me that they are not well defined. It won’t apply to the data used in this research, but here is my attempt at defining them.

A bunt is a batted ball not swung at but intentionally met with the bat. A groundball is a batted ball swung at that lands anywhere between home plate and the outer edge of the infield dirt and would be classified as a line drive if it made contact with a fielder in the air. A line drive is a batted ball swung at that leaves the bat at an angle of at most 20° above parallel to the ground (the launch angle), and either lands in the outfield or makes contact with any fielder before landing (generally through a catch, but sometimes a deflection). A fly ball is a batted ball swung at, with a launch angle between 20° and 60° above parallel (not inclusive), that either lands in the outfield or is caught in the air by a player in the outfield. A popup is a batted ball swung at that either (a) leaves the bat at an angle of 60° or greater above parallel and lands or is caught in the air in the outfield, or (b) leaves the bat at an angle greater than 30° and lands or is caught in the air in the  infield.

This would result in some balls being classified differently than they currently are, and not just because differentiating between a line drive and a fly ball is somewhat difficult with just a pair of eyes. If the defense were to play an infield shift, and the batter were to hit a line drive into the outfield grass into that shift, subsequently being thrown out at first base, it would likely be called a groundout by current standards. Batted balls should not be defined based on defensive success or failure, but by the general path which they take when leaving the bat. It may be unusual to credit a batter with making a line out despite the ball hitting the ground, but it more accurately reflects the type of ball put into play by the batter.

I don’t know that these are the “correct” ways to group together these events, but as we now are using technology that tracks the flight of the baseball from the moment it is released by the pitcher through the end of the play, we should probably have better definitions for types of batted balls than those currently provided by MLB. I don’t expect a human stringer to be able to differentiate between a ball hit with a 15° launch angle or a 25° launch angle, but that doesn’t mean we shouldn’t have some standard definition for which they should aim.

[iii] In theory, xR₅ would attempt to consider details that are even more specific, perhaps the initial velocity of the ball off the bat, the launch angle, and whatever other information can be gleaned from technology like HIT F/X. The xR framework leaves room to consider any further amount of detail that a researcher wants to consider.

[iv] Though not charted here, the r² value based on the correlation between wRAA, the “counting” version of wOBA, and xR₄ is .984. As wRAA is nearly identical to xR₃ but excludes a few of the more rare events from its calculation, it’s not surprising that the r² value between wRAA and xR₄ is just slightly smaller than the r² between xR₃ and xR₄.

It feels icky to create a statistical formula based on what “feels right”.

Last month, I introduced a stat called Leadoff Rating, or LOR. The idea was that most systems to identify great leadoff hitters tab players like Ted Williams and Mickey Mantle, who would always hit closer to the middle of the order. I wanted to distinguish players specially suited to batting leadoff. The formula was simple: OBP minus ISO. By subtracting isolated power, we identified players who get on base a lot but aren’t true sluggers. It’s an easy calculation, and it produced fairly reasonable results. Two particular things bothered me:

1. Bad hitters occasionally had good leadoff ratings because of their very low ISO.

2. Rickey Henderson ranked 45th.

We know that leadoff is one of the two or three most important positions in the batting order. As little impact as lineup construction has on winning percentage, leadoff hitters are important. But LOR saw high OBP and low ISO as equally meaningful, so players with no power sometimes rated as desirable leadoff hitters. That seemed like something to correct.

Rickey Henderson is generally recognized as the greatest leadoff man of all time. LOR did not show this, for two main reasons. One was that the formula did not include baserunning. The other was that the all-time list slanted heavily towards Deadball players. Before Babe Ruth, everyone had low isolated power. Ty Cobb was a terrific power hitter, who led the AL in slugging eight times. Cobb’s career ISO (.146) is basically the same as Rickey’s (.140). Henderson only ranked among the top 10 in slugging twice. The game has changed.

Based on the feedback of FanGraphs readers and on my own muddlings, I’ve reworked the leadoff rating formula. The new system is more complicated — it’s annoying to do without a spreadsheet — and it’s kind of haphazard. OBP – ISO was a nice system because of its simplicity. With the updated formula, I’m guessing, choosing numbers that seem right. If someone better than I am at math would care to suggest revisions, please do so. I am fully prepared to give this stat away to smart people.

The formula I’m using now is — wait. There’s another calculation I abandoned, but it’s important for explaining how we arrived at the current iteration, and that middle step looked like this: OBP – ( .75 * ISO ) + ( ( .005 * BsR ) / ( PA / 600 ) )

On-base percentage is the heart of leadoff rating. A good hitter, and especially a good leadoff hitter, must get on base. But I only subtracted 3/4 of ISO, because (1) low ISO is not as important as high OBP, and (2) the original formula was probably a little too hard on doubles hitters. Guys like Rickey and Tim Raines ranked too low because they had more power than players like Jason Kendall and Ozzie Smith.

Commenter foxinsox suggested adding (Constant * BsR) to the calculation, which was a fine idea I should have seen earlier. The hitch was turning BsR into a rate stat.  By using BsR/PA or BsR/G, we can incorporate that element smoothly.

When I ran the numbers, the historical lists looked great (Rickey Henderson in the top 10!), but for active players, there were hits and misses. Elvis Andrus came back as the ideal leadoff hitter in 2013, and Craig Gentry (.264/.326/.299) ran away with 2014 to date. Even with the adjustments, LOR rewarded low ISO. While a .250 ISO isn’t really the right fit for the top of the batting order, neither is a sub-.050 ISO. We don’t want a guy who only hits singles, we just don’t want a cleanup hitter. Looking at the historical lists, I found that most of the top players had an ISO right around .100, so I created a Goldilocks formula, preferring a minimal absolute difference from .100 ISO. Rather than simply treating low ISO as desirable, we’re looking for the sweet spot between singles and slugging. The new formula is:

OBP –  .75 * | .100 – ISO |  + ( .005 * BsR ) / ( PA / 600 )

That’s on-base percentage, minus 3/4 of the absolute difference between ISO and .100, plus .005 times BsR per 600 plate appearances. Now very low isolated power is punished just as much as very high ISO.

Hopefully you want to see some lists. I’ll show you five: the all-time list, the post-Jackie Robinson list, the leaders for the 2013 season, 2014 to date (through July 31), and 2014 rest-of-season projections (ZiPS). We’ll also look at the 2014 leaders (both to date and projected) for every team in the major leagues. Read the rest of this entry »

Platoon splits have become a major part of baseball today.  The Athletics have ridden a split of Jaso and Norris to production from their catcher position.  Many left-handed starters have had success against righties and many have struggled.  Over the course of his career Cole Hamels has had more success against RHB than LHB (.294 vs .301 wOBA).  Hamels is known for his best pitch — his change up — which has helped him neutralize RHB throughout his career.

So often when a LHP struggles against RHBs the common fix is to use a change up more often or to improve the change up.  However, for some pitchers this model does not work.  Dallas Keuchel, a pitcher who used a change up as his primary offspeed pitch against RHBs in the beginning of his career struggled still against righties.  As shown in this article, from the time his career began until May 31st of this season Keuchel was one of the worst starting pitchers against RHB.  However, by breaking this down season by season it can be seen that Keuchel’s numbers have actually improved as his career’s progressed.

The key to Keuchel’s increased success against opposite-handed hitters seems to be found in his pitch selection.

Keuchel has been an often-discussed topic on this site this season.  The key to his success this season has been his increased use of his rapidly-improving slider which was covered by Eno Sarris here. As Sarris states the slider will allow Keuchel to have increased success against lefties.  However, looking at Keuchel’s splits this season shows he has improved his numbers against righties significantly. According to PitchF/x data Keuchel has used the slider significantly more against righties this season.  He has done this at the expense of four-seam fastballs opting to throw more two-seamers and sliders.

While he is still using the changeup at around his career averages, his heavy increase of sliders in the biggest difference in his way of attacking hitters.  As his numbers for the season have shown he is limiting home runs and striking out the highest percentage of right-handed hitters in his career.  This has also lead to a significant improvement in his wOBA allowed. This season against righties the slider has produced a better than MLB average whiff rate (18% vs 13.%).

Keuchel provides an blueprint for other left-handed starters who struggle against righties.  Contrary to typical belief that in order to improve against opposite-handed batters pitchers must develop their change up, Keuchel has begun using his best offspeed pitch — the slider — more as a putaway pitch against off-handed batters.  Keuchel has become the poster boy for pitching to strengths, riding his sinking two-seam and slider to a breakout season while significantly improving his platoon splits.

Another pitcher who was mentioned as the worst in the league against righties was Eric Stults.  Stults, a lefty like Keuchel, features both a slider and a change up.  Additionally, much like Keuchel, Stults’s best offspeed pitch according to pitch values is his slider.  However, looking at his pitch selection to RHB he has used the change more than twice as much as the slider since 2007 (25.6% vs 10.8%).  If Stults followed in the footsteps of Keuchel and began to use his best pitch more against opposite-handed hitters it could cause him to minimize his platoon split and make him a better all-around starting pitcher.

Earlier this month the Yankees took a chance on Brandon McCarthy, trading Vidal Nuno to the Diamondbacks for the sinker-balling right hander.  While McCarthy’s numbers in Arizona were ugly (5.01 ERA), his FIP was much better (3.82).  Through 4 starts the investment the Yankees made has paid off.  McCarthy has pitched to a 3-0 record with a 2.55 ERA.   Since the trade many of McCarthy’s peripherals have not changed much however, there have been a few differences.

Two keys factors for pitcher success — K% and BB% — have not changed much with K% decreasing slightly and BB% increasing sightly, although neither can be looked at as the reason McCarthy has been so much better since the deal.  Another important stat to look at is his BABIP, which, has improved a few percentage points since the beginning of the season but is still above his career average of .297.  However, a major difference that can be seen in McCarthy’s numbers since the trade is his GB%.  In recent seasons as McCarthy has began featuring his sinker more his groundball percentage has increased significantly.  the 55.3% he showed with the Diamondbacks was more than 7 percentage points higher than the career high he set in 2013.  Seeing this major change in GB% opens the question of what exactly McCarthy has been doing differently with the Yankees.

During a few of McCarthy’s starts with the Yankees, New York broadcaster Michael Kay has mentioned that McCarthy did not throw his cutter as frequently with the Diamondbacks compared to how often he has used it since the trade.  Looking at his PitchF/x pitch selection data does show an increase in the use of his cutter but it also shows several other interesting trends.

As Kay has noted McCarthy has used his cutter more frequently but the increase is minimal compared to several other big changes McCarthy has made.  With the Dbacks McCarthy used his curveball more that a quarter of his pitches making it his second most frequently used pitch.  However, once he was traded McCarthy had been using the cutter as his second most common pitch.  However, the significant drop in his curveball usage did not get added to his cutter usage it instead was added to a pitch he did not use in Arizona, a four seam fastball.

Since his trade to New York from Arizona Brandon McCarthy has been a completely different pitcher.  While his ability has not changed and the park has not been much of an improvement (103 for NY 104 for ARI) the biggest difference in McCarthy as a pitcher has been in his pitch selection, once again featuring a four seam fastball while reducing the usage of his curveball.  To this point the move to the Yankees may have been exactly what McCarthy’s career needed simply because it allowed him to change the way he attacks hitters.

Introduction

A couple months back, I introduced a new method of calculating pitch values using a FIP-based WAR methodology.  That post details the basic framework of these calculations and  can be found here .  The May and June updates can be found here and here respectively.  This post is simply the July 2014 update of the same data.  What follows is predominantly data-heavy but should still provide useful talking points for discussion.  Let’s dive in and see what we can find.  Please note that the same caveats apply as previous months.  We’re at the mercy of pitch classification.  I’m sure your favorite pitcher doesn’t throw that pitch that has been rated as incredibly below average, but we have to go off of the data that is available.  Also, Baseball Prospectus’s PitchF/x leaderboards list only nine pitches (Four-Seam Fastball, Sinker, Cutter, Splitter, Curveball, Slider, Changeup, Screwball, and Knuckleball).  Anything that may be classified outside of these categories is not included.  Also, anything classified as a “slow curve” is not included in Baseball Prospectus’s curveball data.

Constants

Before we begin, we must first update the constants used in calculation for Jule.  As a refresher, we need three different constants for calculation: strikes per strikeout, balls per walk, and a FIP constant to bring the values onto the right scale.  We will tackle them each individually.

First, let’s discuss the strikeout constant.  In July, there were 47,449 strikes thrown by starting pitchers.  Of these 47,449 strikes, 4,585 were turned into hits and 13,750 outs were recorded.  Of these 13,750 outs, 3,725 were converted via the strikeout, leaving us with 10,025 ball-in-play outs.  10,025 ball-in-play strikes and 4,585 hits sum to 14,610 balls-in-play.  Subtracting 14,610 balls-in-play from our original 47,449 strikes leaves us with 32,839 strikes to distribute over our 3,725 strikeouts.  That’s a ratio of 8.82 strikes per strikeout.  This is exactly the same as our from 8.82 strikes per strikeout in June.

The next two constants are much easier to ascertain.  In July, there were 26,244 balls thrown by starters and 1,328 walked batters.  That’s a ratio of 19.76 balls per walk, up from 19.36 balls per walk in June.  This data would suggest that hitters were slightly less likely to walk in July than previously.  The FIP subtotal for all pitches in July was 0.52.  The MLB Run Average for July was 4.17, meaning our FIP constant for May is 3.65.

The following table details how the constants have changed month-to-month.

Pitch Values – July 2014

For reference, the following table details the FIP for each pitch type in the month of July.

As we can see, only three pitches would be classified as below average for the month of July: sinkers, cutters, and changeups.  Four-Seam Fastballs and curveballs also came in right around league average.  Pitchers that were able to stand out in these categories tended to have better overall months than pitchers who excelled at the other pitches.  Now, let’s proceed to the data for the month of July.

Four-Seam Fastball

Sinker

Cutter

Splitter

Curveball

Slider

Changeup

Screwball

Knuckleball

Overall

Pitch Ratings – July 2014

Four-Seam Fastball

Sinker

Cutter

Splitter

Curveball

Slider

Changeup

Screwball

Knuckleball

Monthly Discussion

As we can see, Cole Hamels takes the top for this month due to the  strength of his overall repertoire.  Hamels was classified as throwing five different pitches in July (Four-Seam, Sinker, Cutter, Curveball, and Changeup) and managed to earn at least 0.1 WAR from all five.  The most valuable pitch overall in July was Ian Kennedy’s Four-Seam Fastball.  The least valuable was Drew Hutchison’s Changeup.  As far as offspeed pitches, Garrett Richards’s 0.5 WAR from his slider lead the way.  The least valuable fastball was Hector Noesi’s cutter.

On our 20-80 scale pitch ratings, the highest rated qualifying pitch was Chase Whitley’s changeup.  The lowest rated pitches were the changeups thrown by Drew Hutchison and Mike Minor, Hector Noesi’s cutter, and Josh Beckett’s curveball.  The highest rated fastball was Drew Hutchison’s four-seam fastball.

Pitch Values – 2014 Season

Four-Seam Fastball

Sinker

Cutter

Splitter

Curveball

Slider

Changeup

Screwball

Knuckleball

Overall

Year-to-Date Discussion

If we look at the year-to-date numbers, AL FIP and MLB WAR leader Felix Hernandez still sits in the top spot.  Current MLB FIP leader Clayton Kershaw ranks ninth.  The least valuable starter has been Marco Estrada.  On a per-pitch basis, the most valuable pitch has been Ian Kennedy’s four-seam fastball.  The most valuable offspeed pitch has been Garrett Richards’s slider.  The least valuable pitch has been Marco Estrada’s four-seam fastball.  The least value offspeed pitch has been Marco Estrada’s changeup.  Needless to say, it’s been a rough year for Marco.  Qualitatively, I feel fairly encouraged by the year-to-date results so far.  The leaderboard is topped by two no-doubt aces, both of whom currently in the top two in their respect leagues in FIP, and Marco Estrada comes in at the bottom after posting the highest FIP among qualified starters so far.  For reference, the top five in the year-to-date overall rankings are currently 1st, 12th, 10th, 2nd, and 9th on the FanGraphs WAR leaderboards respectively.

Tip: Click on any acronyms for an explanation in the FanGraphs glossary of terms.

With his final turn in the rotation for July completed, we’ve now had almost exactly one full year of Sonny Gray – one year of the 24-year-old starting pitcher, the up-and-coming staff ace, the dueler of Playoff Verlanders. In that year, we’ve seen him do some great things, like going eight innings with nine Ks and no runs against the Tigers in Game 2 of the 2013 ALDS. We’ve also seen MLB Fan Cave forcing him to prank New Yorkers as a result of some unknown fine print embedded in his rookie contract. Above all else, the one thing we’ve always known is that Sonny Gray has a really good curveball. Let’s take a look at it for all of its 12 to 6, 80-MPH Uncle Charlie glory, from a game against the Astros in August of last year:

How good is his curveball? He has never given up a home run off of the pitch, with the only extra-base hits against the curve in his career being four doubles. In the past calendar year, Sonny Gray has saved more runs with his curveball than any other pitcher in baseball, and is behind only Corey Kluber and Yu Darvish in Runs Saved/100 curveballs. Having watched Kluber a lot, I suspect his slider/slurve is actually being classified as a curveball; I think it looks like a slider, but PITCHf/x doesn’t, so I will defer to the all-knowing pitch computer. Regardless, with the metrics we’re about to examine, Sonny Gray has one of the best curveballs in the game. What we’re going to focus on specifically are the advances in his curve’s effectiveness, spurred on by an adjustment in the way he throws the pitch.

To start, let’s take a look at the top-15 starters by wCB and wCB/C for the past calendar year:

As stated before, Gray is at the top in both of these categories. We should put a little more stock into wCB/C, as it normalizes all pitchers to runs saved per 100 pitches, taking away the advantage that one player might have due to throwing a certain pitch more frequently than another player. This is important for what we’re looking at, because Sonny Gray throws a lot of curveballs. How frequently does he throw curveballs? Here are the leaders for percentage of curveballs thrown over the last calendar year:

The words “second only to Scott Feldman” don’t come up very often, but here they are. Gray throws his curveball a ton. Not only has he always leaned on the curve as a major weapon in his arsenal, but he has actually increased his number of curves thrown since he came into the league every month except for May (when he maintained his % thrown) and June of this year, when he seemed to temporarily lose a feel for the pitch and threw more changeups. However, his first start of July had Gray saying this after holding Toronto to one run over seven innings:

“That was the idea, to really get (it) going again,” Gray said of the curveball. “I think the last five or six starts it’s been OK, but it hasn’t been a big factor. We did some things a little different this week and I was able to find that again.”

Over the last 30 days, Gray has thrown the curveball more than ever, up to over 32% for the month. Not only that, he has found more effectiveness in the pitch, with his whiff % on the curve up to a career-best 19.2% during July. There’s also reason to believe that this isn’t simply a good month for Sonny Gray’s curveball – what we are now seeing is the fruition of a change of approach with the way he throws the pitch that has been coming for some time now. Let’s take a look.

Here we have the release speed of Sonny Gray’s curveball for every start since he was called up:

He’s throwing the curve harder than he ever has, adding over three miles per hour since he started pitching in the majors. That’s not a small change. On top of the speed increase, he’s cut about 2.5 inches of vertical movement off his curve between his first start in the majors and now:

Finally, he’s added more three-dimensional depth to his curve in the form of a top-3 best horizontal movement over the past calendar year. Only Corey Kluber and Charlie Morton have had better horizontal movement on their curves in that time period.

Add all of that up, and we have this 84-MPH curve from his last start against the Orioles:

It now looks more like a slurve, with its high release speed and nasty late break away from right-handed hitters. As Eno Sarris included in his great article from October of last year, Gray said he “adds and subtracts” with the same grip on his curve to move between the 12-to-6 and slurve (which is sometimes classified as a slider) varieties. However, it seems as if he has leaned more toward the slurve option as time has gone on.

One question that arises out of this is “why throw the slurve more?”

Given his whiff % on the curve has increased as he has added velocity, I’d say that fact alone has supported the move to the slurve over the 12-to-6. However, there’s another potential reason that isn’t strictly rooted in statistics, and could be more about what goes into an elite pitching approach: by increasing his arm speed and flattening out the vertical movement of his curve, Gray can further deceive batters into thinking he’s throwing hard pitches before the bottom drops out. His struggles to find consistency with the changeup are well documented, so why shouldn’t he adjust his best breaking pitch to better fool hitters for whiffs and weak contact? As we’ve seen with Yu Darvish, the pinnacle of an ace approach may be one that includes a “great convergence” of arm slots and release points, in which every pitch looks hard until it’s not, or until it is.

Gray’s horizontal release points for all of his pitches are closer to one another than they ever have been during his major league career. Not surprisingly, his curveball and fastball were released on average at the almost identical horizontal point during his May and July starts, when he posted career-best whiff rates on his curveball (18.6% & 19.2%, respectively). June was an aberration, as Gray seemed to lose his release point in general and was tinkering with his delivery, leaning more on the changeup:

Sonny Gray has work to do on parts of his game before he takes the next step into the true elite of starting pitchers. His walk rate has actually increased this year to 8.5%, owing mostly to a lack of fastball command in deep counts, and his changeup is still very much a work in progress as a third pitch. However, his adoption of the hard curve and syncing of arm angles is a positive step toward dominance, and is a sign that he knows what works; he’s now perfecting it.

And now, my first go at a DShep Darvish-like GIF of Sonny Gray’s 12-to-6 curve from last August along with his harder slurve from his last start to compare:

Jose Abreu has seemed a virtual lock for the AL Rookie of the Year Award ever since Masahiro Tanaka went down with what could be a season-ending elbow injury. Abreu has done nothing but hit the hell out of the ball since storming into the Majors after fine-tuning his craft in Cuba. Rookies like Brock Holt and George Springer have had nice seasons of their own, to be sure, but they’re certainly no challengers to the might of Abreu.

Then, of course, there’s the best rookie that mostly nobody’s heard of. Let’s say hello to Mr. Kevin Kiermaier. A 31^st round pick in 2010, Kiermaier fought his way through the minors and finally, after a single game appearance last year, was called up for good on May 17^th. He’s since appeared in every game since May 31^st.  In 57 games (198 plate appearances), he’s hit .311/.362/.544. More importantly, he’s produced a .391 wOBA and 157 wRC+. Those are certainly pretty numbers. But can he keep it up? Kiermaier hit well at almost every level in the minors, but was always known for his superb glove work. Let’s look at his and Abreu’s numbers together, shall we? All stats herein are as they were prior to the inception of action on Monday the 29^th.

Abreu is numerically the greater offensive producer, if only by a slim margin. He’s only out-produced Kiermaier relative to the league (wRC+) by two points, but his wOBA is indicative of the fact that he’s driving the bar a lot further (.619 slugging percentage versus Kiermaier’s .544). Yet while chicks most certainly dig the long ball, it isn’t everything.

Kiermaier’s .362 OBP is a fantastic mark. It’s about twenty points higher than Abreu’s, and it’s incredibly beneficial for his spot in the lineup. For all his famous lineup tinkering, Joe Maddon has been primarily using Kiermaier out of the 9 spot of late. He even went as far as batting the pitcher (Alex Cobb) eighth on July 23^rd in an inter-league game against the Cardinals so that Kiermaier could bat ninth. Putting him there allows him to serve as what basically amounts to a second leadoff hitter when the lineup turns over, and there’s one more runner on base for the big boppers in the heart of the order.

Kiermaier also is much more of a two-way player. UZR and DRS are both very pleased with his defensive skill set. That’s not a surprise, as many scouting reports on Kiermaier always gave glowing reviews of his instincts and range in the field. It’s that defense that has allowed him to be right on Abreu’s tail in total value, in far fewer games. While UZR says that Abreu isn’t a total loss at first base, DRS says he leaves much to be desired. Naturally, first basemen traditionally aren’t employed for their gloves, but nobody complains when someone like Carlos Santana comes along and dazzles on both sides of the ball.

Now, the hard part. We want to extrapolate Kiermaier’s value over the same time span that Abreu’s accrued his, but how? It would be easy to simple do it over the same number of games played. However, “games played” doesn’t account for late-inning substitutions, or early exits. A better (but still not perfect) way of looking at it would be to extrapolate it over innings played. This is still not perfect, as a batter can hit in the top of an inning and then be replaced in the field in the bottom, and vice versa. However, I’m not a good enough statistician to develop my own metric for this (check back with me in a few months) and I can’t find anything out there to show just how much time a player has seen.

I used Baseball-Reference’s game logs for this, as they have a count of how many innings the player saw. Here’s how these numbers work out.

This is far from an exact science. Abreu and Kiermaier didn’t produce exactly that many wins every inning they plated, but it’s how the numbers work out. Now, if Kiermaier’s fWAR/I (fWAR per innings played) is multiplied by Abreu’s total innings played, the result is an fWAR total of 5.425. So far this season, Mike Trout leads all of baseball with 5.7 fWAR. Troy Tulowitzki is in second place with 5.1. That’s assuming, of course, that Kiermaier maintains his current level of play. And as I said before, “But can he keep it up?” The answer to that is “I haven’t a clue.” Here’s why.

There are two glaring realities. One is that Kiermaier hits a lot of ground balls. His BABIP would seem to indicate that’s he’s getting lucky, and his grounders are finding holes in the infield. For what it’s worth, here’s the league leaders in ground ball rate out of everyone who’s qualified for the batting title. It’s a mixed bag for sure. There are some good hitters around Kiermaier’s range, like Yasiel Puig, Alexei Ramirez and Melky Cabrera. Derek Jeter’s made a heck of a career out of a high ground ball rate and good BABIP. So while that isn’t an immediate cause for concern, it’s something worth watching to be sure. For reference, here’s his Brooks Baseball spray chart on the year.

The other is that southpaws have completely owned him. While right-handers are more common (and Kiermaier certainly has no problem with them), in the age of specialized bullpens it’s one awful quality to have. Managers (and the think-tanks in front offices) are surely catching on to this. Eventually, the scouting report will read to get a lefty reliever to face him in high-leverage situations, if they can. This also, of course, could be a result of small samples sizes. Kiermaier only has 43 plate appearances against lefties, and 155 against right-handers. Another thing to keep an eye on going forward.

However, what Kiermaier is doing is not wholly unsustainable. Players have made livings on putting ground balls in the right spots, and good players at that. However, the projections for the rest of the season aren’t too pretty. ZiPS predicts that he’ll produce a .259/.312/.385 line for the rest of the season. Steamer has him at .257/.311/.382. The projection systems are generally not too far off from reality, but it’s fun to think about what could be. Will Kiermaier end up being a 5-win player? The odds aren’t good. It’s certainly possible, though, and if it does happen prepare yourselves for a wonderful offseason debate on whom the rightful winner of the Rookie of the Year Award really was. In the meantime, let’s marvel at how darn good this former 31^st round pick has been.

Nicolas Stellini is a student, college baseball announcer, and amateur baseball writer. Check him out over at @StelliniTweets. 

Over the course of 162 games, there’s only so much influence a manager of any baseball team could have over their outcome. After 105 games the Mets actual record is 3 wins shy of their projected record of 53-52, making this a .500 team. Several factors contribute to this discrepancy like losing your ace pitcher to injury, scrambling for a closer to begin the season, developing a major league catcher, adapting to a new hitting coaches philosophy, and setting the most productive lineup possible just to name a few. What Terry Collins has done with this team to this point can only be admired, but help has arrived and changes must be made to maximize team production.

The move of Curtis Granderson from the cleanup to leadoff role proved to be successful as the team surged from June’s end through July. Daniel Murphy and Curtis Granderson’s slash line numbers are almost identical, batting average is the only big difference which Daniel Murphy leads Granderson by about.060 AVG points and make him a more ideal leadoff hitter. Curtis Granderson hit 6 home runs from the leadoff spot which minimized his RBI potential which essentially is the reason Sandy Alderson signed him. In moving Daniel Murphy into the leadoff spot, the Mets actually increase their leadoff OBP while putting Curtis Granderson into a role where his RBI opportunities increase dramatically.

Daniel Murphy’s SLG% is nearly that of Curtis Granderson with half as many HRs, meaning that Daniel Murphy is doing a better job of getting into scoring position than our current leadoff hitter. The only 2 reasons the Mets have kept Murphy out of the leadoff spot in the past were lack of speed on the basepaths and low OBP. Now Daniel leads our starting players in SB showing he has some speed and base running ability and his OBP is amongst the team leaders. David Wright being the best hitter on the team (despite struggles in 2014) deserves the 2nd spot in the order. His power has declined this season, however his OBP is still respectable and he should remain in a table-setting role followed by Granderson. Lucas Duda has earned his cleanup role as he’s hit over .280 in the past couple of months with at least 5 HRs per month. He is driving the ball to all fields and should be a key contributor to driving in runs once our table-setters do their jobs.

The top 4 lineup spots should be configured as follows:

1  2B Daniel Murphy        (.293/.340/.412) 28 2B, 7HR, 11SB

2  3B David Wright           (.278/.339/.401) 24 2B, 8HR, 5SB

3  RF Curtis Granderson  (.232/.339/.415) 18 2B, 15HR, 8SB

4  1B Lucas Duda               (.259/.356/.500) 22 2B, 18HR, 3SB

For the next spot in the lineup, this player has had a tale of 2 seasons. Travis d’Arnaud has adjusted quickly since his demotion to AAA on June 6th. Since being recalled on June 24th, d’Arnaud has a slash line of (.302/.337/.646). He has lengthened our lineup and has earned the spot of the 5 hitter.

5  C Travis d’Arnaud

Before June 6th demotion    (.180/.271/.320) 3 2B, 3HR

Since June 24th Promotion (.302/.337/.646) 7 2B, 4HR

Season Stats                            (.232/.298/.379) 10 2B, 7HR

Right after Travis d’Arnaud in the Mets order is when they begin to look thin offensively. Having early success in the season but struggling as of recent is Juan Lagares, the defensive wizard and minor league doubles machine. This kid showed an advanced approach to lead off the year and is capable of making the bottom of our order a productive one. He isn’t seeing the ball well like he was in the first half, but we need to remember he is in his first full season in the bigs and known primarily for his route to catch baseballs and cannon for an arm, any offense is a plus.

6  CF Juan Lagares (.271/.306/.375) 16 2B, 2HR, 2SB

7  RF Chris Young/Eric Young/Kirk Nieuwenhuis/Bobby Abreu/denDekker

Our right field position is a question mark. I’m not saying the Mets haven’t produced anything from the position, but they don’t have an everyday right fielder which is a need to be addressed in the off-season or via trade before Thursday’s deadline. Though not one player has stepped up and taken over this position, I still believe they have produced more than my “ideal” 8 hitter, Ruben Tejada. In every championship team there is that one scrappy player that is on the squad solely for defensive prowess. Through the course of the season I have seen many different Ruben Tejadas. I’ve seen the defensive shortstop, the slap hitter, the kid in way over his head, and the wanna-be slugger with warning track power. This player is undoubtedly our 8 hitter and those who look too dependently on his OBP must take into consideration how many times he has walked for the sole reason that the worst hitting pitching staff is just 4 pitches away.

Ruben has been intentionally walked 10 times, twice as much as any player on the Mets. Ruben Tejada hasn’t defended the way he has in the past which quieted his lack of offense. In a New York setting, he shouldn’t start and the Mets executives know that. Ruben is a bridge to the future, an inexpensive filler until we land in a position of contention where an offensive producer is necessary at the position. Until then we have a shortstop with a strong arm and instincts but lacks the speed to get too many balls up the middle or steal a base when we need him to. He has no power and is offensively irrelevant as his slash line below shows. A shortstop with any tools is an upgrade here.

8  SS Ruben Tejada (.226/.351/.281) 9 2B, 2HR, 1SB

Welcome to the fourth installment of xHitting!  As always, reader comments and feedback are super encouraged and appreciated.  (Links to parts one, two, and three)

Briefly recapping the method, the gist is to estimate the expected rate of each individual hit type based on a player’s underlying peripherals, and in turn recover all the needed components to compute expected versions of wOBA, OPS, etc.  The only real change to the model since last time is that I now utilize a “hybrid” predicted home run rate, that averages between actual and (raw) predicted home run rate, with the weight given to actual HR rate increasing in the number of plate appearances.  (This is explained in part three, for those curious.)

Perhaps the more exciting change, though, is that this time I actually have results for an ongoing season, which potentially can help for fantasy purposes.  (Not that most readers need my help necessarily.)  Related to fantasy usage, there were a few requests to see a full spreadsheet of past results (2010-2013 seasons), which I have posted here.  Again feel free to take it or leave it at your leisure.

Note: I collected most of these data at the All-Star Break, so numbers may be a few weeks behind, but they’re still mostly true.  Also, for time considerations I only fetched 2014 stats for qualified leaders.  This even leaves out a few big names, but I couldn’t justify time to fetch every player.

So far, I’ve typically posted the biggest “over-” and “under”-achievers for a given season.  And I suppose I’ll continue that tradition today.  But while these lists are useful for highlighting which players seem most likely to regress, it overlooks another main use of the model, which is to assess the realness of a player’s apparent “breakout” or “decline;” at least in-sample.  (In some cases, the model may think that a player’s breakout is entirely justified, given peripherals, while others it may view more skeptically.)  Thus, today I’ll also post a second list, of players who seem to have taken a pronounced step forward/step back this season, and what the model thinks of their season-to-date performance.

Okay, time for results!  I’ll start with the list of “over-” and “underachievers.”

A general pattern I notice is that, having worked with this model for a while now, there do seem to be players that give the model some trouble and have a disproportionate tendency to appear on this list from year to year.  A few of these players appear on this list… more on that later.

Partly for that reason, I wouldn’t necessarily say to “buy low” the guys on the left, nor “sell high” the guys on the right; although you can if you want.  I won’t address every player, but I have some scattered comments:

• For readers who prefer OPS, .020 wOBA translates to about .050 OPS, on the margin.
• .397 predicted for Teixeira?  Not sure where that came from…
• Poor Segura.  All things considered, I think nobody deserves a big second half more than he does.
• Whatever happened to Casey McGehee’s power?  The guy once hit 23 home runs in a season, but now has ISO of .073, with surprisingly low fly ball distance.
• Although Chisenhall’s breakout is not as impressive if you take out what the model thinks is luck, it’s still a pretty impressive improvement.
• Chris Davis is sort of the reverse of Chisenhall.  Adding back in what the model thinks has been bad luck, he’s still way down from what he did last year, but not nearly as disappointing as he probably has been to many owners thus far.

As mentioned, certain players do seem to be able to over/underperform the model somewhat consistently; the same way we think some pitchers are usually better or worse than their FIP.  With now 4.5 years of data to work with, however, I think I can make educated guesses about which players systematically deviate from the model predictions.  I’ll term this deviation the “player fixed effect.”

(Requiring at least 1000 PA from 2010 through 2014 first half)

Comments:

• Again, .020 wOBA is equivalent to about .050 OPS, on the margin.
• Taking out their apparent fixed effect, Teixeira is only underperforming his xWOBA by about .020, and Brian Roberts is actually doing about par.
• On the reverse side, Mike Trout’s “adjusted” xWOBA jumps up to .408, where really it probably doesn’t surprise us that he’s outperforming even that, since he’s Mike Trout.  And although Giancarlo Stanton misses the Top 10 cutoff above, his apparent fixed effect of +.022 would be 11th; so his “adjusted” xWOBA is more like .385.
• Yasiel Puig (.058) would also be on the list of “positive fixed effects” if we relaxed the PA requirement (he has 826 during this time).  And Matt Adams (~.040) might also be well on his way to that list; although he has fewer plate appearances still than Puig.
• I don’t really have good explanations/know any common themes for players with negative fixed effects.  Maybe readers can help?
• For Trout, home runs are pretty clearly the area where the model underestimates him.  In any given season (2010-2014), he hits about twice as many HR as the model thinks he should in the “raw” prediction.
• And Trout’s not the only “HR rate defier,” either; just the most salient.  In general, the model has never done as well with home runs as it does with singles, doubles, and triples.  It seems there are other important determinants of home run hitting that really should be in the model, but currently are not.  Intuitively, I sort of would like velocity and angle of the ball off the bat, but so far have not found a good data source to actually include these.  (Maybe that will change in the coming years as MLBAM releases “Hit F/X” style data?)  Until then, reader suggestions are also super welcome here.

And now, finally, for the other usage: here’s a partial list of players who have taken either a pronounced step forward or back this season, relative to established norms.

* – To avoid inflation from Coors Field, for these players I’ve taken the total from 2011-13 seasons only

Comments:

• At least in-sample, Brantley’s breakout seems to be pretty much entirely justified.  Of course this doesn’t mean that he won’t regress somewhat, but if I were to guess, I’m a little more optimistic than ZiPS and Steamer (which currently project .341 and .333 RoS, respectively).  Similar deal for some others.
• “Yikes” for Billy Butler and Domonic Brown, whose declines this season seem (at least in-sample) to be entirely justified.
• I’m not sure why the model dislikes Casey McGehee so much.  Obviously his fly ball distance (mentioned earlier) isn’t doing him any favors, and his .369 first-half BABIP is probably unsustainable.  Still, .277 xWOBA?  Seems harsh.

As with any fantasy advice, don’t take any of this too literally…  Take it or leave it as you see fit.

Lastly, although I hyped this piece from a fantasy perspective, the overall goal remains that I would love to see more work done to de-luck hitter stats, the way people do so often for pitchers.  (FIP for pitchers, and xWOBA or xWRC+ for hitters! Is the dream.)

Reader thoughts on how to improve the model, or requests for players not already mentioned?