When examining a batter’s strike zone judgment, the analysis is typically done based on where the pitches passed the plane of the front of the strike zone. However, this analysis usually does not include a discussion of the pitches’ trajectories as they approached the plate, which influences whether or not a batter may choose to swing at a pitch. The aim of this research is to apply a simple model to project a pitch to the plane of the front of the strike zone, from progressively closer distances to home plate, and track how the projected location changes as the pitch nears the plate. In order to quantify the quality of a pitch’s projection as it approaches home plate, we will use a model for the probability of a pitch being called a strike to assess its attractiveness to a batter. While the focus of this will be the projections and results derived from them, a discussion of the strike zone probability model will be given after the main article.

To begin, we can start with a single pitch to explain the methodology. The pitch we will use was one thrown by Yu Darvish to Brett Wallace on April 2nd of 2013 (seen in the GIF below screen-captured from the MLB.tv archives) [Note: I started working on this quite awhile ago, so the data is from 2013, but the methodology could be run for any pitcher or any year].

The pitch is classified by PITCHf/x as a slider and results in a swinging strikeout for Wallace. The pitch ends up inside on Wallace and, based purely on its final location, does not look like a good pitch to swing at, two strikes or not. In order to analyze this pitch in the proposed manner of projecting it to the front of the plate at progressively closer distances, we will start at 50 feet from the back of home plate (from which all distances will be measured) and remove the remaining PITCHf/x definition of movement (as is calculated, for example, for the pfx_x and pfx_z variables at 40 feet) from the pitches to create a projection that has constant velocity in the x-value of the data and only the effects of gravity deviating the z-value from constant velocity. This methodology is adopted from an article by Alan Nathan in 2013 about Mariano Rivera’s cut fastball. At a given distance from the back of home plate, the pitch trajectory between 50 feet and this point is as determined by PITCHf/x, and the remaining trajectory to the front of home plate is extrapolated using the previously discussed method.

If we examine the above Darvish-Wallace pitch in this manner, the projection looks like this from the catcher’s perspective:

In the GIF, the counter at the top, in feet, represents the distance that we are projecting from. The black rectangular shape is the 50% called-strike contour, where 50% of the pitches passing through that point were called strikes, the inside of which we will call our “strike zone” (for a complete explanation of this strike zone, see the end of the article). Within the GIF, the blue circle is the outline of the pitch and the blue dot inside is the PITCHf/x location of the pitch at the front of the plate. The projection appears in red/green where red represents a lower-than-50% chance of a called strike for the projection and green 50% or higher. As one can see, early on, the pitch projects as a strike and as it comes closer to the plate, it projects further and further inside to the left-handed hitter. If we track the probability of the projection being called a strike, with our x-axis being the distance for the projection, we obtain:

Based on this graph, the pitch crosses the 50% called-strike threshold at approximately 29.389 feet (seen as a node on the graph). With this consideration, and the fact that the batter is not able to judge the location of the pitch with PITCHf/x precision, it seems reasonable that Brett Wallace might swing at this pitch.

We can also examine this from two other angles, but first we will present the actual pitch from behind as another point of reference:

Now, we will look at an angle which is close to this new perspective: an overhead view.

The color palette here is the same as the previous GIF (blue is the actual trajectory in this case and red/green is as defined above) with the added line at the front of home plate indicating the 50% called-strike zone for the lefty batter. Note that since the scales of the two axes are not the same, the left-to-right behavior of the pitch appears exaggerated. The pitch projects as having a high probability of being called a strike early on and around 30 feet, starts to project more as a ball.

From the side, the pitch has nominal movement in the vertical direction, and so the projection appears not to move. However, the color-coding of the projected pitch trajectory shows the transition from 50%+ called-strike region to the below-50% region.

With this idea in mind, we can apply this to all pitches of a single type for a pitcher and see what information can be gleaned from it. We will break it down both by pitch type, as identified by PITCHf/x, and the handedness of the batter. We will perform this analysis on Yu Darvish’s 2013 PITCHf/x data and compare with all other right-handed pitchers from the same year.

To begin, we will examine Yu Darvish’s slider, which, according to the data, was Darvish’s most populous pitch in 2013. Since we are dealing with a data set of over 1000 sliders, we will first condense the information into a single graph and then look at the data more in-depth. We will separate the pitches into four categories based on their final location at the front of the strike zone: strike (50%+ chance of being called a strike) or ball (less than 50%), and swing or taken pitch. We will take the average called-strike probability of the projections in each of these four categories and plot it versus distance to the plate for the projection.

For left-handed batters versus Darvish in 2013:

The color-coding is: green = swing/strike, red = take/strike, blue = swing/ball, orange = take/ball. Looking at just pitches that are likely to be called strikes, the pitches swung at have a higher probability of being called strikes throughout their projections, peaking at the node located at 12.167 feet (0.928 average called-strike probability for the projections) for swings and at 1.417 (0.91), the front of home plate, for pitches taken. The swings at pitches in the strike zone end at a 0.924 average called-strike probability. Both curves for pitches outside the strike zone peak very early and remain relatively low in terms of probability throughout the projection.

We can also group all swings together and all pitches taken together to get a two-curve representation.

For sliders to lefties, the probability of a called strike is higher throughout the projection for swings compared to sliders taken. Similar to the previous graph, the swing curve peaks before the plate, at 20 feet with a 0.627 average called-strike probability and ends at 0.613, whereas the pitches taken peak at the front of the plate with a called-strike probability of 0.402.

To examine this in more detail, we can look at the location of the projections as the pitches moves toward the plate, similar to the GIFs for the single pitch to Wallace. Using the same color scheme as the four-curve graph, we will plot each pitch’s projection.

Of interest in this GIF is the observation that most swings outside the zone (blue) are down and to the right from the catcher’s perspective. In particular, based on the projections, there appears to be a subset of the pitches with a strong downward component of movement that are swung at below the strike zone, while most other pitches have more left-to-right movement. In addition, the pitches taken are largely on the outer half of the strike zone to lefties. To better illustrate the progressive contribution of movement to the pitches, we will divide the area around the strike zone into 9 regions: the strike zone and 8 regions around it: up-and-left of the zone, directly above the zone, up-and-right of the zone, directly left of the zone, etc. In each of these 9 regions, we will display the number of swings and number of pitches taken as well as the average direction that the projections are moving as more of the actual trajectory is added in, or in other words, the direction that the movement is carrying the pitch from a straight line trajectory, plus gravity, in the x- and z-coordinates.

Note that the movement of the pitches is predominately to the right, from the catcher’s perspective, with some contribution in the downward direction. In the strike zone, the pitches taken have an average location to the left of those swung at. This may be due to the movement bringing the pitches into the strike zone too late for the hitter to react. Computing the percentage of swings in each region produces the following table:

From the table, where the middle square is the strike zone, we can see that the slider is most effective at inducing swings outside of the strike zone, which has a better percentage of swings than the strike zone itself (Note that some of these regions may contain small samples, but these can be distinguished by the above GIFs). Next is the strike zone, followed by the region directly down-and-right of the strike zone. Going back to the projections, pitches in the two aforementioned non-strike zone regions start by projecting near the bottom of the strike zone and, as they move closer to the plate, project into these two regions.

Putting these observations in context, the movement on the sliders from Yu Darvish to lefties may allow him to get pitches taken on the outer half of the plate, which is generally in the opposite direction of the movement, and swings on pitches down and inside, in the general direction of the pitch movement. This would signify that movement has a noticeable effect on the perception of sliders to lefties. Also of note is that the pitches up and left of the strike zone have very few swings among them, and those that were swung at are close to the zone. Again using movement as the explanation, the pitches project far outside initially and, as they near the plate, project closer to the strike zone, but not enough to incite a swing from a batter.

We can further illustrate these effects on the pitches outside the zone by treating the direction of the movement at 40 feet, taken from the PITCHf/x pfx_x and pfx_z variables, as a characteristic movement vector and finding the angle of it with the vector formed by the final location of the pitch and its minimum distance to the strike zone. So if the movement sends the pitch perpendicularly away from the strike zone, the angle will be 0 degrees; if the movement is parallel to the strike zone, the angle will be 90 degrees; and if the pitch is carried by the movement perpendicularly toward the strike zone, the angle will be 180 degrees. As an illustrative example, consider the aforementioned pitch from Darvish to Wallace:

In this case, the movement vector of the pitch (red dashed vector) is nearly in the same the direction as the vector pointing out perpendicular from the strike zone (blue vector). This means that the angle between the two is going to be small (here, it is 0.276 degrees). If the movement vector in this case were nearly vertical, lying along the right edge of the zone, the angle would be close to 90 degrees.

Taking the movement for all sliders thrown to lefties in 2013 by Darvish and finding the angle it makes relative to the vector perpendicular to the zone, we get the following hexplot:

Summing up the hexplot in terms of a table:

So 31.8% of the sliders thrown outside the strike zone to lefties had an angle of less than 45 degrees between the movement and the vector perpendicular to the strike zone. The average distance of these pitches from the strike zone was 0.779 feet. Increasing the restriction to less than 90 degrees, meaning that some part of the movement is perpendicular to the strike zone, we get 67.9% of pitches outside met this criterion with an average distance from the zone of 0.691 feet. Finally, for all pitches outside, the average distance was 0.608 feet.

As a point of comparison, for all MLB RHP in 2013, the same analogous plot and table are:

Note that the range of possible angles is 0 to 180 degrees, with 25.3% lying in the 0-45 degree range and 52.6% in the 0-90 degree range. So based on this and examining the hexplot visually, the pitches are fairly uniformly distributed across the range of angles.

Comparing Darvish to other RHP in 2013, he threw his slider more in the direction of movement outside the zone. In particular, for angles less than 45 degrees, he threw his slider an average of 1.5 inches further outside compared to other MLB RHP. That disparity shrinks when restricting to less than 90 degrees and is virtually the same for all pitches outside.

While this observation on its own does not have much significance, we can look to see if this was an effective strategy by looking only at swings and seeing the effects.

Examining both the hexplot and the table, Darvish induced most of his swings outside of the strike zone with pitches having its movement at an angle of less than 90 degrees relative to the strike zone. Note that when the pitch is thrown outside the zone in the general direction of movement (an angle of less than 90 degrees), the pitch can still induce the batter to swing while pitches not thrown in this general direction are only swung at when very close to the zone. In particular, the majority of pitches that reach the farthest outside the zone and still lead to swings are in the range of 30 to 60 degrees. This is due to many of the swings outside the zone being below the strike zone, where the angle with the down-and-to-the-right movement will be in the neighborhood of 45 degrees.

For all MLB RHP in 2013, the hexplot for swings produces a similar result:

From the hexplot, we can see that the majority of pitches swung at are at an angle of 90 degrees or less; 64.3% to be precise. For less than a 45-degree angle, the percentage is 31.8%. These are both up from the percentages from all pitches. As seen with the Darvish data, as the angle decreases, the average distance tends to increase.

Finally, for pitches not swung at outside the zone, we get a complementary result to the swing data:

Here, the percentages are lower than for swings and, while the largest distance is for small angles, there is a grouping of pitches present in pitches taken at angles greater than 90 degrees that is virtually nonexistent for swings. So for Darvish, throwing sliders outside the strike zone with an angle greater than 90 degrees does not appear to be a fruitful strategy, unless it plays a larger role in the context of pitch sequencing. To sum up this observation, it would appear that pitching in the general direction of movement outside the strike zone is a necessary but not sufficient condition for inducing swings from left-handed batters.

For MLB right-handed pitchers, this observations appears to still hold:

As with Darvish, the percentages drop when comparing pitches taken to pitches swung at. The hexplot also bears this out, with the largest concentration of pitches taken outside the strike zone having an angle between movement and the strike zone vector of greater than 90 degrees. These results match in general with what we have seen with Darvish, and based on the numbers, Yu Darvish is able to play this effect to his advantage, with a larger-than-MLB-average percentage of sliders outside the zone to lefties with an acute angle.

Next, we will perform a similar analysis on sliders to righties. This will allow for comparison between the effects of the slider on batters from both sides of the plate.

Once again, for pitches in the strike zone, the sliders swung at by righties have a higher probability of being called strikes than those taken. The peak for swings at strikes occurs at 18.333 feet (v. 12.167 feet for LHB) with a 0.945 called-strike probability and ending at 0.931, and taken strikes at 13.667 feet (v. 1.417 feet for LHB) with a 0.892 probability and ending at 0.885.

Just examining swings and pitches taken, the peak projected probability is earlier than for lefties at 26.25 feet with 0.672 probability and finishing at 0.629. It also peaks earlier for pitches taken, at 23.147 feet with peak and ending probabilities of 0.454 and 0.442, respectively. Comparing with the results for lefties, the RHB both swing at and take sliders with a higher probability of being called strikes, but have an earlier peak probability.

Breaking it down again in terms of the individual pitches:

The plot here looks similar to that of the lefties. However, the pitches taken in the strike zone (red) appear more evenly distributed. In addition, the swings outside the zone (blue) appear to be more down and to the right and less directly below the strike zone. To confirm these observations, we can again simplify the plot to arrows indicating the direction of movement in each region and the number of each type of pitch in each region.

The table below gives the percentage of swings on pitches in each of the nine regions for Yu Darvish’s sliders to RHB:

To confirm the first observation, note that the red arrow (pitches taken) virtually overlaps with the green arrow (pitches swung at) in the strike zone. Examining the table, the value that differs the most, among the reasonably populated regions, is directly below the strike zone (42.1% to RHB v. 65.4% to LHB). One possible explanation for this is that some of the sliders ending up in this region to LHB have a stronger downward component of the movement than for RHB. This can be seen by comparing the two GIFs.

Moving on to the results for the angle between the movement and the strike zone vector, the hexplot is heavily populated by pitches thrown in the direction of movement:

Considering the same metrics for interpreting this plot as before:

From the table, we see that Yu Darvish threw 42.3% of his sliders to RHB with an angle of less than 45 degrees between the strike zone vector and the movement vector, up from 31.8% to LHB. Nearly 79% of his sliders outside the zone were thrown with an angle less than 90% degrees, again up from 67.9% to lefties. However, the average distance is down across the board as compared to lefties.

As a point of comparison, for MLB righties to right-handed batters, the distribution looks similar to that of Darvish:

Compared to Darvish, MLB RHP tend to throw a lower percentage of sliders with an angle less than 45 and 90 degrees. However, the MLB average distance from the strike zone is greater across the board.

Now, isolating only swings:

For RHB versus LHB, Darvish’s percentages are up, if only by a few percent. The average distance for less than 45 degrees is down from 0.59 feet to LHB but up in the other two cases. This can be seen in the hexplot since the protrusion in the distribution is around 60 degrees rather than being closer to 45 degrees as before.

The 2013 MLB data shows a similar result, with a roughly triangular pattern in the hexplot, where the distance from the strike zone for swings increases as the angle between the strike zone vector and movement vector decreases.

As in the case of lefties, all metrics for Darvish are above MLB-average.

For the sliders taken by right-handed batters:

For angles less than 45 degrees, the percentage of sliders taken outside is noticeably up, as compared with LHB (39.8% v. 26.3%) as well as for less than 90 degrees (74.9% v. 57.4%). This is not surprising since the distribution for all pitches was markedly different between batters on either side of the plate and, in this case, skewed toward the less-than-90-degrees region. The average distances are, however, down from the case for lefties.

Comparing Darvish to other RHP in 2013, the results are similar:

In contrast to MLB RHP, Darvish’s sliders that are taken outside the strike zone are closer to it across the three measures. As before, Darvish’s sliders taken are thrown more in the direction of movement as compared to MLB righties in 2013.

Discussion

When constructing this algorithm, we need to choose a metric by which to group the pitches at each increment. In this case, we are using distance from the back of home plate. While this may be suitable for analyzing a single pitcher, when dealing with multiple pitchers or flipping the algorithm around and using it for evaluating a hitter, the variance in velocity of pitches in between pitchers may have an effect on the results. Therefore, it may be better, for working with multiple pitchers or a hitter, to use time as a metric instead. So rather than tracking the projections as y feet from home plate, we would use t seconds from home plate.

Using this method, with further refinement, we could potentially try to measure quantities such as “late break”. Granted, the PITCHf/x data is restricted to its parameterization by quadratic functions so even if aberrant behavior occurred near the plate, PITCHf/x would not be able to represent it. However if we define late break as x inches of movement over distance y from home plate (or t seconds from home plate), we could hope to quantify it. Based on how we construct the projection, such as including factors other than the PITCHf/x definition of movement, late break could be considered as a difference in perceived position at a distance versus the location at the front of the plate. As seen in the swing/take curves, after a certain distance, the probability of a called strike starts to drop off for Darvish’s sliders, and we could possibly choose, from that point on, to calculate late break for each pitcher. But to do this, we would first have to figure out all elements we wish to use, including movement, to make up pitch perception. As we have seen, for both Darvish and MLB RHP in general, throwing sliders outside of the strike zone in the general direction of movement (with less than a 90-degree angle between the movement vector and the vector perpendicular to the strike zone) elicits swings at a higher rate farther outside the strike zone. In the hexplot for swings, this takes the form of, roughly, a triangular shape of the data which widens in the distance direction as the angle decreases. This can also be seen in the GIFs for the blue pitches (swings outside of the strike zone).

In addition, other elements could be added into this medley for attempting to model a hitter’s perception of a pitch as it approaches the plate. First, one could remove the drag from the movement, leaving it in the projection. Without running the projections, we can see how this would affect the results by looking at how the “movement” differs at 40 feet with and without drag. Pictured below is a subsample of the movement vectors at 40 feet for Darvish’s sliders based on the PITCHf/x definition, in green, and the movement without drag, in blue. The blue vectors are found based on Alan Nathan’s paper on the subject. The dashed red lines connect the same pitch for the different versions of movement. We can see that the movement without drag is larger in magnitude, and in the downward direction and to the right, meaning the projections would start higher and to the left. Comparing the movement vectors with and without drag, the average change in movement for the entire sample is 1.571 inches and the average change in angle between the pairs of vectors is 5.527 degrees. With drag left in the projection and out of the movement, the swing hexplots would likely take a more triangular shape with the angle between the vectors decreasing and shifting the data downward for the pitches outside the zone that were previously moving more laterally.

One could also affect the time to the plate for the pitches as well. As it stands, this approach assumes that the hitters have perfect timing and track pitches using a simple extrapolation approach. If one were to assume that the remaining velocity in the y-direction (toward the plate) was perceived as constant for the pitches, the hitters would be expecting the pitches to arrive faster than they actually are. This would lead to the projections appearing higher, since gravity would have less time to have an effect.

A rather large assumption that we are making is that batters can decouple vertical movement from gravity. Even in cases where the vertical movement is small, this will have an effect on the projected pitch location. This may also serve as an explanation as to why the sliders swung at below the strike zone do not always have a strong vertical component of movement.

Next time, we will look at Darvish’s four-seam fastballs, followed by his cut fastballs, in a similar manner. As we will see, certain pitches excel at inducing swings outside the strike zone when thrown in the general direction of movement while others show little to no benefit at all. We can also break down the pitches swung at by the result (in play, foul, swing-and-miss) to gain further insight.

Strike Zone Analysis

This section explains the calculation and choice of model for the probability of a called strike used in the above analysis. There have been a lot of excellent articles analyzing the strike zone, such as by Matthew Carruth, Bill Petti, and Jon Roegele, among others, and this method is derivative of those previous works. Our goal is the create an explicit piecewise function that reasonably models the probability that a pitch will be called a strike, based on empirical data. However, rather than treat the data as zero-dimensional (no height, width, or length for each datum), we represent each pitch as a two-dimensional circle with a three-inch diameter. Then, over a sufficiently refined grid, we calculate the number of 2D pitches that intersected each point that were called strikes divided by the number of 2D pitches that were taken (ball or strike). This gives the percentage of pitches that intersected each point that were called strikes. This number provides an empirical estimate of a pitch passing through that point being called a strike. The advantage of taking this approach is that we do not impose any a priori structure on the data, which can happen when using methods such as binning or model fitting to the zero-D data. It also conforms with using a 2D strike zone to perform the analysis by representing the data fully in 2D. Note that since using all MLB data from 2013 to generate these plots, we have a large enough data set that we do not get jumps or discontinuities for the strike zone that may occur for smaller data sets, such as for a single pitcher. As an example, the called-strike probability for LHB in 2013 looks like:

The colormap on the right gives the probability of a pitch at each location being called a strike, based on the data. The solid rectangle represents the textbook strike zone (with 1.5 and 3.5 vertical bounds), and the two dashed lines will be explained concurrently with the model.

For the model, we assume a small region where the probability of a called strike is essentially 1, which, in the graph, is the long-dashed line. Far outside the strike zone, will assume that the probability that a pitch is called a strike is essentially zero. In between, we need a way to model the transition between these two regions. To do this, we will adopt a general exponential decay model of the form exp(-a x^b), where a and b are parameters. In this case, we take x to be the minimum distance to the probability-1 region of the strike zone (long-dashed line). Since there is some flexibility in how we choose the probability-1 region and the subsequent parameters, we will do this less rigorously than could be done in order to keep things simple.

First we examined slices of the empirical data in profile and found that experimenting with the probability-1 region bounds and a, b values, a value around 4 for b worked well at matching the curvature. Then a choice of a equal 4 was found similarly via guess-and-check. Finally the probability-1 region was adjusted to make the model match the data based on a contour plot for each (see below). For lefties, the probability-1 region is [-0.55,0.25] x [2.15,2.85] feet.

Note that we do a decent job of matching the contours outside of the lower-right and upper-left regions, where there is some deviation. This can be adjusted for by changing the shape of the probability-1 area, but this increases the complexity of calculating the minimum distance. When plotting the model for the probability:

Here, the solid and long-dashed lines are as before, and the dotted line is the 50% called-strike contour from the model, which is used as the boundary of the strike zone in the above analysis. While the shape of the strike zone may seem unconventional, it is a natural approach for handling the zero-dimensional PITCHf/x data. For example, if we place a pitch on the edge of the rectangular textbook zone, a so-called borderline pitch, and track the path that the center would make as it moved around the rectangle, it would trace out a similar shape.

For RHB, the heat map is much more balanced, left to right, making the fit much closer than could be achieved for LHB.

Again, the top and bottom of the 50% called-strike contour lies near 3.5 and 1.5 feet, respectively. Examining the contour map:

Here, the identified contours fit well all around. The called-strike probability, with the model applied, is:

In this case the probability-1 region is [-0.43,0.40] x [2.15,2.83] feet.

So, overall, the RHB called-strike probability model fits much better, especially in the corners, than for LHB. In order to properly fit the called-strike probability to such a model, one would first need to have a component of the algorithm that adjusts the probability-1 area, both by location and size, and possibly by shape. Then the parameters for the decay of the strike probability could be fit against the data. The probability-1 area could then be adjusted and fit again, to see if the overall fit is better. This might work similar to a simulated annealing process. However, for our purposes, sacrificing the corners for LHB seems reasonable to maintain simplicity of method and calculations.

In closing, if you made it this far, thank you for reading to the end.

I’ve often wondered if some sort of bizarre connection exists between names and athletic ability, specifically when it comes to the sport of baseball. Considering I grew up in the 90’s, I will always associate certain names with possessing a supreme baseball talent. Names like Ken (Griffey Jr.), Mike (Piazza), Randy (Johnson), Greg (Maddux) and Frank (Thomas) are just a few examples. With a wealth of statistical information available, I thought I’d investigate into the possibility of an abnormal association between names and baseball skill.

I began digging up the most popular given names, by decade, using the 1970’s, 80’s & 90’s as focal points. This information was easily accessible on the official website of the U.S. Social Security Administration, as they provide the 200 most popular given names for male and female babies born during each decade. After scouring through all of the names listed, the records revealed there were 278 unique names appearing during that timespan.

Having narrowed down the most popular names for the timeframe, I wandered over to FanGraphs.com, to begin compiling the “skill” data. I will be using the statistic known as WAR (Wins Above Replacement) as my objective guide for evaluating talent. Sorting through all qualified players from 1970-1999, the data revealed 2,554 players eligible for inclusion. After combining all full names with their corresponding nicknames (i.e.: Michael & Mike), the list was condensed down to 507 unique names.

By comparing the 278 unique names identified via the Social Security Administration’s most popular names data, with the 507 qualified ballplayer names collected through FanGraphs, it was discovered that 193 of the names were present on both lists. The following tables point out some of the more intriguing findings the research was able to provide.

The first table[Table 1], below, is comprised of the 25 most frequent birth names from 1970-1999. The second table[Table 2] consists of the 25 WAR leaders by name, meaning the highest aggregate WAR totals collected by all players with that name. Naturally, many of the names that appear in the 25 most common names list, reappear here as well. Ken, Gary, Ron, Greg, Frank, Don, Chuck, George and Pete are the exceptions. It’s interesting to see that these names seem to have a higher AVG WAR per 1,000 births(as seen on the final table), perhaps indicative of those names’ supremacy as better baseball names? The last table[Table 3] contains the top 25 names by AVG WAR per 1,000 births; here we see some less common names finally begin to appear. These names provide the most proverbial bang (WAR) for your buck (name). Yes, some names, like Barry and Reggie, are inflated in the rankings — probably due to the dominant play of Barry Bonds and Reggie Jackson, but could it not also mean these players were just byproducts of their birth names?!? Probably not, but it’s interesting, nonetheless.

So if you’re looking to increase the chances your child will make it professionally as a baseball player, then you might want to take a look at the names toward the top of the AVG WAR per 1,000 births table, choose your favorite, and hope for the best…OR, you could always just have a daughter.

Please post comments with your thoughts or questions. Charts can be found below.

25 Most Common Birth Names 1970-1999

25 Highest Cumulative WAR, by Name, 1970-1999

25 Highest WAR per 1,000 Births, by Name, 1970-1999

Some team really should take a chance to give Scott Van Slyke a starting OF job next season.  Frankly, I’d find it almost sinful if some team does not go for it.

(Granted, the Dodgers may still use the off-season to relieve their outfield logjam, so maybe Van Slyke works his way into the Dodgers’ own starting lineup.  But I’ll suppose for now that that does not happen.)

First, a summary of his career performance:
.261/.348/.476
.361 wOBA
134 wRC+
(455 PA)

The 134 wRC+ certainly is impressive.  And while he obviously did it only over a limited sample, if he were a full-time player, that would have ranked 24th in 2014; just behind Hanley Ramirez, David Ortiz, and Jose Altuve.  Alternatively, among all players with 450+ PA from 2012-2014, Van Slyke’s wRC+ also ranks 24th.

So he certainly has been good in-sample.  But what should you expect going forward?

There seem to be three key questions:
(1) Can he hit righties well enough?
(2) What is his true talent BABIP?
(3) What is his true talent ISO?

On the first point, Van Slyke’s career-to-date statline has certainly benefited from heavy use against left-handers.  In his career, he’s had slightly over half of his plate appearances against lefties — with a punishing 151 wRC+ — and a more pedestrian 116 wRC+ versus righties.  Taking those numbers at face value, for now, even if you re-weighted his plate appearances to be 70% against righties and 30% against lefties, that still comes out to 126.5, aka plenty good.  At least in-sample, that’s not that different from Josh Donaldson, who mashes lefties and is comparatively average against righties.  And I’m sure most teams would be elated to have Josh Donaldson.

The next question, then, is whether his career-to-date .323 BABIP is his true-talent BABIP.  There are some plausible reasons to think “no.”  Steamer projects him for .295 BABIP next season, and at least this 2012 version of an xBABIP calculator puts him more in the .270 territory.

I’m somewhat more optimistic on his BABIP, though.  His minor league BABIPs were good, after all: .404 over a full season in AA, and .354 and .437 across two half-seasons in AAA.  And ZiPS had him projected for .310 BABIP for 2014, and after a .394 actual showing, it will most likely be higher next season.

For simplicity’s sake, suppose you take everything else about Van Slyke’s career-to-date batting as given (BB and K rates, ISO, etc.), and just do the BABIP adjustment.  (This is not entirely realistic, but again, simplicity.)  What do his stats look like for different BABIP values?  You get:

Even on the low end, that’s still a useful player.  And even lowering everything by .050 for the platoon adjustment,* even the worst-case scenario is about a league-average LF, which this season posted a .720 OPS.  And the more optimistic scenarios put him above average.

* – Remember that 126.5 wRC+ computed earlier?  This would be about a .341 wOBA, which is .020 lower than his unadjusted wOBA.  .020 wOBA is approximately equal to .050 OPS.

Then the last question is: has he also overachieved on ISO in-sample?  Here, I’m a little more convinced that he may have.  His minor league ISOs were not much higher than his Major League career-to-date mark (.215), and you see that Steamer has him projected for just .165 ISO next year.  It’s also possible Steamer is stingy, as ZiPS had him projected for .170 ISO in 2014, and this will only increase after his actual 2014 performance.  But even supposing that increases to something like .182, it still suggests Van Slyke’s true-talent ISO is lower than what he’s shown so far.

Suppose we somewhat conservatively assume Van Slyke’s true talent BABIP is .300, and again take BB and K rates as given, but this time do an ISO adjustment.  What would his career-to-date stats look like?  You get:

(assuming .300 true-talent BABIP; no platoon adjustment)

Or, if you want a full table that allows BABIP and ISO to vary simultaneously, you get:

(OPS value in cells; no platoon adjustment)

Especially after factoring in some platoon adjustment, you see that there definitely are scenarios where Van Slyke could be below a league-average corner OF, despite his promising performance to date.  But these require that he has overachieved in either BABIP or ISO, or both; neither of which is given.  Even using the seemingly conservative Steamer projection for Van Slyke’s 2015 performance, he projects for something like 2 WAR over a full season, which is good enough to start.  And meanwhile there are many scenarios where he could be better than that.  (In-sample he’s been 4.5 WAR per 600 plate appearances!)

Of course the Dodgers know this as well.  Even so, I can’t imagine the price to acquire Van Slyke would be that high, and with the upside, it sounds totally reasonable for teams like Cincinnati, Seattle, or the White Sox, who didn’t get nearly enough production from their outfield last year.

Reader thoughts?

One of the beautiful things about WAR is the way it assigns value to separate, unique elements of player performance. Perhaps one of the lesser-appreciated elements of WAR is BsR, which measures the value of a player’s baserunning.

BsR contains two separate components: wSB and UBR. wSB describes a player’s value added through base stealing, and UBR measures the cumulative value of a player’s base path advancements outside of stealing.

One might imagine that these two components demand similar skill sets. To excel in either, a player must have a: reasonable speed and b: good instincts on the base paths. Indeed, it would be fairly surprising to see a great disparity between the two components for any given player’s baserunning.

In a quest to discover the most puzzling baserunners, I searched for the largest absolute difference between wSB and UBR over a player’s career. There were several noteworthy constraints, a: our UBR data begins in 2002, limiting the search to the past 13 seasons and b: a general difference in magnitude between wSB and UBR. Because UBR governs all base running events outside of stolen bases, players typically see far more opportunities to accrue UBR than wSB value.

To adjust for this factor, I assigned each of the 685 qualified players a percentile rank for wSB and UBR. After sorting by the largest absolute difference in percentile, the truly anomalous base runners became apparent. Consider:

Table 1: From 2002-2014, Largest Absolute Differences in wSB and UBR Percentile

Well, there he is — among the anomalous, David Dejesus reigns supreme. While the average player carries a 22% difference between wSB and UBR percentile, Dejesus clocks in at more than 3.5 standard deviations above the mean. In 123 career stolen base attempts, Dejesus has succeeded in swiping the extra bag only 63 times. That’s certainly a less-than-stellar success rate. Nonetheless, Dejesus’ uncanny knack for taking extra bases on balls in play salvages his value as a baserunner; while Dejesus’ failures as a thief cost his team more than 15 runs, his ability to advance on the basepaths during the course of play has credited his team roughly 20 runs, or 2 wins.

Similarly, Cristian Guzman, Casey Blake, Clint Barmes and Dan Uggla all cost their teams with the stolen base, but ultimately produced positive baserunning value due to their ability to advance extra bases on balls in play. With two exceptions, the top 20 is filled with players who struggled to steal bases but excelled in running them.

Of the top 20 differences, only Barry Bonds and Moises Alou possess a baserunning disparity driven by a positive wSB and negative UBR. Strangely enough, by 2002 both players had already seen a decline in their stolen base totals. Nonetheless, each managed to accrue positive value via thievery, only to give it back (and then some) throughout the course of their time on the base paths.

Ultimately, there exists a relatively easy solution for players who hurt their teams via the stolen base: stop attempting steals. By minimizing their exposure to negative outcomes in base stealing, players can maximize their baserunning value. Unfortunately for players who possess a negative UBR, there is no simple solution. While players can minimize their stolen bases attempted, they cannot avoid the daily labor of running the bases. For most of the “anomalous” players in the table above, a small tweak of strategy could have improved their value over time. In the case of David DeJesus, a league average wSB could have saved his teams close to 20 runs — roughly 2 wins. Although hitting and defense deserve the attention they receive, WAR’s baserunning components play a fascinating role in player valuation.

Statistics courtesy of FanGraphs and Baseball-Reference.

Jonathan Lucroy deserves the NL MVP.  I’ll try to make this short, but first I’ll need to discuss the factors I consider important for MVP candidacy.

Beyond WAR

WAR is a good starting point, but does not give the full picture of a player’s performance in a given year.  It does a great job at combining a hitter’s contributions (hitting, baserunning, defense) across the same units (runs and wins) to allow us to compare players who impact the game and add value in different ways, as well as adjusting for park and league factors, etc.  I also like talking about players in terms of how many wins they add, and the notion of comparing players to replacement level (readily available talent, in theory) as opposed to average has a lot of merit.  That said, there are still things that go uncaptured in WAR (in some cases, as with context/sequencing, this is by design) that make it incomplete when evaluating a player’s MVP candidacy.

For starters, WAR is context-neutral.  In my opinion, context matters.  Others may disagree, and do every time I bring this up, even though I’m saying precisely what others have acknowledged, which is that context is relevant for a backward-looking evaluation of value added to a team.  Take two guys of equal “true talent” levels; if the first guy happens to get more opportunities in high-leverage situations, and/or happens to cluster more of his offensive production in said situations, he’s adding more value than the second guy, if the second guy comes up in the proportionally expected number of high-leverage situations and performs no differently in those situations than low-leverage situations.  Do I project them to repeat their same trends the next year?  No.  But I’m pretty firm in my take that the first guy added more value over the season in question.

Furthermore, WAR does not capture all elements of a player’s contribution.  The most glaring omission at the current time is pitch framing.  Whether or not you believe pitch framing should be a part of the game (which I don’t — use a computer to call a consistent zone already!), it is part of the game, it does have value, and teams do appear to factor it into their evaluation of players.

Let’s look at the top NL position players, starting out with WAR:

Lucroy’s got some ground to make up in the WAR department.

Context

Taking some context into account though, he was significantly more clutch than the other candidates — in fact, the only other player with a positive Clutch score is Posey. The two catchers were the only ones who turned in better performances in higher-leverage situations.  It should be noted that other hitters (Stanton and McCutchen) put up a higher WPA, but this is expected for players whose value comes almost entirely from hitting (WPA measures hitting almost exclusively).  Posey and Lucroy added more value (created more runs) than their WAR represents due to sequencing; all the other candidates added less value.

Pitch Framing

Using Baseball Prospectus’s numbers, Lucroy added 23.3 runs through framing and blocking (almost entirely from framing; in fact his blocking was just slightly negative).  Posey added 13.7.  If we make a back-of-the-envelope calculation of 9.1 Runs Per Win in the NL this year, those come out to 2.6 Wins for Lucroy and 1.5 Wins for Posey.

Add it all up

Taking both context and pitch framing into account easily vaults Lucroy past the other contenders.  I don’t claim to have a perfect method of converting “Clutch” to the same units as WAR (Runs or Wins); one could use something like, the difference between “expected” WPA given a player’s Batting (based on league-wide correlation between Batting and WPA), and then look at their actual WPA, and add the difference to their WAR.  Such a system would give both Lucroy and Posey a bump of 1-1.5 Wins, while penalizing Rendon and Gomez pretty heavily.

Likewise for pitch framing, I’m not comfortable giving the catcher 100% credit for runs saved via framing (which by extension means removing the associated WAR from the pitcher), but based on my subjective opinion from watching good framers and bad framers and the skills they possess, I’m certainly comfortable giving at least half the value to the catcher, probably more.  So again, we’re talking another 1-2 Wins for Lucroy.  By my count, that puts him at upwards of 8-9 Wins, with the rest of the field not coming close.  Posey also sets himself apart from the non-catchers by virtue of both framing and clutching, but not enough to catch Lucroy.

It’s important to call out that using framing isn’t always going to mean a catcher will inevitably win the MVP.  There are plenty of years that the best catcher is not particularly close to the best position players in terms of WAR.  In 2013, Yadier Molina was 2.7 WAR behind MVP McCutchen, a gap too large for pitch framing to cover.  In 2012, Posey had the highest WAR even without framing.  In 2011, Molina had the highest WAR among catchers at 4.4, a full 4.0 behind Matt Kemp (who didn’t win the MVP…).  The same is true in the AL.  Using pitch framing doesn’t mean the MVP is suddenly going to start vaulting catchers over 10+ WAR guys like 2012-13 Mike Trout (we’ll leave that to other position players…).  It just happens that this year, we have two NL catchers who both happen to have exhibited clutch hitting (and who are good hitters in their own right), and who add significant value with their ability to receive the ball in such a way as to convince the umpire to call a strike more often than average.

That other guy

I’m not the type to say pitchers can’t win the MVP, and won’t resort to the “they only play every 5 days!” argument.  Clayton Kershaw has been dominant.  And, if you’re the type, it can be argued he led his team to win their division, while Lucroy’s headed home in September.  Bottom line for me though: Lucroy added more value to his team, using the units of Wins.  He’s a no-brainer for MVP.

All that said, Lucroy has absolutely no chance of winning the MVP.  The rationale in this post is in no way the mindset of the voters and he doesn’t stand a chance.

Season awards time. Not that I am anyone important in baseball and not that my opinions matter, though there is my miniscule contribution to FanGraphs Player of the Year and the Internet Baseball Awards to think about. And so I do. My usual approach had been (1) sort various FanGraphs leaderboards. Then, (2) do this:

Yes, this is a notepad leaned clumsily against the screen to cover up the player names, teams (and win-loss record why not). Why? I have my affections and biases for certain names, so I’m bound to want to come up with reasons to rate those guys higher. I’m also bound to neglect players I don’t see very often, and trick myself into thinking their seasons weren’t as good as they look. If I can’t see the names, I can go on pure statistical evaluation and put one over on my cognitive biases.

But yeah, clumsy. So I wrote up a tool that would do the name hiding in a more graceful way. Behold! The Player Name Hider. This is a fairly simple Greasemonkey script that hides the names of the players and teams on all leaders pages. When installed, I see something more like this:

All the names and teams are hidden after page load. If I want to see one, I just click the text to reveal, as with Zack Greinke and Justin Verlander above. Also note the link tucked just above the leaderboard to reveal all data at once.

Two immediate uses spring to mind:

1. As mentioned above, it’s handy around seasonal award time. You may be surprised whose statistical profile you uncover. (Rhymes with: Shrill Shoes or Schmadison Schmumgarner.)
2. Enjoy hours of delightful life-avoiding trivia games! Sort by RBI and guess who led the league, that sort of thing.

Here’s how you get the tool:

1. Firefox users: install the Greasemonkey browser extension. Chrome users: install the Tampermonkey extension. Safari users: I understand GreaseKit has the same functionality but I have not tested this. IE users: uh, sorry.
2. The script itself is available here. There, click the “Raw” button to install and confirm the prompt.

That’s it! Please let me know if you have any comments or questions.

As we hurtle into what promises to be a dramatic postseason, let’s pause a few moments to remember the rake-steppers, face-planters, and prat-fallers who helped make others’ excellence possible. Far from being stars, these players are the space debris that clogged several MLB rosters this year. So without further ado, here is your All Kuiper Belt team for 2015 (and I don’t mean Duane Kuiper). The team features, if that’s the word, the worst qualifying hitter at each position, and the five worst qualifying starting pitchers, by fWAR.

Catcher: Jason Castro, .222/.286./.386, 1.2 WAR

By far the best player on this ignominious team, Castro is here in part because only nine catchers qualified for the batting title. FanGraphs had this to say in Castro’s pre-season player profile:

Castro turns 27 in June, and there’s not much to suggest regression in his future.

Well, not exactly. This year Castro was durable by catcher standards, but he regressed severely, and you could in fact have seen it coming. A stratospheric .351 BABIP propelled Castro’s breakout year in 2013. This season it sank to .293, not far from his carer mark of .307.  Neither as good as he was in 2013, nor as bad as he was this year, Castro should be a solid, above-average backstop, but always be wary of balls in play bearing gifts.

First Base: Ryan Howard, .222/.310/.380, -0.3 WAR

One of the best Baseball Prospectus player notes ever was for Ryan Howard this year. It consisted of just four words: “We told you so.” Howard’s career has become a coal seam fire, and he still has 2 years left on his deal before what will certainly be a $10 million buyout in 2017. Howard will be a pallbearer at the funeral for Ruben Amaro Jr.’s GM career.

Second Base: Aaron Hill, .244/.287/.367, -0.7 WAR

Hill wasn’t the worst Snake this year; that dishonor goes to Mark Trumbo, who managed to cram -1.3 WAR into just 355 plate appearances. There wasn’t a moist eye in the house when Kevin Towers lost his job, but The Gunslinger won the draw that sent Kelly Johnson to the Blue Jays for Hill in late 2011. Since then, Hill has amassed 6.6 WAR, while Johnson has only put up 1.7.

Third Base: Matt Dominguez, .215/.256/.330, -1.7 WAR

The second Astro on this list, Dominguez is here on the merits. Regressing plate discipline and an oddly consistent but abysmal BABIP have conspired to deprive Dominguez of any run production value. Known in his prospect days for his glove, Dominguez’ UZR is -8.7; only Lonnie Chisenhall had a worse rating at third. Dominguez is only 26, but then again, so was Kevin Orie in his last full major league season.

Shortstop: Derek Jeter, .256/.304/.313, -0.3 WAR

I don’t know about you. I always thought the Bob Sheppard thing was kind of creepy.

Left Field: Domonic Brown, .235./.285/.349, -1.7 WAR

One of the more fascinating what-ifs in baseball is what if Domonic Brown had come up with a different organization.  During most of his time in the Phillies organization, manager and front office were much more vocal about what he couldn’t do than what he could. Left to his own devices at last this year, Brown’s power disappeared. His HR/FB rate of 8.1% is 18th out of 19 qualifying left fielders, meaning that his power surge last year is looking more like a fluke than a step forward.

Center Field: B.J. Upton, .208/.287/.333, 0.4 WAR

This near replacement-level guy made $13.45 million this year, which is the kind of fact that gets GMs fired. He’s actually improved over last year, but every rate stat continues to be worse than his career averages. His K rate of 29.9% is the second worst of his career, and the worst of any qualifying center fielder.

Right Fielder: Jay Bruce, .217/.281/.374, -1.3 WAR

Bruce had surgery for a torn meniscus in his knee in early May and was never the same. Except that’s not true. He came back in June and raked to the tune of an .892 OPS, but then completely fell apart. I have to wonder if fatigue in the knee had something to do with it. For his career Bruce has almost exactly the same number of doubles (181) as homers (182). From July to season’s end, he hit 11 HRs, but just 4 doubles, which suggests he was having trouble getting extra bases without putting the ball in somebody’s beer. Bruce is generally a solid defender, but had a UZR of -8.4 this year, by far the worst of his career, which also suggests he was not fully mobile for much of the season. Bruce is the most likely player on this list to be an All-Star next year.

Designated Hitter: Billy Butler, .271./.323/.379, -0.3 WAR

Four of the seven Royals hitters who qualified for the batting title are home grown. Only one of them, Alex Gordon, had a positive wRC+ (indeed, Gordon is the only such qualifying hitter on the team, period). Butler, Hosmer, and Moustakas were going to be part of the the core of the next Royals playoff team. Instead, the Royals have made the playoffs this year largely despite these guys. Butler’s ISO continued to erode this year, and his walks, which spiked last year, plunged into the root cellar in 2014. These are hard times for DHs, slow and massive beasts whom evolution is passing by, and Butler’s mediocre wRC+ of 97 is just two points off the national average. But if the Royals are going to build a team to get past the Coin Flip Game, they will need to upgrade at this position. Butler is the only player on this list on a playoff team.

Pitchers (ERA/FIP, WAR):

Eric Stults, 4.30/4.63, -0.6 WAR

Sproingggggg! Regression to the mean was mean to Stults this year, as his FIP rocketed from 3.53 to 4.63, or 40 points over his career number.  The gopher ball killed him, no mean feet in cavernous Petco. The Pads won’t offer him arbitration, so he’ll look to take his Veteran Self elsewhere. He won’t be this bad again, but at 34 he may not get the chance to prove it.

Roberto Hernandez, 4.10/4.85, -0.5 WAR

Forced in 2012 to change his name by the International Fausto Carmona Association, whose members no longer wanted to be associated with him.

Chris Young, 3.65/5.02, 0.2 WAR

It’s tough to say goodbye. Young hasn’t been an effective starter since 2007, but he grimly soldiers on, desperately searching for signs of pitching life on this barren world. It’s easy to root for guys like Young, but the M’s had a pennant train to catch this year, and their decision to give Young almost 30 starts probably cost them a seat.

Shelby Miller, 3.74/4.54, 0.2 WAR

If you’ve been playing along at home, most of the guys on this list probably haven’t shocked you, but did you see Shelby Miller coming? Let’s start with that yawning chasm between his ERA and FIP. Miller’s career ERA is 60 points less than his career FIP, so the gap is only somewhat worse than that this year, but still disconcerting. His strikeouts disappeared, not because of any velocity drop, but because of the wholesale failure of his off-speed pitches. He’s still just 23 and he still throws hard. If the Cards can’t fix him, maybe they can trade him to a pitching coach who can. Paging Dr. Cooper …

Kyle Kendrick, 4.61/4.57, 0.4 WAR

The phourth Phillie on this list, Kendrick’s rather offputting FIP is actually 8 points better than his career average. A tolerable innings eating presence on a high-scoring team, Kendrick is now a liability on a team chock full of them. This was his walk year, a strange expression to use in conjunction with Kendrick, since that’s the one thing he doesn’t give up.

Twenty-one year old Brandon Finnegan put his name on the map in Tuesday night’s epic wildcard game, when he tossed scoreless 10th and 11th innings before leading off the 12th with a walk to Josh Reddick, who would eventually come around to score against Jason Frasor. Drafted by the Royals with this year’s 17th overall pick, Finnegan made quick work of the minor leagues, making his big league debut on September 6th at Yankee Stadium, just 81 days — and 27 minor league innings — removed from his last appearance with TCU in this year’s College World Series.

To find comps for Finnegan, I first looked for pitchers with a similar arsenal of pitches. Using a minimum of 1,000 pitches, I sought out left-handed pitchers who threw fastballs, sliders, and changeups — Finnegan’s three pitches — at least 90% of the time since 2008, and threw each of these pitches at least 5% of the time. From there, I turned to the PITCHf/x database to find out how often these pitchers’ pitches fell within Finnegan’s middle 50% of values for velocity, break angle, break length, and spin rate, and spin direction from his eight big-league games. These are the pitchers who threw the highest ratio of pitches comparable to what Finnegan threw. The similarity percentage was calculated by dividing each pitcher’s share of pitches meeting these criteria by the share of pitches met by Finnegan himself. The ERA’s were calculated over the last seven years: 2008-2014.

Finnegan looks to have a bright future ahead of him, as his top two comps are two of the most dominant pitchers in baseball — one a starter (Sale) and one a reliever (Watson). It remains to be seen which path the Royals will choose for their hard-throwing lefty going forward. While it’s tempting to slot him in in a relief role next season, the wiser decision might be to stretch him out as  starter, where he would be able to take full advantage of his three-pitch arsenal. But either way, until the Royals’ playoff run comes to an end, Brandon Finnegan will be allowed to air it out for just an inning or two at a time on easily the biggest stage he’s ever seen. And given his lights-out stuff, he might just end up being this year’s Francisco Rodriguez.

This article originally appeared on Pinstripe Pundits.

With 2014’s baseball season winding down, end of year award discussion is starting to kick into high gear. It seems every day there’s a new article discussing X player’s case for winning Y award, when likely Z will win it.

Mike Petriello wrote an article discussing the NL Rookie of the Year race, and in it stated that it comes down to two players — Billy Hamilton of the Reds or Jacob DeGrom of the Mets. Ken Giles of the Phillies may not be considered a contender for the award, but by every statistical measure Giles’s 2014 rookie season compares  favorably with Craig Kimbrel’s 2011 RoY winning season.

In 2011, the NL Rookie of the Year award was a unanimous decision — Craig Kimbrel! Ice in his veins! 46 saves! Those strike outs! That slider! Could you vote for anyone else in good conscience?

Kimbrel was (and still is) a fantastic pitcher. But if his case for Rookie of the Year was unanimous, does that mean Ken Giles should also garner some consideration? And if Ken Giles had started the season at the Major League level and produced like he has so far, what would that look like? Would he have a better shot then? Let’s dive into the numbers.

Note: I am not an expert with projections. Therefore — all rate stats will stay the same between Ken Giles’s 2014 season and the full-season extrapolation. Sorry to disappoint.

First, some dashboard stats:

Both pitchers allow very low AVG despite having average to below-average luck with BABIP. Their LOB% is well above average, and they don’t allow a lot of home runs. As a result, their accumulated WAR values are both very good. Let’s dig into some rate stats to see how they compare there.

By FIP and xFIP, these pitchers are comparable. By ERA Giles has the advantage, which likely can be explained by the difference in BABIP.

Both pitchers have K rates that are simply awesome. Kimbrel gives up a few more free passes,  but makes up for it with some more K’s. As a result, their xFIP is nearly identical.

Now let’s look at how they achieve these results:

Stuff wise, they mirror one another. Both fastball/slider guys, with some real heat on their fastballs and sliders that fare rather well.
The real eye-opener — they even attack hitters the same way. Take a look at Kimbrel’s Pitch% heat chart in comparison with Giles’s. They are remarkably close to one another.

So we have two pitchers that have great stuff and get great results, but Giles is not considered a candidate. Why? Oh right:

Kimbrel was the closer and Giles was stuck behind the 13 million dollar man.

That should not sway our opinion and lead us to devalue the year Giles has had. We are smarter than that! If Giles had been up since April (and ready to face major-league hitters), in all likelihood we’d be talking about him when it came to NL RoY voting.

One last note: Minimum 40 IP, only two rookies have ever had a lower FIP than Ken Giles. Those occurred in 1884 (Henry Porter, 1.27) and 1908 (Roy Witherup, 1.31). Baseball history is long and filled with many numbers. Ken Giles ranks near the top of that list, and the two players in front of him played in the dead-ball era. What Giles is doing is special, and should be recognized.

Now that the division title belongs to the Nats, and the race for the number one seed in the NL is pretty much locked up, there are still a few reasons to watch the rest of the regular season games (if you are a Nats fan). If I were an unbiased observer, I would find the whole Rafael Soriano situation fascinating. He was having a fantastic first half, and while his ERA was beating his peripherals by a decent margin, his peripherals were still pretty strong. There was reason to expect regression, but not reason to expect a full-on collapse. But Soriano has picked up over two runs on his ERA during this second half and gone from closer to “cross your fingers mop up guy.” While watching another mentally exhausting Soriano “save” on Sunday, I wanted to figure out what exactly had happened to a season that started out so promising.

One thing that is important to remember is that relievers are volatile, and a few bad outings can throw things out of whack. In September, Soriano has given up 7 ER in 7.2 IP. That’s awful, but it’s also only seven innings. You could find quite a few SPs this season who have had a stretch of 7.2 IP giving up 7 runs. Strikeouts haven’t been the problem either, as he is averaging over a K per inning, and a 3:1 K-BB ratio. And despite the recent blow up (by recent I mean the entire second half), Soriano is still sporting a .59 HR/9 ratio for the season, which is much lower than his career of .86 HR/9. His BABIP is the exact same as last year, his strikeouts are up significantly (6.89 in ’13 versus 8.70 this year), and while his walks are up too, the overall K-BB is stronger. Not to mention that he has the second best SwStr% of his career after posting a career low in the same metric last year. So with all these seemingly positive things happening, what’s the deal? Where has this implosion come from if it doesn’t stem from gophers or a high BABIP against?

I think the answer is two-fold: extra-base hits, and a lack of infield fly balls. Below is a chart from 2013 of hit types against Soriano:

Here is 2014:

There are two important takeaways from this chart. One, even though Soriano gave up more dingers in 2013, he has given up significantly more extra-base hits this season. By my count, he gave up 15 extra base hits last year, and 21 this year (including home runs). Six may not sound like a lot, but that’s a 40% increase. When you only throw 60 innings a season, that makes a huge difference.

Two, look at the location of the outs in 2014 compared to 2013. Notice how there are way more silver dots in the infield in 2013. As a pitcher, infield fly balls are the second best thing to strikeouts. They are an out basically 100% of the time and runners can’t advance on an out like they can on a deep fly ball or a grounder. Soriano went from a 16.3% infield fly ball percentage in 2013 to 7.4% this year. A pattern is forming here. For a guy who pitches with runners on base fairly frequently, infield fly balls and strikeouts are a fantastic way to get out of a jam. Even though Soriano has more Ks this year, he also has far fewer IFFB, which almost offset one another. A lower IFFB% despite a higher overall fly ball percentage from 2013 explains a lot of what’s happening here. More balls in the outfield leads to more extra-base hits or even runners advancing/scoring on an out.

I came up with a quick metric I’ll call “Nearly Automatic Out Percentage” to illustrate my point. Soriano has faced 248 total batters this year compared to 277 last year. He has 59 Ks and 6 IFFBs this year (65 total nearly automatic outs) for a NAO% of 23.5%, compared to 51 Ks and 14 IFFBs in 2013 (also 65 nearly automatic outs), a NAO% of 26.2%. These numbers are closer than I would have thought considering how much better Soriano has been with Ks this season. But when you factor in the additional extra-base hits and a few additional walks/HBP, it explains how the end result in 2014 can be so similar to 2013 (nearly the same WAR, ERA, and xFIP in 2013 and 2014) in two completely different ways.