Archive for Player Analysis

The key to winning at fantasy baseball is finding players who will outperform their draft position.  This will be the first of a series of articles addressing undervalued and overvalued players that you should be targeting in your draft.

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It seems that every MLB season, we witness a failed starter turn into a great reliever. The 2014 season was no different, and one of the biggest transformations came from the arm of Orioles RHP Zach Britton. Britton, a former 3rd round pick by the O’s in 2006, was twice named to Baseball America’s top 100 prospects list during his time as an Orioles’ minor leaguer. Britton would go on to join the Orioles’ rotation in 2011, posting a 4.86 ERA and a 4.23 xFIP over 46 starts from ’11-’13, while splitting time at AAA Nofolk. Due to Britton’s inconsistencies, the Orioles decided to try his hand in the bullpen for the 2014 season, and the results speak for themselves:

76.1 IP, 1.65 ERA, 2.82 xFIP, 75.3% GB rate

The ridiculous 75.3% GB rate comes after Britton posted a 55.5% rate over the previous three years. The key difference was Britton relying heavily on his sinking fastball, going from using it 69.6% of the time as a starter, to 91.5% as a reliever. As with most converted starters, Britton also saw a jump in his fastball velocity, going from an average of 92 MPH from ’11-’13, to 95.1 as a reliever. Is there another pitcher out there that could go from being a very average starter to a top notch closer? There is, and he also resides in the AL East.

Joe Kelly and Allen Craig were shipped to Boston in a 2014 trade-deadline deal that sent veteran John Lackey to the St. Louis Cardinals. In his seven starts with the Cards in 2014, St. Louis witnessed a regression from Kelly that was expected by anyone that had kept up with his peripheral statistics in the previous two seasons. Over the 2012 and 2013 seasons, Kelly posted a 3.08 ERA, which tied Madison Bumgarner and Stephen Strasburg, as well as topped teammate Adam Wainwright’s 3.39 ERA during that time frame. But when you look deeper, you see more numbers that don’t belong next to names like Bumgarner, Strasburg and Wainwright: 6.0 K/9, 4.12 xFIP, and 4.22 SIERA. Despite a fastball velocity that tied Jeff Samardzija’s 94.7, and only finished behind Strasburg and Matt Harvey’s 95.5, Kelly’s results were very mixed. In fact, they showed a great resemblance to someone else’s numbers:

               Kelly         Britton (starter)

FIP        4.11            4.25

xFIP      4.14           4.23

K/9        6.05           5.94

BB/9     3.35           4.00

GB%      52.4%        54.9%

While the numbers are not exactly identical, the results are very similar: two pitchers that had very good stuff, but were very inconsistent. As noted earlier, Britton’s move to the ‘pen was also a move to a primarily two-seam/sinking fastball that induced tons of ground balls. While it is unlikely that Kelly’s move to the ‘pen would turn him into a ground ball machine like Britton, it should be noted that Kelly already possesses an average fastball of 94.7 MPH as a starter, while Britton saw his jump from 91.6 as a 2013 starter to an average of 95.1 as reliever in 2014. A few other notable velocity spikes we’ve seen from pitchers with a history of working as a starter, as compared to  fastball velocity as a reliever:

Tommy Hunter 91.6 to 96.0

Andrew Miller 92.5 to 94.9

Wade Davis 91.8 to 93.7

Joba Chamberlain 92.5 to 94.6

Maybe Kelly puts it together as a starter this season. After all, Boston thought very highly of him if they were willing to give up John Lackey last season. Maybe we see Kelly cut down on his walk rates, and finally put together some peripheral stats that match his strong ERA numbers in 2012 and 2013. But what if he doesn’t? What if he continues to be a bottom-of-the-rotation type pitcher? Boston could move him to the ‘pen, see that 95 MPH fastball bump up to the 97-98 range, and reap similar rewards that the O’s received from Britton in 2014.

Last time, we analyzed Yu Darvish’s sliders in terms of when they projected as strikes and how pitch movement affected perception, leading batters to swing at pitches outside of the strike zone in the direction of the pitch movement. This time, we will turn our focus to four-seam fastballs. As before, we are using the 2013 data set since the algorithms for this were run before the completion of the 2014 season. To start, we can examine a four-seam fastball from Yu Darvish, his second-most thrown type of pitch in 2013, via simulation using the nine-parameter PITCHf/x data for its trajectory. The chosen fastball from Darvish was thrown roughly down the middle of the strike zone and we also track the projection of the pitch as it approaches the plate.

Note that the pitch, in this case, is simulated at one-quarter actual speed. The strike zone shown is the standard width of the plate and 1.5 to 3.5 feet vertically. The red circle represents the projection of the pitch after removing the remaining PITCHf/x definition of movement from its current location (Note that while the simulation shown above is a GIF, the actual simulation is an interactive PDF where the controls at the bottom of the image can play, rewind,  slow down, etc. the simulation. This is discussed at the end of the article for the interested reader, including a link to several interactive PDFs as well as a tutorial for the controls and the source code written in TeX). Here, the movement causes the pitch to rise, giving the pitch in the simulation a “floating” quality as it never seems to drop.

As in the previous work on sliders, we will start by splitting the four-seamers into four groups based on the pitch location and the batter’s response: strikes (pitches with a 50% chance or better of being called a strike) and balls (lower than 50% chance of being called a strike), and swings and pitches taken. Working with the projections to the front of the plate after removing the remaining movement on the pitch, we can examine how attractive (in terms of probability that the projection will be called a strike) pitches in each of these four categories, on average, are to batters incrementally as they approach the plate.

To begin, for left-handed batters versus Darvish in 2013:

For both types of pitches in the strike zone (red=taken, green=swung at), the average probability of the pitch being called a strike levels off around 20 feet, with strikes swung at peaking at probability 0.919 at 9.917 feet from home plate, then dropping to 0.917 at the plate. Strikes taken reach their maximum at the front of the plate with probability 0.869. The four-seamers swung at outside of the strike zone (blue) average around 0.5 probability of being called a strike up until around 30 feet, before dropping off. The fastballs taken outside the zone (orange) tend to project as low-probability strikes initially and remain so to the front of the plate.

We can simplify this graph to include only swings and pitches taken.

Once again, pitches swung at project as better pitches throughout than those taken. The peak for swings is at 14.083 feet with probability 0.782, and finishes at 0.777. The pitches taken keep increasing in attractiveness all the way to the front of the plate, reaching a called-strike probability of 0.332.

To further examine what is happening in these graphs, we can view the location of these projections from 50 feet to the front of home plate. The color scheme is the same as the four-curve plot above.

Focusing on the blue projections for the moment (swings outside the strike zone), the projections down and to the right of the zone are carried by movement toward the strike zone and most end up as borderline strikes. Those up and to the left project further and further outside the strike zone as they approach the plate, since their direction of movement is roughly perpendicular to the strike zone contour. To get a better idea of the number of each of the four cases in nine regions in and around the strike zone, we can fade the data into the background and replace it in each region by an arrow indicating the direction that the average projection for that area is moving and the number of pitches of that case located there.

Focusing first on the pitches in the strike zone, there is a dearth of projections in the upper-right area, which would be on the inside half of the plate to LHB. The pitches taken in the strike zone tend to skew slightly down and to the left, relative to those swung at. Note that in many of the regions around the strike zone, the samples are quite small so it may be difficult to draw any strong conclusions. With this in mind, these results can be summarized in the following table where the center cell represents the swing percentage in the strike zone and all other cells contain the percentage of swings in that region.

The region with the highest swing percentage is the strike zone, at 59%. The region with the next highest percentage is above the strike zone, which is in the general direction of movement, but here there are only nine data points to rely on for this percentage. It would seem that the regions that induce swings are those where the pitches project in the strike zone and are carried out by movement (above and above-and-left of the zone) and where the pitches project as balls but movement is carrying them toward the zone (below and below-and-right of the zone). Notice that the area below and left of the strike zone has 47 pitches thrown there and only 2 swings, which is where the movement parallels the strike zone.

It would appear, based on these observations, that the location of the pitch, relative to the direction of the movement, has an influence on generating swings outside the strike zone. As with the sliders in the previous article, we will use, as a measure of if the pitch is thrown outside in the direction of movement, the angle between the movement of the four-seam fastballs at 40 feet (the pfx_x and pfx_z variables in the PITCHf/x data set) outside the zone and a vector perpendicular to the strike zone extending to the final location of the pitch at the front of home plate. An angle of zero indicates that the movement of the pitch carried it perpendicularly away from the strike zone. Ninety degrees means that the pitch projection parallels the strike zone due to movement. A one-eighty degree angle means that the pitch is being carried by movement perpendicularly toward the strike zone. Further explanation, including a visual depiction, can be found in the link to the previous article at the top of this page.

To begin, we will look at the distribution of angle versus distance from the strike zone for all of Darvish’s four-seamers outside the zone to lefties.

The distribution, in this case, seems slightly skewed toward having pitches thrown in the general direction of movement. This visual assessment is supported by the percentages in the table (sorted by angle and average distance from the strike zone contour in feet. e.g., 0.5 = 6 inches, 0.33 = 4 inches), with nearly 29% of pitches having an angle of less than 45 degrees and over 58% with an angle less than 90 degrees. The distribution does not seem to have definitive shape.

For all MLB right-handed pitchers in 2013, including Darvish, the distribution is much more clear. There is a swell of pitches thrown with angle between 0 and 90 degrees and within six inches of the strike zone, with 37.5% thrown with an angle of less than 45 degrees, and 61.9% with an acute angle. In conjunction, as the angle increases, the average distance from the strike zone decreases. To get a better handle on the ramifications of this choice of pitch locations, we can further sort the data into swings and pitches taken.

For Darvish, nearly 44% of the pitches swung at had an angle between the vector perpendicular to the strike zone and the movement vector of less than 45 degrees. For those less than 90 degrees, this percentage jumps to nearly 70%. In addition, the average distance outside with angle less than 45% is an average of 4 inches outside whereas, overall, the average is about 3 inches in all directions. We can compare this to Darvish’s right-handed colleagues in 2013:

For MLB righties, the largest area of swings is right around a 30-degree angle. Close to half of the swings, 46.9% to be exact, occur when the angle is less than 45 degree and over two-thirds are for pitches in the general direction of movement. The average distance on four-seamers swung at outside is close to Darvish’s overall, but is almost an inch further out for Darvish for 45-degree or less angles. So for RHP to LHB, pitches thrown in the neighborhood of 30 degrees and within a half-foot of the strike zone tend to induce swings, which is also seen for Darvish. We can now look at the complement of this, pitches taken outside, to see how this distribution compares to swings.

The distribution for Darvish on pitches taken has some semblance to that for all pitches, but the percentages have dropped in all cases. In addition, the average distances across the board are over six inches outside.

For all MLB RHP in 2013, the pitches taken by LHB outside the strike zone are largely located below 90 degrees, with a large number near 60 degrees. Compared to the case of all pitches outside the strike zone, the percentages are not all that dissimilar, but the distances are slightly larger. Putting the two hexplots together to see how they form the plot for all outside pitches, we see that what appears to be one large grouping of data below 90 degrees for all pitches separates into two smaller groupings: one around 30 degrees for swings and one around 60 degrees for pitches taken.

To examine why it might be the case that pitches thrown in the direction of movement, meaning a small angle between the movement vector and the vector perpendicular to the strike zone, are swung at more frequently and are more effective at inducing swings further from the strike zone than those that are not, we can take a four-seamer thrown by Darvish above the strike zone and examine both the trajectory of the pitch and its projection. We can again simulate such a pitch (at quarter speed) via the PITCHf/x data for Darvish. Note that since the below simulation does not possess the same computational capabilities as the rest of the code, which is done in R, we use the standard strike zone as a reference rather than the 50% contour.

Based on the simulation and associated projection, we can see that the pitch projects as a strike early on and, late in its trajectory, appears to be a ball. The important observation for this is that, for some part of its flight, the pitch does appear that it may be a strike. Similarly, for a pitch below the strike zone, we see the opposite result.

One can see the problem with getting a batter to swing at a pitch such as this. It starts out as looking like a pitch in the dirt and, through its path to the plate, only slightly improves its chances of being called a strike, and at no point really gives the batter much incentive to swing at it. Thus it makes sense that a batter might swing at a four-seam fastball high above the strike zone but not one a similar distance beneath.

Performing the same analysis for right-handed batters, we again start with Darvish’s results for the four-seam fastball in terms of ball/strike and swing/take.

Here, the swing/strike curve peaks at probability 0.94 at 11.667 feet and finishes at 0.937. These probabilities are slightly higher than those for lefties at the maximum and at the front of the plate. The pitches taken in the strike zone peak at the plate with probability 0.904, compared to 0.869 for LHB. For both cases of pitches outside the strike zone, they reach their maximum very early in the trajectory and drop off afterward.

Changing to the two-curve representation for four-seam fastballs to right-handers, the swing curve reaches its apex of probability 0.814 at 19.833 feet and ends with probability 0.797 at the plate. For pitches taken, the average strike probability increases throughout the trajectory, ending at 0.411. Once again, these probabilities are higher than for left-handed batters.

As before, we can switch to the discrete data and their projections as the pitches near the front of home plate. Of note is that the pitches taken (red data points) are, by and large, down and to the right of the strike zone from the catcher’s perspective, which is in the opposite direction that the movement influences the pitches as they approach the plate. In addition, the majority of swings outside the strike zone, the blue data points, leave the strike zone in the direction of movement. Also of interest is that the pitches fill up the strike zone more against RHB, while four-seamers to LHB were lacking for the inner half of the strike zone. For the pitches swung at outside the strike zone in the opposite the direction of movement, down and to the right, they end up very close the strike zone contour, making them boarderline strikes, and thus nominally classified outside the zone. To observe these phenomena more succinctly, we can switch to a vector representation indicating the number of pitches and the direction that the projections are headed for each of the nine regions in and around the strike zone.

Of the 270 pitches in the defined strike zone, the average location of the 112 taken were down and to the right of those swung at, as represented by the red and green arrows. To quantify the percentage of swings in each of the nine regions, we can refer to the below table, aligned spatially with the data from the GIF (center square being in the strike zone).

Based on these results and for regions with more than a handful of pitches, the highest percentages of swings outside the strike zone are in the upper and upper-left regions, in the direction of movement. The lower-left corner is large as well but can be disregarded as it only contains two pitches, one of which was swung at. Also, it is hard to draw any conclusions to the left of the plate since there is no data.

We can now turn our attention to pitches outside the zone for both Darvish and other MLB righties in 2013:

First, for Darvish, the distribution of pitches, when viewed by plotting distance from the strike zone versus angle between the perpendicular vector to the strike zone and the movement vector, appears bimodal with a large grouping both above and below the 90-degree mark.

The four-seamers outside to righties are, on average, over 6 inches outside, with most thrown, 59.9% to be precise, in the opposite direction of movement. However, most of the pitches thrown in the direction of movement, 31.2%, are thrown with an angle of less than 45 degrees. Compared to LHB, the distances are greater and the percentage of pitches with an angle of less than 90 degrees is noticeably lower.

For MLB RHP, the distribution also appears bimodal, with two groupings of data near 30 degrees and 120 degrees. This roughly mirrors Darvish’s distribution, relative to angle versus distance.

As compared to Darvish, RHP threw about the same percentage of pitches with an angle of less than 45%, but more with an angle of less than 90 degrees. In all cases, the MLB RHP four-seamers outside were, on average, closer to the strike zone. Compared to pitches outside to lefties, the percentages for less than 45 and less than 90 degrees are down.

Taking the subset of pitches swung at outside for Darvish, the distribution has become closer to having a single mode near 30 degrees. Despite reaching into small sample sizes for this subset, the below table reinforces these conclusions.

While only around 30% of Darvish’s pitches were thrown with an angle of 45 degrees or less, over two-thirds of his swings outside the strike zone were in this range of angles. This increases to nearly 75% when considering four-seam fastballs thrown in the general direction of movement, meaning 90 degrees or less. Of note here is that the distance that entices a swing decreases as the movement aligns less and less with the vector perpendicular to the strike zone. Here, the distances are greater compared to left-handed batters faced by Darvish in 2013, but the percentages are up.

Switching the larger sample of all 2013 MLB RHP, we retain only one of the modes observed for all pitches. The pitches that are swung at outside are clustered down near 15 degrees and within half a foot of the strike zone.

The percentage of swings with an angle of 45 degrees or less is over 50% and, like Darvish, those less than 90 degrees are up near 75%. The distance again decreases as the angle increases and, compared to Darvish, is much closer to the zone. Versus right-handed batters, the percentages for angles 45 and 90 degrees or less are greater but the distances do not differ greatly as compared to LHB.

The other half of the data, pitches taken outside, gives us the second mode seen originally in Darvish’s data. This mode is a cluster of data above the 90 degree level.

While a quarter of the pitches taken are thrown with an angle of 45 degrees, only a little over one-third were thrown in the general direction of movement. Note that the pitches that are thrown in the direction of movement and are taken tend to average three-quarters of a foot outside, so it makes sense that they would not be swung at. The percentage of pitches taken with an angle of less than 90 degrees is down from 56.8% for LHB and, overall, the pitches are almost an inch further outside.

For the MLB data set, the second mode is located around 120 degrees.

As with Darvish, about one quarter of the pitches taken outside are at an angle of 45 degrees or less and 59% are thrown in the opposite direction of movement. When put up against the pitches taken by LHB, the percentages are down for both 45 and 90 degree or less pitches from 35.1% and 60.7%, respectively.

As with RHP versus LHB, the full distribution, in terms of the hexplots, separates into two clusters: one related to swings and one related to pitches taken. The cluster related to swings sits in the range of 15 degrees while pitches taken are closer to 120 degrees. This is similar to the case for lefties, except the cluster of pitches taken moves from the 60-degree area to the 120-degree area and the cluster related to swings moves down from 30 degrees to 15 degrees. However, in both cases, the swings appear to be separate clusters from the pitches taken.

Discussion

For four-seam fastballs thrown by Yu Darvish in 2013, the maximum attractiveness on swings is in the range of 10-20 feet in front of home plate for left- and right-handed batters, possibly tying into how long a batter can reasonably project a pitch when deciding to swing. The four-seamers also tend to be swung at outside the strike zone in the general direction of movement, which we have seen previously with sliders. This is especially pronounced for RHB vs RHP, with pitches exiting the strike zone in the direction of movement causing swings, and pitches entering the zone opposite the direct of movement being taken. By simulating the PITCHf/x data, we can get an idea of why this might be true: pitches outside thrown in the general direction of movement project in the strike zone for some period of time before projecting outside of it and pitches thrown opposite this direction project outside and, while their probability increases, these pitches never appear as strikes and thus do not usually induce swings from the batter.

Next time, we will finish up with cut fastballs from Yu Darvish and see how movement affects perception in this case. After that, we can switch to the 2014 data set and also turn the algorithm around and apply it to a batter.

PITCHf/x Simulation

For those familiar with the previous installment, we covered a slider thrown by Yu Darvish to Brett Wallace and simulated the projected pitch location in R. To better represent how the pitch projection may tie into perception, we have switched to a more visually appealing representation of simulating the PITCHf/x data in the context of the catcher’s viewpoint (we could presumably display this from the batter’s point of view as well). For the aforementioned slider to Wallace, the simulated PITCHf/x data, based on the 9-parameter model, is:

This would seem to be a better way to represent the data, including a backdrop and accurate scaling of the pitch size and location. As another example, we can simulate a random Darvish curve:

In order to make the GIFs for simulating the PITCHf/x data, we are first using TeX to write the code and then compiling it using MiKTeX with the “animate” package handling the controls. To begin, we place a reference point 6 feet, 1 inch behind the tip of home plate, roughly approximating the location of the catcher (the one inch past six feet is not important but makes the distance to the front of home plate an even 7.5 feet).  The height of the reference point is taken to be 2.5 feet in the z-direction. This is the point by which we will determine perspective. Everything will be projected into the plane at the front of home plate, spanning three feet to the left and right of center and from the ground to five feet high. For a given position of the pitch, we find the associated spherical coordinates, relative to the reference point. To figure out where to display the pitch in the frame, we track the pitch along the line formed between the pitch location and the reference point until it reaches the frame. Since the two angle measures of the spherical coordinates will not change when tracking along this line, we need only find the distance along it that places it in the frame we are displaying.

Once we have the location of the pitch in the frame, we still need to find the size of the pitch as seen from that distance. To do this, we again use the reference point and find the distance to the center and to the top of the baseball. With a third side that goes from the top to the center of the baseball, this creates a triangle. Forming a similar triangle by adding an additional third side where the frame cuts the triangle at the front of the plate, we obtain a smaller triangle contained in the larger one. Using this geometry, we can find the size that the pitch will appear at this distance using trigonometric properties of similar triangles (namely that their sides have the same ratio).

To begin the simulaton, we find the times associated with 55 feet and the front of the plate. We then find the location of the pitch in three dimensions, incrementing in time from release to strike zone and adjusting the location and the size of the pitch to appear positioned and scaled correctly in frame. The simulation in the actual PDF is at 60 frames per second, with most animations lasting around a half a second. For the purposes of creating GIFs, we slow the pitches down to one quarter this speed and capture using a program called LICEcap. The code is written so as to work for any pitch by merely swapping in the chosen 9-parameter PITCHf/x data and recompiling. The projection is shown as a red circle, and is calculated as previously discussed. All background features are scaled appropriately, in a similar manner as the pitch.

Note that while this is, in many ways, an approximation of perception from the catcher’s point of view, it functions well for our purposes of providing a decent replacement for live video since we can overlay the projection and view it from the reverse of the traditional television angle from center field. Included is a link to a Google Drive containing a collection of interactive PDFs for pitchers and pitches from 2013 and 2014. There is also an interactive guide to the controls with the given example being a Clayton Kershaw slider. Finally, the source code is included so the interested reader/programmer can input any chosen PITCHf/x parameters and compile to get a representation of the pitch, that includes distance to home plate, the velocity of the pitch, and the time since release.

Jon Lester just got paid. Early Wednesday morning, it was announced that Jon Lester accepted a 6-year $155M contract with a vesting option for a 7th year to become a member of the Chicago Cubs. This is over $20M more than the FanGraphs readers predicted in the offseason free agent crowdsourcing. This is the same pitcher that the Red Sox offered a 4-year $70M extension to prior to the season and proceed to nearly double their offer to $135M in the off-season. Before 2014, it was reasonable to think Jon Lester might be looking at a deal similar to the 5 years $90-95M that has been predicted for James Shields. What did Lester do to convince teams he is closer to a $200M pitcher than a $100M pitcher? He looked like the elite Lester we saw in 2009-2010, and not the merely very good pitcher he was in the past few years. How did he improve in his contract year? Let’s take a look.

Lester was one of the top pitchers in the game during the 2009 and 2010 seasons, and even placed 4th in AL Cy Young voting in 2010. His peers were Felix Hernandez and Adam Wainwright, along with Lincecum and Sabathia (before they became husks of their former selves on the mound). While WAR still treated Lester well in 2011-2013, partially due to the sheer number of IP and park and league statistical adjustments, his FIP placed him in the leagues of Kyle Lohse, Homer Bailey, Yovani Gallardo and Ian Kennedy. During that period, Lester’s opponent contact rate and swinging-strike rate were the same as those of Clay Buchholz. So comparing a theoretical Lester deal to the Bailey extension, which was looked at as an overpay by some, didn’t seem unfair. From 2011-2013, Lester trailed Shields by over 2 WAR, so a similar contract didn’t seem unfair prior to 2014.

What caused Lester to fall from the ranks of the elite and become more of a number-two starter? The strikeouts disappeared.

The problem was especially apparent against righties, whom he struck out at only a 17.9% clip in 2013 (compared to 26.3% in 2010). What caused this decline? Let’s take a quick look at the Pitch F/X data. A lot of people are under impression that the decline was due to velocity loss that occurs with aging. This isn’t really the case for Lester. Lester’s fastball velocity has declined by less than one mile per hour, from 94 MPH in 2010 to 93.2 MPH in 2014. While the cutter and curveball have lost velocity, they aren’t as dependent on velocity as the fastball:

If we take a look at his pitch usage, we notice two things: He has for the most part ditched the sinker and changeup in favor of the fastball, cutter, curveball combination that our Matt Trueblood detailed in his recent piece.

Let’s take a look for the most telling data for any pitcher: the ability to get hitters to swing and miss.

Lester’s fastball generated whiffs on 16.3 percent of all swings in 2014, his best mark since 17.8 percent in 2010. That is around the league average of 16.4 percent, according to Eno Sarris’s benchmarks. The cutter, which he is throwing at a career-high frequency of 31.0% of all his pitches, is generating a whiff-per-swing rate of 23.8 percent (5th in MLB, minimum 500 pitches thrown), above the league average of 21.4%. The pitch isn’t as good as it used to be, when it generated whiffs on over 28 percent of swings, for reasons Sarris detailed last year. However, it’s still a very good pitch that was even better in 2014 due to improvements in the horizontal movement.

The curveball, though, is what’s special. In 2014, Lester’s curveball generated whiffs-per-swing on 40.8 percent of opponents’ swings, the highest mark of his career—and 12 percentage points more than in 2013, when his curveball was league-average in that regard. In 2014, It was the best curveball in the league in terms of whiffs per swing, with pitchers like A.J. Burnett, Adam Wainwright and Sonny Gray behind him.

Now what happened to the curveball? It didn’t gain velocity. It didn’t gain movement. It gained a more consistent vertical release point. Look at 2014. Lester changed his release point on all pitches, but as you can see, the release points of the fastball, cutter, and curveball are tightly grouped together. Now look at 2009 and 2010, and compare it to 2011-2013. It appears that in his “peak years,” Lester maintained a better-disguised release point on the curve than he did in 2011-2013.

If the curveball was being tipped or was less deceptive, righties would have the best look at it.  Let’s check that out:

There is something incredibly satisfying in finding the results we might expect. The curveball generated 41.4% Whiffs/Swing vs. righties compared to the 25.8% in 2013. Lester’s Curveball caused 25 Ks against righties, 6th in baseball compared to 2014 when it only caused 6. This is exceptional because he only generated 55 whiffs on the CB vs. RHH. When righties do make contact, they aren’t hitting it hard, only slugging .151 against the hook last year, compared to .338 in 2013. Righties aren’t picking up the curveball like they were before, and in 2014, that solved the platoon issues he had against them in previous years. Trueblood said, “Lester might have tapped into something that will allow him to dominate right-handed batters in the future.” That something is the curveball. It allowed him to ditch his changeup with little consequence.

The gist is that 2014 Jon Lester was more like 2009-2010 Jon Lester than 2011-2013 Jon Lester. A mechanical adjustment allowed the curveball to reemerge as an elite weapon in Lester’s arsenal, complementing a very good cutter and an above average fastball. It also allowed him to enjoy success versus righties again. The question is: How sustainable is this change? If Lester can lose his mechanics for years, is there serious risk of him losing them again? Can he continue to improve? If he can bring the curveball back to its former glory, can he regain a bit more movement on the cutter again? Can he bring back an effective sinker, giving him a deeper arsenal? These are the questions on which the Cubs will be betting $155 million on. If these changes can be sustained, it wouldn’t be surprising if Lester is worth the contract barring injury. Just one 5 WAR season can be worth $40M on this market. Lester made a slight change to his mechanics and it just might have earned him tens of millions of dollars.

When the Yankees signed Mark Teixeira to an 8-year, $180 million contract in the 2008-2009 offseason, they knew fully well that they were getting a hitter who liked to pull the ball. Like Jason Giambi, his predecessor at first base, it was believed that his superb power would make up for a batting average that was likely to decline throughout the deal, especially with the short porch in right field at Yankee Stadium. However, Teixeira’s 2014 line of .215/.305/.413 against righties was probably not what they had in mind for their switch-hitting first baseman.

Naturally, many have jumped to blame Teixeira’s woes on the drastic defensive shift that is employed when he hits left-handed. But the shift was there in 2009, when Teixeira finished 2^nd in the AL MVP voting with a .292/.383/.565 line and 39 home runs. The fact is Mark Teixeira, spray chart included, was once good enough of a hitter to earn a $180 million contract. Defenses could basically know where he was going to hit the ball and still shook in their boots when he came up to bat.

However, one factor has not remained constant: Teixeira’s production against fastballs. In his prime, Teixeira wasn’t just good against heaters: from 2003-2012, his wFB/C of 1.70 ranks 16^th among qualified hitters. But his numbers against fastballs has consistently diminished during his Yankee years. Brooks Baseball gives some additional information (note: wFB/C is from FanGraphs and is not against RHP only):

2014 saw Teixeira whiffing on more fastballs then ever before and hitting more grounders when he did make contact. Even more alarming is the fact that his wFB/C is negative, suggesting that he was a liability against what was once his favorite pitch. Baseball Savant shows a similar downward trend against righties throwing four seam fastballs, two seam fastballs, cutters, or sinkers:

Teixeira’s decreasing offensive value makes sense when one considers the fact that what was once his greatest strength as a hitter is now a weakness. And considering the fact that FanGraphs has had pitchers throwing 57.8% fastballs to Teixeira throughout his career, it is definitely not a problem that can be avoided by trying to do damage against other pitches. However, this trend also suggests that Teixeira, who put up wRC+’s of 142, 128, 124, and 116 in the first 4 years of his deal, can become a force on offense again if he can start hitting heaters like he used to.

Unfortunately, I have very little no expertise that can assuredly help Teixeira regain his prowess against fastballs. The only “shot in the dark” idea I have for Teixeira is for him to level out his notorious uppercut swing. The fact that Teixiera is whiffing on more fastballs and hitting more groundballs suggests that his ability to make solid contact has diminished with age and injury. Straightening the path of his swing would give him more of a margin for error.

He could maintain his power by guessing on more pitches, which is what I believe fellow Yankee Brett Gardner did in 2014, when he hit 17 of his 40 career home runs. According to Baseball Savant, 15 of his 17 home runs came from four seam fastballs, two seam fastballs, sinkers or cutters. The fact that all of them were pulled to right field, despite greater velocity, leads me to believe that Gardner was sitting on them more often than not.

Alternatively Teixeira’s lingering wrist injury (which is why I left his 15-game 2013 season off the tables above) might be making it harder for him to turn on pitches with high velocity. Conversely, Teixeira could be correct in suggesting that a full offseason workout program could allow him to return to form. In any case, Teixeira needs to regain his ability to destroy fastballs if he has any hope of being a force on offense again.

Being a Toronto native, I had a fair share of complaints last season. Seeing hefty division leads evaporate with the blink of an eye stinks, as does Brett Lawrie’s 3rd failed attempt at a breakout. I could complain about a lack of financial commitments from management (on the field) and about the overall middle of the pack finish.

When Alex Anthopoulous acquired R.A. Dickey before the 2013 season, expectations were high. When initially reviewing his first season in Toronto, a Cy Young winner who puts up a 4+ ERA the following season is disheartening. Yet in March 2013, were you really expecting a fly ball pitcher in the Rogers homerdome of the AL to perform on par with what he did in the NL while throwing to the pitcher every other inning? The last two seasons have had disappointments, but R.A. Dickey has been consistent in a reliable and also amusing way.

Dickey ended 2013 with a 14-13 record, and coincidentally, this past season’s win-loss record was an identical 14-13. I am in no way soliciting win-loss records, and this is saying nothing about how he threw the ball. The identical records merely add to the interesting couple seasons the now 40-year-old knuckleballer has had with the Jays.

To achieve the identical 14-13 records, each of the last two years Dickey made 34 starts. While it may not sound impressive, only nine other pitchers made as many starts in 2014, and only “Big Game” James Shields has made the cumulative 68 starts since 2013 that Dickey has. Since 2013, he ranks 4th in innings pitched, trailing only Felix Hernandez, Adam Wainwright and Shields himself. While many Jays fans would infer that doom looms when Dickey jogs on for his 6th and 7th inning of a start, the overall results were at the very least, respectable.

Although eating innings is certainly an important quality, nobody is congratulating Edwin Jackson every five days. Our best overall performance indicator is probably WAR, and wouldn’t you know it, Dickey’s fWAR was 2.1 in 2013 and 2.1 in 2014. 2 Wins Above Replacement matches up with expectations for your average starting pitcher, so it is no surprise that Dickey’s number is in line. On a runs allowed basis, his bWAR is 2.0 and 2.5 in 2013 and 2014, respectively. To fuel the similarity fire, his strikeout percentage in 2013 was 18.8%. As you guessed, his 2014 figure is one Josh Thole framing blunder away, at 18.9%. And, without shock, the strikeout to walk rate budged a mere .15 percentage points from year to year. As you can see, if one tempers their Cy Young expectations, Dickey has been plenty useful and stable for the Blue Jays. If you buy that case, then the remaining wonder is whether he has value relative to the investment.

When last year ended, Dickey took home $5 million in salary, not including signing bonuses offsetting Canada-US tax discrepancies. With the price of a WAR being roughly $7 million annually nowadays, Dickey was a bargain at $5 million in 2013. His extension had him making $12 million this past season, so if we are auditing to the penny, he was slightly below market rate.

With regards to the initial trade, the package for Dickey included Travis d’Arnaud, the big Noah Syndergaard, a low end outfield prospect and John Buck, who was set to earn $6 million in 2013. Of course, the Jays also received two catchers in Mike Nickeas and Josh Thole (there were a lot of catchers in this trade). Although d’Arnaud was a major piece at the time, it is worth noting that come next season, he will be a 26 year old catcher with a grand total of 533 plate appearances at the MLB level. On the other hand, Syndergaard is still only 22, and has very good stuff. However, given the increase in the frequency of pitcher arm injuries nowadays, he remains miles away from being a middle of the rotation starter.

Blue Jays fans have seen Bautista and Encarnacion as the significant bright spots for the team over the last two years. With both of them only under contract for a couple more seasons, in addition to them having likely put their best years behind them, Dickey has certainly given the team a better chance to win – at the appropriate time. This was Anthopoulous’ thinking when he made the acquisition, and although the overall results have not been perfect, it was a reasonable gamble. Not to mention the positive return on investment the team yielded from Dickey himself.

The overstated reality is that R.A. Dickey has been a good pitcher. The guy had a better ERA- than Hisashi Iwakuma last season. He had a better ERA- than Francisco Liriano too, and the latter is likely to get nearly $40 million in free agency despite having not started 30 games since 2010. Dickey has given the Jays a good chance to win in a tough environment. Sure, nobody is happy to have Thole in the lineup once a month, let alone once every five days. But hey, at least we are fortunate enough to not have had Jose Molina and his 23 wRC+ (not a misprint) frequent the lineup card.

If you read or surf through once in a while, you will surely venture across baseball job openings. It is enlightening to see an increasing amount of analytics positions looking to be filled, especially with major league teams. Advanced stats and sabermetrics have emerged in the last decade. This is clear. What is not clear yet is if the once niche perspective is fully sunk into mainstream baseball culture. Certainly, this wasn’t true a couple years ago, or Mike Trout’s MVP award would not be his only one.

It would be a lie to say that advanced statistics have been beyond the peripherals in major award voting in recent years. I am fairly certain that a league leading ERA and WHIP were not enough to win an AL Cy Young award this past season (poor Felix). Not to mention that the same Mariner took home the glory with a murky 13-12 record a few years ago. On the Gold Glove circuit, I can make the open claim that defensive metric leaders and Gold Glove victors lined up much more this year than they have in the past. Even Adam Jones’ defensive season was arguably deserving of a gold glove (not his 4^th though…).

Let us focus on baseball’s best player (I can say that now, right?). Mike Trout has been juiced out of at least one MVP, and maybe two depending on what side of the fence you sit. From the table below you can see his “traditional” stats in both those years.

From analyzing the first table, he still had fantastic years. And as we know, he scored back-to-back 10 fWAR seasons. On the other hand, here is what Miguel Cabrera’s corresponding numbers look like:

Based on the tables, the main drivers year over year seem to be home runs and RBI. From 1993 to 2007, every single AL MVP had 30 homers and 100 RBI – aside from leadoff hitting Ichiro in 2001. In the NL, the song remains the same with only Barry Larkin failing to reach the 30 homer mark and two others merely totalling 90+ RBI. While this was a steroid heavy era, there is not enough reason to discredit the data, as with an even larger sample of MVPs, the same trends can be drawn. In 2012, Miguel’s “box” looks significantly better – 137 to 83 RBI is quite a large gap. To avoid sounding like a broken record, I will not mention the poor defense and baserunning that the Tigers corner infielder accounted for. That is why Trout’s standalone fWAR numbers are second to none. In 2013, it was more of the same from Trout. The 10 fWAR season was almost double Cabrera’s, but a 179 OPS+ (park- and league-adjusted) put him behind Cabrera’s 190 OPS+. With the defense and baserunning, it was still likely another Trout miss by the voters.

Arriving back to present time with Trout holding his trophy, it is worth understanding what he did differently. In short, he started being more aggressive and his whiff rate (number of swings and misses per pitch) rose. I would also speculate that with Statcast data, we would see ball speed off his bat is faster this year. As for his results, there is no surprise his strikeout rate jumped, nor is there for the home run total. As they positively correlate, the RBI came up too, leaving his “traditional” numbers looking like this. His fWAR total is also alongside.

While it is common on a typical defense and baserunning aging curve, the former and the latter did, in fact, take dives as well this year. Trout’s willingness to run decreased by more than 50% (18 total stolen base attempts) and he actually graded out as a relatively bad center fielder.

My claim here is simple. Mike Trout, whether acting purposeful or not, did what the classic MVP voting criteria wanted him to do – hit homers and drive in runs. This past season, Trout was significantly less valuable than he was in his previous two years, but according to the traditional measures, he was fabulous in the now hitting-depressed baseball. In September of 2012, Trout was quoted saying “I was trying to do too much, trying to hit home runs when I shouldn’t be.” Clearly, he has discarded this mentality, and because of it, he unanimously captured the MVP – the first American Leaguer to do so since Ken Griffey Jr. in 1997.

Can you see the irony here? Mike Trout manages two consecutive 10 fWAR seasons, a feat only done by Barry Bonds, Willie Mays and Mickey Mantle. He doesn’t win the MVP in either one. The next year he cuts his fWAR by almost 3 wins but adds 28 RBI and half a dozen homers to his totals. All of the foregoing occurs, in the era in which sabermetrics are undoubtedly now integrated into modern baseball. (Fortunately for him, he didn’t need 10 WAR to be seen as baseball’s best player). The fact is that Mike Trout just won the MVP – the traditional way.

Back in 2007, B.J. Upton was thought to be a future megastar, a young tools-filled player, whose future seemed to almost certainly include MVP consideration and numerous other awards. As a 24-year-old he put up back-to-back 4+ WAR seasons. For the past two years however, he has posted a total -0.2 WAR. What has happened to B.J. Upton? He is only 30 years old, which for an athlete of his caliber, is still potentially only the tail end of his prime.

Defensively speaking, he has had ups and downs, but the swings in performance were never too drastic, posting a -7 DRS and -1.9 UZR/150 in 2014 compared to -3 and 8.4 back in 2008. His Defense rating has dropped from 9.8 to 0.1 from 2008 to 2014. Again, obviously not good, but not enough to account for such a big drop in WAR. So his troubles must mostly be tied to his offensive production.

I first tried to identify the problem with his hitting by dividing our options into two groups. The biomechanics processing results versus possible telling statistics on his approach. Let us look at the latter to start. A couple things I want to focus on would be his O-Swing% and how he performs in hitter-friendly counts. From 2008 to 2014, his O-Swing% has jumped up 11% from 16.8% to 27.8%, which is not a great indication that he has a plan when stepping into the box. Hitter’s counts are all about the approach, not only working yourself into this count, but being ready for your pitch because you can be selective at the plate.

Now obviously the wRC+ numbers will be inflated due to the higher walk rates when in a hitter’s count, so I am focusing more on average. To help put this into perspective, in 2014, through 0-2 counts, Upton hit .085. That is scarily similar to his performance in hitter’s counts. Clearly something is off. When in a hitter’s count, the batter typically sits on a fastball, so naturally my next focus was to look at his wFB. His wFB in 2008 was 1.1 and in 2014 was -11.1. So while he used to be above average, he has now become much worse at handling fastballs, which would correlate to his lack of success hitting in hitter’s counts. In order to survive in this league as a hitter, you must be successful hitting against the fastball. His pitches seen rate has remained relatively consistent except for a slight increase in sliders seen. His lack of production in these areas really makes me question whether he is ready to hit when stepping into the box.

If you have ever seen a B.J. Upton swing you know there are a lot of moving parts. This in and of itself is part of the problem. Double loads, bat wraps, too much rotational and not enough linear movement, dipping, and changing eye levels are all apparent. But first let’s start with the numbers. A couple of things jump out when looking at the numbers. To begin with, his GB/FB rate in 2008 was 1.65 in comparison to a rate of 1.11 in 2014 while his 2014 HR/FB rate is lower than his career rate, it is higher than his 2008 All Star rate at 9% compared to 7.4%. His line drive rate is consistent throughout.

However Upton’s BABIP tells you most of the story. Upon entering the league full time in 2007, B.J.’s BABIP was .393 then .344 in 2007-08 compared to .286 in 2014. This is ridiculous and impossible to sustain! The question is, does this then make his two best years a fluke? Back in 2010 and 2011 he had near-league average BABIP years and posted close to a 4 WAR in both. So as ridiculous as those 2007-08 numbers were, I can’t blame his entire decline on not being as lucky.

Another one of the most worrisome numbers is to see his Z-Contact% drop 10%, almost as much as his overall Contact% which dropped 12%. This to me screams mechanics. So let’s take a look (and I apologize in advance for the youtube link, but it serves our purposes decently enough — I still haven’t figured out the .Gifs). Ironically enough, he hits a home run in this video. It just goes to show all the holes in his best of swings.

The first thing you see is at the peak of his negative move his hips slant upwards (2 second mark). The reason this happens, is because his leg kick doesn’t gain any ground. It immediately makes him more likely to have a bit more uppercut in his swing and it also changes his eye level due to the flexion in his knees changing while the distance between his feet do not change. Both factors make it more difficult to produce solid contact. Once at toe-touch, he then loads again and inverts his front leg, making it a double load (5-6 second mark). This leads to the potential to over-rotate (think Newton’s 3^rd law — for every action there is an equal and opposite reaction). By inverting and coiling his body, he will uncoil, or over-rotate off the pitch, causing his shoulders and hips to pull off the pitch and not stay square.

Once he finishes inverting his front side he commits to swinging. His hands/upper half look okay up to this point (7 second mark), and they’re very active. However his upper half and lower half are completely out of sync. Once he initiates his swing, his bat immediately wraps because he still hasn’t come set with his barrel, the bat has been moving the entire time. At this point there is nothing going forward at all, no backside drive. It is all rotational, making it harder to stay on the ball if his timing isn’t near perfect. In other words, due to having a more rotational swing versus a linear swing, his margin of error with timing is much narrower.

Once at the contact position (11 second mark) he looks okay. He hits the ball off his front foot, his elbows are at slightly obtuse angles, and his front side is stiff. During his bat path his hands dip a bit, giving him a high finish, most likely due to the pitch being low.

B.J. Upton’s biggest problems in his swing come before his contact position. This is a very good explanation for why he struggles against fastballs and in hitter’s counts. Simply put, he isn’t ready to hit. He has way too much going on, the main problems being his double load and lack of linear movement. In an age of power bullpens and power fastballs it is no wonder that he is struggling as badly as he is. B.J. Upton needs to simplify and settle everything down in the box. His swing is fixable, but these issues need to be addressed and changes have to be made if he is ever to be successful again.

2014 wasn’t a good year to be a starting pitcher on the New York Yankees. With injuries to CC Sabathia, Masahiro Tanaka, Michael Pineda, Ivan Nova and David Phelps, jokes about Andy Pettitte coming back from retirement again started to find “but really though” tacked on at the end. Out of the rotation vacuum emerged Shane Greene, an unlikely success story from Daytona Beach Community College. If the Yankees manage to put together a healthy starting rotation for opening day, Greene will likely be shifted to the bullpen, where I believe he will flourish.

In 78.1 IP as a starter, he posted a 3.79 ERA, a 3.64 FIP, a WHIP of 1.37, and K/9 and BB/9 rates of 9.19 and 2.99 respectively. His WHIP would lead many to think he overachieved, but aside from that and his walk rate, he was an above average pitcher.

What stands out specifically about Greene is his 2-seam fastball. To make a long story short, Pitch f/x would suggest that it is very hard to hit:

Note that while his scouting report does not specifically mention him as throwing a sinker, Pitch f/x occasionally registered his 2-seamer as one. While this is pretty common (Kelvin Herrera’s 90 mph changeup routinely registers as a 4-seamer), I believe that it is a telling sign when it comes to the life on Greene’s fastball.

Unsurprisingly, his fastball is harder to hit with increasing velocity. Hitters put up a mere .136 BA and SLG% in an admittedly small sample size against Greene’s 2-seamers above 94 mph. Those slower than 94 mph were hit to the tune of a .340 BA and a .447 SLG%. It is well known that pitchers experience an increase in velocity after a starting rotation to bullpen transition. Greene’s 2-seam fastball, which averaged at 92.8 mph, could easily creep up to the mid 90’s if he were put in the bullpen.

Of course, one reason why he might not ever succeed out of the bullpen is because he could remain a starter. He showed flashes of dominance in 2014, the most noteworthy being his shutout of the potent Tigers lineup. But even if the Yankees do pencil Greene into the 5^th spot of their rotation, something will have to give when Ivan Nova comes back from Tommy John surgery.

Like Joba Chamberlain when he became a starter in 2009, those few extra miles per hour on his fastball could make a huge impact on Greene’s numbers. As a fan, I appreciate David Robertson both as an excellent pitcher and a superb role model. But if the Yankees do not want to pay him the closer money he will deservedly get on the free agent market, Greene might be a cost-effective late-inning option.

Note: Stats not taken from FanGraphs are from baseballsavant.com

2014 looked to be a positive year for the Rangers. However, injuries took their toll on the Rangers and though they could keep up for a few months, they were too much to handle. Jurickson Profar was out for the whole season due to injury, this led for an opportunity for young prospects Rougned Odor and Luis Sardinas. Odor eventually outplayed Sardinas and won the starting job, as Sardinas was sent back to the minor leagues. As 2015 approaches the Rangers now have four middle infielders who could potentially play shortstop and second base.

Elvis Andrus has been the shortstop for the Rangers since 2009, and signed a huge eight-year contract in 2013 and seems to be the guy for shortstop in 2015, however since batting.286 in 2012 has failed to hit over .275 in the past two seasons, and hit just .263 in 2014. His OBP was just .314 in 2014 and OPS was only .647.

Jurickson Profar remains a big question mark. Labeled the number-one prospect in all of baseball before the 2013 season, he played in 85 games and hit just .234 for the Rangers. Named the everyday second baseman in spring training before hurting his shoulder, the Rangers were expecting big things from Profar.

Rougned Odor was called up from Double-A in May of 2014 and exceeded all expectations the Rangers had for him. He hit .259 with 9 home runs, and was the youngest player in the major leagues at 20 years old. His defense improved as the year progressed and was certainly a nice surprise in a season of dismay.

Luis Sardinas played in just 43 games for the Rangers, but sure did showcase a strong set of skills. Hitting .261 with six extra-base hits and 8 R.B.I. Sardinas played well in the role the Rangers gave him, and he will be competing for a spot in 2015.

As Elvis has played below expectations for the past two seasons, I do not see the Rangers trading him away. The only way the Rangers could get rid of him is to trade him, and with his big contract, if a team is willing to take it on the return for him will not be worth it. Though he had an incredibly poor year last year Andrus offers a veteran presence, and consistency defensively. With Andrus, the Rangers know they’ll get above-average defense, a bunch of stolen bases, and a decent batting average, with little power. Because of this he will be the shortstop for the Rangers in 2015.

Jurickson Profar puts the Rangers in an odd spot. Essentially he has missed a year and half of development with his injuries, and there is no telling what he will be in 2015, or if he will even be healthy. In 2013 the Rangers moved him all over the field and that played a part as to why he did not perform to his potential. He could not get comfortable in a position, and it hindered him. In 2015 if healthy, he should break spring as the second baseman, and play well at second base offensively and defensively.

Rougned Odor performed incredibly well for the Rangers in 2014. He improved defensively at second base and after a tough month of August where he hit just .221 turned it around in September hitting .296, and he had a much improved OBP at .345. Odor was the youngest player in the MLB last season, and should start 2015 in Triple-A where he can work on pitch selection, working the count, and improving at second base defensively.

Luis Sardinas has the slimmest of chances to win a job on the Major League club out of spring training. His best chance is as a utility man, however the Rangers would rather have him in the minor leagues getting to play everyday. Sardinas performed well in his time with the Rangers, but as he was the 3rd youngest player in the MLB will likely find himself as the everyday Triple-A shortstop for the Round Rock Express.

To sum it up, The Rangers middle infield is going to be Andrus at short and Profar at second, with Odor and Sardinas starting the season in Triple-A. With the youth of Profar, Odor, and Sardinas they can be traded, and used for a package deal potentially for a starting pitcher pitcher like Andrew Cashner or Ian Kennedy of the Padres. It will be an interesting off-season for the Rangers, as they try to put 2014 behind them.