December 17, 2018

About Stuart Wallace

Stuart Wallace is a Contributor to District Sports Page. A neuroscientist by day, the Nevada native also moonlights as an Associate Managing Editor for Beyond the Box Score, stats intern at Baseball Prospectus, and a contributor at Camden Depot. A former pitcher, his brief career is sadly highlighted by giving up a lot of home runs to former National Johnny Estrada. You can follow Stu on Twitter @TClippardsSpecs.

Statistically Speaking: Free Swingers or Patient Producers?

As a team that employs one of the more balanced offenses in the National League, it’s no big surprise that the Washington Nationals sit atop myriad offensive categories, on the team and individual levels. Currently in third place behind a pair of NL West foes—the Colorado Rockies and Los Angeles Dodgers—in runs scored per game (at 4.24), the Nats runs have come from a number of expected and surprising sources, up and down the lineup card.

Whether your statistic of choice is of a more traditional flavor (4th in home runs, 5th in runs batted in) or something a little more nuanced (4th in wOBA and OPS), the Nats are more than likely in the top five of said league offensive category.

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Statistically Speaking: Aaron Barrett’s Third Pitch

In a continuation of my personal infatuation with the members of the Washington Nationals bullpen, this week’s Statistically Speaking looks at the contributions and success of one of the non ‘Big Three’ relievers, rookie Aaron Barrett. A 2.79/2.52/3.09 ERA/FIP/xFIP pitching slash line to go along with a 0.6 fWAR has all been made possible for the righthander due to a solid fastball-slider combination, with the breaking pitch being particularly tough on hitters, as alluded to by Barrett’s 28.7% strikeout rate.

Not immune to many of the foils of a rookie season, Barrett’s 2014 has been bisected by a brief return to the minors—the entire month of August was spent at Class AAA Syracuse—which saw him work not only on a small mechanical hiccup, but also on a third pitch, a circle changeup.

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Statistically Speaking: Drew Storen’s Pitch Selection Tendencies As A Closer

With much fanfare, Drew Storen has returned to the ninth inning, after the unceremonious departure of Rafael Soriano due to inconsistent mechanics and overall poor performance. Thus far*, the formerly-but-now-current closer has done a bang up job with the ‘promotion’, going a perfect three-for-three in saves and doing so in dominant fashion, facing only the minimum number of batters and amassing seven strikeouts in the process.

As tumultuous 2012 and 2013 seasons become but mere specks in Storen’s and the Washington Nationals’ rearview mirror, talk now returns to the righthander’s devastating repertoire, which has quietly propelled him to a fantastic 1.29/2.79/3.49 ERA/FIP/xFIP slash line and a 11.54 RE24, eighth-best in among National League relievers this season.

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Statistically Speaking: Bryce Harper’s Hot Hitting

Bryce Harper sporting his fantastic stirrups (1st inning) - Atlanta Braves v. Washington Nationals, 8/22/2012. (Cheryl Nichols/District Sports Page)

Bryce Harper bats against Atlanta Braves in 2012. (Cheryl Nichols/District Sports Page)

It’s another table-filled post this week at Statistically Speaking, but in a welcome twist, this week’s data dump and analysis will be looking at hitting, and in particular, the resurgent swinging of Bryce Harper. It’s been a lost season of sorts for Harper, with the lion’s share of the year spent recovering (disabled list and otherwise) from a torn ligament in his left thumb.

Not only has it been a long row to hoe from the anatomical aspects of the injury and surgical procedure involved, but also one from a mechanical and timing perspective of his prodigious but oftentimes complicated swing. Even with a clean bill of health, missing significant time and having an injury to the hand have briefly made Harper’s swing a bit of a reclamation project, with stretches seen where his mechanics were inconsistent, erratic, and ever-changing in an effort to once again find comfort and confidence in his hitting abilities.

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Statistically Speaking: Craig Stammen’s Success Against American League Teams

As the season begins to wind down and the Washington Nationals gear up to face the Seattle Mariners later this week for their final interleague series of 2014, it is easy to use the matchup as a way to gauge how prepared the team is to face American League competition in the playoffs, as their chance for a postseason berth becomes more and more inevitable. While this sort of talk is pretty premature, it nonetheless gives us as good of a real time advance scouting report as we can get.

One of the more crucial components to the success of the season thus far and any extended playoff appearance is the bullpen. While many will focus on the performances of the ‘Big Three’ of the relief corps of Tyler Clippard, Rafael Soriano, and Drew Storen as key to the team’s success and potential long playoff run, it very well could be the play of oft-forgotten middle reliever Craig Stammen, who has been just as productive and impressive as the aforementioned trio, putting up a 3.56/3.06/3.41 ERA/FIP/xFIP pitcher ‘slash line’ in 65.2 innings in 2014, that sways a decision towards the win column.

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Statistically Speaking: Stephen Strasburg and Bearing Down

As frustrating and mercurial as Stephen Strasburg can be, he does provide a wealth of topics to cover, especially when they pertain to the statistical application and translation of potential to performance. Never short on talent, the righthander has shown to be a day late and a dollar short when it comes to the final box score numbers, with this season proving to be particularly challenging for Strasburg to make the most of his health and talent.

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Statistically Speaking: Is Stephen Strasburg’s Slider Hurting His Curveball?

As 2014 begins to slowly loom larger in the rear view mirror as the remaining games trickle in, fans of the Washington Nationals are yet again at a place where the promise of Stephen Strasburg lies somewhat orthogonal to his results. Despite some encouraging stats—in particular, a 3.00 fielding independent pitching and a 3.0 wins above replacement, all in the top-10 for National League starting pitchers—there is also plenty to point to a discouraging season for the righthander.

Hopes were high earlier in the season, as 2014 looked to be a year where Strasburg was finally completely healthy; add to it an addition to his already devastating four pitch repertoire in the form of a slider, and it appeared that the NL Cy Young Award and the NL East were all but wrapped up, courtesy of D.C.’s once and future ace.

Much was made of the inclusion (then the scrapping, only to be brought back again) of the slider, with most feeling this was a subtraction by addition. Recently, a great article by the venerable Pat Jordan added some body and tangibility to the underlying discouragement of the new pitch by Strasburg, with a particular passage being at the heart of the issue:

There’s a longtime axiom in baseball that a slider ruins a pitcher’s curveball. They are diametrically opposed pitches, a stiff-wrist slider and a loose-wrist curveball. When a curveball pitcher adds a slider to his repertoire, pretty soon he won’t have either. He’ll have a slurve. A slurve is a big, fat, right-to-left breaking pitch that loses the best qualities of both pitches. It’s slower than a slider and begins to reveal itself too soon, and it has a less definable break than a curveball. The greatest breaking pitches are the ones that break late, sharp, down and a lot.

In essence, the slider appeared to be a waste of time; Strasburg’s otherworldly fastballs, curve, and changeup were plenty to not only get hitters out, but dominate them. At best, it would be an infrequently used pitch, occasionally flipped up there to set up another pitch. At worst, it would make his once-in-a-lifetime curveball less effective—a slurve—and something that was in between the curve and slider, possibly losing the bite and movement in the process, as alluded to in Jordan’s piece on overhand curves. Adding insult to injury, it could make his other pitches also a little less effective in the process, thereby making Strasburg’s stuff a little more human.

Could the trials and tribulations of 2014 be caused by Strasburg’s flirtation with the slider, making his repertoire a little less effective and more hittable?

First, let’s figure out how often he has thrown the slider; for this and any following stats, this season will be compared to 2013, the most recent (and most healthiest) season where he threw his usual four pitch mix: a four-seam fastball (FA), two-seam fastball (FT), curve (CU), and changeup (CH). For the table below and in the sake of data robustness, the number on the left is taken from FanGraphs PITCHf/x data and the number on the right is from Brooks Baseball, which has an additional manual classification correction implemented.

Season FA% FT% SL% CU% CH%
2013 48.7/57.7 12.2/3.5  — 22.8/22.8 16.2/16.0
2014 40.2/57.5 18.3/1.3 0.5/1.9 17.9/17.6 22.8/21.3

For Strasburg, the slider is a rare pitch, being used at most two percent of the time. This being said, has its incorporation affected the success of his other pitches? To answer that, here is another table, including 2013-14 data on each pitch type, including their respective walk (BB%) and strikeout (K%) percentages as well as weighted on-base average against (wOBA) and pitch type linear weights per 100 pitches thrown (LW/100), with LW/100 in particular especially useful telling us how effective a particular pitch was, in terms of minimizing run expectancy.

Season Pitch BB% K% wOBA LW/100
2013 FA 8.70% 12.60% 0.325 0.62
2013 FT 10.10% 10.10% 0.351 -0.27
2013 CH 8.30% 43.80% 0.172 1.86
2013 CU 2.40% 50.60% 0.150 2.16
* * * * * *
2014 FA 5.50% 18.60% 0.353 -0.18
2014 FT 6.00% 13.50% 0.406 -1.72
2014 CH 5.50% 43.20% 0.210 1.65
2014 CU 1.60% 47.20% 0.252 0.30
2014 SL 0.00% 14.30% 0.310 -1.55

Between last season and current, all of Strasburg’s main four pitches have dropped in effectiveness, per LW/100, with the slider overall not being a terribly effective pitch, given its greatly negative linear weights value. wOBA shows the same trends, with all of the ‘big four’ getting hit around a little more this season. Strasburg’s strikeout percentages appear to be as strong as ever this season, however, with each enjoying a moderate hike in rate compared to last season.

These numbers are valuable, but don’t provide the entire story. Variables such as release points, pitch movements, and spin rates, to name a few, all play a role in a pitch’s effectiveness and aren’t necessarily reflected in the above numbers; in short, a pitch and its ultimate effectiveness is comprised of a number of dimensions.

Let’s look at those dimensions and see if we can visualize any changes from last year to today that might help answer whether the slider is hurting Strasburg’s other pitches. One way to do this is with the help of a statistical method that is used to reduce the dimensionality of a dataset: principal component analysis (PCA).

An approach that is commonly seen in psychology and sociology and used in things such as intelligence and personality tests, PCA is used to identify and analyze underlying linear structures and previously unsuspected relationships in order to reduce large datasets into more palatable results, using their variance to help collapse and parse out themes that help in explaining the underlying structure of the data.

For the curious, a great reference for PCA can be found here. For the purposes of this article, it isn’t so much reducing factors, but visualizing how much the factors ‘cluster’ for each pitch in 2013 and seeing if that clustering pattern is the same (or different) this year, so the math and nuts and bolts of the PCA will be glossed over, simply because we are just interested in a snapshot of the process versus the process itself.

The variables in question are primarily PITCHf/x based and include:

spin direction
break angle
pitch horizontal movement
pitch vertical movement
velocity
break length
spin rate
horizontal release point
vertical release point
vertical height of the pitch where it crosses hoke plate
horizontal height of the pitch where it crosses home plate

After applying a PCA to Strasburg’s 2013 and 2014 data, the following loading plots of the respective principal components were created (PC1 and PC2, which explain 99.9% of the variance of the factors discussed), which reveal the relationships between variables; here, it is with respect to pitch type.

First 2013:

Strasburg_13_PCAEssentially, Strasburg’s fastballs and changeup ‘run together’, with his curveball and the respective factors at play behind it being a separate and disparate entity. Thus, we have our template for what the various factors and dimensions of each pitch for comparison to this year, with the new pitch.

2014 looks like this:

Strasburg_14_PCAWith the new pitch, we do see some less-clustered aspects of the curveball, but more drastically, with the changeup; there is perhaps a smidgen of truth to the idea that the slider, with all other factors remaining the same, could perhaps lend itself to hurting Strasburg’s other pitches, though not necessarily the curveball in isolation. However, it isn’t such a cut-and-dried ‘his curve looks like his slider’ explanation, which was discussed in the Jordan article, or at least, not yet.

While the few specks of the slider are tough to discern, we do see them drifting between the two trend lines, with some bleed of the curve and change into an in-between area. However, we don’t have a grasp of what underlying factors are at play with this visualization. Interpreting some of the factor loadings (not presented), the biggest difference between this season and last is the exchange of importance between the horizontal release point (x0) and pitch height as it crosses the plate (pz), with x0 having a slightly larger role as a factor in the loading of the variables used this season.

remain stable across the two seasons of interest. Other factors could be at play with respect to Strasburg’s somewhat shaky 2014; injury and even simply being a year older and the ramifications that has on a pitcher’s ultimate success could be variables that are responsible for the hiccups that the PCA does not capture.

Overall, through the interpretation and visualization of many of the factors and their respective dimensions at play with respect to Strasburg’s pitching, we do find that while his curve still might ‘look’ like his curve after the incorporation of a slider, there is some potential for some trending towards a slurvier offering if this small amount of data are to be trusted. With that said, Strasburg might do well to scrap the slider, as not only is the curve at risk of looking less like a curve, but his changeup as well is threatened by the unfavorable, neither here nor there characteristics of his slider.

***

Data courtesy of Baseball Savant, Brooks Baseball, and FanGraphs.
__________________

Stuart Wallace is a Contributor to District Sports Page. A neuroscientist by day, the Nevada native also moonlights as an Associate Managing Editor for Beyond the Box Score and a contributor at Camden Depot and Gammons Daily. A former pitcher, his brief career is sadly highlighted by giving up a lot of home runs to former National Johnny Estrada. You can follow him on Twitter @TClippardsSpecs.

Statistically Speaking: Measuring Ryan Zimmerman’s value

Ryan Zimmerman has been a catalyst for the Washington Nationals offense from what seems to be time immemorial. Boasting a career .357 weighted on-base average (wOBA), which ranks second amongst third baseman and ninth in the National League since 2005 (minimum 5000 plate appearances), Zimmerman has been a consistent, potent offensive weapon for a team that has endured its share of toothless lineups. To the chagrin of the team and fans, this offense has sputtered in recent years, primarily due to a number of injuries that have forced him to miss significant time out of the lineup.

It’s been felt by many this season that when Zimmerman’s not penciled in the lineup card, the chances of runs being scored drop precipitously; the numbers confirm this to a certain extent, with the Nationals averaging 4.66 runs per game with Zimmerman in the lineup and 3.77 runs a game with him out. Compare this to the team’s overall scoring average—4.19 runs per game, fourth in the NL—and to the NL’s average runs scored per game—3.96 runs per game—and we pull back the curtain a little more as to how important Zimmerman’s bat is to the Nats; with him, they’re league beaters, but without him, they’re not even league average when it comes to plating runs.

Let’s keep pulling said curtain back and go back to wOBA to get a better grasp of the importance of Zimmerman in (and out of) the lineup, now, from a teammate’s perspective. With wOBA, we can better measure and apply a player’s offensive value and what exactly they contribute to the run scoring environment. It does require a little math in order to accurately weight each offensive contribution (singles, walks, and so on) for the current run environment, but thankfully, FanGraphs helps us with this process.

The wOBA formula for the 2014 season is:

wOBA = (0.691×uBB + 0.723×HBP + 0.892×1B + 1.280×2B + 1.630×3B + 2.126×HR) / (AB + BB – IBB + SF + HBP)

…and after plugging and chugging and some shuffling of stats into two ‘bins’—stats with Zimmerman (‘Zim’) and stats without him (‘no Zim’), we get the following numbers for the ‘Big 8′ of Nats players who get the lion’s share of starts: Ian Desmond, Danny Espinosa, Bryce Harper, Adam LaRoche, Wilson Ramos, Anthony Rendon, Denard Span, and Jayson Werth:

Name wOBA Zim wOBA, no Zim PA, Zim PA, no Zim
Desmond 0.363 0.282 212 236
Espinosa 0.314 0.278 123 184
Harper 0.340 0.319 102 95
LaRoche 0.347 0.377 228 157
Ramos 0.357 0.293 97 106
Rendon 0.397 0.316 223 249
Span 0.325 0.338 224 235
Werth 0.399 0.318 220 237

*PA: plate appearances

Using the following Rule of Thumb courtesy again of FanGraphs:

Rules of Thumb

Rating wOBA
Excellent .400
Great .370
Above Average .340
Average .320
Below Average .310
Poor .300
Awful .290

…we see that Zimmerman’s presence in the lineup makes Rendon and Werth borderline excellent and the others above average, except for Espinosa, who enjoys league average wOBA with him in the lineup. However, without him in the lineup, things change and for some of Zimmerman’s teammates, quite drastically.

Without Zimmerman, Ian Desmond’s offense takes a huge nosedive, going from above average, to worse than awful, per our rule of thumb; Espinosa suffers similar production drops, as does Ramos, Werth, and Rendon. Oddly enough, LaRoche’s and Span’s production actually improve ever so slightly without Zimmerman’s presence, with Span’s offense the least affected overall by Zimmerman’s bat.

Let’s go one further with the numbers and look at weighted runs created plus (wRC+), a stat that is built off of wOBA, but adds additional granularity in the form of park and league-adjustments, allowing the comparison of these stats with respect to the leagues and parks played in to be performed. Again, FanGraphs provides us the formula:

wRC+ = (((wRAA/PA + League R/PA) + (League R/PA – Park Factor* League R/PA))/ (AL or NL wRC/PA excluding pitchers))*100

Here, the calculations are a little hairier than wOBA. Thankfully, the heavy lifting has been done for us, courtesy Neil Weinberg over at New English D, where you can find a very nifty wRC+ calculator that you can use once you have the proper constants for a given metric and season, which you can find in several places over at FanGraphs.

With wRC+, we can again better measure a players worth (like wOBA), both can now look at these results from both a current and historical perspective. 100 is considered league average, with any number above or below 100 providing us the percentage difference better or worse a player is to average. An as example, we can say Zimmerman’s career 121 wRC+ means he has been 21 percent better than the league average hitter.

Without further ado, the Nats offense with and without Zimmerman, through the lens of wRC+:

Name wRC+, Zim wRC+, no Zim
Desmond 133 73
Espinosa 95 71
Harper 113 99
LaRoche 118 139
Ramos 125 81
Rendon 153 97
Span 103 103
Werth 154 98
Average 124.25 95.13

It should be no surprise that the numbers trend similar to wOBA, given wRC+ being based on wOBA. In general, the Nats are currently and historically a below average offensive team without Zimmerman in the lineup (95.13 average) and are roughly 25 percent better than average with him healthy and taking his hacks. What’s also interesting is how much the team’s offensive leaders of 2014—Desmond, Rendon, and Werth—rely upon Zim’s contributions. Again, the oddballs are LaRoche, who still shows improved numbers without Zimmerman, and Span, whose numbers are exactly the same with and without the Nat’s elder statesman in the lineup. This all being said, caution should be exercised when interpreting Harper’s and Ramos’s number, simply due to sample size considerations, with both having limited PA’s this year due to their own injuries.

Zimmerman’s presence in the Nationals lineup, while always desired, at times has been one that is often under-appreciated, given the talents of his teammates and his difficulties in staying on the field. The numbers presented reflect this, but should nonetheless be taken with a grain of salt, as other variables, in particular, the effects of where each player hits in the lineup and even where they play defensively, can all play potential roles in these results. While the team-level numbers obviously show his worth in the heart of the order, when parsing out the effect of his presence across each of his teammates, we see a much deeper need and reliance upon his pop and his importance to his teammates’ overall offensive successes.

Data courtesy of Baseball-Reference and FanGraphs and current as of August 5th.
__________________

Stuart Wallace is a Contributor to District Sports Page. A neuroscientist by day, the Nevada native also moonlights as an Associate Managing Editor for Beyond the Box Score and a contributor at Camden Depot and Gammons Daily. A former pitcher, his brief career is sadly highlighted by giving up a lot of home runs to former National Johnny Estrada. You can follow him on Twitter @TClippardsSpecs.

Statistically Speaking: Soriano’s Historic (?) Implosion

What was expected to be a straightforward 6-0 drubbing of a National League East foe turned into a bit of a laugher come the ninth inning for the Washington Nationals on Monday night and unfortunately, for the wrong reasons. The inning and the game was lost by the usually steadfast closer, Rafael Soriano, whose stat line was a veritable house of horrors for a team in need of a strong showing against the Miami Marlins in order to stay atop the NL East standings:

IP H R ER BB SO RE24
0.1 3 4 4 1 0 -3.68

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Statistically Speaking: Finding the Nats’ pipe shots

Much like last week’s Statistically Speaking article, this week’s will have a bit of an All-Star flavor to it. While this season’s game has left a sour taste in the mouths of many Nationals fans due to the lack of some very deserving players, the team ultimately selected, Nats player or otherwise, appeared to be a reasonable representation of the respective leagues. Adding insult to injury for the National League, however, was this peachy comment from the NL’s starting pitcher, St. Louis Cardinals righthander Adam Wainwright:

“I was gonna give him a couple pipe shots. He deserved it,” Wainwright said. “I didn’t know he was gonna hit a double or I might have changed my mind.”

The player deserving of said pipe shots—a pitch grooved right down the middle of the plate—was of course soon-to-be-retired New York Yankees shortstop Derek Jeter. Not surprisingly, Jeter did exactly what Wainwright (and everyone else) expected him to do with the gimmie, knocking the 90 mile-per-hour offering into the outfield for a double. Upon realizing the gravity of his ‘pipe shot’ comment, Wainwright about-faced on giving Jeter the mulligan:

“Sometimes my humor gets taken the wrong way,” Wainwright said in a dugout interview in the eighth inning. “I feel terrible about this if anyone is taking any credit away from what Derek Jeter’s done today or off me. It was mis-said. I made a mistake.

Regardless of the ultimate result or intention of the pipe shot, the pitch was exactly as published:

numlocation.php

The PITCHf/x data also shows us (courtesy of Brooks Baseball), the pitch’s ‘px’ value was 0.1545 feet and its ‘pz’ value was 2.320 feet, which are the left/right distance of the pitch from the middle of the plate as it crosses the plate and the height of the pitch as it crosses the plate, respectively, while having 0.3206 inches of horizontal movement and 9.667 inches of vertical movement. Add it all up, and it was about as close as a pitcher could get to putting the ball on a tee for a hitter.

For Wainwright, this location and ‘grooving’ was intentional; sometimes, it isn’t quite the case, and pitches end up rolling down that pipe and right into a hitter’s sweet spot; has this been an issue for Nats pitcher this year, as talented as they are? First, let’s look at what Nats pitcher’s have done in terms of pitch location for all fastball types (the pitch of choice when you’re looking to groove a pitch), with Wainwright’s pitch in red for reference:

Screen Shot 2014-07-22 at 11.10.26 PMThere appears to be quite a few pitches that could fit the bill as a pipe shot, so let’s slim the field down with some additional criteria, with some help from an old Greek. By taking the px and pz information from Wainwright’s pitch and considering that the bulls eye for all pips shots, we can use the following calculation to figure out how close each of the above 8935 fastballs were to being pipe shots:

(x-center_x)^2 + (y - center_y)^2 < radius^2 

where x is a given pitch’s px value, center_x is the Wainwright pitch px, y is a given pitch’s pz value, and center_y is the pz for Wainwright’s pitch. From here, we apply a numeric value to the radius to shrink our sphere of influence for what we will consider pipe shots. To cut to the chase and to keep numbers to a dull roar, I selected a radius of 0.001 for our pipe shot ‘winners’, which are displayed below, with the Wainwright’s pitch again in red and the average strike zone outlined in black for reference:

Screen Shot 2014-07-22 at 11.09.46 PMHere, we find seven winning pitches, from, surprisingly, seven different pitchers; for those curious the table below provides additional information as to count, velocity, and pitch movement (HMov and VMov):

name pitch_type pitch_result start_speed HMov VMov balls strikes
Clippard, Tyler FF Flyout 90.7 -1.22 11.59 1 1
Fister, Doug FF Groundout 89.2 -7.278 6.27 1 2
Gonzalez, Gio FF Called Strike 92.5 6.475 9.676 0 0
Jordan, Taylor FT Called Strike 88.2 -9.67 6.25 3 0
Roark, Tanner FF Called Strike 92.6 -7.61 8.37 1 0
Stammen, Craig FT Called Strike 91.4 -10.97 4.82 2 1
Strasburg, Stephen FT Called Strike 94.5 -9.03 10.17 0 0

Overall, the pipe shots from the Nats haven’t been terribly egregious, with a pair being first pitch strikes and only one grooved in a hitters count, courtesy of Taylor Jordan. Thankfully for the Nats, all of these grooved pitches ended up without any damage being done in the form of hits balls or runs scored, unlike Wainwright’s cookie to Jeter; despite this sliver of luck with the approach, the infamous pipe shot probably isn’t the best method of garnering strikes and outs, and should be best left to the Home Run Derby.

***

Data courtesy of Baseball Savant, unless otherwise noted.
__________________

Stuart Wallace is a Contributor to District Sports Page. A neuroscientist by day, the Nevada native also moonlights as an Associate Managing Editor for Beyond the Box Score and a contributor at Camden Depot and Gammons Daily. A former pitcher, his brief career is sadly highlighted by giving up a lot of home runs to former National Johnny Estrada. You can follow him on Twitter @TClippardsSpecs.

 

 

 

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