September 24, 2020

Statistically Speaking: The Nats Home Run Derby

Taking our cue from Major League Baseball, we’re going take a breather this week and throttle back with the statistically complex discussions and celebrate one of the (somewhat) simpler aspects of the game, or at least one of the more pure and visceral components of baseball—the home run. While we won’t be looking back on the 82 dingers the Nationals have hit so far in 2014 in a manner totally devoid of analysis, we will take a more lighthearted approach to the numbers and focus more on the result more so than the process or the ramifications of the play, for a change. So, without further ado…long balls!

A quick perusal of FanGraphs will tell you that these 82 homers the Nats have hit thus far ranks 14th in MLB and sixth in the National League, well behind the 112 hit by the Colorado Rockies. Seventeen Nats have hit one, with Ian Desmond leading the team with 16 long flys.

Taking those 82 homers, let’s now look at what pitch and pitch location in the strike zone was ‘preferred’ by each Nat with a homer:

Screen Shot 2014-07-14 at 9.41.08 PMNot a huge surprise, but the hitters like to drive fastballs—here, I’ve collapsed fourseamers, twoseamers, cutters, and sinkers into the ‘FA’ variable—with Adam LaRoche (AL) changing things up and walloping the occasional slider and Ian Desmond (ID) showing himself to be an equal opportunity hitter, with five changeups and three sliders complementing the eight fastball he’s hit out. Here’s how the team level results parse out for pitch type:

 

Pitch n %
CH 8 9.76
CU 3 3.66
FA 56 68.29
FS 2 2.44
SL 13 15.85

 

How about counts—is there a particular point in the at bat that each player has enjoyed more homers in?

HR by countAnthony Rendon likes to jump on the first pitch, while LaRoche has seen a lot of success in 2-2 counts; Desmond and Jayson Werth (JW) show a fairly even spread across all pitch counts, with Werth showing the interesting quirk of doing quite a bit of damage behind in the count (0-1 and 0-2, in particular). Here’s the team breakdown of homers by count:

Count n %
0-0 10 12.20
0-1 12 14.63
0-2 4 4.88
1-0 6 7.32
1-1 8 9.76
1-2 6 7.32
2-0 8 9.76
2-1 6 7.32
2-2 13 15.85
3-0 1 1.22
3-1 3 3.66
3-2 5 6.10

Of note in the above table is the lack of homers in the ultimate hitter’s count—3-0—with the fewest number of Nats homers (just one!) coming on the count you’re most likely to see the most common pitch type hit out for a homer, a fastball.

Homers are great and the further hit, the better; with that in mind, let’s look at home run distances by player:

Screen Shot 2014-07-15 at 12.22.40 AMLaRoche concomitantly leads the pack and brings up the rear in homer distances, owning the shortest and longest dingers of 2014. A list of the average homer distances by player is displayed below:

Player n Avg., ft.
Zimmerman 4 378.47
Frandsen 1 377.46
Ramos 3 370.81
Desmond 16 368.68
Werth 12 365.78
LaRoche 12 365.73
Rendon 13 364.68
Span 1 357.90
Moore 3 357.70
Harper 2 354.94
Lobaton 2 352.23
Gonzalez 1 351.88
McLouth 1 347.12
Espinosa 6 346.93
Leon 1 343.44
Walters 3 329.64
Hairston 1 319.51

Not surprisingly, Ryan Zimmerman, while lacking in sheer numbers, leads the pack in average homer distance, showing that when healthy and locked in with his swing, still packs a potent punch.

Let’s get fancy. Let’s now look at home run distances across pitch types:

Screen Shot 2014-07-14 at 11.07.15 PMThe colored lines in the graph above are the average distances (in feet) for a given pitch type; sliders lead the way in distance hit:

Pitch Avg., ft.
CH 362.38
CU 357.30
FA 361.12
FS 365.16
SL 367.34

While the graph’s x-axis (game date) is a bit tough to see, the above graph also shows us that right around mid-May was when the balls started going a tad further, perhaps due to the weather warming up.

…and last, but not least, a table full of player-specific gory homer details, including average pitch velocities, for those so inclined:

Player Pitch n Avg. Speed (MPH) Avg. Distance (ft.)
Desmond CH 5 82.82 356.95
Desmond FA 8 90.94 374.02
Desmond SL 3 85.40 373.98
Espinosa FA 5 92.58 351.16
Espinosa SL 1 77.30 325.76
Frandsen FA 1 90.40 377.46
Gonzalez FA 1 89.80 351.88
Hairston FA 1 87.60 319.51
Harper FA 2 92.10 354.94
LaRoche CH 1 82.40 374.50
LaRoche FA 6 90.97 352.54
LaRoche FS 1 86.80 366.19
LaRoche SL 4 84.05 383.20
Leon FA 1 87.20 343.44
Lobaton FA 2 91.25 352.23
McLouth FA 1 93.80 347.12
Moore CH 1 78.70 404.78
Moore FA 2 88.95 334.16
Ramos FA 3 93.87 370.81
Rendon CH 1 82.20 335.02
Rendon CU 1 74.40 362.43
Rendon FA 10 91.55 366.98
Rendon SL 1 83.40 373.56
Span FA 1 85.70 357.90
Walters FA 2 92.15 332.50
Walters SL 1 87.00 323.93
Werth CU 2 77.95 354.73
Werth FA 7 93.17 370.56
Werth FS 1 83.40 364.12
Werth SL 2 83.25 360.93
Zimmerman FA 3 90.77 379.42
Zimmerman SL 1 88.90 375.63

No matter how you slice or parse it, the home run remains one of the more exciting plays in the game. With our version of the Home Run Derby, Nats fans now have a better idea of when the dinger is coming and how far it’s going. And to at least my surprise, it isn’t on a 3-0 fastball.

***

Data courtesy of Baseball Savant, unless otherwise noted.
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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 him on Twitter @TClippardsSpecs.

Statistically Speaking: Batting Expectations

From an offensive standpoint, the first half of the Washington Nationals’ 2014 has been fair to middling. Ranking sixth, seventh, and tenth in weighted on base average, weighted runs created plus, and wins above replacement, respectively, in the National League, the team thus far as produced runs at a slightly disappointing level, given the level and depth of hitting and run producing talent the lineup carries. Despite this mildly disappointing aspect of the Nationals’ 2014 season, the team has remained within shouting distance of first place in the NL East, making the expected unfulfilled, at least, as of yet.

A statistic that can be used to gauge the variation between expected and observed tendencies in hitting and help discern whether a spike or a slump in production is a product of skill or some other variable is batting average on balls in play, otherwise known as BABIP. Simply put, it measures how often a ball put in play by a hitter ends up a hit by taking their batted ball profile into account. As a rule of thumb, BABIP sits around .300, but can vary greatly between players and even between individual player seasons. From BABIP, additional calculations can be performed to derive a hitter’s expected BABIP (xBABIP), which can further refine the ramifications of a batted ball profile. While there are a number a methods to calculate xBABIP, the following is felt to be the most accurate:

xBABIP = 0.392 + (LD% x 0.287709436) + ((GB% – (GB% * IFH%)) x -0.152 ) + ((FB% – (FB% x HR/FB%) – (FB% x IFFB%)) x -0.188) + ((IFFB% * FB%) x -0.835) + ((IFH% * GB%) x 0.500)

…where LD% is line drive rate, GB% is ground ball rate, IFH% is infield hit rate, FB% is fly ball rate, HR/FB% is home runs per fly ball rate, and IFFB% is infield fly ball rate.

With the combination of BABIP and xBABIP, some of the more finicky aspects of a player’s season can be parsed out and determined as something that is indicative of a player’s skill, or something outside of his control and is one way to take stock of player performance at the halfway point and determine whether a streak or a slump will carry on into the summer months. Below, I have provided the career (cBABIP), 2013 (BABIP 2013), and 2014 (2014 BABIP) BABIPs as well as the projected 2014 BABIP based on 2013 numbers and the expected BABIP for the rest of the season (xBABIP 2014) based on this year’s performance thus far for the eleven Nats hitters who have had at last 100 plate appearances this year. With these values, we can identify Nats hitters who might be due for an uptick or drop in production based on their batted ball rates thus far; this can also be compared to last year’s numbers as well as career values to find help determine whether the waxing or waning of their 2014 BABIP is something that could be indicative of skill, or perhaps other variables, such as an injury, a change in hitting approach, a change in pitcher approach, or how a defense plays a hitter in terms of alignment or shifting:

red=decrease greater than 5 points in BABIP; yellow=increase or decrease of 0-5 BABIP points; green= increase in BABIP greater than 5 points.

cBABIP = career BABIP; xBABIP_proj = xBABIP using 2013 end of season stats. Red = decrease greater than 5 points in BABIP; yellow = increase or decrease of 0-5 BABIP points; green = increase in BABIP greater than 5 points. Difference in BABIP points measured based on previous column.

With the help of the color coding, we see that Ryan Zimmerman’s BABIP is pretty resistant to change, with the respective BABIP values over his career, 2013, and throughout this year staying within a couple of points of one another. On the other hand, Jayson Werth’s fantastic start to this year hasn’t fulfilled expectations that were in place using his final 2013 batted ball values, but is still in line with his career BABIP, which is encouraging. However, using up-to-date values and calculating his 2014 xBABIP, it appears he will possibly suffer a light drop in productivity. Adam LaRoche’s season has been a positive across the board in comparison to both last year and his career averages and appears to have the potential to get even better. We can also hope to see a over-correction in Denard Span’s BABIP later this season, eclipsing both his current and career BABIP.

The calculations for BABIP/xBABIP are based on batted ball data and as such, the swings in these values across and within a season can be caused by changed in one or many of these stats. Research has found that while BABIP itself does not correlate strongly year to year, metrics like GB% and HR/FB% can, thus providing additional layers of complexity when looking at the above table. With that in mind, provided below are each player’s change in the batted ball rates inherent to xBABIP, to help identify what is truly at the root of any egregious disparities in BABIP or xBABIP. First, differences between 2014 and 2013 data:

 

Player dLD% dGB% dFB% dIFFB% dHR/FB% dIFH%
Adam LaRoche 3.20% -2.10% -1.10% 1.30% 2.80% -8.10%
Anthony Rendon -5.50% -1.30% 6.80% -2.20% 3.50% -0.70%
Jayson Werth -7.80% 3.80% 3.90% 1.00% -10.60% -11.20%
Ryan Zimmerman -2.30% 0.10% 2.20% -4.10% -10.90% -12.20%
Wilson Ramos 5.70% -5.40% -0.30% 0.80% -19.30% -23.80%
Ian Desmond -6.70% 4.80% 1.90% 4.40% 5.40% -4.70%
Bryce Harper 0.10% -1.00% 0.90% -2.10% -13.80% -11.70%
Denard Span 0.30% -10.80% 10.50% -1.40% -2.40% 2.20%
Danny Espinosa 12.00% -8.80% -3.20% 7.50% 5.40% -1.90%
Kevin Frandsen 2.40% -5.50% 3.00% 10.10% -6.00% -7.40%
Nate McLouth -17.00% 15.80% 1.20% 1.90% -4.50% -3.30%
Jose Lobaton -1.40% 3.30% -1.90% -7.20% -3.20% -5.70%

…and here, differences in 2014 data compared to career averages:

Player dcLD% dcGB% dcFB% dcIFFB% dcHR/FB% dcIFH%
Adam LaRoche 3.90% -3.00% -0.90% -1.50% 0.50% 1.50%
Anthony Rendon -2.80% -0.60% 3.40% -1.00% 1.60% 0.70%
Jayson Werth -2.60% 1.30% 1.30% 2.60% -6.80% -0.30%
Ryan Zimmerman 0.10% 0.60% -0.70% -2.80% -6.70% -2.60%
Wilson Ramos 7.90% -1.50% -6.30% -2.20% -7.40% -0.50%
Ian Desmond -2.20% -0.70% 2.90% 4.20% 5.90% 1.00%
Bryce Harper -1.20% 0.20% 1.00% -2.80% -11.70% 0.60%
Denard Span 2.20% -9.40% 7.30% 2.70% -2.80% -1.80%
Danny Espinosa 5.30% -3.20% -2.10% 1.50% -0.30% 0.10%
Kevin Frandsen 1.70% -2.70% 1.00% 6.40% -2.40% -3.30%
Nate McLouth -11.30% 14.90% -3.60% -0.50% -6.50% -2.50%
Jose Lobaton 0.80% 1.30% -2.10% -5.20% -0.30% -1.60%

 

With both of these tables, positive numbers indicate 2014 data being an improvement over either 2013 or career averages. Overall, we see the volatility in year-to-year BABIP values reflected in the batted ball data, consistent with the effects of injury and game-to-game changes in hitting approach and defensive alignments being played out over a small period of time. Looking at the 2014 compared to career averages, we do see some significant changes in Denard Span’s ground ball rates, as well as with Bryce Harper’s HR/FB%; however, given the comparative lack of games played by Harper due to both MLB service time and injury, these values can be expected to swing a wildly as his year-to-year values for the moment. Other changes of interest include the career decline reflected in Nate McLouth’s numbers and the change in line drive and homer run rates for Wilson Ramos, possibly a reflection of an injury-marred career more so than a change in hitting philosophy.

Converting expectations into actual results is a precarious endeavor and can take unexpected turns during the course of a season; slumps, injuries, even the fashion in which opposing defenses line up for a given hitter can all make the most obvious and conservative of projections worthless, or at the least, frivolous.  However, with xBABIP, we are provided a more refined and data-driven approach to prognosticating what’s in store for Nats hitter come the second half of the season.

***

Statistics courtesy of FanGraphs; current as of July 7th.

Statistically Speaking: A Tale of Two Strike Zones

In terms of the offense, how Ian Desmond and Jayson Werth go, the Nationals go. Both are productively dynamic hitters that approach their time in the batter’s box in very different ways.

For Desmond, it’s an aggressive plate discipline that paces the shortstop’s offense, with every first pitch of a plate appearance one that simply cannot be passed up. For Werth, it’s the exact opposite; there are very few pitches that are worthy of putting a swing on, as evidenced by his career 4.39 pitches seen per plate appearance average. To compare, Desmond has a career 3.59 pitches per plate appearance average.

With these disparate approaches to hitting, you would think that pitchers would have a different methods of getting each player out—for Desmond’s aggressive, undiscerning approach, getting him to chase pitches just out of the strike zone and for Werth, a more balanced plan of attack with more pitches in the strike zone to counter his discerning eye—and those potentially opposing approach would show up in their respective PITCHf/x data.

Using the aforementioned PITCHf/x data, we can determine how Desmond and Werth have been pitched, either with the pitcher avoiding the strike zone or by attacking the hitter and throwing pitches that get a lot of the zone, with little fear that they will put a good swing on a given pitch.

A recent article has shown that how close and how often a pitcher throws pitches to the strike zone can help identify breakout and breakdown candidates, with the greater distances indicative of a pitcher wanting to work around a hitter and not get beat by them and smaller distances from the zone showing a possible lack of respect of a hitter’s ability to turn on a strike.

For our purposes, I calculated distances from the center of the strike zone by applying the distance formula to the px and pz variables of each pitch:

distance equation, Pythagorean theorem

…with the center of the strike zone estimated using the average strike zone location, referenced here.

The distances (labeled ‘xy’) were plotted against the chronological order in which the pitch was seen over the course of the 2014 season; scatter plots were also created for each pitch type seen, with all fastball types collapsed into on category and the usual offspeed and breaking pitch type categorized separately.

The results:

Screen Shot 2014-07-01 at 10.34.26 PM
Screen Shot 2014-07-01 at 10.35.01 PM

Here, we find some interesting deviations between the two hitters. For Desmond, there is an overall slight downturn in the distance from the center of the strike zone on the pitches he’s seeing as of late, alluding to some potential breakdown in productivity. This is especially the case with the fastballs he has seen, with some of the uptick in zone distance seen in secondary pitches indicative of pitchers wanting Desmond to chase.

For Werth, the overall trend opposes Desmond’s with pitchers less likely to give him anything close to the plate to hit. Looking at the trend across pitch types, we see very subtle downticks in distance form teh zone in sliders and changeups, possibly a ramification of Werth’s reputation as a hitter with a very keen eye and pitchers aware that he is unlikely to chase soft stuff out of the zone.

A breakdown of the average zone distances for each pitch type for both hitters is as follows:

Player Pitch Avg Distance from Zone (ft.)
Desmond CH 1.263348
Desmond CU 1.318966
Desmond FA 1.067716
Desmond SL 1.269305
Werth CH 1.306862
Werth CU 1.408313
Werth FA 1.022414
Werth SL 1.219673

Again, very subtle differences are seen, but when extrapolated out, the differences can be vast. While there are a number of factors playing a role in how each hitter is pitched to and the interpretation of the very minute fluctuations in where pitches are ending up in reference to the strike zone, it is an interesting example of how despite both being notoriously streaky hitters, the more discerning eye of Werth has possibly prevented him from suffering from any extended slumps thus far this season. It is also a tacit revelation that in many instances, it’s the pitcher who will be the first to tell you how well you’re hitting.

Data courtesy of Baseball Savant.
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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 him on Twitter @TClippardsSpecs.

Statistically Speaking: Bullpen Efficiency

The Washington Nationals bullpen as a unit are having a fantastic season in support of their more acclaimed starting rotation brethren. While the actual ranks differ by which all-encompssing statistic you prefer to use—the bullpen ranks tied for second in MLB with 2.8 wins above replacement (WAR) and fourth in RE24 at 27.53—the overall sentiment that the team’s relief corps is among the best in the business is not lost without the statistical confirmation.

It hasn’t been a smooth ride throughout the course of the season overall, with the likes of ever-dependable setup man Tyler Clippard and immensely talented former starter Ross Detwiler taking their lumps in the form of blown leads and inherited runners scoring. Yet, these shaky outings have been countered and exceeded by the efforts of Drew Storen, Rafael Soriano, and rookie Aaron Barrett, among others, and has kept the bullpen ledger in the black and the team in whispering distance of first place in the NL East.

Looking further at the polarizing outings of Clippard led me to come to this particular stat last week:

With the polarizing outings of Clippard to go along with the some similar clean outings by polarizing personality of Soriano, the Nats have a pair of relievers that face the minimum number of hitters half of their outings, which goes a long way to accruing the WAR and RE24 values the bullpen has thus far. It also speaks to how efficient the guys in the ‘pen are in getting hitters out and preventing the big inning for the opposing team. Do the rest of the Nats relievers follow suit and could this ability to keep additional runners (and potential runs) at bay be a reason for the success of 2014 from a group that hasn’t changed much in terms of roster from last year’s staff that finished 18th and 20th in MLB in WAR and RE24, respectively?

First, let’s outline what bullpen efficiency means. Efficiency is essentially how many batters a pitcher faces over the number that was expected from an outing. From there, we will also look at ‘clean outings’, where a pitcher faces the minimum number of batters for a given outing, with game situation considered. The fewer batters faced over the minimum, the better, as this obviously keeps runners off the base paths.

Let’s look at some data.

Name G IP xIP IP, Diff TBF xBF BF, Diff Efficiency(%) AppClean/Pct. RE24
Aaron Barrett 28 25.2 26.2 1 108 77 31 59.74 13/46.4% 2.77
Craig Stammen 22 38.1 39 0.2 152 115 37 67.83 6/27.3% 6.72
Drew Storen 29 24.1 26.2 2.1 93 73 20 72.80 16/55.2% 5.94
Jerry Blevins 33 27 29.1 2.1 116 81 35 56.80 16/48.5% 2.82
Rafael Soriano 31 31 31 0 114 93 21 77.42 17/54.8% 9.83
Ross Detwiler 20 29 31.2 2.2 137 87 50 42.53 4/20% -7.08
Tyler Clippard 37 34 36.1 2.1 137 102 35 65.70 19/51.4% 2.11

 

The table above is a little busy, but the explanations of the various columns are very straightforward and on the lighter side, mathematically. Aside from the standard games, innings pitched, and RE24 values, we also have a couple of variables that were calculated to help capture efficiency.

The first of these is expected innings pitched (xIP), which is the number of inning pitched that were expected from a pitcher, with game and outing specific information included. For example, if a pitcher has an outing where he pitched 0.2 IP, he could have an xIP of 0.2 if he came in relief with one out in the inning—he was only expected to get the other two outs to complete the inning.

Conversely, he could have a xIP of 1, but failed to get the third out of the inning before being pulled. Calculating xIP and confirming game situations was dine using game log data from Baseball Reference. Total batters faced (TBF) is simply that and expected batters faced (xBF) is calculated similar to xIP, with game situation taken into account. With xIP and xBF, care was taken with the Nats bullpen members who are more situational relivers, in particular, Jerry Blevins, to account for how they were pulled.

If they left an outing due to poor performance with runs scored or runners put in scoring position, then they were allotted the full inning of work expected and the batters faced. If they were pulled due to situation—bringing in Blevins to face a tough lefty, for example—then a full inning pitched was not assumed. Differences between actual performance and expected data re capture with the ‘Diff’ categories. From the game log data also comes the clean outing data (AppClean/Pct.), where the number of clean outings specific to game situation were tallied, with percentages also provided for comparison.

With the variables exhaustively described, let’s talk results. Not surprisingly, the Big Three of the Nats bullpen—Clippard, Soriano, and Storen—lead the way in clean outings, with Soriano and Storen also showing the most efficiency in terms of batters faced over the minimum (BF, Diff.).  Percent efficiency was calculated by taking the percentage difference between xBF and TBF and then subtracting this value from 100 and again shows how well both Soriano and Storen have been, not only in terms of performance, but in terms of being economical.

Not to be forgotten are the performances of Barrett and long man Craig Stammen, who both show a high rate of efficiency, despite subpar clean appearance numbers. Despite some encouraging recent outings, a very rough start to the season skews Ross Detwiler’s numbers greatly and shows a propensity for big innings and difficulties in keeping hitters off of the base paths.

Does this idea of efficiency trend with performance?

Screen Shot 2014-06-24 at 10.45.04 PM

In our very small sample, it indeed does, as the above graph of RE24 by number of batter faced of the minimum (BF, Diff in our table above) shows. As the number of extra hitters faced rises, RE24 drops, which makes this a negative correlation with a very strong R-squared of 0.72, providing us confirmation of good fit of the data. However, with seven data points, it would be very unwise to make any grand inferences out of these results. Despite this, we do see an interesting aspect of the bullpen’s success that doesn’t necessarily show up in the box score or in the formulas of the numerous advanced metrics available—not only are they keeping runs off of the scoreboard, they’re doing so in tidy fashion.

Data courtesy of FanGraphs and Baseball-Reference and current through 6/24/2014.
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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 him on Twitter @TClippardsSpecs.

Statistically Speaking: Is the Bunting Working?

If there’s anything in baseball that will get a crowd (or a Twitter conversation) going, it’s discussion of the merits of the bunt. While philosophies have evolved over the years showing that the visceral call for a batter—more often that not a pitcher—to square around and give the team a productive out isn’t nearly the requirement many fell it is, it is a play that remains firmly in place in the game and in the tactics of many managers.

For the Washington Nationals, the bunt remains a common used weapon, both in the form of the sacrifice bunt (currently tied for third in the National League with 39 sacrifice attempts and tied for ninth with 23 successes, for a 14th best 59% success rate) and the bunt hit (first in the NL, with 19). It’s a tactic that belies the hitting prowess of the team, whose reputation as a lineup full of tough outs and hitters who can crush mistakes has preceded them, but it also one that they have failed to live up to.

Currently scoring runs at a clip (4.07 per game per Baseball Reference) just above league average (4.02 runs per game), it has been the sacrifice bunts and bunt hits that have kept a lineup afloat, as they await the power and scoring surge expected of the lineup.

Have these sac bunts and bunt hits been savvy, or haphazard attempts at generating runs, void of acknowledgement of the situation? Has the bunt helped more than hindered the Nats quest for scoring?

To answer these questions, let’s grab some data and see how the bunt attempts shake out; first, let’s look at the running environment has looked when a hitter has squared to sacrifice or to bunt hit.

Runners n=
24
—1 1
-1- 7
1– 21
11- 5

It’s a fairly mixed bag, but the 58 situations where bunts have occurred are dominated by the no runners on (noted with a ‘—‘ and indicative of an attempt at a bunt hit) and runner on first base (‘1–‘) situations, with a smattering of other base states, each with a runner in scoring position.

Speaking of bunt hits, who has them and how successful were they?

Player Bunt Hits Runs*
Danny Espinosa 6 2
Anthony Rendon 2 1
Bryce Harper 2 1
Denard Span 4 4
Kevin Frandsen 2 0
Nate McLouth 2 0
Taylor Jordan 1 0

*runs scored via runners put into scoring position by bunt; includes errors

Danny Espinosa leads the way with six bunt hits, with two runs being the result of the bunt, either from his eventual scoring or his putting a runner into scoring position via the bunt. Denard Span’s four bunt hits and four runs following the bunts are nothing to sneeze at, either. Overall, eight runs have been scored via the bunt for a hit for the Nats.

Let’s add sacrifice situations—responsible for six runs scored thus far—and tally up how much of a difference they have meant to the Nats and their run expectancies. Using the tenets of The Book and looking at run expectancies of each of the base-out situations before and after a bunt was attempted (RE change in the table below), we can get an idea of how beneficial the bunt was in improving the chances of the Nats scoring in a given at bat. The more positive the number, the more likely runs were produced:

Situation RE change Runs
Sac -4.8978 6
Hit 4.875 8
Total -0.0228 14

Overall, the Nats are playing a negative-sum game with bunting, actually worsening their scoring chances ever so slightly (-0.0228 run expectancy) by their bunting habits, which has netted then 14 runs, or 4.9 percent of their total runs. Yet, we do see that when hitters are bunting for a hit, the team is in good shape with generating runs.

Of course, the sacrifices are predominantly courtesy of the Nats pitchers; even when you consider that most of your starters will swing the bat just well enough to not have them required to automatically bunt with runners on base, there are still situations where the sacrifice will be called for. For the Nats, it’s been called on 18 times for their starters, with the poor-hitting Tanner Roark leading the pack with seven sacrifice bunt outs.

However, there have been five sacrifice bunt outs made by position players—twice by Kevin Frandsen and once each by Denard Span, Nate McLouth, and Anthony Rendon. These five bunt outs change the run expectancy in a fairly significant fashion, dropping the above -4.8978 run expectancy to just -3.8656, good for just a little over a run’s worth of improvement in run expectancy.

While this isn’t to say that all of the position player bunts would magically turn into positive batted ball results, it does show that preventing hitters from swinging the bat can and will lead to fewer runs; the same could be said for some of the pitching staff as well, in certain situations.

It’s hoped that with the mathematical gymnastics completed, a better appreciation of when and where bunting should occur and who should be doing it was reached. In some ways, the bunt—be it for a hit or for a productive out—is similar to a butter knife. It has a particular purpose that it serves quite well, but just because you can use it for other makeshift purposes, doesn’t mean you should.

Data courtesy of Baseball Savant unless otherwise noted as of 6/17/2014.
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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 him on Twitter @TClippardsSpecs.

Statistically Speaking: Jordan Zimmermann’s Pitching With A Lead

Jordan Zimmermann’s last start, a dominating 6-0 win against the San Diego Padres, was one for the Nationals record books. Perfect through 5.1 innings, the righthander ended his night with a two-hit, twelve-strikeout, no-walk shutout, good for a game score of 95, besting the previous Nats record of 90 by John Patterson in 2005 as well as Josh Beckett’s no-hitter earlier in the season.

A man of few words, Zimmermann was decidedly chatty after game, with much of the discourse surrounding the run support provided him; in particular, Zimmermann discussed his approach, with the added luxury of runs and an expansive ballpark:

Pour strikes in the zone…It’s a big ballpark. Just let them hit the ball.

And throw strikes he did, with 73 percent of his 114 pitches during the complete game effort being strikes. a five percent hike in strike rate compared to his other 2014 starts. However, the propensity to throw strikes from a guy known for his ability to effortlessly pound the zone isn’t necessarily news, or even interesting. What does raise an eyebrow is Zimmermann’s quote regarding just letting hitters hit the ball, given the lead he was afforded.

Did his willingness to throw strikes and ‘let them hit the ball’ reflect in his pitch data? With a lead of varying size, does Zimmermann become a more flippant pitcher, relaxing his normally dogged approach to getting hitter out?

For this exercise, we are looking at how Zimmermann’s approach, both in terms of pitch selection and location, changes, given the lead he has in a given inning. Right away, I must provide some discussion to the methods of data collection, as I have admittedly taken a shortcut, one thoughtfully provided by Zimmermann this year in the form of 27.1 shutout innings. We will discuss the potential relaxed approach through the lens of these innings, as it involves fewer data collection and management gymnastics, simply due to the fact that in these innings, he never pitched behind, runs-wise.

With this important caveat in mind, what do we have in these 27.1 innings? Overall, we have range of data where Zimmermann had a lead of one through seven runs; I have also included first inning data, where he had no lead, as a sort of control variable, assuming that these data could be thought of as his baseline approach with respect to concentration and intensity.

First, let’s look at pitch type and frequency; percentages of each pitch thrown are on the y-axis, while the number of runs ahead are on the x-axis:

Pct pitch bar

By the looks of this graph, Zimmermann is essentially the same guy with respect to pitch selection regardless of lead, pounding hitters with a great fastball. The bigger the lead, however, we do see him going to the slider more frequently than in lower leads, where he is more wont to mix in all of his four pitch repertoire.

Let’s now shift attention to the results of these pitches. Here, I have categorized outcomes into a handful of ‘bins': ball, called strike, swinging strike, foul balls, and balls in play. If we take Zimmermann’s comment at face value, we should see more balls in play, the larger the lead:

Cat Pct

Lo and behold, we do see a big hike in balls in play, the bigger the lead; there’s a great deal of variability of how many called strikes Zimmermann gets given the lead, but this is offset somewhat by a relatively stable swinging strike rate. We also find JZimm having a bit of an aberrant spike in balls with a four run lead, but settles down with larger leads.

And that fantastic command of the strike zone—how does that change, if at all, across runs ahead and pitch result?

ZNN_result

The bigger the lead, the more we find Zimmermann pepper the heart of the plate with pitches, with most of them resulting in a ball in play, just as he had described after his shutout. The more an opposing team is with respect to tying or going ahead, the more likely he is to paint the corners, per his usual approach.

One last graphic, this time, taking into account pitch velocity as well as inning; there’s a slight chance that the additional variable of inning to go along with runs ahead metric might shed extra light on the data.

Screen Shot 2014-06-10 at 11.53.52 PM

Overall, Zimmermann’s velocity remains consistent, regardless of inning or size of lead, with perhaps some slight increase in velocity with a bigger lead in later innings. However, the effect isn’t huge, and in general, we see no dramatic trends in velocity.

While the data as sampled do have some limitations, they do show Zimmermann as being true to his words, showing a propensity to put the fate of his ability to get outs in games where he has a large lead into the hands of the batter. He does this by throwing almost exclusively fastballs while also throttling back on his desire to paint corners, allowing his pitches to get more of the plate than in situations where his lead could suddenly become a defecit in a swing of the bat. It’s an approach that is simple in theory, but difficult to carry out with any consistent success, making Zimmermann’s mastery of the approach all the more enjoyable to watch.

Statistically Speaking: Ian Desmond’s First Pitch Swinging

It’s an oft-denounced approach, but swinging at the first pitch of an at bat is something shortstop Ian Desmond has fared well with over his career. Proponents of the 0-0 count hacks (such as Desmond) will tell you that in many respects, it’s the best time to swing, as it gives you the best chance for getting a good pitch to hit, as it is almost always a fastball and around the strike zone, due to pitchers wanting to get ahead in the count. As the table below shows, Desmond has taken full advantage of this approach, both over his career and this season: [Read more…]

Statistically Speaking: To Pull Or Not To Pull Your Starter

Among the many on-the-job lessons Matt Williams is learning in his first season as Washington Nationals’ manager has been the fine art of knowing when to pull a starting pitcher, due to ineffectiveness or fatigue. It’s an elusive skill and between it and bullpen management, can make or break a team’s season and a manager’s career. Unfortunately for many, it is a skill that is more art than science, with a lot of trial and error involved in the process.

For Williams, there has been lots of error in his trials thus far. But recently it appears a corner has been possibly turned, with his handling of Doug Fister’s most recent outing. Fister, only a handful of starts into his 2014 season after suffering a right lat strain, had been a mixed bag results-wise in his first three starts, and after 83 pitches in the sixth inning May 25th against the Pittsburgh Pirates, Williams had seen enough, despite going well over 100 pitches in his previous two starts and pitching well enough against the Pirates to get the win. However, vigilance surrounding the now-healthy lat ruled the day, and the righthander was relieved by Craig Stammen, an ever-lowering and inconsistent release point—possibly a result of fatigue—the culprit behind the early curtain for Fister.

Here’s a look at Fister’s release points for the 83 pitches, broken down by pitch type and inning, with the last pitch that sealed his fate circled in red. As noted in a previous article, x0 is the horizontal component of the release point (also labeled HRel) and z0 is the vertical component (VRel):

Fister RelPt

It was a savvy move by a green manager, who up until this point, had all too often been the victim of his moves backfiring; it appeared that Williams’ handling of his pitching staff was taking a turn for the better. However, looking at the release point graph, it appears that the incriminating pitch wasn’t necessarily that bad and there were plenty of other pitches that reflected a dropping release point that is often a red flag for fatigue. Yet, it does provide an interesting jumping off point for what the threshold could be for pulling a starter due to fatigue, with the caveat that game situations can dictate whether a pitcher gets pulled immediately upon exhibiting fatigue or if they stay in to finish an at bat.

Using this 83rd pitch as the threshold pitch, let’s calculate some z-scores in an effort to standardize the release point data—both HRel and VRel individually—so that we can compare all pitches, in light of the fact that each pitcher will have a slightly different average release point for each of their pitch types. The closer a z-score is to zero, the closer a pitch is to his average release point, with large negative values consistent with a drop in arm angle and release point. Calculating the respective z-scores across pitch type for Fister and then plotting these values across pitch count and inning gives us the following results: first, HRel data:

Fister_HRel

I have done the liberty of drawing the line across the data (arrow pointing at pitch 83) as a reference to this fatigue threshold; pitches below the line can be considered worse pitches from a release point perspective.

Here’s the same graph for z-scores of the vertical component of Fister’s release point:

Fister_VRel

It appears the vertical aspect has more ‘danger zone’ pitches, with a large number of four-seam fastballs (FF) seen, reflecting some inconsistencies with getting on top of the pitch, especially later in the game. VRel data also has larger negative values associated with it compared to HRel, which could possibly lend it being more affected by fatigue than HRel.

Overall with Fister, we do see a very cautious approach taken by Williams—Fister spent most of the game with z-scores at or above average, with only a handful of pitches going past the threshold of pitch 83.

Let’s go back to the previous day’s game, with Stephen Strasburg on the hill. Unfortunately for Strasburg, he ran into a little more trouble over the course of the last inning he pitched and there are many who felt that Williams kept him in too long. With a pitch count of 91 after completing six innings—but laboring through a 22-pitch sixth inning in the process—and surrendering a solitary run up to this point, Strasburg sputtered in the seventh, tallying another 17 pitches and giving up two runs on the way to a loss at the hands of the Pirates.

Like we did for Fister, let’s look at his raw release point data across pitch type and inning, with his last, ‘red zone’ pitch circled:

Strasburg RelPt

While the situation is a little different here compared to Fister, in that Strasburg was allowed to finish the inning and did not get the hook as abruptly as Fister, we do see some similarities between the pitches. With Strasburg, we again see the last pitch not being egregiously erratic with respect to the release point, but we do see some steady drop in arm slot in previous innings along with some inconsistent arm slots in the final frame, a possible clue that Strasburg was fatigued before the seventh inning.

Let’s move on to z-score data, again starting with HRel values:

Strasburg_HRel

…and the VRel values:

Strasburg_VRel

The data are slightly more damning compared to Fister’s; there are a large number of sub-threshold values in both horizontal and vertical components of release point; also Strasburg essentially spent the last two innings in this release point ‘danger zone’, potentially indicative of the 22- and 17-pitch innings he finished his outing with were not only stressful, but inconsistent due to arm fatigue.

The number of pitches under the threshold, across pitch type and release point component, are provided below in order to compare the duo:

Pitch Type HRel VRel
Fister CH 1 3
CU 2 3
FC 1 3
FF 7 9
FT 2 3
Strasburg CH 11 17
CU 6 12
FF 18 28
FT 1 3

CH: changeup, CU: curveball, FC: cut fastball, FF: four-seam fastball, FT: two-seam fastball

It is again clear that Strasburg’s outing, one where he battled hard and still put up a respectable pitching line, was less than ideal, compared to Fister’s. This is also an interesting contrast in how Williams handled his staff in the span of a day—one pitcher, despite some apparent clues that he was not long for his outing, is left to pitch through some bumps in the road, while another, who despite recently coming off of injury, still appeared to have a little more left in the tank, is quickly taken out of the game.

While not all variables have been taken into consideration—injury status, game situation, bullpen status, among many others—this exercise does potentially provide a very rough method of not only monitoring fatigue (albeit retrospectively), but also gauging a manager’s tendencies with how he handles his pitching staff as a whole and as individuals when they begin to labor in their outings.

***

Data courtesy of Brooks Baseball.
____________________

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 him on Twitter @TClippardsSpecs.

Statistically Speaking: Gio Gonzalez’ 2014 Through the Eyes of PITCHf/x

The Nationals’ starting rotation was dealt a big blow when Gio Gonzalez was sent to the 15-day disabled list for the first time in his career, with shoulder inflammation. The lefthander had been complaining of discomfort during a start against the Los Angeles Angels of Anaheim on April 23, but it was his two most recent starts that Gonzalez had confessed to having problems, in particular, with his arm slot and release point being lower than normal. Let’s take a look at Gio’s comment and see if it holds any water, through a perusal of his PITCHf/x data.

Here’s what Gonzalez’ release point across all of his pitches between 2013 and 2014—’z0′ is the vertical (north-south) component and ‘x0′ is the horizontal (east-west) component of the release point, measured in feet. For pitch types, four-seam fastballs are labeled ‘FF”, two-seamers are ‘FT’, changeups are ‘CH’, and curveballs are ‘CU':

Release point 2013-14 Gio

Grossly, there aren’t any tremendous differences between this year and last with Gonzalez; however, there are some subtle points to take away from this graph. First, his release point for the curveball is pretty distinct from his other pitches, with the release point being higher and closer to his body, likely a result of focusing on getting on top of the pitch in order to generate the snap and spin the pitch requires. Second, we can see some of the ‘arm dropping’ Gonalez had mentioned, especially with the curve and two-seam fastball. Here’s what the average release points across pitch type and year look like, in tabular form:

Pitch Type Year HRel (x0) VRel (z0)
CH 2013 1.846 5.427
CH 2014 1.903 5.400
CU 2013 1.840 5.806
CU 2014 1.899 5.796
FF 2013 1.693 5.479
FF 2014 1.761 5.462
FT 2013 1.888 5.386
FT 2014 1.986 5.337

Anecdotally, all of the changes within pitch type across seasons are statistically significant (Student’s t-test, p-values < 0.5) except for two values—z0 for both FT (p = 0.053) and CH (p = 0.051), which reach near significance. While these minute differences aren’t large enough to be seen from a batter’s perspective, from a biomechanical perspective, these slight differences can wreak havoc on a pitcher’s timing. So, each pitch is being thrown from a lower point vertically and further away from his body horizontally this season—how consistent is this trend occurring?

Pitch Type Year HRel (x0) SD VRel (z0) SD
CH 2013 0.165 0.140
CH 2014 0.176 0.125
CU 2013 0.205 0.160
CU 2014 0.232 0.163
FF 2013 0.221 0.160
FF 2014 0.232 0.142
FT 2013 0.198 0.136
FT 2014 0.192 0.118

With these standard deviations, we find that this season, Gonzalez has a little more release point spread in HRel, with VRel showing less spread, except for the curveball, which is consistent with a release point a little less repeated in terms of the east-west direction for all pitches and north-south with the breaker. While the vertical component is a little less erratic generally, the horizontal aspect of Gonzalez’ release point appears to be fairly inconsistent, compared to his healthy 2013 season.

Let’s continue our focus on this season’s data and again look at release points across pitch type and each of Gio’s outings:

Screen Shot 2014-05-21 at 10.18.41 AM

While Gio had commented on feeling some stiffness on April 23, we don’t really see any significant changes in his release point until his next start, April 29 against the Houston Astros. In particular, we see a drop with FT and CU pitches. From there, we see a bit of a zig-zag pattern with respect to his release points, never getting comfortable, which we eventually learned arose from the shoulder inflammation.

Let’s look at a couple of other PITCHf/x values to see if we can garner any more details about Gio’s outings and identify any outliers or deviations that could help pinpoint where Gio went off the rails, prompting or exacerbating the shoulder problem. Here, we look at pitch spin direction and spin rate, again across pitch type and using 2013 as out ‘healthy’ control sample. Spin direction tells us which way a pitch breaks, while spin rate tells us how much the pitch will break:

spin rate dir

Here, we have some other variables that show 2014-vintage Gio is overall a little different than last year’s Gio—his fastball in particular are getting more spin on them—Garrett Hooe over at Federal Baseball has a nice piece on spin rates as they pertain to Nationals starters, for those looking for finer grain details. For those curious, there were statistically significant increases in spin rate between 2013 and 2014 for FT and CU, but not FF or CH. Regarding spin direction, we don’t see any drastic year-to-year differences, but applying a t-test to the data, we find statistically significant decreases in spin direction for FF, FT, and CU, but not CH. These changes were again minute; however, there were a handful of outliers in the 2014 curveball data (circled in red above) that warrant further investigation:

Screen Shot 2014-05-21 at 12.09.21 PM

Again breaking out the data across starts, we find the spin direction outliers arising from the start against Houston. With the outliers, we also see Gio’s curveball having a higher than usual spin direction in this game, with it returning to previous averages in later starts. Here are those near-zero spin direction outliers:

Result x0 z0
3/Called Strike 2.265 5.838
4/Ball 2.163 5.688
5/Ball 1.832 5.582
5/Swinging Strike 1.934 5.608
5/Called Strike 2.083 5.741

From a release point perspective, we see that most of these curveballs were thrown well away from his body in the east-west/horizontal plane, considering the average x0 has been right around 1.89. Looking at the vertical/z0 component and remembering the average for 2014 for Gonzalez is right around 5.8, we see these outliers being thrown with a lower arm slot. With these, Gonzalez threw his curve in almost antithetical fashion for how it should be thrown—with a low vertical slot and his hand far far distance from his body in the horizontal. While the shoulder issues apparently started in an earlier start, the outliers seen in the Astros start in the table above along with their PITCHf/x fingerprint and biomechanical ramifications possibly point to Gonzalez’ pitching through his shoulder soreness and possibly fatigue came back to haunt him against the Astros. It is possible that the efforts to pitch through the discomfort resulted in difficulties repeating his usual mechanics and arm slot, with the tipping point arising from an effort to get on top of the curveball later in the game and in doing so, losing the timing, strength, and arm slot to properly generate the torque and momentum necessary to get the pitch to spin and break.

Thankfully for Gonzalez, the shoulder woes are free of structural damage and should hopefully subside with rest and some minor physical therapy. However, a discerning eye on his PITCHf/x data, especially for the curveball, might help provide additional information as to when and where Gonzalez (and perhaps other pitchers) starts to lose his mechanics, due to fatigue or injury. While this data isn’t exhaustive and the conclusions drawn might be tenuous, it does show that the Gonzalez of this season is a potentially different beast than the Nationals fan have enjoyed in years prior, shoulder discomfort or otherwise.

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Data courtesy of Baseball Savant.

Statistically Speaking: Stephen Strasburg’s Heat

The modern day version of Stephen Strasburg has been confounding. Despite fantastic stuff and most of his statistics showing an above average pitcher, there still remains a disconnect between expectations and outcome. As Strasburg continues to flounder in light of above average talent, there are many who point to his use of the fastball as the tipping point between his being simply a frustrating talent versus a perennial All-Star and undisputed ace. Whether it is frequency of use or the failure of a full return to pre-Tommy John surgery velocity that is the culprit is debatable, the evolution of Strasburg’s fastballs and his trust in the pitch is nonetheless an interesting development.

First, let’s take a look at Strasburg’s pitch selection over his career—post-Tommy John surgery, he has apparently scrapped the two-seam fastball (here labeled a sinker) in exchange for more offspeed pitches, including the slider, which is new to 2014.

SS_pitchpct

The fourseamer is still Strasburg’s go-to weapon, but there does appear to be an initial drop in usage this season, with a return to using it nearly half of the time starting in May, again with more offspeed offerings in the form of the changeup.

How about that fastball velocity?

SS_Velo

As many have noted over the last couple of seasons, Strasburg’s ultra elite velocity is now just…elite—the fourseamer averages right at 95 miles per hour, while the twoseamer/sinker is a little under 95, down roughly three miles per hour compared to his pre-injury days. Despite the dramatic drop, this is velocity that still has Strasburg in the top ten for National League average fastball velocities for starters. However, both of Strasburg’s fastballs have lost some of their effectiveness thus far in 2014, with the fourseamer at a 0.03 pitch value per 100 pitches and the twoseamer at -0.74 pitch value per 100 pitches, per FanGraphs. While there is a sample size component to this statistic, especially with the twoseamer, this drop from last year’s numbers—0.62 and -0.27, respectively, is potentially troubling.

In some respects, the Strasburg of recent vintage is a tale of four different fastballs—two grips, but also two speeds. Gone are the days of the pure gas that defined him in earlier days, with a line of velocity delineation being 95 miles per hour. Looking at all of Strasburg pitches thrown over the past two-plus seasons, we find 95+ miles per hour fastballs accounting for roughly 45 percent of pitches thrown in 2012 to 36 percent in 2013 and finally to 16 percent in 2014. With this in mind, let’s move forward and take a look at these fastballs in this fashion—twoseamers and fourseamers above and below 95 miles per hour—and see if this distinction provides any additional insight.

First, a look at Strasburg’s sub-95 fourseamer outcomes in 2014:

FF14l

…and here, sub-95 twoseamers of 2014:

FT14l

Roughly a third of each fastball are balls, with a quarter to a third are called strikes, with the remainder a mix of various types of batted balls along with some whiffs. Now, let’s take a look at 95-plus fastballs, but adding some additional data, looking at each across the 2012, 2013, and 2014 seasons, from left to right. First, fourseamers:

FF_all

…and the same thing for twoseamers, from 2012, 2013, and 2014, left to right:

FT_all

Reviewing the fourseamer data, it appears that Strasburg has better command of the sub-95 stuff, with fewer balls called. This comes with fewer balls in play, but slightly more hits with the lower velocity fourseamer, as well as less ability to get hitters to swing and miss. For the twoseamer data, it’s a somewhat different story. More called strikes are seen with the sub-95 heat, with a dramatic drop in called strikes across year with the 95-plus twoseamers. Interestingly, Strasburg’s sub-95 twoseamers are getting hit less and are getting more whiffs compared to the high heat, with rates similar to previous year’s 95-plus pitches. Fewer balls and fewer balls in play also make the sub-95 twoseamer on the surface a different beast altogether, and perhaps a better one, compared to the 95-plus twoseamer.

Briefly, let’s take a look at location, again comparing the sub-95 two- and four-seam fastballs of 2014 compared to 2012-2014 95-plus heat.

First the 2014 data—fourseamers are on the left and twoseamers are on the right:

FF14hlFT14hl

…and here are fourseamer (top) and twoseamer (bottom) data for 95-plus pitches across the post-Tommy John era:

FFh_all

FTh_all

It’s a lot of data to digest, but there are some subtle differences seen. Not surprisingly, Strasburg’s command of the sub-95 stuff is a tad better than the 95-plus heat. Another trend seen is Strasburg not really using the corners of the plate with the 95-plus fastballs, appearing to almost nibble the outside corner with the twoseamer and leaving the fourseamer up in the zone and down the middle of the plate. While this is presented context free with no references to batter handedness and counts accounted for, an overarching thought looking at these heat maps is perhaps this is a ramification of overthrowing the fastball, either out of frustration or perhaps in order to ramp up the velocity to blow a guy away with strike three.

While much of these results are somewhat arcane and subtle, it does show an interesting trend with respect to the waning fastball velocities of Stephen Strasburg. Fewer high octane fastballs, a disappearance of the twoseamer in favor of a new pitch and other offspeed pitches all have the potential to account for some of the disparities between Strasburg’s stuff and his outcomes, with a return to the twoseamer and a scrapping of the slider possibly an attractive change for the better. Looking at our first graph of pitch selection, this is perhaps what we are starting to see.

While they aren’t the fastballs of old, the sub-95 twoseamers and fourseamers do have some advantages over the pure heat and could potentially help Strasburg in the long run with respect to remaining economical with his pitches and also applying a more simplified approach to getting hitters out. Strasburg remains a special talent who is in rarefied air in his ability to dominate with his fastball above all else, despite tangible velocity decreases post Tommy John surgery.

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Data courtesy of Brooks Baseball and Baseball Savant.

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