May 24, 2022

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: 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
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: 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.

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.

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=
—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.

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: Catcher Effects on Pitching Pace

The job responsibilities of catching position can be very nuanced and many of the things that make a good backstop are attributes that rarely get noticed by fans. As an example, a recent ‘Fancy Stats‘ article by Neil Greenberg discussed the effect that Nationals catcher Jose Lobaton has on getting his pitchers extra strikes due to his pitch framing ability, a very subtle skill that is near intangible in contrast to abilities like hitting prowess or handling an opponent’s running game with your throwing arm.

A similar skill that can also often go unnoticed  from a pitcher’s perspective is pace—how quickly you are able to make a pitch, collect yourself, get the sign, and throw the next pitch. Given the effects of timing on the ultimate success of an at bat for a hitter and the need for a pitcher to disrupt this timing in order to get outs, pace can play an unheralded role in a pitcher’s performance.

Pace goes beyond a pitcher’s internal clock, with many factors based on the rapport a pitcher and catcher have with one another playing a role in the outcome and whether a pitcher’s pace is quick or slow; ultimately, there is a particular level of comfort that a pitcher has with a catcher with respect to pitch calling that can affect pace.

With this in mind, let’s take a look at how the Nats starting rotation’s pace stats look, with the trio of catchers used so far in 2014—Sandy Leon, Jose Lobaton, and Wilson Ramos—taken into consideration. First, let me briefly discuss the data. PITCHf/x data from Nats games through May 5th was collected to calculate pace between pitches, with careful curation of the data done in order to remove outliers.

Ultimately, curation involved removing data points that were longer than 60 seconds and less than 10 seconds. This was done to remove first pitches of an inning, pitches after a home run (in order to counter the various lengths of time it took for hitters to jog around the bases), pitches where replay was involved, and other data that was felt to be physically impossible, with the hope that this pruning would give us the best picture possible of the effects of catcher on pitching pace. With these considerations in mind, let’s look at some pace results:

Pitcher Catcher Pace (secs)
Gio Gonzalez Jose Lobaton 25.068
Gio Gonzalez Sandy Leon 24.174
Jordan Zimmermann Jose Lobaton 26.310
Jordan Zimmermann Sandy Leon 25.927
Stephen Strasburg Jose Lobaton 26.349
Stephen Strasburg Sandy Leon 25.975
Stephen Strasburg Wilson Ramos 27.075
Tanner Roark Jose Lobaton 25.621
Tanner Roark Sandy Leon 24.483
Taylor Jordan Jose Lobaton 27.286
Taylor Jordan Sandy Leon 26.603

For reference, here are each player’s average pace—note that these averages were calculated using the aforementioned criteria, for those who use FanGraphs’ pace statistic and find a roughly four second shift in the pitcher’s averages:

Pos Name Pace (secs)
C Jose Lobaton 25.841
C Sandy Leon 25.664
C Wilson Ramos 27.075
P Gio Gonzalez 24.906
P Jordan Zimmermann 26.077
P Stephen Strasburg 26.360
P Tanner Roark 25.259
P Taylor Jordan 26.898

Across the board, pitchers are a little quicker when Sandy Leon is behind the dish. With the pitchers, Taylor Jordan appears to be the slow poke, even slowing down Leon’s typically quicker pace with the staff by roughly a second. Overall, we do see some effects of the catcher on a pitcher’s pace.

Is this a significant effect? Let’s run an analysis of variance (ANOVA) to see if it is—for those numbers averse, feel free to skip to the pretty picture further down the page.

Using pace as our dependent variable and pitcher and catcher as our independent variables, the ANOVA results are as follows:

Screen Shot 2014-05-07 at 11.12.04 AM

Cutting to the chase, we find that catcher does not have a significant effect on pace, but (no surprise here) the pitcher toeing the rubber does (p=0.022). Briefly, a Tukey’s test to look at the average differences between catchers:

Difference Lower Upper p adj
Sandy Leon-Jose Lobaton -0.176 -1.262 0.908 0.923
Wilson Ramos-Jose Lobaton 1.234 -1.588 4.056 0.561
Wilson Ramos-Sandy Leon 1.411 -1.457 4.278 0.481

Regarding the statistically significant results between pitchers, this stat was driven by the differences in pace between Gio Gonzlaez and Taylro Jordan, the quickest and slowest members of the rotation, with a difference of roughly two seconds in average notching a p-value of 0.04, which is just satisfies the criteria for significance of a p-value at or below 0.05. Additional ANOVA modeling including pitch type and inning did not show any statistically significant differences in average pace.

For the numbers averse crowd, welcome back! Overall, we did not find any statistically significant effects of catcher on average pace (or inning or pitch type), but did with pitcher. For those who a little more visual, the scatterplots below show show pace across inning, broken down by both pitcher and catcher, confirming the first table of results showing Leon getting pitchers to work quicker than Lobaton or Ramos:

Pace Across Pitcher and Catcher

While we don’t see any statistically significant results, pace is nonetheless an important aspect of the pitcher-catcher battery, and while again not a significant result, the quicker a starter works, the more success he tends to have, using RE24 as our marker of success:

Data courtesy of FanGraphs

Data courtesy of FanGraphs

While statistically these results aren’t terribly robust, the effects of pace (and the catcher) on the game are innately important, not only in its potential to disrupt hitter timing and rhythm, but also on a pitcher’s teammates. The longer a pitcher takes to decide what to throw, the longer his defense sits in their crouches, awaiting the ball to be put in play. The longer they wait, the greater potential to lose focus on the game and become distracted.

Pace also plays a role in length of game. In a recent interview, Boston Red Sox manager John Farrell discussed how starting pitcher pace can negatively affect game length. Like many things related to the position, the catcher’s role on pitcher pace will remain a potentially critical piece in a game’s outcome, despite its statistically small effects.

Statistically Speaking: Anthony Rendon’s Swing

Sitting atop the Washington Nationals leaderboard in several offensive categories, Anthony Rendon is having himself quite the start to 2014. The Texan’s long-coveted swing and bat-to-ball skills appear to be in full bloom, also displaying some tantalizing pop that some felt he might not fully develop. Here’s a quick look at some of Rendon’s numbers, compared to his rookie season:

2013 394 7.90% 17.50% 0.131 0.307 0.265 0.329 0.396 0.318 100
2014 59 6.80% 15.30%* 0.273* 0.386 0.345* 0.390 0.618* 0.426* 166*

* denotes team leader

Comparing his start to 2014 to his 2013, we see Rendon is not only making lots of contact, but is making harder contact (per his isolated power), while also continuing to develop his already keen eye for the strike zone. Let’s delve a little deeper into that eye for the strike zone and its development; here, we have Rendon’s swing and contact rates for pitches in (labeled with the prefix ‘Z-‘) and out (labeled ‘O-‘) of the strike zone:

Screen shot 2014-04-16 at 9.59.42 AM

Here, we see an interesting trend—Rendon is swinging at more pitches, but making less contact compared to last season. In fact, he is swinging more at pitches outside of the zone, which is also flies in the face of his slight uptick in walk rate in 2014. Despite the slight rise in chasing pitches outside of the zone, he still shows the most restraint when comparing his O-Swing rates to his Nats cohorts; Rendon trails only Adam LaRoche and Jayson Werth with respect to Z-Contact rate (89.6% versus 90.6%), but leads the team in overall contact rate, connecting with 85.6% of pitches he has seen.

Much of this possibly points to pitchers attacking Rendon differently—is this the case? Let’s take a look at Rendon’s heatmaps for pitches seen from this and last season, courtesy of Brooks Baseball; 2013 pitches are on the left, with this year’s on the right:

Screen shot 2014-04-16 at 10.32.13 AM

By the looks of it, pitchers are taking a slightly different approach with Rendon, busting him inside with pitches more so than last year, when they went down and away with their most of their offerings. How is he faring with this tweaked approach? Let’s look at his batting average heatmaps, again with last season on the left, 2014 on the right:

Screen shot 2014-04-16 at 10.55.28 AM

The colors are a tad misleading for 2014 simply due to sample sizes—he is still making lots of contact and getting hits on pitches in the strike zone. However, we also see that Rendon is taking those inside, slightly off the plate pitches and doing more with them this year, which is not only reflected here, but in his BABIP, currently at .386.

Let’s discuss BABIP briefly. Overall, the stat doesn’t have a strong year-to-year correlation, so the chances of Rendon maintaining and continuing his current average isn’t likely. However, popup rate (PU%) is pretty correlative year to year and is also relatively predictive of BABIP. It is also a nice way to gauge how hard a hitter is hitting the ball. With this in mind, we can take a look at Rendon’s popups and not only see the potential of his BABIP to remain above average, but also how well he is hitting the ball, not only in terms of accumulating base hits, but also how hard the contact he is making really is.

Using the formula IFFB / (FB+LD+GB) * 100, we can calculate PU%. First, let’s do this for 2013:

9 / (97+73+116) * 100 = 3.16%

To put this into contrast, Joey Votto had a 0.22 PU% in 2013, having had one IFFB; teammate Bryce Harper had a 2.1% popup rate. Comparing him to a similar hitter in terms of BABIP, Manny Machado had a 5.1% popup rate.

For 2014, Rendon, like most of the league, has a 0 PU%, so we can’t really say much about popup rate improvements just yet, as we simply don’t have enough data points. However, this correlation is something to keep in the back of our minds as the season progresses. However, looking at last year’s numbers, we do see Rendon as someone who projects to hit the ball hard as he continues to develop.

Last, let’s briefly look at Rendon’s swing. Overall, it’s one that spends a long time in the strike zone, allowing for more opportunities to make contact. He does show some ‘noisy’ hands, exhibiting lots of extra movement. However, as you can see, his hands appear to be a little less noisy:

rendon3 09-09-55-127

Click to start gif

…compared to 2013:


Click to start gif. Courtesy of

While these gifs aren’t the best for comparison given the first is on a fastball, while the second is on a curveball, it does show the changes in how his hands and feet are set and work throughout his approach. With that caveat noted, it appears that Rendon has also removed some extra movement with his lower half, making an already compact swing quicker, allowing him to cover all corners of the plate and also turn on those high and tight fastballs he appears to be getting more of in 2014.

Despite counter intuitive statistical changes from this season to last, we see an improved approach by Rendon, possibly brought on by some slight mechanical tweaks. He is not only taking what he is given in terms of pitches in the strike zone, but is also showing pitchers that he can turn on the inside pitch, thus opening up the outside corner for Rendon in future at bats. In terms of his BABIP and the ability to consistently make hard contact, the trends bode well; however, it’s a little to early to say with much conviction whether the BABIP we have seen from Rendon in the first month of the season will remain through the year or his career, but nonetheless, we should enjoy the show one of the best pure hitters in the game is putting on.


Data courtesy of FanGraphs, unless otherwise noted


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