Archive for April, 2017

What Happened to Adam Wainwright?

At 24 years old, Adam Wainwright burst onto the scene, closing out the 2006 World Series and bringing the Cardinals their first championship in almost 25 years. Over the next 10 years, he was the opening day starter five times and racked up the 7th-most wins of any pitcher despite missing the entire 2011 season and most of the 2015 campaign. If you prefer to measure a pitcher’s performance in Wins Above Replacement as opposed to wins, Wainwright was still a top-10 pitcher from 2007-2016.

Adam Wainwright

If you’re still not convinced, he finished in the top three of Cy Young voting four times and received MVP votes in 2009, ’10, ’13, and ’14. By all accounts, Adam Wainwright was an elite player for the better part of a decade, even after recovering from Tommy John surgery in 2011. Still, by all accounts, he has been anything but elite since.

What happened?

In 2014, Wainwright’s last dominant year (6.1 WAR), he threw almost 230 innings on his way to a 20-9 record with a 2.38 ERA. Since then, he has posted a 4.35 ERA — almost a full 2 runs worse than 2014. Is this just the decline that comes with age, especially as he pitches in his age-35 season? Is it lingering effects from his devastating Achilles injury in early 2015? Admittedly, it’s probably a combination of both. They key, however, is the pitch he used to break onto the scene way back in 2006.


The curveball, which was his go-to pitch for years, is no longer devastating hitters with the consistency that it once did.

There has been a consistent decline in the percentage of whiffs Adam gets on his curveball. It peaked at 17.22% in 2014 and has fallen to 10.26% early in 2017 — a 40% decrease.

Curveball Whiff Rate

But a decreasing whiff rate doesn’t necessarily mean the pitch is failing him; maybe the batters are just making more contact?

When looking at how the vertical movement of his curveball has changed over the last few years, it is evident that the pitch has regressed.

Vertical Curveball Movement

From 9.22 inches in 2014 to 7.54 inches in 2014, the movement on Wainwright’s curveball has decreased by almost 20%. No wonder the whiff rate decreased by so much — the curveball doesn’t have the necessary movement to trick hitters.

This is probably a result of age. How many pitchers maintain the efficacy of their best pitch for their entire careers? None. But the most important thing to do, especially with a curveball, after realizing you can’t get the ball to move like you once could, is to selectively pick when to use it. If Wainwright used his regressing curveball more sparingly, it might be more successful. Instead, he has taken an obviously less effective pitch and increased how often he uses it.

Pitch Usage

Wouldn’t it make sense to use a decreasingly-effective pitch less often? As you can see, instead of markedly decreasing how often he uses his curveball, the long-time Cardinals ace actually increased its usage rate. Now, the graph shows a variance of only a few percentage points — relatively insignificant. Notably, the 28.89% usage rate is the most Wainwright has ever used his curveball.

Let’s do a quick recap. Adam Wainwright is not getting whiffs from hitters because his curveball doesn’t have the movement it once did. And instead of lowering its usage rate, he increased it to an all-time high.

What should we expect to happen when a less-effective pitch is used more and more?

Batting Average by Pitch Type

The batting average against his curveball has risen to .380 in 2017 — more than doubling since 2014. This is alarming. It leaves hitters hoping Wainwright throws them a curve — a stark contrast from a pitch that used to buckle knees.

Recently, we’ve seen less of Wainwright’s dominant curveball, and more of his less-effective curveball, leading to more hits, runs, and losses. Wainwright’s success is directly linked to the effectiveness of this pitch. While it may no longer be elite, it can be effective if he saves it for the right moments.

Pitch-Framing and Twins Pitchers

On Wednesday, November 30, 2016 the Twins announced the signing of free-agent catcher, Jason Castro to a 3-year, $24.5MM contract, a move that was widely attributed to the Twins’ new front-office comfort with advanced analytics. Jason Castro is widely regarded as very good defensive catcher, due in large part to his ability to frame pitches and steal strikes for his pitchers. In 2016, Castro ranked third in all of baseball in Baseball Prospectus’ Framing Runs statistic, with 16.3. Kurt Suzuki, the Twins primary catcher in 2016, ranked 92nd at -6.8. Suzuki’s main backup, Juan Centeno, ranked 97th with -9.7.

Castro is a roughly average offensive catcher. He put together a 88 wRC+ in 2016, which ranked 17th among catchers with at least 250 PAs, via FanGraphs. For reference, the league-average wRC+ for catchers in 2016 was 87. But, he got a $24.5MM contract primarily because of his framing and the Twins are expecting him to make an impact on their pitching staff.

So where might the Twins pitchers benefit from better framing? Let’s look at the Twins pitchers (that are still with the organization in 2017) that threw at least 50 innings in 2016, sorted by innings pitched:

Table 1 Twins 50 IP

Using this list of pitchers, we can utilize FanGraphs’ excellent heatmaps tool to explore each pitcher’s distribution of pitches around the strike zone. For example, here is Kyle Gibson’s 2016 pitch% heatmap, which displays the percentage of pitches thrown to each particular segment in and around the strike zone (from the pitcher’s perspective). The rulebook-defined strike zone is outlined in black.

Gibson Pitch% Heat Map

There are not many surprises here, as we can see Gibson most often pitches down in the zone, and to his arm side, which is likely driven in large part to the high number of 2-seam sinking fastballs he throws (27.2% of total pitches in 2016, per PITCHf/x data available on FanGraphs).

What this data also lets us do, is explore each pitcher’s propensity for pitching to the edges of the strike zone. Let’s assume much of the benefit of pitch framing occurs at the edges of the strike zone, where pitches are less definitively a ball or a strike to the eyes of the umpire. By focusing on the edges of the zone we can identify which Twins pitchers might benefit most from better framing.

For this analysis, I focused explicitly on the strike-zone segments just inside and just outside the rulebook strike zone, which are the areas between the gold lines in the graphic below:

Gibson Total Edge Pitch%

Using the pitch data in these sections, I calculated a metric for each Twins pitcher, Total Edge%. These data points are summarized in the table below and show us the percentage of pitches thrown on the edge, or just off the edge of the strike zone, by each Twins pitcher:

Table 2 Twins Total Edge%

What we can see is the Twins’ starting pitchers seemed to pitch toward the edges of the strike zone more than the league average and more than their reliever teammates in 2016, with the exception of Brandon Kintzler. Ervin Santana is approximately at league average, which was 44.7%. Kyle Gibson is significantly above, at almost 49%. Jose Berrios, Phil Hughes, and Hector Santiago are all up around 47%.  So, as a starting point, we can assert that Gibson, Berrios, Hughes, and Santiago are the primary candidates to benefit from better framing.

But how do they fare in getting called strikes around the edges of the zone?

Using the same heatmaps tool, we are also able to visualize each pitcher’s called strike percentage (cStrike%), in each segment of the strike zone. Here is Gibson’s for 2016:

Gibson Total Edge cStrike%

As we would expect, pitches located in the middle of the zone are nearly always called a strike, evidenced by the bright red boxes and rates at or near 100%.

Our interest is just on and just off the edge of the strike zone, which I again outline in gold. Here, we see more variation, with the called strike percentage ranging from as high as 88% in the zone to Gibson’s arm side, to as low as 27% inside the zone up and to his glove side. We also see, pitches just off the strike zone are called strikes at a much lower percentage than pitches just in the zone, as you would expect. We need a reference point. How do the Twins compare against the rest of baseball?

Using this data, I calculated two additional metrics, In-Zone Edge cStrike% and Out-Zone Edge cStrike%, which delineate the called strike percentage on the edge and in the zone, and on the edge and out of the zone. Focusing on these strike zone segments, I calculated the called strike percentage for each Twins pitcher. Also included are the MLB averages for each metric.

Twins In Zone Edge cStrike%

What we see above is that six of the 10 Twins pitchers to throw 50 innings last season had a lower than league-average called strike rate on pitches on the edge and inside the legal strike zone. Ryan Pressly and Jose Berrios appear to be the most impacted, with called strike rates of significantly less than the league average of 64.9%, at 52.8% and 57.5% respectively.

But what about just off the edge?

Twins Out Zone Edge cStrike%

When we focus on the segments just off the strike zone, we see this same trend play out, but even more significantly. The visual above shows that eight of the 10 Twins hurlers had lower than league-average called strike rates on pitches just off the strike zone. This indicates that they were not getting many strikes stolen in their favor. In most cases for the Twins, the difference from league average is quite significant. Berrios, Michael Tonkin, Pressly, Taylor Rogers, and Santiago each have rates right around half the league average of 10.4%. The net result, when we add up the In-Zone and Out-Zone Edge cStrike% for Total Edge cStrike%, is that seven of the 10 Twins pitchers studied had called strikes rates around the edges of the strike zone that were decidedly less than league average.

Now, this probably isn’t all that surprising intuitively. We know the Twins as a whole did not pitch well last year (29th in ERA, 27th in FIP, per FanGraphs), and we know the Twins catchers did not rate well as pitch-framers. Kurt Suzuki and Juan Centeno combined to catch nearly 86% of the Twins’ defensive innings last season. But for as bad as the team pitched, it is also clear the pitchers were not getting much help from their catchers.

But how many pitches are we talking about here? If we assume a league average called strike rate on the edges of the strike zone (which was 36.1% in 2016) for the Twins, we can estimate an additional number of pitches that would be called strikes. This is what we find:

Table 3 Estimated Called Strike delta

By this analysis, it seems that Jose Berrios, Ryan Pressly, and Ervin Santana would benefit the most from better pitch-framing, with each gaining roughly 20 additional called strikes over the course of the season.

But how much does a pitch being called a ball, instead of a strike, matter?

Let’s look at the major-league batting average by count in a plate appearance. The data in the table below is from a 2014 Grantland article written by Joe Lemire, and calculates the batting average for plate appearances ending on specific counts. For example, the batting average on plate appearances ending on the 0-1 pitch is .321. The data fluctuates slightly year to year, but in any given season, you’ll find a table that generally looks like this:

Table 4 Batting Average By count

By this measure, the value of a strike, depending on the count is quite significant. In a 1-1 count, for example, if the next pitch is called a strike, making the count 1-2, the batter’s expected batting average drops from .319 to .164. Similarly, if the pitch is a ball, making the count 2-1, the batter’s expected average increases to .327. That’s a .163 swing in expected batting average.

Others have approached this differently by trying to calculating the expected outcomes by the result of the at bat that reaches each count. So for example, what is the expected outcome for all plate appearances that reached an 0-1 count, regardless of whether it was the 0-1 pitch that the outcome of the plate appearance was created. Nonetheless, we find a similar result. This is a revisit of the idea by Matt Hartzell published on RO Baseball in 2016:

Chart 1 Batting Average By count

Chart 2 OBP By count


While the differences here are not quite as steep as before, we still see the swings matter. Batting average after a 1-2 count is .178, where after a 2-1 count it is .247. That’s still a .069 swing in batting average. We also have added on-base percentage, and the trend holds. OBP after a 2-1 count in 2016 was .383, versus just .229 after a 1-2 count.

So, all of this helps us show the Twins have a pitch-framing problem and pitch-framing matters because getting more pitches called strikes leads to fewer runners on base.

But can Jason Castro fix it?

To try to find out, let’s look at the Houston Astros, Castro’s former employer. Using the same methodology as with the Twins pitchers, I again calculated the cStrike% on the edges of the strike zone for the all Astros pitchers that threw more than 50 IP in 2016.  What we find is pretty telling:

Astros Total Edge cStrike%


Of the 12 Astros to throw more than 50 IP, only one, Michael Feliz, had a lower than league-average called strike rate on the edge. But even he was roughly league average at 36.06%, compared to league average of 36.11%. The rest of the pitchers studied were above league average, and in most cases, quite comfortably so. Six of them are clustered close together right around 41.0%.

Now, to be fair, not all of this is directly attributable to Castro. These are different, and arguably, better pitchers. And Castro didn’t catch every pitch thrown (he caught 61.9% of the Astros’ defensive innings in 2016). But, the difference is stark and by this rough measure, it seems Jason Castro will make a positive impact for the Twins pitchers.

To the Twins’ credit, they recognized they had a weakness, and they used the free-agent market to acquire a player they hope can help address it.

Let’s Talk About That Michael Lorenzen Appearance

You may have heard that the Reds are approaching their bullpen a bit differently than other teams this season. The Reds aren’t expected to be particularly good this season, and as such, they are a bit more free to experiment.

One recent game highlighted the new-school approach of manager Bryan Price. In the third inning of Monday’s game, Brandon Finnegan started the 3rd inning with a 5-run lead and proceeded to implode, loading the bases before walking the first run of the game in. With no outs recorded yet in the inning, Finnegan was primed to give up several more. Price pulled the trigger on a highly unusual move: He went to the bullpen in favor of Michael Lorenzen, one of his better bullpen arms.

This decision was lauded by quite a few writers and pundits, including those here at FanGraphs. Craig Edwards used it as the impetus for examining the overall usage in the Reds bullpen so far this year, and Ben Lindbergh and Jeff Sullivan called it out in their latest “Effectively Wild” episode. The emphasis, in both cases, was on the decision to bring Lorenzen into the game. Which was a great decision! It was weird! It was wonderful! Most importantly, it worked!

There’s another aspect of this Lorenzen appearance that shouldn’t go overlooked, though. After Lorenzen worked the 3rd inning with great success, he stayed on for the 4th inning, in which he maintained their 5-1 lead. He retired the side in order with 10 pitches in the 4th, having thrown 14 in the 3rd. The Reds tacked on another run in the top of the 5th, and Price stuck with Lorenzen again for the bottom of the inning, now with a 5-run cushion. Lorenzen, once again, set down the side in order, this time on just 8 pitches. With 32 total pitches on the day, Price elected to turn to a lesser pair of arms in Cody Reed and Wandy Peralta to finish out the game (although not before allowing Lorenzen to lead off the top of the 6th at the plate).

While the 3rd inning represented a quintessential high-leverage situation, the 4th contained much less leverage, and the 5th, still less. The numbers bear this out: In the third inning, Lorenzen faced three batters in situations commanding a Leverage Index of 2.68, 2.66, and 2.53. The total Leverage Index of these three batters was a whopping 7.87. By contrast, the total LI associated with Lorenzen’s work in the 4th and 5th innings was 2.40. The six outs Lorenzen got in those innings weren’t as important, cumulatively, as the least important hitter in the 3rd inning!

(Click the graph for an interactive version)

Price was rightfully lauded for bringing one of his best pitchers into one of the most critical moments of the game. That’s only half of the equation, though. Knowing when to take a key reliever out of the game, in the context of the season as a whole, is just as important as knowing when to put him in.

As Edwards rightly notes, Andrew Miller is only on pace for about 88 innings this season. Andrew Miller threw 74.1 IP last season, and it was the most he had ever thrown in his tenure as a full-time reliever. It’s not as though the Yankees or Indians were trying to limit his usage — it’s that a reliever, any reliever, has a limit to the number of innings (and more appropriately, the number of pitches) they can throw in a season without breaking down or losing their effectiveness.

The question, then, is how to maximize the value of these innings. Lorenzen threw 2 innings and 18 pitches that he, quite possibly, didn’t need to throw. He consumed 2.40 “units” of leverage in the process. The next day, he was (quite predictably) unavailable. Price, faced with a close/late game situation, had to throw Peralta in the 7th inning of a one-run game, where he retired the top of the Pirates’ lineup in order, but consumed 4.22 “units” of leverage — 75% more than Lorenzen did in those two innings the day before.

This isn’t to say that “perfect” bullpen usage is achievable. The nature of the game is to guess when the situation you’re faced with will be the most important in the remainder of the game, or the remainder of the series, or the remainder of the homestand. In some cases, a more important, later, closer, more tense situation will arise in the same game, and you’ll have used your most effective bullets. In other cases, you’ll have used a pitcher in a big spot one day, and he’ll be unavailable in an even bigger spot the next day. In still others, the team will go on a run of 4-5 close games in a row, and lesser parts are needed to fill the surplus of close/late innings.

CIN 040317 - 041217
(Click the graph for an interactive version)

But the concept of “perfect” bullpen usage must start with the recognition of constraints, and an approach that optimizes the total leverage that a pitcher can consume within those constraints. It’s not enough to pick the right person for the job when the job is hard; it’s also necessary to pick the right person for the job when the job is somewhat easier, so that the right person for the next hard job is available. Michael Lorenzen did the hard job, but he also did an easier one, and as a result, wasn’t available for the next hard job.

Why Mike Trout Will Never Be Mickey Mantle

Let me begin by clarifying that this article is not conceived as an attempt to downplay Trout’s historic greatness. I am a Trout devotee, fully in awe of his talent and willing to debate with Baby Boomers about his worthiness of winning the AL MVP just about every season (Miguel Cabrera’s Triple Crown be damned!). However, there is one baseball player I worship above all the rest – Mickey Mantle.

As a child growing up with an undying love for baseball and reading, I devoured just about every baseball book I could. Player biographies, compendiums claiming they could list the top 100 (or even 1,001) ballplayers of all time, and wacky collections of random fun facts. My favorite of all of these was “The Mick,” Mantle’s autobiography. This book made me fall in love with his story, and I acquired an appreciation for him as one of the all-time greats beyond his stat line. And that, folks, is why Mike Trout will never be Mickey Mantle – the overwhelming power of narrative vaults Mantle to a level beyond just about any player this side of Babe Ruth. In his book The Truth About Stories, Thomas King tells us that “the truth about stories is that’s all we are.” Our entire humanity is shaped by stories. No collection of numbers or in-depth analysis of WAR, launch angles, and exit velocities can combat the enormous influence of stories. Trout, legendary talent and all, will (barring extraordinary future events) never have a story that lives up to Mantle’s.

If you’re unfamiliar with Mickey Mantle’s story, maybe it’s been a while since you’ve listened to Bob Costas pontificate. Let me refresh you. Here’s really all that matters; the life-blood of Mantle’s legend. He was supposed to be even better than he actually was! Here’s a photo of the injury that derailed everything.

You can just see DiMaggio standing over him, lamenting the fact Mantle will only accumulate a mere 111 career WAR from this moment onward. This career-destroying injury caused Mantle to hit only 523 home runs from this moment onward.

Let’s talk about home runs for a second. Mantle’s mammoth power from both sides of the plate is the skill he is best known for. The term “tape-measure home run” was coined because of his power, in case you didn’t know. Mike Trout hits some tape-measure home runs himself. Here’s the longest home run of Trout’s career, a 489-foot blast at Kaufman Stadium.

Impressive, no doubt. That’s a long way to hit a baseball. We can see that from the video. In fact, the video provides irrefutable evidence of the event occurring, with ESPN’s Home Run Tracker giving us accurate statistical information. Had the home run been hit in the Statcast era we’d have an even more detailed account of the home run.

Mantle’s home runs are historic, legendary, and record-setting. We know this because we’re told this. We have no Statcast data to back this up, but the stories we’re told are compelling. Purportedly, Mantle once hit a ball 734 feet. Well, he would have, had the ball not hit the façade atop Yankee Stadium while still reaching the apex of its flight. Don’t believe it? Check out the definitive list of Mantle home runs.

Diagram of Mickey Mantle's mammoth home run at Yankee Stadium on May 22, 1963 that hit the facade and bounced back to the infield - it was the closest anyone has ever come to hitting a ball out of Yankee Stadium

It’s unbelievable, right? With no Statcast or ESPN mathematicians to aid us, all we have are eyewitness accounts and crude trigonometric measures to give us a less-than-definitive representation of reality. What matters isn’t how far the ball was actually hit. What matters is that there is a story surrounding the home run. In the same way his early-career knee injury begs the question of “What if?”, so too does this home run leave room for imagination. The façade acts as the sprinkler head, an impediment between what was and what would have been.

What could Mike Trout be? We’ll have to wait for time to answer that question. In any given moment, though, we’ll never have to question what he is. We’ll know the exact speed the ball comes off his bat, the exact distance of his home runs, and the precise amount of ground he covers as he tracks down fly balls. That’s the real crux of the issue – we know too much. Even though Trout plays on the West Coast and arguably doesn’t receive enough recognition or publicity, we have all the information we need and more. We can quantify his achievements in a way we never could, except retroactively, with Mantle.

Mickey Mantle’s stellar career was plagued by knee injuries, alcoholism, and pesky stadium facades. Mike Trout’s career will always be followed by WAR-calculating analysts, 24/7 media coverage, and the omnipresence of Statcast. Subjectivity will always be more compelling than objectivity. It’s human nature. The truth about stories is that’s all we are, and the truth is that Mike Trout will never be Mickey Mantle.

Platooning Kolten Wong and Jedd Gyorko

Last week, the Cardinals announced that Kolten Wong would be part of a platoon with Jedd Gyorko. As Mark Saxon noted, Wong did not react well to the news (although he later clarified that he would prefer to stay in St. Louis). Kolten and the Redbirds agreed to a five-year, $25.5-million contract last spring, but what might have served as a confidence booster for the young second baseman resulted in a slash line of .240/.327/.355 over 121 games.

The reason for this platoon is Gyorko’s bat. He did lead the Cardinals with 30 home runs in 2016, a number that had him tied for 11th in the National League. But is Gyorko that much better offensively to offset Wong’s defensive attributes?

Let’s look at this from two different perspectives: in the field and at the plate.

In the Field

Using data from 2015 and 2016, Wong and Gyorko played over 1000 innings at second base — a large enough sample size to use in an analysis. Examining the Ultimate Zone Rating per 150 games (UZR/150), we see that Kolten and Jedd have scores of 3.1 and 4.2, respectively. So, while Gyorko seems to have an advantage here, over the course of a 162-game season, this is a relatively insignificant difference.

Looking at Def, which measures the number of runs above or below average a player is worth, we see that Wong scores 8.2, while Gyorko scores 5.3. According to FanGraphs Rules of Thumb for interpreting this statistic, both players are between “above average” and “great defenders.” Wong’s advantage here equates to about 1/3 of a win. Again, no significant difference in their fielding abilities.

If we look at the Inside Edge Fielding statistics from FanGraphs, we see, as a whole, that Kolten makes more difficult plays, but Jedd makes the easier play a greater percentage of the time. For instance, look at the percentage of “unlikely” plays that each player made. An “unlikely” play is a play that is made 10-40% of the time. Kolten made 27% of these plays, while Jedd did not make a single one. At the same time, looking at plays that are “likely,” Kolten made 73% of them, while Jedd made significantly more (88%).

An analysis of these statistics shows us that, in the field, Kolten may make more web gems, but Jedd is the more consistent everyday second baseman. Nevertheless, there is not much separating these two on the defensive end.

At the Plate

At first, this part of the debate seems relatively simple. Gyorko led the team in HRs last year, he is clearly the much better hitter, right? Let’s take a look. At first glance, the players look very similar, with Jedd posting a line of .245/.301/.445 and Kolten producing a line of .254/.323/.375.

One key aspect of a platoon is starting the right-handed hitter against southpaws and vice versa. So let’s look at the zone profiles.

Kolten vs. Righties

Jedd vs. Lefties

Kolten is, far and away, the better hitter in this platoon in terms of average. But Gyorko’s greatest success was his power, right? So let’s look at the slugging zones.

Kolten slugging vs. Righties

Jedd slugging vs. Lefties

Although there are places where Jedd has the higher slugging percentage, Kolten has slightly lower, but similar zone ratings over a longer period of time. Even with advanced statistics, these two players are very difficult to separate.

By the eye test, Kolten seems to have the advantage in the field, but the statistics tell us that these two players are actually very similar. In addition, Jedd seems like the better hitter, but the statistics tell us that, again, they are very similar. Perhaps there is one thing that we can glean from this analysis: Kolten should be put in a place where he can reach base in front of players who drive the ball and Jedd should be placed where he can drive runners in.

To respond to the question asked at the beginning, should this platoon continue? The statistics tell us yes. As a younger player who just signed a large extension, Kolten has more upside. However, if we are to make a decision for this year, not the future, the numbers tell us that the platoon should continue because neither player has separated himself from the other.

Measuring Offensive Efficiency

Runs Created was one the first sabermetric statistics I took it upon myself to learn about.  After all, it was one of the first statistics developed by Bill James himself.  I am also pretty sure RC is the formula written on a whiteboard in Moneyball (the most influential Brad Pitt movie I have ever seen).  Anyways, Runs Created is not discussed much because there are other, more sophisticated alternatives – wRC, wRC+, etc.  I still appreciate RC because of its simplicity, and it is can still be used as an effective tool for measuring the efficiency of offensive production.

That is precisely what I set out to do.  The question I sought to answer with this study is, “which teams were the most efficient in scoring runs?”  A pretty basic question — which I decided to complicate.  Using team statistics from last year, I calculated the Runs Created for each team’s offense.  The largest separation between Runs Created and actual runs scored came from the San Diego Padres, who scored 686 times, despite “creating” only 621.38 runs.

While ranking in 19th in total runs, the Padres were actually incredibly efficient. I discovered this after trying to develop a way to measure offensive efficiency.  To do so, I created the Runs Conversion Rate (RCR).  While relatively rudimentary, this ratio between runs scored and Runs Created provides, in my mind, a good measurement for the efficiency of offenses.

Run Conversion Rate = Runs Scored / Runs Created

The purpose of this, again, is to gauge the overall efficiency of offenses.  All I really did was give a fancy name to the margin of error of Runs Created.  However, what I sought to do was use this statistic in a different way — to examine which teams made the most of what they produced (efficiency), and which did not.  Think of this article as a new way of looking at an old statistic, not me trying “discover” a new stat.  Below is a table, sorted by runs scored (i.e. from most productive offenses to least productive).  Green values represent teams in the top 10 of a category, and red the bottom 10.

2016 Run Conversion Rates
TEAM Runs Created Runs Scored Run Conversion Rate
Red Sox 905.26 878 0.970
Rockies 856.84 845 0.986
Cubs 790.93 808 1.022
Cardinals 784.92 779 0.992
Indians 770.06 777 1.009
Mariners 769.39 768 0.998
Rangers 755.83 765 1.012
Nationals 752.18 763 1.014
Blue Jays 759.72 759 0.999
D-Backs 775.15 752 0.970
Tigers 791.98 750 0.947
Orioles 768.79 744 0.968
Pirates 724.74 729 1.006
Dodgers 709.32 725 1.022
Astros 727.58 724 0.995
Angels 700.20 717 1.024
Giants 725.10 715 0.986
Twins 742.03 690 0.930
Padres 621.38 686 1.104
White Sox 713.38 686 0.962
Reds 699.02 678 0.970
Royals 685.69 675 0.984
Rays 701.08 672 0.959
Brewers 694.02 671 0.967
Mets 707.39 671 0.949
Marlins 695.80 655 0.941
Athletics 655.47 653 0.996
Braves 671.35 649 0.967
Yankees 690.17 647 0.937
Phillies 617.22 610 0.988

After looking at the table, I noted a few observations to be made: teams ranked top 10 in scoring and top 10 RCR last year were, for the most part, the best teams in the league, the two highest-scoring teams did not score as many runs as they could have, and some teams capped out their production, albeit not a high level of scoring.

First, let’s look at the teams who ranked top 10 in scoring and top 10 in RCR in 2016: the World Champion Chicago Cubs, the American League Champion Cleveland Indians, the Seattle Mariners (second in AL West), the Texas Rangers (AL West Champs), the Washington Nationals (NL East Champs), and the Toronto Blue Jays (AL Wild Card).  All these teams were both productive and efficient.  Both are key indicators of good ball clubs.  They created an equal balance of the two, and, outside of the Mariners, played postseason baseball.

While the last paragraph was basically a no-brainer, this is where the study got interesting.  The Boston Red Sox scored 878 runs last year — short of their roughly 905 “created” runs.  According to their RCR, they were only 97% efficient.  So, what does this mean? The Red Sox, while more productive than anyone else, did not hit their ceiling.  They came close (RCR of 0.970), but still only ranked in the middle third of offensive efficiency.  What if the post-Ortiz Red Sox put up around the same numbers they did last year, but became more efficient in doing so?  In my opinion, the AL East should be scared.  Other teams falling into the top 10 scoring, middle 10 RCR category are the Colorado Rockies, St. Louis Cardinals, and Arizona Diamondbacks.  The Rockies certainly receive a boost in production because they played 81 games in Coors Field.  The Cardinals and Diamondbacks, like the Red Sox, scored often, but not as often as they could have.  So maybe their problem is not a low ceiling, but rather getting away from their floor troubles them.

Our third group of relatively important teams in this study are those who ranked in the middle 10 in scoring and top 10 in RCR: the Pittsburgh Pirates, Los Angeles Dodgers, the Los Angeles Angels, and the San Diego Padres.  Essentially, these offenses were middle of the road in terms of productivity, but scored as many runs as possible given their level of production.  The Angels, ranked in the bottom 10 in Runs Created by their offense in 2016, but were second in RCR, scoring 2.4% more runs than they “created.”  The only team ahead them were the lowly San Diego Padres, who turned in 10.4% more runs.  The Dodgers, who won 91 games in a comparatively weak NL West division, were middle-of-the-road in terms of offensive production, and came in third in terms of RCR.  These teams were ruthlessly efficient, milking the most out of what their offense provided.

I do not know what qualities are common in high-RCR teams.  Maybe a high average with runners in position, a low number of runners left on base, or maybe just plain luck.  That could be the topic of an entirely different study, perhaps.

To sum things up, a high RCR was a common denominator in the teams who saw great success in 2016, and I would like to think it is useful in measuring the efficiency of teams’ offenses.  It will be exciting to see who will rise in 2017 as the most potent offense.  For me, it will be just as exciting to see who is the most efficient.


FanGraphs and were instrumental in the production of this article.  Theodore Hooper is an undergraduate student at the University of Tennessee in Knoxville.  He can be found on LinkedIn at or on Twitter at @_superhooper_

wOBA Using Exit Velocity and Launch Angle

After reading this post on predicting batted-ball type based on exit velocity and launch angle, I thought it might be neat to see how it could be applied to reducing the effects of luck and defense on wOBA.

The idea is that wOBA correctly values outcomes; however, wOBA implicitly assumes the batter has total control over the outcome of a batted-ball event based on his input to the system. I tried awarding the batter value based on the expected value of balls hit with similar launch angles and exit velocities. Instead of treating the outcomes as events, I assigned them a value based on the wOBA weights used for the season of interest.

I used the random forest classifier from the referenced post but looked at outcomes relevant to the wOBA formula. The probabilities of each outcome based on the exit velocity and launch angle of the batted-ball events are multiplied by the wOBA weights to give an expected value for the batted-ball event.

The classifier was trained on all batted-ball events from the 2015 season. The model accurately classified only 70% of the 2016 batted-ball events, so there may be a problem with over-fitting. The use of probabilities rather than plain classification should help to reduce error.

The plot below is a graphical representation of the classifier. This shows what the classifier believes to be the most likely outcome for different levels of EV and LA. When using the individual probabilities, the model is more smooth.

I compared the number of at-bats for a player to regress to their season-long mean for this value metric with the number of at-bats for the player to regress to their season-long mean for the corresponding section of their wOBA. It should be noted I only counted at-bats where the batter put the ball in play.

It looks like it is capturing some information about the batter that is lost by considering only the true outcome of the batted-ball event. It takes fewer at-bats for the error to stabilize.

Luis Severino, a Changeup, and a Little Bit of Luck

Luis Severino had a great 2015 in his two-month debut, and was supposed to take a big step forward in 2016. Luis Severino took a big step backward in 2016. He was sent back to Triple-A in May, and when he came back late in the season, he was almost exclusively used as a reliever (albeit with incredible results). The reason for his struggles is pretty clear, even on the back of his baseball card – a 1.39 HR/9 rate. So clearly, the homer problem is what we’ll be looking into today, but what if I showed you this?

Severino 1

*as SP

You’ll see that I only used his numbers as a starting pitcher; they would be skewed in his favor if I included his dominant innings as a reliever. Even so, the number of fly balls were extremely low, he wasn’t being hit very hard…in fact, he was generating a lot of infield pop-ups and a lot of soft contact in general. This is the opposite of what one would expect from a pitcher with a homer problem. It gets even weirder.

Severino 2

His issue in 2016 was homers, but in the roughly one-third of his innings as a starter in the first time against an opposing batting order, he beat the league average and his own 2015 mark, which had actually been below league average. And for good measure, in the other roughly one-third of his innings that he was a relief pitcher, he gave up zero home runs. That’s befuddling. But there is a silver lining to this; we now know where the problem is. Clearly, Severino’s problems lie in the second and third time through the opposing order. We can finally find the glaring problems there.

Severino 3
Severino’s problems can be summed up in this table. While his groundball rate was still healthy and even above league average, there was still a significant decrease from the 51.9% mark he posted in 2015 in the same circumstances. When his groundball rate dropped, his line-drive rate rose to the point that would tie Mike Fiers for the league lead (and that isn’t a good thing). So the increased line drives were part of the problem, and as the saying goes, increased line-drive rate equals higher BABIP. In Severino’s case, “higher” means .394, which is, by the nature of the statistic, absolutely unsustainable. Additionally, the 3.0 HR/9 is extremely ugly (as expected, since this is where we deduced the homer problem to be), and yet the HR/FB rate can expect positive regression as well.

Now, before we pin all of Severino’s troubles on luck, let’s remember that he is responsible for turning those ground balls he was getting in 2015 into line drives. Severino went from throwing his changeup 12.8% of the time in 2015 to only 8.9% in 2016, essentially becoming a fastball/slider only pitcher. Without an offspeed pitch or even a third offering of any sort, Severino naturally became rather predictable as the game went on. I am not sure exactly why Severino scrapped his changeup, and this Eno Sarris chart you’re about to see will only puzzle us further.

eno sarris changeup

The entire article deserves a full read for context, but by Sarris’ metric, Luis Severino had the 16th-best changeup among starters in 2015. Maybe there are some imperfections with PITCHf/x, maybe the sample size was too small, but batters only hit .222/.323/.259 against it, so the results back up the metric. That kind of effectiveness would have gone a long way for Severino in 2016. To the Yankees’ credit, the only instructions they gave Severino when he was sent back to the minors were to work on his changeup. Later in the season, after the Yankees called him back up to New York, Severino was deployed as a relief pitcher, allowing him to continue his two-pitch ways.

Clearly, there are two factors we were able to pin down that derailed Luis Severino. Some of the problem for him was terrible luck, as the .394 BABIP and 31.0% HR/FB are likely to come back down to earth. Some of the problem was self-inflicted, like the abandoning of his changeup, which at 57.9% in 2015 was a groundball machine, leading to more line drives in 2016. The recipe for success is there for Severino, both in his right arm and in the wishes of the Yankee management. Take spring-training stories for what you feel they are worth, but it sounds like Severino is working on reincorporating that 2015 changeup back into his repertoire. If it is indeed back, beginning with his first start scheduled for April 7, expect Severino to take the step forward in 2017 that he was supposed to take in 2016.

All stats via FanGraphs

The Jeff Samardzija Experiment

Jeff Samardzija is incredibly frustrating at times.  For the first few months of 2016, Giants fans saw a pitcher who would more than earn the five-year, $90-million contract he had signed in the offseason.  In April and May, Samardzija posted FIPs of 3.67 and 2.45, as well 11.9% and 19.1% K-BB rates.  Those numbers are pretty worthwhile considering Samardzija has forged himself into a workhorse, averaging over 200 IP over the past four seasons.  The Giants would be plenty happy with that for a full season.  All seemed well in Giants land.  The free agents were proving their worth, Madison Bumgarner’s greatest concern was with his own hitting (that may always be true), and Buster Posey was healthy.  The even-year sorcery seemed to be working.

June and July came around, though, and Samardzija saw himself regress into what looked like the 2015 version of himself.  In June and July Samardzija posted FIPs of 7.09 and 5.06.  Samardzija was giving up homers at an alarming pace and he was desperately struggling to strike people out.  Oddly enough, Samardzija was drastically altering his pitch mix in the middle of the year.


Holy cow.

That looks experimental more than anything else.  For Samardzija to maintain his level of performance even in his good months is pretty solid given such drastic changes in pitch mixes.

For reference, here is Samardzija’s FIP throughout the course of last year.


You can see the success I mentioned earlier before June and July came around, but Samardzija also set out on a strong end to the season, posting a 3.67 FIP in August and a 2.38 FIP in September/October to somehow bring his FIP below the league average.  That final stretch also saw Samardzija posting a 21.8 K-BB% as well, maintaining a similar walk rate he posted all season while striking out 28.6% of batters.

Staring through the bevy of pitches Samardzija featured through the season, you can see where he was getting to in the end.  He almost entirely ditched his cutter and found a balance between his four-seam and two-seam fastballs.  The curveball usage held steady, the slider usage went down, and the splitter continued to emerge as a favorite.  The splitter usage has appeared to come about as Samardzija’s neutralizer towards lefties, and it has worked well.  Lefties have given Samardzija trouble for his whole career and the near-60-point difference in wOBA versus lefties last year is fairly alarming (.331 vs .276), so an offspeed pitch that moves away from lefties is crucial.

That splitter itself is fairly similar in movement to Masahiro Tanaka’s.

Samardzija: -6.7 x, 3.9 z

Tanaka: -6.7 x, 3.3 z

Should Samardzija use the splitter versus lefties as much as Tanaka does (nearly 30%!) and locate it as Tanaka does (low and away from lefties), it should be effective, given his SwStr% with the pitch throughout his career (19.5%).

Here is Samardzija in his last tune-up before the season.

(Skip to 0:13 for the nasty nasty.)

In those final two months last year, Samardzija was able to continually do better against righties while limiting lefties to a somewhat manageable .410 SLG.  Should Samardzija maintain a similar pitch mix, he would look more like his four-win 2014 campaign.  Pitching isn’t that simple, but he’s making his way back to something that had worked quite well for him in the past.

The 2016 Giants season became all about the monstrous second-half collapse, but hidden in there was a bit of a Jeff Samardzija resurgence.  In 2017, Samardzija will almost assuredly be worth his salary in durability alone.  But if he can continue to utilize his splitter as he had toward the end of 2016, I would expect him to outperform his projections (Steamer 3.84 ERA 3.78 FIP 4.09 xFIP) and deliver a performance more in line with his 2014 season.  The Giants rotation already runs deep, but they could be looking at one of the most durable and effective groups of front-line starters in the game.

Vince Velasquez Is the Future of the Phillies, If…

On April 7th, Vince Velasquez pitches the Phillies’ home opener. His electric talent makes him a linchpin for the team’s plan to return to contention, and his four-seam fastball could be the key. It’s his best pitch, and it had the 12th-best weighted value among pitchers who threw at least 130 innings last year. MLB’s 5 Statcast Storylines for the team features him and the 27.4% swinging-strike rate he got on it, tops in the league.

And oddly, even more than his dubious health, it could be his biggest obstacle to stardom.

Corinne Landrey at Crashburn Alley found that Velasquez was in the top 15 for overall fastball usage last year, and top three in two-strike counts. Immediately, we could reason he threw it too much, even when acknowledging the rankings above. But it’s worth noting how, exactly, it looked.

380 of his 428 two-strike fastballs were four-seamers. They accounted for 60% of his two-strike pitches. It’s not just that he threw a ton of heat when hitters had their backs against the wall. It’s that he didn’t use his secondary offerings to keep hitters honest.


The top 10 qualified pitchers by K/9 last year  —  and some of the best pitchers in the game  —  present various paths that can be taken with two strikes. The range between their most used and second-most used pitches in those counts goes from 1.1% to 27.3%. The range for Velasquez screams from the page: 43.7%.

It might be easiest to think of this like kids on a seesaw. His four-seamer was like a particularly stout kid (maybe Billy Butler) and his curveball was like a particularly scrawny kid (say, Jose Altuve). The way he used these pitches in two-strike counts didn’t lend itself to a fluid, balanced approach during the most advantageous situations.

The problems Velasquez’s fastball created were subtle because overall it was so good. Guys weren’t driving it out of the park or putting up crooked numbers against it, but they were letting him wear himself out. While he got a whiff nearly 26% of the time in a two-strike count when using his four-seamer, there was also better than a 2:1 chance the at-bat would continue because it was either fouled off or called a ball. The foul balls were a major reason he worked a ton of deep counts last year, and what made making it through even six innings a coin flip all year.

There’s a chance that could be due to where he was locating his heat, too.

A look at his heat maps shows Velasquez hammered the zone with his four-seamer when behind (left). When ahead, as he would be with two strikes, he threw it higher (right). That’s generally good when thinking about sequencing, changing the eye level of hitters, and possibly the concept of having a pitch to spare.

But Velasquez’s fastball is a riser  —  it averaged 9.75 inches of positive vertical movement last year, or about a full inch more than the league average. While the maps of different counts will show slightly different locations, the big picture suggests his four-seamer could have been easier to take when higher in the zone because hitters and umps alike perceived it was already up.

Landrey also found that Velasquez was beginning to favor his changeup toward the end of the year as a lead secondary offering. While that’s positive, it’s bizarre that it took so long to show up in the majors since it was lauded through the minor leagues. Right now, the opposition knows he’s dynamic but can be worn down and sent out before the sixth inning. As he matures, he could become a force they genuinely dread.