If you’ve spent any time observing some of the nerdier battles over baseball statistics in the last decade or two, you’re probably familiar with the arguments made for and against certain metrics. Beginning with the relatively simple matter of batting average versus on-base percentage, these debates tend generally to take the same shape. And generally, one recurring blind spot of such debates is that they tend to dwell on what certain statistics don’t do instead of best identifying what they do do.*

*Author’s note: /Nailed It

The last few years has seen the release, by MLB Advanced Media (MLBAM), of a flurry of new data and statistics, generally referred to as “Statcast data.” We’ve also seen advances in the measurement of catcher-framing by the people at Baseball Prospectus, who have also continued making improvements in the evaluation of pitchers in the form of Deserved Run Average (DRA). When new data and metrics emerge, there is inevitably a period of uncertainty that follows. What does this stat mean? What’s the best way to use this data set? Equally inevitable is the misapplication of new statistics. That aspect of potential statistical innovation is not really new.

Today, what is new is xwOBA — and, in part due to the wide proliferation of Statcast data by means of telecasts and MLB itself, more fans are finding and using stats like xwOBA than might have been in previous generations. As with other new metrics, we are still attempting to identify how xwOBA might best be used.

One such study into the potential utility of xwOBA was recently published by Jonathan Judge at Baseball Prospectus. The study is a good one, with Judge focusing on xwOBA against pitchers. While not ultimately his point, Judge does, along the way, object to the “x” in xwOBA, as he feels that “expected” implies predictive power. While I have always interpreted the “expected” to mean “what might have been expected to happen given neutral park and defense” — that is, without assuming a predictive measure — it does appear that reasonable people can disagree on that interpretation.

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At a basic level, nearly every piece at FanGraphs represents an attempt to answer a question. What is the value of an opt-out in a contract? Why do the Brewers continue to fare so poorly in the projected standings? How do people behave in the eighth inning of a spring-training game? Those were the questions asked, either explicitly or implicitly, by Jeff Sullivan, Jay Jaffe, and Meg Rowley just yesterday.

This piece also begins with question — probably one that has occurred to a number of readers. It concerns how we evaluate pitchers and how best to evaluate pitchers. I’ll present the question momentarily. First, a bit of background.

Fielding Independent Pitching, or FIP, is a well-known tool for estimating ERA. FIP attempts to isolate a pitcher’s contribution to run-prevention. It also serves as a better predictor of future ERA than ERA itself. The formula for FIP is elegant, including just three variables: strikeouts, walks, and homers. It does not include balls in play. That said, one would be mistaken for assuming that FIP excludes any kind of measurement for what happens when the bat hits the ball. Let this be a gentle reminder that home runs both (a) are a type of batted ball and (b) represent a major component of FIP. There is, in other words, some consideration of contact quality in FIP.

Expected wOBA, or xwOBA, is a newer metric, the product of Statcast data. xwOBA is calculated with run-value estimates derived from exit velocity and launch angle. Basically, xwOBA calculates the average run value of every batted ball for a hitter (or allowed by a pitcher), adds in the defense-independent numbers, and arrives as a wOBA-like figure. The advantage of xwOBA is that it removes the variance of batted-ball results and uses a “Platonic” value instead.

The introduction of Statcast’s batted-ball data is exciting and seems like it might help to better isolate a pitcher’s contributions. But does it? This is where I was compelled to ask my own, relatively simple question — namely, is xwOBA better for assessing pitcher performance than the more traditional FIP? What I found, however, is that the answer isn’t so simple.

The differences between FIP and xwOBA, as well as the similarities, deserve some exploration.

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Given the relative novelty of Statcast data, it remains unclear for the moment just how useful the information produced by it can and will be. As with any new metric or collection of metrics, it’s necessary to establish baselines for success. How good is an average exit velocity of 90 mph? What does a 10-degree launch angle mean for a hitter? How does sprint speed translate to stolen bases or defensive ability?

In an effort to begin answering such questions, the Statcast team has rolled out a few different metrics over the past few years that attempt to translate some of the raw material into more familiar terms. Hit probability uses launch angle and exit velocity to determine the likelihood that a batted ball will drop safely. Another metric, xwOBA, takes that idea a step further, using batted-ball data to estimate what a player should be hitting.

Another example is Outs Above Average. In the case of OAA, the Statcast team has accounted for all the balls that are hit into the outfield, determined how often catches are made based on a fielder’s distance from the ball, and then distilled those numbers down to find what an average outfielder would do. The final result: a single number above or below average.

At Reddit, mysterious user 903124 has published research showing that the year-to-year reliability for Outs Above Average has been considerably higher than the Range component for UZR. The user was kind enough (or foolish enough) to create a Twitter account reproduce some graphs of his results, which are shown below. There is a subsequent tweet in the thread that shows left field.

Recalculate correlation of RngR and OAA with per 1000 inning data and for each outfield position (min. 400 innings). OAA still clearly has a lower year to year variance. @mitchellichtman @tangotiger pic.twitter.com/MZ4xB6YICV

— 903124 (@903124S) January 3, 2018

 

 

For those who can’t see the charts and would prefer not to open up a new window, what you’d see here is that, for the group selected, the r-squared is much higher for Outs Above Average than for the Range component for UZR. If Statcast could produce something that is much more reliable and much more accurate than the Range component of UZR, that would be a pretty significant breakthrough and win for Statcast, potentially improving the way WAR is calculated and providing a better measure of a player’s talent and results on the field.

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Over in the American League, there’s a clear two-horse race between Chris Sale and Corey Kluber for the Cy Young Award. Both are head and shoulders above the rest of the league and both have very strong cases for the honor, depending on what metrics you prefer.

Over in the National League, that isn’t quite the case. Max Scherzer is the clear front-runner at this point, with a host of other pitchers behind him all trying to make an argument why they might have had better seasons. Clayton Kershaw has a lower ERA. Zack Greinke pitches in a much tougher park. Teammate Stephen Strasburg has a lower FIP.

Those are just the stats that measure outcomes, though. Let’s see what Statcast has to say about the sort of contact the other candidates are allowing to see if anybody has a real case against Scherzer.

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Last week, I took a look at Statcast data from 2016 and 2017 and attempted to find contact-management skills among pitchers. The basic conclusion of that study? Pitchers might well have skills to manage contact once the ball hits the bat; if they do, however, neither xwOBA nor Statcast classifications seem to reveal it. Quality of contact didn’t hold up from year to year — i.e. last year’s results on contact aren’t likely to inform much of this year’s results on contact.

In the comments section, however, one reader wondered if in-season results might create a different result. That’s what I’d like to examine in this post. Here we go.

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While there are certain events (like strikeouts, walks, and home runs) over which a pitcher exerts more or less direct control, it seems pretty clear at this point that there are some pitchers who are better at managing contact than others. It’s also also seems clear that, if a pitcher can’t manage contact at all, he’s unlikely to reach or stay in the big leagues for any length of time.

Consider: since the conclusion of World War II, about 750 pitchers have recorded at least 1,000 innings; of those 750 or so, all but nine of them have conceded a batting average on balls in play (BABIP) of .310 or less. Even that group of nine is pretty concentrated, the middle two-thirds separated by .029 BABIP. The difference between the guy ranked 125 out of 751 and the guy ranked 625 out of 751 is just three hits out of 100 balls in play. Those three hits can add up over a long period of time, of course, but it still represents a rather small difference even between players with lengthy careers. For that reason, attempting to discern batted-ball skills among pitchers with just a few seasons of data is difficult. Thanks to the emergence of Statcast, however, we have some better tools than just plain BABIP to evaluate a pitcher’s ability to manage contact. Let’s take a look at what the more granular batted-ball data reveals.

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Over at Baseball Savant, another Statcast leaderboard has been rolled out. This one relates to speed. They are calling it Sprint Speed, and the definition is as follows:

Sprint Speed is Statcast’s foot speed metric, defined as “feet per second in a player’s fastest one-second window.” The Major League average on a “max effort” play is 27 ft/sec, and the max effort range is roughly from 23 ft/sec (poor) to 30 ft/sec (elite). A player must have at least 10 max effort runs to qualify for this leaderboard.

While Sprint Speed has been used for a while, we didn’t have leaderboards until now. Mike Petriello over at MLB.com has a full article on the rollout which I would recommend. Among the highlights: Sprint Speed correlates well from year to year; it doesn’t require a large sample to become reflective of true talent (Petriello compares speed to fastball velocity); and it might be useful when attempting to identify injuries that could be slowing players down.

So, we know that the metric can tell us who is fast and who is not. That’s helpful. I wondered if it might also be able tell us anything about any other statistics.

Before trying to predict the future or look at past years, I thought it might be useful to compare speed to the stats we have and see how they compare. While the leaderboard over at Statcast features nearly 350 names (every player who’s produced 10 or more max-speed data points), those sample sizes might be a bit too small when looking at other statistics. As a result, I narrowed the sample for this study down to the 166 players who were qualified at the end of Monday’s games.

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On Tuesday, I discussed MLB’s expected wOBA (or xwOBA) metric and one of its problems — namely, that guys with great speed might have the ability to outperform their xwOBA on a regular basis. I also pointed out that, despite this drawback, xwOBA should have considerable utility. This post looks at one potential aspect of that utility when it comes to projecting future performance when we have only completed just a small portion of the season.

Comparing wxOBA and wOBA for individual players over the course of a season, one find a pretty strong relationship — a point which I establish in that Tuesday post. To take things a step further, I’d like to look here at the relationships of these stats over the course of a couple seasons and see how they correlate from year to year. In order to establish a baseline, let’s look at how players with at least 400 at-bats in both 2015 and 2016 fared by wOBA.

So we see a decent relationship between wOBA marks in consecutive season. It certainly would be strange if there weren’t some relationship between a player’s offensive statistics from year to year, as players generally don’t get a lot better or a lot worse in such a short span of time — even if the players who do meet those criteria make for more interesting stories and analysis. So we see that, from 2015 to 2016, there is a relationship with wOBA. What about xwOBA?

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One of the more fascinating rollouts from Baseball Savant this season has been xwOBA, a metric that utilizes launch angle and exit velocity to assign a hit value (single, double, triple, home run, or out) to every batted ball and then translates the results to “expected” wOBA. Why does it matter? By stripping out the influence of luck and defense, it gets closer to something like a “deserved” hitting number.

Here’s what the glossary at MLB.com says about the metric:

xwOBA is more indicative of a player’s skill than regular wOBA, as xwOBA removes defense from the equation. Hitters, and likewise pitchers, are able to influence exit velocity and launch angle but have no control over what happens to a batted ball once it is put into play.

For instance, Tigers first baseman Miguel Cabrera produced a .399 wOBA in 2016. But based on the quality of his contact, his xwOBA was .459.

For the most part, those claims make sense. But that’s not to say xwOBA can’t be beaten. To understand how, let’s look a little bit at how wOBA compares to xwOBA. Let’s begin by looking at all players from last season who recorded at least 400 at bats and compared their wOBAs to their xwOBAs. The scatter plot looks like this.

There’s a pretty strong relationship there. Of the 183 players represented above, 150 had a disparity between wOBA and xwOBA under 30 points. That seems pretty conclusive.

So what are we to do with this data? We could look at the outliers on either end, presume that they were either unlucky or lucky when it came to batted balls, and then move on with the analysis. However, before we do that, we might want to look at other reasons for the potential disparity. To that end, I did an eye test of sorts. I took all players with at least 400 at bats in both 2015 and 2016 and looked at their xwOBA minus wOBA in both seasons. If a player had a negative number, he might be considered to have had some good luck. If the numbers were positive, he might have had some bad luck.

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An increase in publicly available data can often help our understanding of the sport. The rollout of Statcast data has been fascinating. Learning how hard Giancarlo Stanton hits a ball, how fast baserunners and fielders move to steal bases and make catches, and how hard outfielders and catchers throw the ball is all very interesting information. Up to this point, it can be tough to determine if the information is useful or if it is more akin to trivia knowledge, like batting average on Wednesdays or pitcher wins. An examination of the batted ball velocity against pitchers provides some hope of providing potentially important information, but until we have more data — and more accurate data — conclusions will be difficult.

Looking at the top of the leaderboard in exit velocity, it is easy to see why linking a low exit velocity with good performance is enticing. I looked at all pitchers with at least 150 batted balls in the first half and 100 batted balls in the second half. Here are the top-five pitchers in batted-ball exit velocity this season, per Baseball Savant, along with their ERA and FIP.

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