Archive for Statcast

I spend a lot of time bothering Connor Kurcon. He’s a smart dude with a certain intuition about baseball and a certain ability to apply that intuition to produce tangible results that invariably reflect his hypotheses. He devised Predictive Classified Run Average (pCRA), an ERA estimator that outperforms the big three (FIP, xFIP, and SIERA). He also created a dynamic hard-hit rate which, to me, was astoundingly clever and a superior accomplishment to pCRA (although maybe he disagrees).

Anyway, like I said, I bother him a lot, he tolerates me, we bounce ideas off each other. The journey starts there, with my incessant annoyance of him, but also it starts here, with this Tom Tango axiom: exit velocity (EV) is the primary predictive element of hitter performance (as measured by weighted on-base average on contact, aka wOBAcon) — significantly more so than launch angle (LA). Some of the inner machinations of Tango’s mind:

That's not that interesting. What I was more interested is its relationship to NEXT YEAR's wOBA.

Not surprisingly, the performance of the 5% hardest batted balls is what indicates talent in the following year, and by a huge amount. If you've been following my barrels thread.. pic.twitter.com/hvLWr0rCSr

— Tangotiger 🍁 (@tangotiger) February 27, 2020

I won’t speak for Kurcon, but I think this finding helped guide his work on the dynamic hard-hit rate. I also think it inspired his foray into replicating this effort for pitchers or, at the very least, his attempts to determine the most predictive element of pitcher performance. Which leads us to this tweet that (spoiler alert) is actually not stupid at all:

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I semi-recently had the honor of presenting at PitcherList’s PitchCon online conference, which raised a good chunk of money for Feeding America. My presentation, “ERA Estimators: Past, Present, and Future,” discussed, well, exactly what it sounds like. Over three posts, I will recap and elaborate upon various talking points from the presentation.

However, if this does happen to be your first exposure to ERA estimators or you are familiar with them but don’t necessarily understand their innards, then I hope you find this launching-off point beneficial.

ERA Estimators, Part I: Past

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In rounding out my series on the most important factors influencing components of BABIP, I will be looking into what most affects a hitter’s batting average on ground balls. So far, the results of these analyses have been consistent. Launch angle has been the main driver of BABIP for pitchers, and it has been for hitters as well, at least when they are launching flyballs. The story is a different one, though, when hitters put the ball on the ground. Ground ball launch angle was not a significant factor in determining a hitter’s ground ball batting average, and neither was ground ball exit velocity.

Whether or not a hitter pulls grounders has much to say about whether that player will hit for average on grounders. There is a negative relationship between these variables that is significant at p < .0001 and with a Pearson’s r of .27. Even more important is how fast the hitter is. The Pearson’s r for the positive correlation between average sprint speed and ground ball batting average was .45.
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In a pair of recent columns, I looked into what factors have impacted flyball BABIP (or FB BABIP) and ground ball batting average for pitchers, and those analyses were linked by a common finding. Whether pitchers are allowing balls that are in play in the air or on the ground, the launch angles of those batted balls go a long way towards explaining whether they become base hits. Now I am turning my attention to flyball BABIP for hitters, and the trend continues. While flyball pull rate, average flyball distance and average exit velocity, both on flyballs and line drives combined and on flyballs alone, did not have significant relationships with FB BABIP for hitters, average flyball launch angle (FB LA) turned out to be a statistically significant factor yet again (p < .0006, r = .19)
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In the second installment in my series on the factors impacting components of BABIP, I move on from flyball BABIP for pitchers to ground ball batting average for pitchers. This analysis produced one result that really surprised me: whether or not a pitcher has a tendency to allowed pulled grounders does not have much of an impact on the ground ball batting average they allow. I didn’t anticipate this, because hitters put up a collective .180 batting average on pulled grounders in 2019, but a .306 average on all other grounders. For pitchers who allowed at least 225 grounders in seasons between 2015 and 2019 (n=286), the negative relationship between pull rate and ground ball batting average allowed (GB Avg) was significant at p < .05, but with just an .012 Pearson’s r.
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A little more than a year ago, Al Melchior had the brilliant and beautifully straightforward idea of investigating how strongly pretty much ever Statcast metric correlated with various traditional power metrics and compiling them in one post. He asked me to help out, which I was more than glad to do.

Recently, I saw folks talking about this again, and someone asked specifically about the 2019 season. I figured I could refresh the values from the original post quickly enough (certainly a lot more quickly than I did last time), and it would also help bring pertinent information to the fore for folks neck-deep in draft prep.

Spoiler alert: the results barely changed. But! I do feel more confident in this particular set of values, as I nerded out with programming instead of pulling dozens of different queries from the Baseball Savant search function and constantly getting frazzled.

OK, here’s the goods. For 2019 hitters:

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Expected weighted on-base average (xwOBA) is one of Statcast’s most important additions to the Sabermetric sphere. It’s a simple premise — estimate a hitter’s deserved production based, simply, on his combinations of exit velocity (EV) and launch angle (LA) — with robust implications and applications. It’s remarkable how powerful the metric is with just two inputs.

However, the metric is not without its faults (or complaints from those who use it). Its simplicity is beautiful but inherently and knowingly lacking, accounting minimally or not at all for:

1. spray (lateral) angle (touched upon here),
2. a player’s foot speed (discussed more thoroughly here),
3. park factors, and
4. opposing defense.

None of this necessarily serves as an indictment of xwOBA. The number of inputs you include affects the purpose you want it to serve. That is, do you want it to be descriptive or predictive? How about both? Maybe defense shouldn’t be included, then, if we can’t reasonably expect a hitter to face the same caliber of defense each year, something that is out of his control.

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Using a format similar to the one Paul Sporer recently posted for 2020 Roto player rankings, below is the 2020 ranking of the Top 75 Outfielders for Ottoneu fantasy baseball.  Ottoneu leagues are auction style, but with no salaries listed (league dependent), think of these lists as simplified “snake draft” rankings (“which player would I take before the next”), or a value ranking of players above replacement level for 2020. Players with multi-position eligibility may receive a slight bump in value (2020 positions listed).  You can reference average Ottoneu player salaries here, but keep in mind these salaries fluctuate throughout the winter as rosters shape up towards the January 31st keeper deadline for all leagues.

Previous 2020 Ottoneu rankings:

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Last year, I took a stab at developing what might be loosely defined as park factors using Statcast data. (I called them park “impacts” because they lacked the requisite rigor to be true factors, although it’s all semantics, truly.) I sought to use Statcast’s expected wOBA (xwOBA) metric, specifically on batted ball events (BBEs), such that we would have a measure of xwOBA on contact (or xwOBAcon). This metric accounts for exit velocity (EV), launch angle (LA), and little else — which makes it perfect for this purpose.

The difference between actual and expected wOBA on contact indicates the amount of luck, whether good or bad, a hitter might have incurred on a particular batted ball event. In other words, given ‘X’ exit velocity and ‘Y’ launch angle, what is the most common wOBA outcome, and how much did the actual wOBA outcome differ from it?

The beautiful part about xwOBAcon is it strips away all other context. It removes elements that confound other park factor calculations, such as hitter and pitcher quality or even sequencing (vis-à-vis run-scoring). Except for fielding. Can’t control for fielding, unfortunately.

With this approach, we have the exit velocity. We have the launch angle. We have historical results for that particular combination of EV and LA to use as a benchmark. And then we compare.

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The hot stove season is a time of discovery. In responding to the news of trades and signings, and in doing research to prepare our annual preseason rankings, we learn things that we missed during the regular season.

After the Rays traded Tommy Pham to the Padres earlier this month, I wrote about how Pham mysteriously started hitting grounders with less authority one-third of the way into the season. (Also, thank you to commenters randplaty and zwibi, who pointed out that Pham was playing through hand and elbow injuries over several weeks late in the season.) Of 130 hitters who saw at least 1,500 pitches and hit at least 100 ground balls in both 2018 and 2019, Pham experienced the sixth-largest year-to-year decline in average exit velocity on grounders (EV GB), and that was even with his decline not beginning until two months into the season.
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