Archive for expected

Hi! Disclaimer: In this post I use raw Statcast data to calculate expected batting average (xBA). Evidently the raw data do not include the sprint speed adjustment that the Statcast folks said they made. That adjustment only shows up on player pages and in the search. This explains why it seemed to me an adjustment had not been made! The xBA values on player pages are much closer than the raw values and look similar to what I have presented below, and it explains my confusion herein regarding the matter.

Thanks for reading!

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It has always seemed rather obvious to me that Statcast’s expected batting average (xBA) failed to properly account for a hitter’s speed (“sprint speed”). It seemed like fast hitters routinely outperformed their xBAs while slower hitters underperformed. In looking at a Statcast-era leaderboard (2015-21) of differentials between actual and expected batting averages on ground balls, obvious names rise to the top: Delino DeShields, Dee Strange-Gordon, Eduardo Núñez, Billy Hamilton, Jose Altuve, Jonathan Villar, Norichika Aoki, Mallex Smith, Jean Segura, Adam Eaton, Starling Marte… the list of players who have historically outperformed their xBAs by the widest margins are (were) all elite speedsters. At the other end of the spectrum, post-prime sluggers: Justin Smoak, Chris Davis, Logan Morrison, Jay Bruce, Kendrys Morales, etc. etc.

I thought this exact phenomenon, which is not a revelation by now, had once nudged the Statcast team to apply a sprint speed adjustment to xBA. Apparently, this happened sometime between the 2018 and 2019 seasons. Here’s the original snippet, which I very lightly edited for clarity:

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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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A couple of weeks ago, I devised a deserved barrel rate (where barrel rate is calculated as a percentage of batted ball events, or Barrel/BBE) based exclusively on a hitter’s average exit velocity (EV) and average launch angle (LA). To employ such a simple model, I made a broad but accurate assumption: the average hitter’s average EV (or LA) has a distribution of EVs (or LAs) centered around it, and this distribution does not differ dramatically from other hitters’ distributions.

In layman’s terms, the typical hitter’s average launch angle is his — he owns it, and it reflects his swing plane and mechanics — but he is no better than any other typical hitter in repeating his average launch angle. He, like everyone else, will likely vary from the mean by a certain margin of error. I make the same assumption of exit velocity as well. The two variables bear almost zero correlation to each other. In light of this assumption, the best thing a hitter can do is maximize his exit velocity and hopes it coincides with an optimal launch angle.

(Some folks have suggested I include the percentage of balls hit 95+ mph to refine deserved barrels. The notion intrigues me. However, to illustrate a point: if you have two hitters with identical average EVs, would you expect their distribution of EVs to be dramatically different? Probably not. The inclusion of hard-hit rate accepts as fact that one hitter might be better at hitting 95+ mph more frequently — which would also suggest he hits more softly more frequently as well, and with certainty. This doesn’t stand out to me as a repeatable, let alone necessarily desirable, trait.)

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Yesterday, I devised a new expected strikeout rate for pitchers and used it to identify qualified starting pitchers who over- or under-performed in 2018. I’m reluctant to make out the exercise to be more than it is. I simply wanted to take the most intuitive approach to describing a pitcher’s strikeout rate (K%): by using the plate discipline exhibited by opposing hitters. Today, I seek to do the same for hitters. I can tell you now the discussion will be much more qualitative than quantitative.

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Playing time can make or break a baseball player’s fantasy value. An elite player may not finish above replacement level if he suffers an injury and plays only half the season, and a lackluster player could finish above replacement level simply by playing every single day. This is all intuitive, and the fantasy community generally approaches these kinds of things rationally. In other words, most players are appropriately valued, outside of the market inefficiencies that inevitably warp player values.

One-dimensional speedsters — dudes who steal a bunch of bases and do little else — are much harder to peg. Their value is tied up primarily in one category, as stolen bases (SBs) do not directly correlate with other categories the way home runs would with runs and RBI, for example. The issue becomes all the more confounding when one considers the contemporaneous scarcity of SBs relative to home runs. There’s more to value than just SBs and plate appearances (PAs), but the fact of the matter is the two statistics by themselves correlate very strongly with a player’s end-of-season (EOS) value (which, here, are informed by Razzball’s Player Rater).

In the last five years, baseball has seen 75 player-seasons of 30-plus SBs — 15 steals a year on average, a trend that didn’t fundamentally change in 2016 (although that doesn’t mean SBs aren’t scarce). A simple linear regression of SBs and PAs, the latter of which serves as a proxy for other counting stats such as runs and RBI, against EOS value produces a remarkable 0.71 adjusted R²:

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I committed Matt Holliday to my disabled list Monday, marking the 14th(!!!!) DL move I’ve made for my primary team this season. Perhaps the state of my team is implied by the length of its disabled list. If not, I’ll make it clear: my team has been bad. Pretty darn bad.

All of my drafts were especially poor. I drafted the same terrible, injured, underachieving players in every league, so it has been generally a nightmare all around. The hole I dug for myself is deep. Kyle Lohse broke ground on said hole with an 8-run Opening Day outing that lasted all of 3-1/3 innings, and we never looked back. Woe is me. Alas, it’s barely the second week of June, and I have already resorted to my Hail Mary play: buy low on everyone in sight.

Calling it “buying low,” however, is a bit misleading. It’s a shallow league, so there is arguably a stronger incentive for owners to cut bait on underachieving name-brand players in order to ride the hot streaks of unknown quantities, given they crop up more abundantly. What I’m actually doing, then, is loading up on underachievers from waivers. My team is already underachieving. These guys are already underachieving. How much worse could it get?

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