Archive for Meta Analysis

Every year, I pit my Pod Projections against the Steamer projections in various categories. Today I’m going to continue the annual smackdowns by calculating AB/HR rates and then extrapolating them over 600 at-bats. At that point, I’ll compare how many home runs each system is forecasting, given a 600 at-bat projection. I’ll start by sharing the names of hitters Pod is projecting for significantly more home runs than Steamer. Many of these players figure to be part-timers, so consider them sleepers in deeper leagues.

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Yesterday, I used my pitcher xK% equation to highlight four starting pitchers whose underlying skills suggested a dramatically higher strikeout rate than actually posted, hinting at upside this year if the pitcher could maintain those skills. Today, let’s talk about the starting pitchers who most outperformed their xK% marks, heightening the risk of a decline this season, absent an improvement in underlying skills.

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Three years ago, I shared an updated version of my pitcher xK% metric, which correlated strongly with actual strikeout rate, given its 0.931 R-squared. While some of my other xMetrics I calculate and then use them to serve as a historical guide to assist in my Pod Projection forecasts, I actually project the underlying components of xK% myself and the vast majority of the time, keep the projected strikeout rate those components spit out. There are instances where I do change the forecast though, as some pitchers have a history of outperforming or underperforming their xK% marks, for whatever reason. Anyhow, let’s discuss four starting pitchers who posted xK% marks above their actual marks, hinting at some upside this year.

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Last week, I used my hitter xBABIP equation to identify and discuss 8 hitters who could enjoy significant BABIP spikes this season, if they maintained the underlying skills driving those marks. Today, I’ll talk about the other side of the coin, those hitters whose xBABIP marks suggests serious downside this season, unless they improve their underlying skills.

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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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Two years ago, I introduced you to the latest iteration of my hitter xBABIP equation, this time incorporating the effects of defensive shifts. Let’s find out which fantasy relevant hitters most underperformed their xBABIP marks in 2019, suggesting the potential for dramatic upside in 2020…if the hitter maintains those underlying skills.

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I’m like everyone else out there, grinding away from every little nugget to gain an edge. The following players I’ve heard something intriguing about during various podcasts or discussion. While I should trust the analysts, I don’t. I prefer to verify what they said and see if I’ll need to adjust my projections.

The Rockies Garrett Hampson reworked his swing in the second half of last season.

Hampson was a late-round darling in the previous draft season for those hoping to get some late steals. He did nothing until September when he went off with a .903 OPS and nine steals. Several people mentioned a swing change and a quick search later, I found out about the toe tap.

“I was thinking of way too much mechanically in the box this year,” Hampson said. “What (the toe tap) has allowed me to do is just be way more in rhythm with the pitcher and (get) started, and my hands and everything else are natural from there. I don’t think about what my hands are doing. There’s a lot of things that weren’t synching up with my leg kick, and now they seem to synch up more naturally.”
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Beginning with that Aug. 25 game, Hampson has hit .344 (21-for-61) with a .397 on-base percentage and a .905 OPS in 22 games (16 starts). His 19 percent strikeout rate during this span is a marked improvement from earlier. And Hampson has two homers, six RBI, 10 runs scored – he scored 23 runs in his previous 74 games – and six stolen bases without being caught during this stretch. Indeed, with 11 stolen bases in 14 attempts, Hampson is the sixth Rockies rookie with at least 10 steals in a single season and the first since Eric Young Jr., in 2010.

So what did the top tap change in his profile Here is his season divided up into three sections with his demotion on May 12th is the first division and the August 25th game mentioned in the article being the second. Try to find a smoking gun.

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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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Today, I move on from xHR/FB rate and its components to my xBABIP equation. Every year, there are some seemingly obvious candidate to regress (like the guy who just BABIP’d .390) or improve (like the guy who just BABIP’d .210). But what about the guys hovering within range of the league average of .298? Just because their BABIP marks were close to average doesn’t mean their skills supported such a mark. So let’s discuss hitters who posted a seemingly sustainable BABIP, but xBABIP suggests the mark was either way over their head or actually below what their skills suggested.

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Yesterday, I used my xHR/FB rate equation to identify and discuss 7 hitters who could enjoy a HR/FB rate spike in 2020, assuming they maintain the component skills they displayed in 2019. Now let’s turn to the other side, those hitters who dramatically outperformed their xHR/FB rates and could be in for regression this year, unless they improve their underlying skills, or paid off Lady Luck once again to ensure fortune is on their side. As you might expect given the home run explosion, there were more overperformers than underperformers, so I had to cut it off at 6% over xHR/FB rate.

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