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The absence of Mike Trout, however unfortunate, has the possible effect of opening up the field for the American League’s MVP award. It’s also possible we’ll see a position player other than Trout lead the league in wins above replacement for the first time since 2011, when Jacoby Ellsbury posted a 9.4-win season. Trout still leads the league this season (3.3). Given that he could miss two months of time to injury, however, he could be hard pressed to finish ahead of his peers for a sixth consecutive campaign.

So that brings us to the No. 2 player on the American League leaderboard, Aaron Judge.

I received some questions about MVP odds during my Monday chat and Judge’s name came up. As impressive as Judge has been this season, including a 510-foot batting practice shot in Toronto…

… I sense there are questions, concerns about the league’s ability to punch back when it accumulates more scouting material against an inexperienced hitter who possesses both a long swing and unusual baseball body. Judge doesn’t yet have the same type of exposure to major-league pitching and defenses that other MVP candidates have. When pitchers began to refine their approach against him, will Judge be able to counter punch?

There’s already some evidence that a considerable slow down is imminent: Judge’s strikeout rate has been inching up.

While Judge deserves credit for his offseason work — including a swing adjustment that has resulted in dramatically improved bat-to-ball skills and allowed his raw power to translate into games — the forecasts call for merely a good player, not a great one. Our Depth Chart projections, which are a combination of Steamer and ZiPS with curated playing-time estimates, have Judge slashing .253/.338/.500 for the rest of the season, with 22 more homers and 2.2 wins.

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Devised originally in response to a challenge issued by sabermetric forefather Rob Neyer, and expanded at the request of nobody, NERD scores represent an attempt to summarize in one number (and on a scale of 0-10) the likely aesthetic appeal or watchability, for the learned fan, of a player or team or game.

How are they calculated? Haphazardly, is how. An explanation of the components and formulae which produce these NERD scores is available here. All objections to the numbers here are probably justified, on account of how this entire endeavor is absurd.

Most Highly Rated Game
Washington at Los Angeles NL | 15:10 ET
Strasburg (74.1 IP, 79 xFIP-) vs. Kershaw (83.0 IP, 65 xFIP-)
Once again, the author’s haphazardly constructed algorithm confirms what most sentient beings could have determined for themselves — namely, in this case, that a game featuring two of the sport’s top pitchers might possesses some interest for viewers. The projections suggest that, at this point, Stephen Strasburg is likely to record the highest WAR of his career, whether one calculates that figure with FIP or runs allowed. He’s excelled by either version. As for Clayton Kershaw, he isn’t expected to post a career year and yet he remains one of the majors’ best pitchers.

Readers’ Preferred Broadcast: Los Angeles NL Television.

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I read a FiveThirtyEight article not too long ago from Michael Salfino, and it had the following headline:

Who Needs A DH? The NL Is Outhitting The AL, Somehow

Now, I’d never actually thought before to just look at league-by-league OPS. And, since that article was published, the point around which it was built has become untrue. Nevertheless, by OPS, the leagues are close. Courtesy of Baseball Reference, here’s how the leagues have compared over more than the past century:

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Episode 747
Lead prospect analyst Eric Longenhagen is the guest on this edition of the pod. Since his most recent appearance on the program, he’s been many places, if not technically everywhere, including Cincinnati and Louisville and Cincinnati again and Reading and Lehigh Valley and Fayetteville and so on. Discussed on the pod: Phillies prospects Dylan Cozens, Rhys Hoskins, and Cam Perkins; other Phillies prospects Scott Kingery and Andrew Pullin; likely first-rounders Adam Haseley (of Virginia) and Brendan McKay (of Louisville); plus no fewer than one (1) player from the NCAA Regional in Fayetteville.

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Don’t hesitate to direct pod-related correspondence to @cistulli on Twitter.

You can subscribe to the podcast via iTunes or other feeder things.

Audio after the jump. (Approximately 1 hr 8 min play time.)

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Not very long ago, Sam Dyson was a revelation. He was acquired quietly, but deliberately, and he played a major role in turning around what had been an unstable 2015 Rangers bullpen. Down the stretch in 2015, and then again throughout the year in 2016, Dyson pitched like one of the more valuable relievers around, providing the Rangers the luxury of riding his sinker to one- and two-run victorious margins. When one would try to explain the Rangers’ success, you’d have to talk about the relievers, and you couldn’t talk about all of them without talking about one of them in particular.

Not very long ago, Sam Dyson was designated for assignment. The Rangers ran out of patience, and although Dyson’s going to get another opportunity, it won’t be with Texas. The team won’t be getting much back. By WPA, already, Dyson has been worth what he was a season ago, only this time with a minus sign in front of it. At -3.45, Dyson owns the lowest WPA in the game. He’s been worse even than Francisco Rodriguez. WPA usually is not a very good analytical tool. It doesn’t always reflect the true totality of a player’s worth. Yet it’s sure had Dyson figured out.

The weird thing is how little has changed. I know that Dyson’s going to be moved any minute now, but the industry doesn’t know all that much more than we do. When it comes to trying to see Sam Dyson’s future, it’s simply a whole lot of guesswork.

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Much of the focus in this still young season has been on higher launch angles, the three true outcomes, and a handful of newly minted sluggers who have quickly made an impact on the game. This week, let’s take a look at the players who have done the most damage on contact this year, analyze how they’ve done it, and assess what might lie in store for them. First, we’ll look at the AL.

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This is now the third consecutive year in which I’ve written a post about the potential misuse of defensive metrics early in the season. We all want as large a sample size as possible to gather data and make sure what we are looking at is real. That is especially true with defensive statistics, which are reliable, but take longer than other stats to become so.

While the reminder is still a useful one, this year’s edition is a bit different. Past years have necessitated the publication of two posts on UZR outliers. This year, due to the lack of outliers at the moment, one post will be sufficient.

First, let’s begin with an excerpt from the UZR primer by Mitchel Lichtman:

Most of you are familiar with OPS, on base percentage plus slugging average. That is a very reliable metric even after one season of performance, or around 600 PA. In fact, the year-to-year correlation of OPS for full-time players, somewhat of a proxy for reliability, is almost .7. UZR, in contrast, depending on the position, has a year-to-year correlation of around .5. So a year of OPS data is roughly equivalent to a year and half to two years of UZR.

Last season, I identified 10 players whose defensive numbers one-third of the way into the season didn’t line up with their career numbers: six who were underperforming and four who were overperforming. The players in the table below were all at least six runs worse than their three-year averages from previous seasons. If they had kept that pace, they would have lost two WAR in one season just from defense alone. None of those six players kept that pace, and all improved their numbers over the course of the season.

The next table depicts the guys who appeared to be overperforming early on. If these players were to keep pace with their early-season exploits, the rest-of-season column would be double the one-third column. Brandon Crawford actually came fairly close to reaching that mark; nobody else did, however, as the other three put up worse numbers over the last two-thirds of the season than they had in its first third.

Just like with the underperfomers, all four of overperformers had recorded defensive marks six runs off their established levels. Replicating those figures over the rest of the season would have meant a two-win gain on defense alone. Again, no one accomplished that particular feat.

A funny thing happened when I ran the numbers for this season. There weren’t any outliers of a magnitude similar to last season or the season before. It’s possible you missed the announcement at the end of April, but there have been some changes made to UZR to help improve the metric.

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