Looking at five big performances from unexpected sources to determine their staying power in the coming weeks and months.

Christian Arroyo | BOS, 2B

Arroyo has been a surprise standout for the Red Sox, hitting .302/.353/.413 in 69 PA and occasionally hitting atop the lineup. He was a top 100 prospect multiple times back when he was the Giants but has become a bit of journeyman since then moving from the San Francisco to Tampa Bay to Cleveland and finally to Boston. At the risk of being simplistic, the fast start seems almost entirely attributable to his .404 BABIP. There has been virtually zero power (.111 ISO) thanks to a massive groundball lean (62%).

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We’re about one sixth of the way through the season now, so while the sample sizes of performance and advanced underlying metrics remain small, they aren’t completely meaningless like they were a week or two into the season. So let’s begin evaluating surface results on the hitting side by looking at Statcast’s xwOBA and comparing it to actual wOBA. As a reminder, xwOBA is not a predictive metric. It’s backwards looking and should be used the same way you might use SIERA for evaluate pitching performance. It’s more of a “how should the hitter have performed”, rather than a “how will the hitter perform”. That’s because it assumes all the underlying metrics driving xwOBA, like walk rate, strikeout rate, launch angle, exit velocity, etc, are all sustainable skills, which certainly isn’t true for all players and all metrics.

So don’t read this table and automatically believe these hitters will match or come close to matching their xwOBA marks over the rest of the season. That’s not how it works. However, do figure these batters have been “unlucky” and should perform significantly better the rest of the way, assuming their underlying skills don’t dramatically change. As a result, this could be your initial acquisition target list before diving deeper.

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Today’s article is an experiment that I’d like feedback on. I’m not finding enough information that doesn’t immediately become stale for Mining the News. So, I’m going to take those nuggets and try to combine them with the weekly lineup analysis. I’m not sure if this format will work so just let me know in the comments. Thanks. -Jeff

American League

Angels

• Consistent. Everyone just moved up a slot with Anthony Rendon out.

• With Max Stassi on the IL, Kurt Suzuki (.233/.314/.367) is the regular catcher.

• Jose Rojas (.385 OPS) and Luis Rengifo (.258 OPS) are now splitting time at third base.
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Yesterday, I listed and discussed the hitters who had raised their FB% marks by at least 10% versus 2020. Let’s now switch gears to those hitters who have seen their FB% marks decline by at least 10%. Remember these are still very small sample sizes, so the odds are the majority of these hitters return to their normal batted ball distributions by the end of the season. Still, it’s worth monitoring these names as FB% changes could have a big impact on a hitter’s fantasy value.

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There still isn’t a whole lot to evaluate just about two and a half weeks into the season, but batted ball profiles are one of the few that could signal a change in plate approach that lasts all year. As one of the primary drivers of hitting home runs, let’s look to fly ball rate to see who has increased their marks versus 2020 so far. All else being equal, a higher fly ball rate will result in more homers, so paying attention to a hitter’s batted ball profile is important.

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Yesterday, I discussed a whole bunch of hitting metrics and who currently leads and lags the league. Things be crazy this early in a season. I didn’t quite finish diving into all the metrics I wanted to, so today I’ll check in on the batted ball type related metrics. Let’s find out who is part of the weird and wonderful today.

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What I love about the first couple of weeks of the baseball season is all the crazy rates players are posting. In small samples, the range recorded in all metrics is much wider than over a larger sample. Obviously, the larger the sample, the more a player’s performance will revert toward his true talent level. In only eight to nine games, anything goes! So for fun, let’s take a gander at some of the weird and wonderful rates that have been posted by hitters so far.

So there’s at least some care given to sample size, I limited my dive to hitters who have recorded at least 30 plate appearances.

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You may remember, in the before times, a game called Beat the Streak. The game challenged baseball fans to hypothetically beat one of the greatest records of all time. The idea was to pick one player each day to get a hit and to do that 57 consecutive times, beating Joe DiMaggio’s 56 game hit streak record. Way back in preseason 2020 I wrote about my ambitions of becoming a millionaire by using predictive, machine learning models to aid in winning the competition. The game’s 2020 cancellation gave me time to think, time to read, time to learn how noisy my upstairs neighbors are, and time to build a better model.

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Typically after just a couple of games, we would be screaming small sample size for nearly every metric quoted as a reason to pick up or drop a player. But maxEV is different. Sure, the more batted balls, the greater chance of a hitter posting an EV that matches his maximum skill. When looking at maxEV declines, sample size definitely does matter, as all it takes is one batted ball to set that maximum. When we talk gainers, sample size is far less important. A hitter could post their max of the entire season with their first batted ball. For that reason, it’s never too early too look at the maxEV gainers so far. That max will remain at least that high all season long and could suggest an increase in power skill, which might result in a higher HR/FB rate. so let’s check out the gainers versus 2020. I required a minimum of 10 “events” last year to qualify here so we’re not seeing a gainer because he only had one or two poorly hit batted balls last season.

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Every season, in addition to posting my standard bold predictions (which I’ll publish next week), I up the ante with my bold league leaders. If you thought nailing a bold prediction was tough, the bold league leaders are even more difficult! Just getting one right is worthy of celebration. Because these are bold, I automatically disqualify players I don’t personally believe would be considered bold or is already projected to finish top five in the category. So I challenge myself and it typically causes me to bat .000, though I actually have hit on a couple over the years. This is more for fun and dreaming of what could be, rather than any serious attempt at being right. Naturally, I use my Pod Projections to identify players with that 80th-90th percentile upside to vault to the top of the category mountains.

Today, I’ll start with the bold hitting league leaders in each of the five categories, split up by league. Next Monday I’ll move on to the pitchers.

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