In the last two days, I reviewed league average hitting and pitching metrics to understand what was driving the decline in offense. Now let’s return to discussing individual players. We know that Statcast’s xwOBA has its flaws, as most (all?) estimator metrics do. But it’s the best we have and it doesn’t need to be perfect to provide us with actionable information. Furthermore, we have historical data, so when we see a hitter consistently underperforming or overperforming, we could reasonably conclude that the metric is not accounting for something and ignore the player’s placement on the list.

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We’re about a month into the season (holy cow, seriously, it’s whizzed by so far!), and offense is down at levels we haven’t witnessed in a loooooong time. So I’m going to take a break from my usual player specific leader and laggard boards and review some leaguewide metrics. You may have noticed in your fantasy league standings that pitching ratios are significantly better than we’re used to. In my shallow 12-team mixed league, four teams are sitting pretty with a sub-3.00 ERA! Two teams have a sub-1.00 WHIP (I’m barely above at a 1.0057)…whaaaaaaaaaaat?! Even in my AL-Only Tout Wars, four teams have posted a sub-3.00 ERA, which is just insane! Naturally, these strong pitching results must mean that offense has been missing. Let’s review some of the most basic of metrics to find out what’s driving the decline in offense.

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Choose one batter to get a hit in tomorrow’s games. You can choose any batter you’d like. This game is simple. This game is called Beat the Streak. This game is hard. The trick is that you need to do it 56 times in a row. As of this writing, there have been 743 games played and 27,440 at-bats recorded in 2022. You can only choose one per day, however, and currently, the leader of the Beat the Streak challenge has chosen 29 consecutive hitters to hit. They are just over halfway there. I enjoy playing this game, but it can be really demoralizing, and I’m just doing it from my couch, relying on other people to get a hit. I can’t imagine what it’s like to have a stadium full of people yelling at you and then actually trying to get a real hit. I’ve written a few times about a model I’m using to aid in the choosing of my daily picks, but I’m starting to wonder if there’s a better way. It would be nice to just look at one or two metrics that help decide, rather than running a daily model, merging matchups, and splicing in park factors. In this post, I’ll walk through an experimental process that simply asks the question, what 2022 season statistics from the leaderboards here at FanGraphs can help us choose likely hitters.

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American League

Angels

• Since returning from the IL, Taylor Ward (.985, 1 HR, 1 SB) has hit either second, third, or fourth.

• Jared Walsh (vs RHP) and Matt Duffy (vs LHP) are in a first base platoon.

• For now, Tyler Wade (.760 OPS, 2 SB) and Andrew Velazquez (.429 OPS) are getting full-time with David Fletcher on the IL. Read the rest of this entry »

There were a few years in my ESPN home league where Gary Sánchez was the first catcher off the board. Take a look at his 2016 end-of-season game logs and it’s easy to see why. Sánchez made the Yankees appear as if they had struck gold and found a franchise catcher that could have been the Yankee Posey (Ohh how I can just hear the cringing of Yankee fans behind computer screens after reading that sentence). But, things don’t always work out in such a storybook kind of way and Gary Sánchez is now in Minnesota splitting time with Ryan Jeffers. Read the rest of this entry »

Let’s finish running through the extreme performances on the hitting side before moving to the pitchers. Today I’ll check in on the batted ball type related metrics.

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Let’s continue on our journey of identifying the extreme offensive performers so far over the first week and a half of the season.

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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 nine to 11 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 know how the old saying goes, “Fool me once, shame on you. Fool me twice…uh, well, you can’t fool me again cause you already fooled me” (ref).  Rowdy Tellez must have said something similar, maybe even the proper phrase, while sitting in the dugout waiting to get another shot at Kyle Hendricks‘ changeup. That’s because Tellez struck out swinging in his first two at-bats against Hendricks on opening day. Read the rest of this entry »

One of my favorite Topps baseball card series is the Gypsy Queen collection. Typically released at the beginning of the season, this series has special inserts, such as the Crystal Gazing Die-Cuts, that serve as the Topps version of a bold prediction. I like to think of myself as a machine learning, gypsy fortune telling, bold predictor. I’ve created a model, one that I’m still partially developing, named Jolt after Joe “Joltin’ Joe” DiMaggio himself. Read the rest of this entry »