Archive for Hitters

Last Thursday, Jonathan Schoop signed a one-year deal with the Twins, bringing him back to the American League after a short stint with the Brewers in the National League. After posting the highest wOBA of his career in 2017, his offense sunk this season, as his wOBA dipped below .300, thanks primarily to a dramatic decline in BABIP. Will the move to Minnesota help him rebound? Let’s see what the park factors have to say. Since h played nearly his entire career in Baltimore, amassing just 134 plate appearances in Milwaukee, we’ll keep the comparison between Baltimore and Minnesota.

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This study came about from a comment made in passing by Jeff Erikson and Scott Jenstad in a RotoWire podcast. They were discussing how they were worried more about hitters who were striking out on fastballs instead of breaking balls. They figured it was worse to strikeout on fastballs and it showed the hitter was in decline. I don’t remember the exact show but I agreed and now have time verify. And as usual, the pair was right with three-hitter groups differentiating themselves from the pack.

The first key was that I wasn’t interested in the batter ability to tell balls from strikes. Instead, I wanted to focus on pitches right down the heart of the plate while keeping the strike zone as big as possible to increase the sample size. In the end, the taken pitches in the zone used were called strikes 97% of the time.

Next, I found the swinging strike rate for pitches in the strike zone. I grouped the pitches into several groups.

• All fastballs
• Fastballs > 94 mph
• Fastball < 94 mph
• All non-fastball
• All changeups including splitters
• All breaking balls

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Another day, another blockbuster. I literally went “ohhh my God” when I read the news on Rotoworld. Obviously, you know by now that Paul Goldschmidt was traded to the Cardinals. Was he on the block? I had no idea, though admittedly I pay little attention to speculation and rumors. Let’s try to figure out how the switch in home parks might affect his performance.

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For fantasy baseball writers, this time of year means only one thing, player profiles. Dozens and dozens of player profiles to slog through. While writing up some hitters, I can to the realization I didn’t have a quick method to flag hitters who may be shifted. While I could navigate to the splits page, scroll down, and calculate each player’s shift rate, I wanted a quick and dirty method to flag them. While I went over several methods, I settled on one stat and two values.

I did some shift work for an older edition of a The Hardball Times Annual but I have stayed away from the topic over the last few years. My initial conclusions stand which are the shift really affected some hitters and the rate of effectiveness slowly declines more players are shifted. The shift isn’t going away though. It’s still effective against slow pull hitters.

I initially thought a hitter’s pulled groundball rate would be a major factor, but it was not. Just pull rate was enough.

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It was easy to mock the Padres signing of Eric Hosmer to an 8-year $144 million deal. It’s a real possibility that literally everyone outside the key decision makers in the Padres front office believed that it was a bad deal. After his first season, the doubters are up 1-0. He posted the second lowest wOBA of his career and lowest since 2012. This after coming off his best career mark, meaning that his wOBA plummeted an exorbitant 0.67 points. Since you’re generally paying a first baseman for his offense, a .309 wOBA simply doesn’t cut it. So what went wrong?

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I’ve always been a week or two behind evaluating hitters so I continue trying to find ways to gain an edge. Today’s stab in the dark is trying to see if StatCast data can determine if a player is hurt. A recent study of mine found no correlation between playing through an injury and exceeding their projection in the next season. Instead of looking at preseason projections, I’m going to go a little more in-season today and determine how much of an impact an injury has on a hitter’s in-season production.

As Al showed, there are a ton of metric available StatCast batted ball metrics to use. I started down the path of using several of them but quickly found there is more to injuries than just power. A hitter’s plate discipline and speed results (e.g. turning singles into doubles) also matter. Instead of incorporation all of them in, I decided to use BaseballSavant’s xwOBA metric to measure a hitter’s production since it combines all of these factors.

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Oops. I was very wrong about Javier Baez, but man what a year he enjoyed. With a .290/.326/.554 triple slash en route to a .366 wOBA, this was a full breakout for the former top prospect. For his reward, he finished in second place in this year’s National League MVP voting. So let’s discuss what drove the breakout.

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The Braves made an early off-season splash by signing veteran third baseman Josh Donaldson to a one-year contract. He’ll take over at the hot corner and will likely hit cleanup behind a trio of established and rising stars. Now on his third team in one year, let’s dive into the park factors to figure out how the move might affect his hitting performance.

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One of the keys to fantasy success involves finding when projections can be systematically off. The hard part for fantasy owners is that most of these findings, like pitch velocity, get quickly incorporated into projections. Since it’s difficult to find these discrepancies, I was intrigued when I saw this quote by Mitchel Lichtman (MGL) in an article he wrote:

So, the substantial under-projections seem to occur when a player gains speed but his wOBA remains about the same.

And by substantial, it was a 22 point difference is wOBA. This is a major difference and could point owners to some nice upside plays. I decided to go ahead and dive in.

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Every pre-season, I compare my rankings to the RotoGraphs consensus (which excludes my ranking of course) to discover which players I’m apparently more bullish or bearish on than the others. I was strapped for time this year and so only published a bullish piece for hitters, dubbed Pod’s Picks. Let’s see how these hitters ended up performing and ultimately ranking. Remember that the rankings are positional and not overall, which makes this exercise a bit more difficult. I’ll be using the FanGraphs auction calculator to compare values.

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