Archive for Hitters

Quickly identifying legitimate breakout hitters is tough. Is the hot streak just that, a streak or is something more? With pitchers, it is easier to find the breakout’s cause. New pitch. Added velocity. Improved control. These traits can be seen in a single start after facing 25+ batters. A hitter has only about five plate appearances a game to display a new skill. It’s a different world with them. Today, I am going to try to find a simple process with a few key stats to focus on.

With hitters, their data contains so much noise, especially once the ball is put into play. To get rid of some of this noise, I started to find with the following stats:

• Power: Hard%, HR/FB
• Plate Discipline: BB%, K%, O-Swing%, Contact%
• Batted Ball Distribution: Pull%, Flyball%

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Publicly available Statcast data is just over two years old. Eric Logenhagen posted his first set of prospect grades before this season started. I have decided to say screw it to small samples and see how well Eric’s power grades match up with exit velocity number. Even with the limited sample, the results ended up fairly consistent.

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Are hitters consciously deciding to hit more balls in the air, now that more data is available? I don’t know. There is certainly evidence that more players have hit the ball in the air over the past 2 years. Maybe it’s the ball. Maybe it’s the availability of StatCast, though teams have had more batted ball data than what is publicly available for some time. Would more widely available data be all that was needed in order for a player to see the merits of hitting the ball in the air?

I’m skeptical. Perhaps, hitters have always known that hitting the ball in the air is valuable, but now we have a standardized way of articulating these changes? These are just a couple of questions that sift through my head as we sort our way through changes in league wide batted ball tendencies over the past 2 years.

We know that expected production based on qualify of contact changes as we change launch angles and increase exit velocities, with the least valuable batted balls being those hit under 0 degrees and over 39 degrees. Balls in play fall into these buckets nearly 50% of the time.

Given this, I was curious if players increasing their launch angle are, on average, increasing their expected production. Much could be said about Yonder Alonso deciding to hit more fly balls. It’s worked out well for him, but there’s some survivor bias involved here. We remember Alonso because he has succeeded. Has everyone who has added more lift to their swing seen similar improvements? Read the rest of this entry »

While we feel like pitchers change far more quickly than hitters, that doesn’t mean that hitters never adjust, regress, or improve during the season, moving their true talent level to another level. So let’s take a look at some of the more interesting names on the HR/FB rate leaderboard over the last 30 days.

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It’s been a forgettable year for Jonathan Lucroy. He has posted the lowest wOBA of his career and despite playing in a historical time for home run power, his ISO has slipped below .100 for the first time since his 2010 debut and HR/FB rate sits at just 6%. His Brls/BBE has dipped to a lowly 2.4%, versus 7.7% last year, so his weak performance doesn’t appear to be a fluke. He has simply lost all semblance of power. Where did it go? Who knows. But if anything could wake up a dead bat, it’s Coors Field!

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Over the past three years I’ve had a lot of fun studying quality of contact stats, and I’ve tried to put everything I have learned into xStats.  This uses exit velocity, launch angle, running speed, park factors, temperature and more to measure a player’s offensive performance and displays this information in the form of basic slash line items to make it easy to understand for any baseball fan.  You’ll see stats like expected batting average, slugging, BABIP, and wOBA. (xAVG, xSLG, xBABIP, and xOBA, respectively).

Recently I’ve run 7 day, 30 day, and 60 day stats, giving an insight into players who have been out or under performing their game results in recent weeks. Today I want to touch on a few of the batters I find particularly interesting, and I’ll include a little table showing a few other notable players as well. Read the rest of this entry »

Jonathan Schoop is having one heck of a season, with a breakout that’s produced some gaudy numbers. He’s hitting over .300, he’s already hit 21 bombs, he’s tied for 8th in the league in RBI with 70 — basically, he’s doing everything you want as a fantasy owner, except stealing bases. It’s no surprise that he’s currently the No. 6 second baseman in standard leagues, even in a year that’s seen incredible production at the keystone.

With a career .192 isolated power, the 25-year-old already had good power production in the majors, but this year he’s upped that mark to a .246 ISO, supplementing his 21 homers with 26 doubles. Even more impressively, Schoop is hitting .307, a significant jump from his .261 career average.

Of course, with marked improvements like this come questions of sustainability, and justifiably so. With this in mind, let’s try and figure out what to expect from young Mr. Schoop moving forward.

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There are a lot of players in Major League Baseball. There are even more in the minors. I’m not a prospect guy and rarely know names much outside the top 10 in baseball. So when a player is called up, I often times never heard of the player and unless I know him as a top 10 prospect, I generally don’t even bother to do the research to determine if he’s worth a pickup. In a shallow league, 90% of the time, the answer is no. But then that hitter keeps hitting home runs and I wonder to myself “who the heck is this guy?!” Let’s discuss some of those players that led to such a reaction.

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Yesterday, I took the first dive into the intriguing waters of Statcast’s Sprint Speed metric. After comparing correlations with other speed-related stats to Spd Score, I now want to see which players are actually faster and slower than Spd would have use believe. Because Spd uses some context-dependent metrics, it could sometimes give us a false impression of a batter’s true raw speed.

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Yesterday, I identified a group of hitters worth buying for their home run upside, given the discrepancy between their actual HR/FB rate and xHR/FB rate. Today, I move over to batting average, as I identify the hitters whose xBABIP marks most exceed their actual BABIP marks. These are the guys to target for batting average that you may be able to get at a discount.

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