Archive for Meta Analysis

Identifying pitching breakouts as they happen can be hard. Anticipating them, even harder. Savvy readers of sites such as this rely on plate discipline metrics, batted ball data, and other indicators designed to tease out luck from results in order to uncover which performances have staying power and which are fleeting. And while we’ve arrived at the point in the season when most pitching rates have stabilized, a simple change in pitch mix can render those indicators obsolete. Think Matt Shoemaker and his splitter, Jake Arrieta and his cutter, Max Scherzer and his curveball, or Sonny Gray and his slider.

If you can spot a pitcher pulling unexpected arrows from his quiver as he does it, then cheers to you. But it’s difficult to do that at scale. While trying to anticipate a change in pitch mix before it happens may seem futile at times, doing so is a bet on potential. And what we talk about when we talk about “stuff,” is really potential. One of the components upon which that potential relies, aside from command and health, is an optimal pitch mix.

To identify which pitchers could benefit by throwing their more effective offerings a little more frequently, we have a number of tools at our disposal. Today, we’ll use some of those tools to identify the league’s underthrown four-seam fast balls and sinkers and the pitchers who underthrow them.
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At the end of January, I introduced the latest hitter version of xHR/FB, featuring the Statcast fueled metric, Brls/BBE (barrels per batted ball event). I failed to discuss the pitcher side of the ledger, but their barrels against are tracked too, of course. While I haven’t performed any sort of analysis or run a regression, I have to assume that just like for hitters, Brls/BBE allowed by pitchers are going to correlate rather highly with their own HR/FB rates. Unlike for hitters though, it’s probably very inconsistent from year to year, which is why I never bothered to look into it.

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As a Royals fan, I had my doubts Jorge Bonifacio would be a major league contributor. Over the past couple of seasons, my opinion has changed as he showed some power in AA in 2015 (17 HR) and AAA in 2016 (19 HR). I fostered some reservations on the AAA power because he played in the offensive happy Pacific Coast League.

He’s started 2017 off great with 3 HR and a 10% HR/FB ratio in AAA before getting his major league call-up. Since the promotion, he’s hit six bombs with a 29% HR/FB rate. Owners may be expecting some heavy regression from Bonifacio but they shouldn’t. The “juiced” ball era has reversed a trend of position players hitting for less power once getting a major league promotion.

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In early February, I used our Splits Leaderboard to develop the latest xBABIP equation, the first of which that accounts for defensive shifts. I then talked quite a bit about defensive shifts over the years and how its usage has risen significantly since 2012. So let’s take a look at the fantasy relevant names that have faced a defensive shift far more often than last year, as well as those that have seen fewer of them during their plate appearances.

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The sheer amount of data points we have at our fingertips these days, while immensely useful, can be overwhelming. Seemingly, any one player, particularly this early in the season, can surge to the top of the leaderboard of your most trusted stat. One of my favorite heuristics for identifying sustainable breakout performances is very simple. Based on a handful of my favorite peripherals, I filter the leaderboards for those performing above average across all of them, in effect isolating standouts while hedging against the inherent volatility of performing well in a single stat. It’s not perfect but it’s a quick-and-dirty methodology that serves its purpose. 

Last week, I presented a list of pitchers who were better than average across K-BB%, swinging strike rate, Zone-Contact percentage, and ground ball rate. I focused on a few available starters while promising to cover some of the relievers this week. Lowering the innings threshold a tad (min. 14 IP), here are the relief pitchers all boasting better than average performances across the board in the aforementioned stats along with their ownership rates in Yahoo! leagues.

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Last week, I discussed five surprising American League starting pitchers sitting among the leaders in SwStk%. All of them had enjoyed surging versus 2016. Today, I’ll discuss pitchers on the other side of the coin, surprises toward the bottom of the SwStk% leaderboard, all of whom have suffered declines compared to last season.

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Yesterday, I used my xHR/FB rate to validate which of the surprise HR/FB rate leaders appear to have legitimately enjoyed a power surge in the early going. Today, I’ll look at the surprising names closer to the bottom of the HR/FB rate leaderboard, all of whom are validated by shockingly low xHR/FB rate marks. Like for the leaders, this doesn’t necessarily mean it’s panic time if you’re an owner, but simply that it appears poor fortune has little to do with the power outage. The batter simply hasn’t been barreling it up like he typically has, for whatever reason.

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Earlier this week, I recommended Zack Godley in all leagues. A little while later, I got the following tweet

@jeffwzimmerman @rotographs Great Godley piece Jeff. I’m torn. Do i drop Shoemaker Berrios or my 3rd RP Betances to grab him in 14 team weekly points lg? 5 keepers.

— Marcus Aurelius (@WilcoWacko) May 17, 2017

It’s a tough call and one I face in my own home league where everyone is not 100% invested in their team. Just because a player should be owned, he may not be a fit on a team. Here’s how owners should operate when the situation arises.

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Yesterday, I used my xHR/FB rate equation to discuss the hitters whose actual HR/FB rate marks sat significantly below their expected marks. Today, I’ll check in on the hitters whose actual HR/FB rates are significantly higher than their xHR/FB rates. As a reminder, these are the marks unadjusted for home park.

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Last week, I analyzed the 2016 home-to-first times for hitters. With the background information out of the way, I’ll examine at the 2017 speed data to find who’s running the faster and slowest, who’s changed the most since 2016, and how home-to-first times compare to Bill James’s speed score.

With all the Statcast batted ball data getting analyzed, I continue examining the home-to-first times. Fantasy owners may believe speed is mainly used to determine stolen base threats. It’s more than that.

It’s an input to many other fantasy related factors which can help explain a player’s age-related decline. Faster players will beat out a few extra ground balls for hits thereby raising their batting average and on-base percentage. Speed allows a player to score more once on base. It can add to a hitter’s power profile. Also, speed can help keep a player maintain their fielding range at a premium defensive position instead of moving to a statue-like position (e.g. first base). Finally, a drop in running speed may point to an injured player.

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