Archive for Meta Analysis

Long has the relationship between pitcher performance and batted ball metrics been dubious. The Sabermetric community has a solid understanding of why, fundamentally, a pitcher is good or bad. Strikeouts are good. Walks are bad. Hits by pitch are also bad. Home runs allowed are especially bad. So on, so forth. And by no means are batted ball metrics useless. It’s how we know ground balls allowed are superior to fly balls allowed, for example.

The community had hoped, however, that more granular batted ball metrics would help us better explain some of the more nuanced elements of pitcher performance, including those related to luck, such as batting average on balls in play (BABIP) and the percentage of home runs per fly ball (HR/FB). Since their introduction to the public sphere in 2015, and even with the inclusion of more granular Statcast data in 2016, any relationships that might exist between the physics and outcomes for batted balls during an individual pitcher’s season are still poorly explained. The following table depicts the correlations between pitcher BABIP and various batted ball metrics, sorted by the strength of the relationship (all qualified seasons, 2007-17, n = 898):

Read the rest of this entry »

A few days back, I noticed Domingo German’s fastball velocities and spin rates were unique. Since starting, his sinker (or two-seamer) was faster and had more spin than his four-seam fastball. This setup rarely ever occurs. Pitchers try to get as much velocity and spin as possible on their four-seamer with the intention of generating swings-and-misses up in the strike zone. A sinker usually has less of both to help create sink and the resulting groundballs. Throwing a higher spin sinker than a four-seamer historically is not helpful.

Normally, the other Jeff examines one-offs, but I was dumbfounded when I saw German has this feature. Here the two pitches in action.

Sinker

Four-seamer

Read the rest of this entry »

Earlier this week, I looked at Statcast’s expected metrics to identify hitters who may improve or decline from their current ISO marks. Today, let’s talk pitchers. I’m simply going to sort the Expected Stats leaderboard by difference between wOBA and xwOBA and discuss the fantasy relevant starting pitchers with the biggest negative gaps between wOBA and xwOBA (higher wOBA than xwOBA). Remember that xwOBA fails to account for home ballpark, defensive support, quality and opposition, and perhaps other factors I’m not able to come up with at the moment. It’s why I stick to the extremes, as even with all factors accounted for, it’s highly likely these guys are still due for the same directional move in wOBA, though perhaps not to the same degree.

Read the rest of this entry »

In my last article, I did a Quick Look at Freddy Peralta and found his fastball fascinating. He manipulated the pitch to provide the look of different pitches by changing his grip. What I wanted to know is if he could get by with just a good, varying fastball.

First, everyone needs to take a trip over to Peralta’s game page at BrooksBaseball.net and examine the pitch groupings. Usually, different pitches form clusters when examining variations in break, velocity, and spin. Ignoring the possible changeup, he has two groups, fastball and curve. His fastball has an estimated spin which varies from 1300 rpm to 2500 rpm. Its velocity differs from 88 mph to 97 mph. The spin values on Brooks are interpreted based on the ball’s break. The spin rates may be off because Peralta releases really close to home as Jeff Sullivan documented.

Read the rest of this entry »

Yesterday, I used the Statcast expected metrics to calculate player xISO marks, then comparing them to actuals. I began by looking at those hitters who have posted ISO marks most below their xISO marks, suggesting the potential for significant upside in the near future. Today, let’s discuss hitters whose actual ISO marks have most exceeded their xISO marks, suggesting possible downside.

Read the rest of this entry »

This year, the Baseball Savant pages at MLB.com have shared even more data, as new metrics are being calculated and published for our analytic pleasure. The Expected Statistics page calculates a hitter’s expected batting average, slugging percentage, and wOBA. Sweet! Though I can’t recall any exhaustive research studies on the predictive power of their expected metrics, I know the Statcast guys are top notch and trust they have done a good job.

Read the rest of this entry »

A week ago, I examined how prospect rankings could add more context to hitter projections. It’s time to take the research a step further by dividing up the prospect list to see if projections can be refined. And they can be.

Initially, I shied away from dividing up the prospect lists because the sample size quickly gets into single digits. I started dissecting the data hoping to keep reasonable sample sizes. I sort of achieved my goal.

I used the same parameters in the last article. I compared a hitter’s Steamer projected OPS (on-base percentage plus slugging percentage) from 2010 to 2017 to the actual results in their debut season. To designate prospects, I used Baseball America’s top-100 which has been compiled since 1990. I collected the average and median change in OPS. The median value helps to smooth out any major outliers.

Read the rest of this entry »

Rarely is OPS (on-base percentage plus slugging percentage) a fantasy stat. It’s off many people’s radar but it’s widely available and closely mimics a position player’s overall hitting talent. While other stats (e.g. wOBA and wRC+) also give a hitter an overall value, these stats aren’t available at every website. Most sites have their own unique blend but OPS is commonly available. Because of this availability, I’ve been using it as a baseline in recent articles on adjusting projections based on prospect pedigree and when hitters get platooned ($$). Now, it’s time to use OPS to help predict the individual categories.

The process I used for this study was to simply see how much various stats changed when OPS changed a certain amount. For rate stats (e.g. batting average) the conversion is straightforward. For counting categories, I put the stats on a per 600 plate appearance scale. Additionally, I only compared data from 2015 to 2017 during the current “juiced” ball era. I know the process is not close to being 100% precise and that is fine. I’m just trying to create general adjustments and can look to hone the process later. I’m putting in 20% effort to get 80% of the answer.

Read the rest of this entry »

Yesterday, I used my xHR/FB rate equation to identify and discuss ten hitters who have enjoyed a surprise HR/FB rate surge that is actually justified. Today, let’s dive into the disappointing HR/FB guys who xHR/FB rate confirms the power outage. An appearance here doesn’t guarantee doom and gloom the rest of the way, but that these hitters haven’t been unlucky so far, but actually powerless.

Read the rest of this entry »

Last week, I calculated every hitter’s xHR/FB rate, and then listed and discussed those whose marks were well above their actual HR/FB rates (suggesting imminent improvement) and those whose marks were most below their actual HR/FB rates (suggesting imminent decline). Those posts provided some actionable advice, in the form of buy low targets and guys you might want to consider selling high. But what about the hitters whose underlying skills actually justify their HR/FB rate spikes? These are the guys you might consider buying high, or if you already own the player, shouldn’t rush to sell high.

Read the rest of this entry »