Archive for Starting Pitchers

Quick Looks: Faria & Castillo

Jacob Faria

• For Faria, I watched his July 6th game against the Red Sox. The game was the most recent with a decent camera angle.

• The nearly 24-year-old righty used a 3/4 release with decent command and control of his pitches.

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Jaime Garcia & Trevor Cahill Move to the AL

We still have about a week to go before the non-waiver trade deadline, but already deals are being made. Yesterday, both Jaime Garcia and Trevor Cahill were shipped off of their non-contending teams to the American League. Let’s see how the league, park, and team switches could affect their values.

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Two Intriguing Flamethrowing Righties – Castillo and Kuhl

A couple of National League Central starting pitchers have caught my eye. Both are owned in a lower percentage of fantasy leagues than they should be, and both are flame throwers. One of the two has already received not just one endorsement from Paul Sporer earlier this month, but also a second after pitching decent in Colorado and brilliantly in Arizona. The other hurler hasn’t received the same level of praise, but Jeff Zimmerman noted a new breaking ball this righty’s added to his mix, and his results of late are intriguing. Read the rest of this entry »


Let’s Talk AL SP, Brls/BBE Allowed, & HR/FB

Earlier this year, I introduced the new xHR/FB rate that uses Statcast’s Brls/BBE (barrels per batted ball event) metric. While the equation was for hitters, Brls/BBE is still a useful data point for pitchers. Luckily, the Statcast leaderboard has the exact same metrics for pitchers as hitters, including Brls/BBE. Let’s lets discuss some American league starting pitchers from a Brls/BBE and HR/FB rate perspective. Given the hitter xHR/FB rate formula, you know that Brls/BBE and HR/FB rate correlate rather strongly. A mismatch will typically mean some sort of regression in either of the stats.

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The SP Omnibus

I’m sure no one noticed but I took last week off and am now back in the saddle. I needed to catch up on starting pitchers to see who is or isn’t performing as expected. Here are some my thoughts while catching up on the news.

Joe Ross and Michael Pineda will need Tommy John surgery

I was a little surprised to see both of these injuries happen during the All-Star break. After reading the news, Ross’s injury shouldn’t have been a surprise for someone paying attention with him exiting his July 9th start early. His fastball velocity for the start was down 3 mph compared to earlier in the season.
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Reconciling Pitcher (x)BABIP and Hard Contact Allowed

This is a long one. I appreciate your patience in advance.

Mike Podhorzer, I and sporadic others have — but primarily Mike has — carried the torch on developing ‘expected’ metrics, such as xBABIP (expected batting average on balls in play), xHR/FB (expected home run-to-fly ball ratio) and xK% (expected strikeout rate), all and the rest of which can be found here. For the uninitiated, these xMetrics help describe how a hitter or pitcher should have performed based on various measurements of the events that unfolded and typically are more predictive of future performance than the original metric. They’re not perfect, but, like other advanced metrics, they give us a better understanding of player performance and ability.

Each metric — xHR/FB, xK%, etc. — has formulas for both hitters and pitchers, with the hitter metrics typically having stronger correlations than those for pitchers. Unfortunately, pitcher xBABIP has always eluded us. It’s inappropriate to repurpose hitter xBABIP for pitchers, but it’s because the model coefficients (weights) would be different, not because the theory underpinning the model is flawed.

That’s the problem, though: hard hits, line drives, infield fly balls — these all should affect a pitcher’s BABIP allowed. Our intuition begs it to be true. Yet there’s a resounding lack of evidence that suggest otherwise. The correlation between BABIP and hard-hit rate (Hard%), line drive rate (LD%) and infield fly ball rate (IFFB%), among others, borders on nonexistent:

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Starting Pitcher Rankings Update – July

Here is my latest starting pitcher rankings update! Keep in mind that this is a narrower focus than just “rest of season” as I’ll be updating these again in mid-to-late August for the stretch run. Pitching is just too volatile to have confidence in a single ranking set for more than 4-6 weeks at a time. The tiers are what’s important.

I’ll reiterate again that the Must-Starts aren’t automatically the best pitchers, but rather the guys that you can’t sit with any confidence (they don’t have a platoon split or home-road split and their track record speaks more than the first three months of this season. Please leave your questions and comments below!

Previous Updates:

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Mid Season Pitcher Workloads

At the midway point of the season, it’s always interesting to see how teams are utilizing their pitching staffs. I’ve been examining workloads through the metric I created, called Fatigue Units. This metric accounts for days between appearances, stress during pitching, and time between pitches – you can read more about it here. TLDR; Fatigue Units appear to be a more accurate indicator of “overworked” pitchers than pitches, or innings pitched.

To start off – what does the midway point indicate about a pitcher’s workload? It doesn’t necessarily indicate what the workload will be by season’s end, but it does say that the pitcher has worked hard in the first half. It definitely says that the team has trusted that pitcher, and, that pitcher is very good. Here’s what the halfway point workloads looked like in 2016.

2016 All Star Break Workloads
Rank Name Fatigue Units Average Days Between Games SD of Days Between Games Appearances Inning Appearances 5 or More Days Rest 2 – 4 Days Rest 1 Day Rest Pitch Count
1 Nate Jones 14.46 2.42 1.93 49 63 8 21 20 685
2 Travis Wood 14.15 2.34 1.48 51 64 6 27 18 693
3 Chris Sale 13.85 6.05 1.47 20 144 20 0 0 2117
4 David Phelps 13.48 2.37 1.32 50 61 6 29 15 923
5 Max Scherzer 13.44 5.52 0.87 22 150 22 0 0 2327
6 Seung Hwan Oh 13.36 2.27 1.13 52 57 4 33 15 895
7 Zach Duke 13.14 2.23 1.26 53 62 5 31 17 615
8 Dellin Betances 12.94 2.33 1.48 49 56 5 30 14 817
9 Ryan Pressly 12.68 2.35 1.32 50 63 3 33 14 843
10 John Lackey 12.39 5.60 0.88 21 141 21 0 0 2099
11 Chris Archer 12.22 5.43 0.60 22 135 22 0 0 2254
12 David Hernandez 12.04 2.50 1.28 47 55 5 30 12 832
13 Johnny Cueto 12.04 5.42 1.38 21 151 21 0 0 2226
14 Madison Bumgarner 12.03 5.43 0.60 22 154 22 0 0 2331
15 Bud Norris 12.02 4.11 1.76 28 109 15 12 1 1693
SOURCE: PITCHf/x

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Poll 2017: Which Group of Pitchers Performs Better?

Since 2013, I have polled you dashingly attractive readers on which group of pitchers you think will post the better aggregate ERA post all-star break. The two groups were determined based on ERA-SIERA disparity, pitting the underperformers versus the overperformers during the pre-all-star break period.

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Mid-season Arsenal Scores

About a month ago, I started searching for the league’s underthrown pitches. Pitches that despite inducing elite swing-and-miss, ground ball, and pop-up rates, are thrown with scarcity relative to other inferior offerings. It was as an enlightening a topic to research, as it dealt with untapped potential, as it was a fun series to write. Though to be honest, there wasn’t a whole lot of actionable fantasy advice to be gleamed. But in the process of writing those pieces, I had to grade each pitch. And arsenal scores, a subject of interest to the RotoGraphs community over the years, were just a stone’s throw from away from the groundwork I’d already laid.
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