Archive for Starting Pitchers

SSNS: Buxton, Lucroy, Hamels, Tanaka

#2: April 24
#1: April 13

If you’ve tuned in before, you know what this is about. If not: the Small-Sample Normalization Service (SSNS) seeks to, ah, normalize a player’s performance in the context of his own previous achievements (or lack thereof). Most of us are human, and our humanity leaves us vulnerable to the biases that cloud rational thought and critical analysis. Such vulnerability is eagerly exploited by the small sample size, never more so than in April. While midseason small samples cower under the cover of hundreds more plate appearances, April performances have no such luxury.

A month’s worth of playing time is certainly more worthwhile to assess than one week’s worth, but 30 innings or 100 plate appearances can still be pretty volatile. Here are a few still-small samples that recently caught my eye.

All graphs pulled prior to yesterday’s games.

Name: Byron Buxton, MIN OF
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The Sleeper and the Bust Episode: 452 – 10 SP Value Changes

5/2/17

The latest episode of “The Sleeper and the Bust” is brought to you by Out of the Park Baseball 18, the best baseball strategy game ever made – available NOW on PC, Mac, and Linux platforms! Go to ootpdevelopments.com to order now and save 10% with the code SLEEPER18!

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Strategy Section: 10 SP Value Changes

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Patrick Corbin’s Electric Slider is Back

If you’re a nostalgic fantasy baseballer, you’ll remember that Patrick Corbin generated 3.5 wins above replacement (WAR) in his first full season of baseball before suffering the dreaded curse of Tommy John. (If you’re even more nostalgic, or more likely an Angels fan, you’ll remember he was traded alongside Tyler Skaggs for Dan Haren.) Corbin returned to baseball in 2015, and he shoved, seemingly indicating he suffered no ill effects of his surgery.

Yet 2016 was an unmitigated disaster, culminating in a midseason move to the bullpen and a full-season 5.15 ERA. A low strand rate (LOB%) is the blame — virtually no one suffers a 64.8% strand rate for a full season without some bad luck — but poor control and a home run problem complicated things. It appears to me Corbin ran afoul in two distinct ways in 2016.

It also appears to me he may have recalibrated himself. In his last three starts, he has struck out 23 and walked only four across 19.1 innings, good for a 1.86 ERA / 2.53 xFIP / 2.59 FIP.

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PITCHf/x Forensics: Shelby Miller

Poor Shelby Miller. After last year’s disastrous debut with the Diamondbacks, Miller was looking forward to getting back to his previous Cardinals and Braves form. The season started well enough – his spring training was filled with reports about his increased velocity, and how well he was pitching.

In my initial calculations of STUFF for the 2017 season – Miller was up from 0.56 in 2017, to 0.84 in 2017. That is a huge increase that shows great promise for turning a career around. But now – the dreaded third opinion from Dr. James Andrews, and the discovery of a strained flexor muscle, and a torn UCL, which could lead to Tommy John Surgery, or at minimum, a year off with rehabilitation.

What risk factors were present that could have lead to Shelby Miller’s UCL tear? Let’s look at the research.

Table 1. Known risk factors for UCL reconstruction from research.

The interesting thing about this analysis – is that the 2016 iteration of Shelby Miller has no risk factors that particularly jump out at you. He’s right in the moderate area for everything, save for pitches per game – but that value is quite comparable to every other starting pitcher, if not lower. At the same time, this is a case of the sum of all parts – a compounding situation where it’s death by 1000 paper cuts.

Miller broke into the league in 2012, and his Stuff has remained relatively stable since. The biggest change in his Stuff has been this season – and, I’ve tried to take into account the change in data by re-normalizing Stuff to only 2017 data, and subtracting 0.4 mph from the fastball velocity.

Figure 2. Shelby Miller fastball velocity and Stuff, 2012 to 2017.

2017 is clearly a change here – the velocity is up significantly, and that’s not really something that happens when someone gets older. There are a lot of variables in play that we can’t quantify – how quickly did Miller gain this velocity in the off season? What did his training regime look like?

Interestingly, it has been noted that Miller suffered a flexor muscle strain, as well as the UCL tear. Given the urgency that Miller and the Diamondbacks had in accelerating Miller’s return to good-ness, there is a chance signs of discomfort were ignored along the way. When a flexor-pronator muscle is strained, the tension that the muscle originally supported during pitching is now transferred to the ligament. If you’d like to know more about this – there’s a very interesting discussion on flexor-pronator muscle tears/strains on the “fixing pitchers” podcast – http://fixingpitchers.com/podcast/baseball-pitchers-ice-games/.

This is a very important note for young pitchers – do not ignore your body’s warning signs. If something doesn’t feel right, tell your coach and get it looked at by a doctor.

There are no red flags in this analysis for Shelby Miller – but had the strain been noticed a bit sooner, there is a chance he wouldn’t have torn his UCL.

References

Whiteside, D., Martini, D. N., Lepley, A. S., Zernicke, R. F., & Goulet, G. C. (2016). Predictors of Ulnar Collateral Ligament Reconstruction in Major League Baseball Pitchers. The American journal of sports medicine, 0363546516643812.


AL Starting Pitcher Z-Contact% Regressers

Last week, I discussed the American League starting pitchers that have improved their Z-Contact% the most. Let’s now check in on the pitchers whose Z-Contact% has risen the most.

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SSNS: Vargas, Bautista, Miley, Gausman

Last week, I inaugurated RotoGraphs’ Small-Sample Normalization Services, or SSNS. Said services attempt to contextualize good and bad starts within a particular player’s history of achievements (or lack thereof). Assessing player performance based on small samples seems distinctly difficult in April, when, for whatever reason, we perceive players with tattered histories as blank slates. Occasionally, there’s merit to these perceptions. More often, we find out a player’s April is no different than his May or June or July, for example, when a small-sample performance might go less noticed than it would when starting from zeroes.

Here are a handful of players that have caught my eye lately.

Name: Jason Vargas, KCR SP
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MASH Report: Greinke, Lackey, and Tomlin

With the 2017 changes MLB Advanced Media implemented with their StatCast pitch tracking data, I’ve been scrambling to recode my pitcher injury finder. Well, it seems to be working fine and here are some pitchers it found to be concerned about.

Note: I have bumped up all 2016 and earlier values to be equal with higher 2017 readings.

 

John Lackey

It’s tough to tell if Lackey is hurt or he’s at a new, lower talent level. His last start was the most concerning. Here are his velocity and spin rates over the past two seasons.

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Tipping Pitches: Jason Vargas is Dominating

Jason Vargas is pitching out of his mind right now. He has allowed just one run in 20.7 innings (0.44 ERA) with a 0.77 WHIP. Perhaps more impressive than the surface results is the fact that he has a 31% strikeout rate powered by a 13% swinging strike rate. His 3% walk rate is great, too, though less surprising given his career 7% mark. Three starts of a 3% for a guy who rarely walks batters isn’t crazy. The rest is just insane, though.

His velocity has always been underwhelming (~86-88 mph) and it’s on the low end this year at 86.6. Vargas is using essentially the same pitch mix, too: 55% fastballs, 30% changeups, and 15% curveballs. By the way, it’s worth noting that this kind of started last year in three late-September as he managed a 2.25 ERA, 0.92 WHIP, 23% K rate, and 10% SwStr rate in 12 IP. So comparing the 32.7 innings from 2016-17 to his 380 from 2013-15, the biggest differences I found were with the fastball and changeup against righties. There’s also a wholesale zone percentage change, up to 51% against a career 45% and sitting 39-41% from 2013-15, which shows up in the fastball/changeup against righties.

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AL Starting Pitcher Z-Contact% Improvers

What do we do to analyze players this early in the season? Focus on the underlying metrics that stabilize quickest and try to spot early changes. While our sample size stabilization points don’t include any advanced metrics, I would bet that the plate discipline metrics, including Z-Contact%, would sit on the low end in terms of how many plate appearances or batters faced they require to reach the calculated stabilization points. So let’s dive into the American League starting pitchers that have improved their Z-Contact% most from last year. Is there anything more illustrative of the quality of a pitcher’s stuff than making a batter miss a pitch in the strike zone?

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Minors to the Majors: MiLB Batted Ball Baselines & Leaders

 Note: Do to a calculation error involving popups, the values initially report were off by a small bit. Everything is corrected now.

Our Dark Overlord continues to install enhancements to FanGraphs. One item which he has sneaked in over the weekend in Swinging Strike (must add to custom dashboard) and Groundball Rates for minor league pitchers (example). With the data now available to query, it’s time to find the league specific baselines and compare some highly touted prospects. Today, I will just concentrate just on the batted ball data.

Anytime new data becomes available, the baselines values are the starting point for an analysis so comparisons can be made. First, here are the overall league ground ball rates from 2016.

MiLB Batted Ball Averages
Level GB% LD% FB% PU%
MLB 44.7% 20.7% 34.6% 3.4%
AAA 44.9% 20.6% 34.6% 7.3%
AA 45.4% 20.1% 34.5% 7.3%
A+ 45.5% 19.8% 34.7% 7.5%
A 46.0% 19.3% 34.7% 7.5%
A- 47.9% 18.8% 33.3% 7.8%
Rookie 47.9% 20.8% 31.3% 8.6%

There is some funkiness going on in Rookie Ball and the Majors but the general trend is for ground ball rates to drop as the level approached the majors. Generally, the numbers are steady. With the league averages out of the way, I will move onto pitchers.

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