2015 Starting Pitcher Ball-in-Play Retrospective – AL West

With just over a week of the regular season in the books, it’s high time we concluded our division-by-division, ball-in-play-based analysis of 2015 starting-pitcher performance. Last time, we considered the AL Central. Today, it’s the AL West.

First, some ground rules. To come up with an overall player population roughly equal to one starting rotation per team, the minimum number of batted balls allowed with Statcast readings was set at 243. Pitchers are listed with their 2015 division mates; those who were traded during the season will appear in the division in which they compiled the most innings. Pitchers are listed in “tru” ERA order. For those who have not read my previous articles on the topic, “tru” ERA is the ERA pitchers “should” have compiled based on the actual BIP frequency and authority they allowed relative to the league. Here we go:

Starting Pitcher BIP Profiles – AL West
AVG MPH FB/LD MPH GB MPH POP % FLY % LD % GB % ADJ C K % BB % ERA – FIP – TRU –
Keuchel 85.78 90.55 83.52 2.4% 17.2% 18.7% 61.7% 83 23.7% 5.6% 62 73 69
McHugh 86.16 89.25 85.12 3.9% 30.7% 20.0% 45.4% 85 19.9% 6.2% 97 89 78
F.Hernandez 88.81 92.10 87.70 2.0% 24.9% 16.9% 56.2% 92 23.1% 7.0% 88 95 79
Gray 88.85 91.89 87.55 2.5% 28.2% 16.6% 52.7% 86 20.3% 7.1% 68 86 80
Iwakuma 88.71 91.87 87.20 2.1% 29.0% 18.5% 50.4% 100 21.5% 4.1% 88 93 82
McCullers 89.16 92.62 85.87 3.0% 28.8% 21.8% 46.5% 99 24.8% 8.3% 80 81 83
Richards 87.48 92.35 85.20 2.9% 25.1% 17.1% 54.9% 88 20.4% 8.8% 91 96 85
Weaver 86.82 91.59 82.47 6.0% 40.5% 19.0% 34.4% 86 13.5% 4.9% 116 120 89
Shoemaker 87.37 91.81 83.55 3.9% 38.5% 18.5% 39.2% 101 20.4% 6.2% 111 114 90
Happ 89.72 91.82 89.71 4.1% 30.0% 24.3% 41.6% 104 21.1% 6.3% 90 85 91
Hahn 86.56 90.70 84.23 1.3% 21.6% 24.5% 52.6% 92 15.8% 6.2% 84 88 92
Kazmir 87.67 92.37 84.43 2.6% 34.7% 19.8% 42.9% 101 20.3% 7.7% 77 99 93
Santiago 89.38 92.96 85.73 5.9% 47.7% 16.5% 29.9% 99 20.9% 9.2% 90 119 94
Elias 88.40 91.68 86.41 3.3% 33.1% 19.4% 44.2% 98 19.8% 9.0% 103 113 94
T.Walker 90.60 92.96 88.48 3.9% 35.1% 22.4% 38.6% 115 22.2% 5.7% 114 101 95
CJ.Wilson 90.07 92.52 88.70 3.4% 31.6% 21.9% 43.1% 103 19.9% 8.3% 97 100 96
J.Chavez 89.21 93.11 85.85 5.4% 28.6% 22.9% 43.1% 110 20.2% 7.1% 104 96 99
Gallardo 88.53 89.92 87.69 2.4% 26.3% 22.0% 49.3% 96 15.3% 8.6% 85 100 102
C.Lewis 89.47 92.22 86.47 3.5% 40.8% 22.0% 33.7% 111 16.5% 4.9% 116 104 104
Feldman 88.34 90.80 87.63 1.7% 25.8% 23.6% 48.9% 101 13.5% 6.0% 97 108 105
Heaney 89.95 93.39 86.67 4.0% 35.5% 22.2% 38.3% 117 17.8% 6.4% 87 93 109
Graveman 90.07 93.10 87.59 1.3% 27.3% 21.4% 50.0% 115 15.3% 7.6% 101 115 116
N.Martinez 89.04 91.44 87.38 3.6% 30.1% 24.0% 42.3% 109 13.8% 8.2% 99 124 116
AVERAGE 88.53 91.87 86.31 3.3% 30.9% 20.6% 45.2% 100 19.1% 6.9% 93 100 93

Most of the column headers are self-explanatory, including average BIP speed (overall and by BIP type), BIP type frequency, K and BB rates, and traditional ERA-, FIP-, and “tru” ERA-. Each pitchers’ Adjusted Contact Score (ADJ C) is also listed. Again, for those of you who have not read my articles on the topic, Unadjusted Contact Score is derived by removing Ks and BBs from opposing hitters’ batting lines, assigning run values to all other events, and comparing them to a league average of 100. Adjusted Contact Score applies league-average production to each pitchers’ individual actual BIP type and velocity mix, and compares it to league average of 100.

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The Rays Are Becoming Baseball’s Most Aggressive Team

Yesterday I published my annual reminder that it’s never too early to look at the standings. That is, even though we’re through just one week, the Orioles have done themselves a hell of a favor by starting out 6-0. Now, on the flip side, it can be too early to look at the leaderboards. Like, Tyler White is first in baseball in WAR. If you want to find some real signal, you just have to be patient. But sometimes I just can’t help myself. I mean, I practically live on this website, so of course I’m going to go exploring. And, related to that — it’s been just six games, but the Rays are already up to something.

It’s not something entirely new. I wrote about this when the Rays traded for Corey Dickerson, but during last season, the Rays switched to taking a more aggressive offensive approach. So if you were curious, no, that hasn’t been abandoned. The Rays hitters remain aggressive today, and based on the early indications, they’re going to be more aggressive than anyone else.

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KATOH Projects: Tampa Bay Rays Prospects

Previous editions: Arizona / Atlanta / Baltimore / Boston / Chicago AL / Chicago NL / Cincinnati  / Cleveland / Colorado / Detroit / Houston / Kansas City / Los Angeles (AL) / Los Angeles (NL)Miami / Milwaukee / Minnesota / New York (AL) / New York (NL)  / Oakland / Philadelphia / Pittsburgh / San Diego / San Francisco / Seattle / St. Louis.

Last week, lead prospect analyst Dan Farnsworth published his excellently in-depth prospect list for the Tampa Bay Rays. In this companion piece, I look at that same Tampa farm system through the lens of my recently refined KATOH projection system. The Rays have the eighth-best farm system in baseball according to KATOH.

There’s way more to prospect evaluation than just the stats, so if you haven’t already, I highly recommend you read Dan’s piece in addition to this one. KATOH has no idea how hard a pitcher throws, how good a hitter’s bat speed is, or what a player’s makeup is like. So it’s liable to miss big on players whose tools don’t line up with their performances. However, when paired with more scouting-based analyses, KATOH’s objectivity can be useful in identifying talented players who might be overlooked by the industry consensus or highly-touted prospects who might be over-hyped.

Below, I’ve grouped prospects into three groups: those who are forecast for two or more wins through their first six major-league seasons, those who receive a projection between 1.0 and 2.0 WAR though their first six seasons, and then any residual players who received Future Value (FV) grades of 45 or higher from Dan. Note that I generated forecasts only for players who accrued at least 200 plate appearances or batters faced last season. Also note that the projections for players over a relatively small sample are less reliable, especially when those samples came in the low minors.

*****

1. Jake Bauers, 1B (Profile)

KATOH Projection: 8.2 WAR
Dan’s Grade: 45 FV

Bauers spent his age-19 season squaring off against High-A and Double-A pitchers, and more than held his own. Bauers didn’t show any glaring weaknesses offensively, and rode a 14% strikeout rate to a .273/.347/.422 batting line. Bauers doesn’t have the power of a traditional first baseman, but he excels in every other offensive area. Considering how young he’s been for his level, that’s quite an accomplishment.

Jake Bauer’s Mahalanobis Comps
Rank Name Proj. WAR Actual WAR
1 James Loney 6.0 6.7
2 Adrian Gonzalez 5.2 19.1
3 Justin Morneau 6.5 14.9
4 Randall Simon 5.7 1.5
5 Rico Brogna 6.6 4.4
6 Paul Konerko 8.9 9.2
7 Prince Fielder 11.3 24.9
8 Kyle Blanks 5.4 3.7
9 Derrek Lee 6.4 14.6
10 Roberto Petagine 5.5 1.0

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Effectively Wild Episode 860: Week One in Baseball Confirmation Bias

Ben and Sam banter about the most popular picks in BP’s “Beat PECOTA” game, then discuss Rich Hill, the Cubs’ strong start, Ray Searage’s promising pupils, and more.


Job Posting: Toronto Blue Jays Baseball Operations Data Architect

Position: Toronto Blue Jays Manager of Baseball Analytics

Location: Toronto

Description:

The primary focus of this position will be to support the Baseball Analytics Department. Incumbent will learn and understand how decisions are made in all areas of Baseball Operations, develop a familiarity with the data required to make those decisions and create tools and systems to display and expedite the access to that data.

Responsibilities:

  • Understand and document current database structures, historical design decisions, format, definitions, limitations and content of currently used external and internal data feeds, and establish future requirements.
  • Maintain and support the current end to end data warehousing process within Baseball Operations, starting with structured and unstructured data, conceiving and designing appropriate data structures, performing ETL processes to house the data in the data structures and exposing the data structures to end users.
  • Design and manage a new data warehouse capable of supporting reporting and analytics to improve the currently deployed systems.
  • Develop and maintain data quality assurance processes to ensure database integrity in the future.
  • Complete ad-hoc database queries and analysis as dictated by circumstances.
  • Oversee the evaluation, selection, implementation and support of new database systems.
  • Collaborate with members of the Baseball Operations department to develop best practices for storing and displaying baseball data.
  • Recommend new data sources for purchase and/or new techniques to gather data.
  • Develop and maintain conceptual, logical and physical data models.

Qualifications:

  • Bachelor’s degree in computer science, computer engineering, or equivalent professional experience required. Master’s degree a plus.
  • 5-8 years of related work experience is required, including demonstrated knowledge about data management best practices, long-term maintainability of code and ability to effectively solve problems pertaining to data infrastructure and integrity.
  • Must have demonstrated some ability to complete baseball-specific statistical analysis.
  • Previous experience with baseball-specific data, either publically available or otherwise (i.e. Pitch F/X, TrackMan, Statcast, etc.).
  • Proven background in the ability to relate to and communicate effectively with people of varied backgrounds (programmers, analysts, outside data vendors, other front office members, and Major and Minor League coaches, possibly players).
  • Demonstrated ability to successfully design and execute data warehousing projects.
  • Expertise with SQL and relational databases is required.
  • Experience with at least one of Python, Ruby, Perl, C++ and/or other programming languages’ is required.
  • Experience processing large amounts of JSON formatted data strongly preferred.
  • Represent the Blue Jays in a positive fashion to all business partners and the general public.
  • Ability to work evening, weekend and holiday hours as dictated by the baseball calendar.
  • Willing and able to relocate to Toronto.

To Apply:
Interested applicants must do the following:

  1. Why do you want to work in baseball?
  2. Describe a time when you either built a production-level database from scratch or added a new data source to an existing production-level database and explain the steps you took to make sure the process went smoothly.
  3. Please describe any work you’ve done with any publicly available baseball databases.

Riding the Waves of BABIP Variance with Chris Colabello

When Chris Colabello’s first ball in play this season, a line drive with a recorded exit velocity of 103 mph, went directly into the glove of opposing shortstop Brad Miller, it seemed a cruel yet fitting reminder that nothing is given at the start of a new season.

Not even for Colabello, who appears to have used a strong 2015 season to finally lock down a secure job in the major leagues. He produced offense at a level 42% above league average last year when controlling for park factors, and he did so for a playoff team, eventually forcing his way into more than the short side of a platoon with Justin Smoak. He’s not set to play every day for the Toronto Blue Jays this year, but he should have the larger share of a time-split at first.

He appears to have, at long last, made it. Assuming he can keep it up, that is, which few think is a certainty. For most of his baseball career, people have been looking for reasons why Colabello won’t succeed, even now that he’s doing so.

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August Fagerstrom FanGraphs Chat — 4/12/16

11:49
august fagerstrom: Hello!

11:50
august fagerstrom: Quick programming note: I’m in the process of moving, and very shortly someone is coming to my apartment to buy my dining room table and chairs. Due to that, we may get this thing going a few minutes late today. Apologies

11:51
august fagerstrom: In the meantime, listen to MF DOOM:

12:28
august fagerstrom: OK!

12:28
august fagerstrom: Table: sold

12:28
august fagerstrom: Will chat for an hour starting now

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How to Strike Out Bryce Harper

Bryce Harper’s at-bats have become events. Maybe more so than any other player in baseball, a Harper at-bat is the kind of thing that you set an alert for on MLB.TV so you can switch over to the Nationals’ feed when he comes up. He averaged more than four pitches per plate appearance last year, so you’re probably getting your money’s worth, and the allure of seeing a baseball hit 450 to center is ever present. A Harper at-bat is a spectacle, not only because of the raw power, but because of the craft.

I was one of those people keeping tabs on each Harper at-bat yesterday, except this time it wasn’t because I was enticed by the power. This time, it was because I wanted to see if he’d strike out. He did. Which is a pretty normal thing for baseball players to do. Except this time, it was noteworthy, because Harper hadn’t yet struck out this year. Entering the game, he was just one of two qualified hitters to have not yet K’d, and the other was Melky Cabrera, who never K’s. Cabrera’s offensive game is built around putting the bat on the ball, without much care for authority. Harper is all about authority, and it’s already been on display, which makes his strikeout-averse start to the season feel like it means more than Cabrera’s.

Harper went 21 plate appearances into the season without being sat down on strikes, a streak which lasted four games and then some. Last year, he only went four games without a strikeout once, and never beyond that. Last year’s streak lasted 22 place appearances. There was a 22-plate-appearance run in 2013. He didn’t set any personal records — though if you want to get technical, you could extend back to last year and say he actually went 28 consecutive plate appearances without a whiff — but it also means the first we’ve seen of Harper this year is, in this one particular way, Harper at his best. For a 23-year-old coming off a historic MVP season, that’s fun, because we spent the offseason wondering what he’d do next. Maybe it’s “never strike out.”

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Quick Study: Cold-Weather Effects on Velocity

Last week, the Astros’ Dallas Keuchel and the Yankees’ Masahiro Tanaka faced off on Opening Day in New York. The first Opening Day was pushed back a day because of wintry weather. This day was no different, with a 36-degree first-pitch temperature and 18 mph wind. The next day, I ran my daily velocity report and both of the above names were on the list of starters who’d exhibited huge velocity drops. Keuchel was down 2.5 mph and Tanaka was down 2.3 mph. By running a quick study, I found that colder weather does have a fairly dramatic effect on pitcher velocity.

To find the data, I ran a query using the PITCHf/x database. For the years 2008 to 2015, I compared average velocity from a game which started at 40 degrees or less and the average from the rest of their games. The average change in velocity was -0.95 mph, with a median value of -0.92 mph. A pitcher throwing in a cold game should expect some velocity decline.

Note: Reader yaboynate quickly pointed out that the drop because it is early in the season. I change the query around a bit, and found the average and median change to -0.58 mph. Now the rest of April would be colder and the whole month is lower to start with.

Here are how the velocity changes were distributed.

Cold-Weather Effects, 2008-15
Velocity Change # of Pitchers % Change
> 2 mph 3 0.7%
1 to 2 mph 17 3.7%
-1 to 1 mph 221 48.3%
-2 to -1 mph 136 30.0%
< -2 mph 81 17.7%
Total 458 100%

Well, the cold weather definitely limits any upside and almost half the pitchers in the sample experienced a 1 mph loss — with one in every eight experiencing a 2 mph loss relative to the rest of the season. The differences shrink as the games warm up. From 40 to 50 degrees, the gap is around -0.6 mph; from 50 to 60 degrees, around -0.4 mph.

So, it’s simple: when looking at early season velocity declines, look at game temperatures. Part of the reason for the decline could be attributed to the cold weather.


Baseball’s New Approach to the Changeup

Baseball can be slow to change. We’ve had this idea for decades that certain pitch types have platoon splits, and that you should avoid them in certain situations because of it. Righties, don’t throw sliders to lefties! It’s Baseball 101.

Think of the changeup, too. “Does he have a changeup?” or some variation on the theme is the first question uttered of any prospect on the way up. It’s shorthand for “can he be a starter?” because we think of changeups as weapons against the opposite hand. A righty will need one to get lefties out and turn the lineup over, back to the other righties, who will be dispatched using breaking balls.

As with all conventional wisdom, this notion of handedness and pitch types should be rife for manipulation. Say you could use your changeup effectively against same-handed hitters, for example. You could have a fastball/changeup starter that was equally effective against both hands, despite the history of platoon splits on the pitch.

To the innovators go the spoils. And we’re starting to see some innovators.

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