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06-14

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Prospectus Feature: MLB Draft Day 3: Gut-Feel Guys
by
Steve Givarz

06-13

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1

Prospectus Feature: MLB Draft Day 2: Quality Seniors
by
Steve Givarz

06-12

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3

Prospectus Feature: The 2012 Re-Draft
by
Baseball Prospectus

05-29

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13

Prospectus Feature: The Ervin Santana Disagreement
by
Jonathan Judge

05-19

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2

Prospectus Feature: It Finally Clicks for Aaron Hicks
by
David Brown

04-21

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5

Prospectus Feature: TIDES Report: Gender and Race in MLB
by
Kate Morrison

04-14

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2

Prospectus Feature: Graveman Comes to Grip With His Destiny
by
David Brown

04-14

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Prospectus Feature: Taylor Motter's Flow
by
Daniel Rathman

04-13

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3

Prospectus Feature: Lew Fonseca and the Myth of Democratic Baseball
by
Mary Craig

04-05

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1

Prospectus Feature: Estimating Release Point Using Gameday's New Start_Speed
by
Dan Brooks and Alan M. Nathan

04-03

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13

Prospectus Feature: Pre-Season Staff Predictions
by
BP Staff

03-27

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1

Prospectus Feature: Christian Bethancourt and Fun
by
Meg Rowley and Patrick Dubuque

03-09

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27

Prospectus Feature: DRA 2017: The Convergence
by
Jonathan Judge

03-01

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8

Prospectus Feature: The Marketing of Baseball
by
Kate Morrison

02-20

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3

Prospectus Feature: Arbitration Clash
by
Jarrett Seidler

02-07

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12

Prospectus Feature: Using the PFM and BP's Fantasy Tools
by
Mike Gianella and Bret Sayre

01-30

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17

Prospectus Feature: Choose Your Own Adventure: Padres Rotation
by
Patrick Dubuque, Ben Carsley, Craig Goldstein and Bret Sayre

01-27

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Prospectus Feature: Modeling Tunnels: The Path Forward
by
Jonathan Judge

01-26

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5

Prospectus Feature: Unlocking Kyle Hendricks
by
Jeff Long, Jonathan Judge and Harry Pavlidis

01-25

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3

Prospectus Feature: Two Ways to Tunnel
by
Jeff Long, Jonathan Judge and Harry Pavlidis

01-24

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17

Prospectus Feature: Introducing Pitch Tunnels
by
Jeff Long, Jonathan Judge and Harry Pavlidis

01-23

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17

Prospectus Feature: Command and Control
by
Jeff Long, Jonathan Judge and Harry Pavlidis

01-09

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Prospectus Feature: Passed Balls and Wild Pitches (Again)
by
Jonathan Judge

12-23

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3

Prospectus Feature: The 2016 All Out-of-Position Team
by
Andrew Mearns

12-19

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9

Prospectus Feature: Rule 5 Review
by
BP Prospect Staff

12-07

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Prospectus Feature: Narrative Nothings
by
Trevor Strunk

11-22

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4

Prospectus Feature: MLB's Ongoing Search for Front Office Diversity
by
Russell A. Carleton and Kate Morrison

11-14

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10

Prospectus Feature: The Cy Young and the Unfair Advantage of Defense
by
Jonathan Judge

10-28

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5

Prospectus Feature: Dominican Winter League Q&A
by
Grant Jones

10-13

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9

Prospectus Feature: Tal's Hill, the Performative Quirk
by
Emma Baccellieri

10-12

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Prospectus Feature: AFL Preview: Peoria Javelinas
by
Steve Givarz, Brendan Gawlowski and Wilson Karaman

10-12

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2

Prospectus Feature: AFL Preview: Glendale Desert Dogs
by
Steve Givarz, Mauricio Rubio and Jarrett Seidler

10-11

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Prospectus Feature: AFL Preview: Surprise Saguaros
by
Steve Givarz, Kate Morrison, Jarrett Seidler and Wilson Karaman

10-11

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Prospectus Feature: AFL Preview: Salt River Rafters
by
Brendan Gawlowski, Steve Givarz, Mark Anderson and Wilson Karaman

10-10

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Prospectus Feature: AFL Preview: Glendale Desert Dogs
by
Wilson Karaman, Steve Givarz and Mauricio Rubio

10-10

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Prospectus Feature: AFL Preview: Scottsdale Scorpions
by
Jarrett Seidler, Wilson Karaman and Steve Givarz

10-06

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12

Prospectus Feature: Imagining a Position-Less Baseball
by
Emma Baccellieri

09-27

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Prospectus Feature: The Joy of Adrian Beltre
by
Kate Morrison

09-26

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7

Prospectus Feature: The Comp-less Mike Trout
by
Henry Druschel

09-26

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7

Prospectus Feature: The Song of Jose Fernandez
by
Mauricio Rubio

09-21

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4

Prospectus Feature: The Six Archetypes of Famous Baseball Men LinkedIn Profiles
by
Emma Baccellieri

09-18

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Prospectus Feature: Baseball's Peek-A-Boo
by
Trevor Strunk

09-16

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10

Prospectus Feature: A Brief, Modern History of Reliever Name Foreshadowing
by
Ben Carsley

09-15

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18

Prospectus Feature: The Active Player Hall of Fame Draft
by
Brendan Gawlowski and Meg Rowley

09-15

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3

Prospectus Feature: The Best Pitcher Nobody Cares About
by
Bryan Grosnick

09-12

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16

Prospectus Feature: Two Visions of October
by
Henry Druschel

09-09

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Prospectus Feature: Burning Up The Track In September, Part 2
by
Rob Mains

09-08

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5

Prospectus Feature: The Giants Are Making History!
by
Rob Mains

09-04

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Prospectus Feature: The September Slumber
by
Trevor Strunk

09-02

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Prospectus Feature: That Old Story About Teams Never Trading Prospects Anymore
by
Julien Assouline

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What if you could have a metric that accurately describes what a pitcher did while also reliably forecasting the skills that pitcher would bring to the future?

Two years ago, I wrote the first DRA essay, focusing on the challenge of modeling descriptive versus predictive player performance. At the time, my prognosis for threading that needle was rather grim:

What is it, exactly, that you want to know? For example:

(1) Do you care primarily about a pitcher’s past performance?

(2) Are you more worried about how many runs the pitcher will allow going forward?

(3) Or do you want to know how truly talented the pitcher is, divorced from his results this year or next?

The reader’s likely response is: “I’d like one metric that excels at all three!” Sadly, when it comes to composite pitcher metrics, this might not be possible.

Read the full article...

Baseball is trying to reach a bigger, younger audience, but its marketing has yet to reach the 21st century.

For an industry with no direct competitors, a brighter inside future than ever, and a very owner-and-league-friendly system of dispensing with profits, Major League Baseball sure seems convinced that they’re dying. And for a company publicly despairing, they don’t seem to have any understanding of what little things they could do to make life easier on themselves. Nowhere is this more apparent than in their seeming inability to move their marketing efforts into the 21st century.

No matter what kind of organization you run, from a small start-up to a multinational telecom[1], the fundamentals of the game are the same: How do you communicate your message to the people you want to reach? How do you determine who you want to communicate with? What image of yourself do you want to communicate?

These three questions are what it all boils down to. It is extremely easy to get lost in the day-to-day of marketing, in the buzz of new ideas and what’s “hot” at the moment. It’s more difficult to refine down to the fundamentals.

Read the full article...

After beating Dellin Betances in arbitration the Yankees added to the drama by going public with criticism of the star reliever.

The arbitration process sucks. It sucks for the team. It sucks for the player. The player, his agent, and key front office personnel go into a room where their lawyers and contractors argue why the player is worse or better than he initially appears. At the end of the day, three professional arbitrators who don’t necessarily have intimate knowledge of MLB player value decide between the player’s submitted salary number and the team's submitted salary number.

These decisions are almost always fitted on a player’s service time, past salary, and the closest comps based on antiquated box score-level stats like wins, saves, batting average, home runs, and RBI, as those stats are generally what the arbitrators understand. The process has been around long enough that there are almost always comparables. Because of this, groups like the Pace Law baseball arbitration team are able to project arbitration awards with stunning accuracy without even being in the room, and an annual national law school arbitration competition occurs with MLB’s system as the model. Often, this is all about a couple hundred-thousand dollars, a pittance in the overall budget of MLB teams.

The Yankees reached arbitration settlements with six of the players they tendered. The seventh was Dellin Betances, one of the best relievers in baseball, entering arbitration for the first time. The Yankees offered $3 million and Betances countered at $5 million. The Yankees are a "file-and-trial" team, which means once the arbitration numbers are officially exchanged they will no longer negotiate a one-year deal.

Economist Matt Swartz of MLB Trade Rumors went a step beyond looking at cases individually and fitted a statistical model to project arbitration salaries across the league, since the comparables are so stable. Swartz’s model for relievers is pretty clear: saves get paid and holds don’t. Swartz also found that the arbitration panel hews so closely to past precedents that a player is unlikely to get more than $1 million beyond the previously highest-paid player for his role and service time, no matter how much better he was than that past comparable. Swartz’s model is generally well-regarded and projected Betances’ median arbitration award at $3.4 million for 2017, far closer to the team filing than the player filing. It’s no surprise that the Yankees won the case, no matter how unfairly light that $3 million number may seem at first glance.

I suspect nothing further would’ve happened here except perhaps a generic disappointment quote from Betances, but then Yankees president Randy Levine went to the media. You certainly wouldn’t be reading about it here on BP—across town, Wilmer Flores’ arbitration victory over the Mets floated through the papers as a couple of sentences in a pre-spring training slice of life story, garnering no major regional or national attention.

Why Levine chose to go after Betances in the media after winning is a question only Levine himself can answer. Arbitration proceedings are often rancorous. It often puts the team in a position where it has to trash its own player for financial advantage, pointing out things like how slow he is to the plate. Occasionally these things boil over; Jerry Blevins’ arbitration win over the Nationals in 2015 was reportedly a factor in his trade a few weeks later to the Mets. This proceeding was apparently particularly bad, but again, the Yankees won.

Read the full article...

A primer on how the Player Forecast Manager and PECOTA projections can guide you before, during, and after drafts and auctions this spring.

Before the Auction

The advice below is designed primarily for mono league, auction formats. However, the same principles apply for mixed league formats as well.

For fantasy players, the unveiling of PECOTA means the simultaneous unveiling of the PFM, our Player Forecast Manager. One of the most versatile valuation tools in the industry, the PFM allows you to customize valuation based on your league’s format. This is particularly useful if you are not playing in a “standard” 5x5 Roto format, as most “expert” commentary (including mine) focuses almost entirely on 5x5, Rotisserie valuations.

Read the full article...

The stories behind the Padres' starters of 2017.

Read the full article...

Where do we go from here?

It has become fashionable to bemoan the absence of novel, raw baseball data on which the next generation of would-be analysts can hone their skills. In the case of Statcast, that certainly describes both the status quo and the foreseeable future, as far as public analysis is concerned.

However, Statcast isn’t the only potential source of fresh baseball data. This week, we’d like to think we have made at least a small contribution along these lines: by reviewing our newly-released data bearing upon pitcher command, control, pitch tunnels, and pitch sequencing, both novice and seasoned analysts can unleash their creativity and hopefully teach the baseball community a thing or three.

That said, this data presents some rather unique challenges that might be overlooked in the rush to “see what Excel can do” or apply the trendiest machine-learning technique. So, while I encourage readers to do whatever they want with our new data, I will also start you off with a few words of advice.

Inference Versus Prediction

First, effectively using tunnels data will almost certainly require you to appreciate the distinction statisticians make between “inference” and “prediction.” By “inference,” statisticians describe the process of isolating predictors that tend to be associated with certain outcomes. This usually occurs by isolating certain coefficients in a regression or classification problem, and exploring whether they are consistently meaningful. Examples of inference would be comparing a new drug to a placebo in preventing disease, or in the baseball context, looking at the effect of ballparks on run-scoring. In both cases the outcome is important, but it is not the focus of the investigation.

“Prediction,” on the other hand, is not particularly concerned with the precise contribution each input makes to an outcome. Rather, prediction seeks to forecast the outcome as correctly as possible as often as possible. Many baseball models tend to focus on prediction, deriving an “expected” rate of some event or another, such as a batter’s home run rate or a pitcher’s strikeout rate. Prediction is right in the wheelhouse of your most advanced machine-learning algorithms, which tend to build the shiniest, blackest box imaginable in exchange for terrific results. You often don’t really know how the algorithm got there; all you know is that it did a great job—whatever the hell it did.[1]

Read the full article...

Kyle Hendricks might be a lot closer to Greg Maddux than he thinks.

One of the challenges of bringing BP's new pitching data to light is figuring out whether it’s useful and how we can leverage it to better understand what is happening on the field. As mentioned previously, we look at this in much the same way we look at pitch movement or velocity; we need to figure out how these tunnels data points interact with other components of a player’s performance to unlock a deeper understanding of what is happening.

Cubs right-hander Kyle Hendricks is a perfect subject to start with. As we mentioned in "Two Ways to Tunnel," Hendricks has some of the smallest pitch tunnels in all of baseball. Hendricks is often compared to Greg Maddux (including by us!), and we can see how he is in fact like Maddux in certain respects. It gives us an idea of how he’s successful, but only an abstract one. That is, we rationalize Hendricks’ success because we’ve seen Maddux do it before, but we don’t really know how all of the moving pieces come together.

In order to better understand how Hendricks is successful, we’ll have to dig into some of our new data to see what that can tell us about how he pitches.

Hendricks has steadily learned how to strike out opposing batters, increasing his K% by 55 percent from 2014 to 2015 and 2016, and it’s clear the effect that has had on his game. In fact, Hendricks’ new-found ability to strike batters out has resulted in him becoming one of the best pitchers in baseball as he has posted a sub-3.50 DRA over each of the past two seasons despite getting dinged for pitching (and winning an ERA title) in front of an elite defense.

Read the full article...

Tunneling from Greg Maddux and Barry Zito to Kyle Hendricks and Rich Hill, and everything in between.

The new pitch tunnels data released by Baseball Prospectus gives us a new glimpse into the repertoires of pitchers across the major leagues. Of course, this data is only as useful as the analysis it helps produce. To showcase how pitch tunnels data can help us better understand the success, or lack thereof, of certain pitchers, we’ll need to better understand how pitch tunnels manifest themselves in the real world.

The title of this article— “Two Ways to Tunnel”—already signals that there isn’t a one-size-fits-all approach to this new data. While game theory might suggest that each individual pitcher has an optimal approach (or approaches), there can be dramatic differences in how different pitchers attack major-league hitters. As such, we should look at this tunnels data much like we would PITCHf/x data. It’s descriptive, and there are many ways to interpret and utilize the data.

We’ll use modern pitchers to explain these concepts with requisite data, but first it’s worth revisiting a historical example. Jeff Long's very first post for BP over two years ago included the following quote about Greg Maddux, the patron saint of tunneling (yes, we know the majority of this quote is included in the introductory post about pitch tunnels, but it’s so good that it merits inclusion once again):

Read the full article...

Greg Maddux was on to something, whether he knew it or not.

One day I sat a dozen feet behind Maddux’s catcher as three Braves pitchers, all in a row, did their throwing sessions side-by-side. Lefty Steve Avery made his catcher’s glove explode with noise from his 95-mph fastball. His curve looked like it broke a foot-and-a-half. He was terrifying. Yet I could barely tell the difference between Greg’s pitches. Was that a slider, a changeup, a two-seam or four-seam fastball? Maddux certainly looked better than most college pitchers, but not much. Nothing was scary.

Afterward, I asked him how it went, how he felt, everything except “Is your arm okay?” He picked up the tone. With a cocked grin, like a Mad Dog whose table scrap doesn’t taste quite right, he said, “That’s all I got.”

Then he explained that I couldn’t tell his pitches apart because his goal was late quick break, not big impressive break. The bigger the break, the sooner the ball must start to swerve and the more milliseconds the hitter has to react; the later the break, the less reaction time. Deny the batter as much information—speed or type of last-instant deviation—until it is almost too late.

- "Greg Maddux used methodical approach to get to Cooperstown" by Thomas Boswell

Greg Maddux may have known about the concept of pitch tunnels. He may not have. Regardless, he knew how to put the concept into practice, and really that’s the important part. Maddux:

Read the full article...

Introducing new tools to evaluate command and control through the lens of strikes.

About a year and a half ago, Baseball Prospectus revealed a suite of catching stats that formed the basis for our industry-leading valuation of catchers. These new stats would shape how we perceived and discussed catcher value, but they also opened the door to better understanding the performance of pitchers.

Two key statistics—CSAA and CS Prob—serve as the basis for the pitch framing portion of our catching metrics. Today, we’ll show how those same statistics can tell us a great deal about pitching as well. CS Prob was initially introduced in 2014 with Harry Pavlidis and Dan Brooks’ first catcher framing model. Early the next year, Jonathan Judge joined the effort and the team introduced CSAA, officially moving our framing models beyond WOWY.

Of the two, CS Prob—short for Called Strike Probability—is the more straightforward: the likelihood of a given pitch being a strike. CS Prob goes beyond what the strike zone ought to be and instead reflects what it is: a set of probabilities that depends on batter and pitcher handedness, pitch location, pitch type, and count. Good pitchers understand that while the strike zone is a dynamic construct, it nonetheless has some consistencies depending on which combinations of these factors are present. We calculate CS Prob for every pitch regardless of the eventual outcome.

The other statistic, CSAA, stands for Called Strikes Above Average; a measure of how many called strikes the player in question creates for his team. In the case of catchers, we isolate the effects of the pitcher, umpire, and other situational factors which allows us to identify how many additional called strikes the catcher is generating, above or below average. For catchers, this skill is commonly described as “framing” or, in more polite company, “presentation.”

For pitchers, we can apply a similar methodology—controlling for the catcher, umpire, etc. to identify the additional called strikes created by the pitcher. CSAA is calculated only on taken pitches, an important nuance. A pitch must be taken in order to be eligible to be called a strike by the umpire, so while CS Prob looks at all pitches, CSAA only takes into account pitches where the outcome is left up to the umpire.

What can these two statistics tell us about pitcher performance and skill? First, we should define a few important things:

Read the full article...

Have we been underrating the value catchers add via blocking skills?

About this same time last year, I was in the midst of a trial in West Virginia when I got to thinking about wild pitches, as one does. In doing so, I realized that modeling passed balls and wild pitches as simple binomials—as we had been doing—did not fit the data as well as it should. To address the problem (or so I thought), I tweaked the parameters, recognized that a Poisson distribution seemed to be a better fit, and remodeled them accordingly.

However, in reviewing those revised numbers after this season, Harry Pavlidis and I came to the same conclusion: our predicted numbers were still not quite right. Specifically, they are too low. In raw numbers, catchers tend to be worth anywhere from plus or minus five runs a season when it comes to blocking, but our models were giving them credit for only about one or two runs above or below average.

Why were our models still underestimating the value of pitch blocking? The answer is that wild pitches follow an even more complex distribution than I had thought. Specifically, what I had decided to be a simple Poisson distribution was in fact a mixture distribution. Mixture distributions, in turn, require a more sophisticated approach.

To understand mixture distributions, we need to start with non-mixture distributions and work our way up. The most famous probability distribution, typically described as the normal distribution, or “bell curve”, looks like this:

Read the full article...

The best of weird baseball in 2016.

Imagine that you're a member of the New York Philharmonic. You’ve practiced countless hours on the viola, earned a bachelor’s degree in performance, taken out student loans to get yourself through a music program in graduate school, and spent several years auditioning, waiting for a break to finally go your way. It’s been a long, long journey, but at last you're finally in the role you dreamed about.

Then one day when you’re on tour in Europe, the oboist begins suffering from horrific food poisoning at intermission of a show in Prague. There is no time for the conductor to ask a ringer to bail them out.* Remembering that you also dabbled at oboe many years ago while an undergrad, you are asked to step up and fill in.

Congratulations, you are now Robin Ventura playing second base in a heated 2003 Yankees-Red Sox showdown.

Some of my favorite baseball moments have come from professionals suddenly being forced into positions far from the ordinary. It’s the ultimate #Weirdball, and I’ve written about the yearly All Out-of-Position Team for three years in a row. Here it is once more, the most spectacular group of randomly placed players who saw at least one game at the position in 2016.

*Quiet, musicians of the baseball world. I know there’s a decent chance that a ringer exists or that the orchestra would simply proceed without the oboist. The fact that the very idea of this happening in a professional orchestra is so ludicrous only makes this better, because in how many forms of entertainment other than baseball can people be so out of place?

Pitcher: Miguel Montero

Read the full article...

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