Over the last few years, there have been some players who have publicly acknowledged that they enjoy and use analytics to help themselves on the field. Zack Greinke, Brandon McCarthy, and Glen Perkins are some of the more notable players who have embraced nerd stats, and guys like Craig Breslow — with his molecular biophysics degree from Yale — buck the stereotype of the typical jock. But thanks to this great Travis Sawchik profile on Cole Figueroa, we might have a new contender for the title of King of the Ballplayer Nerds.

But he wasn’t interested in baseball analytics until he was traded to the pioneering Tampa Bay Rays in 2010, his third year in pro baseball.

“I said ‘OK, it’s important to them. Why isn’t it important to me?’” said Figueroa of analytics.

Figueroa said in the spring, in an auditorium setting, the Rays hold hitters’ meetings. He had never been in meetings like these. For example, Rays coaches spoke about evaluating the quality of hitter not by batting average but by batted ball exit velocity.

“Depending on who you were you could sleep through (the meetings), you could take exactly what they are giving you,” Figueroa said, “or you could expand upon it.”

Figueroa expanded.

“What can I do to become the most optimal player?” Figueroa asked himself. “What are my strengths? What are my weaknesses?”

Seeing the vast amount of data pouring into the game, and thinking about how to take advantage of it, he began to teach himself code, ‘R,’ or programming language.

He spent hours at Coursera.org — the Web site reassures a new visitor one can “Code Yourself!” — where there are step-by-step instructions in learning how to code and program.

With his nascent coding skills, he began to research and refine data given to him by the Rays, though the Rays kept much of their data off limits from their proprietary database.

He created models to understand how a player with his skills would age. He studied players with similar physical and statistical profiles. He studied what skills would age well, which would age poorly. In three consecutive seasons in Triple-A, he improved his on-base percentage.

“People think coding is some foreign language … in a sense where it’s only something really intelligent people can do,” Figueroa said. “And it’s really totally the opposite. Anyone can code.”

The whole thing is a great read, and even includes Figueroa giving hitting instruction to a teammate based on things he’d read from Alan Nathan, but the fact that Figueroa is writing his own code puts him on another level. I’d imagine that even if he doesn’t end up having a particularly great career on the field, every team in baseball has probably already penciled Figueroa’s name down for a potential front office or coaching job.

As we move closer to April 3, our Depth Charts have a better estimates for roster composition and playing time for the upcoming season. The Depth Charts use a blend of ZiPS and Steamer projections, which adjusts for playing time and calculates many of the stats we host on FanGraphs. Last year, I made a modified box plot which compares the teams by showing their distribution of players using their offensive stats and playing time. I’ve improved upon the concept slightly by adding more stats: OPS, wOBA, wRC+, K%, BB%, AVG, WAR. All these projections come from the same data the teams’ Depth Chart pages are built on.

In the plot below each player is represented by a green circle, whose area is proportional to the number of plate appearances the player is expected to have over the course of the season. The players are grouped by their teams, which have team stats represented by yellow lines. This represents the what the team AVG, wRC+, wOBA, OPS, etc. are. (Differing from traditional box plots, these marks do not represent the median of the data points.) The gray box representing the middle 50% of players on the roster. The box gives you an idea of the roster composition. Teams with a few star or elite players will skew the team stat higher than a team with a more balanced roster.

The visualization is interactive with tooltips when hovering over a player’s dot. Clicking a player’s dot will take you to his player stat page. The data will be current to when you load the page up until the day before Opening Day (4/2/2016).
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It’s become a practice of the present author in recent years to begin, towards the end of winter, a painstaking search for the new ballcap that will express his entire being. It’s also become a practice in recent years to parlay that search into web content so that the author might “remain” “employed.”

Two years ago, this pursuit yielded a Winston-Salem Spirits cap from 1994 with a weird red sun and melancholy eagle on it. Last year, I had the fortune of procuring a handsome Diablos Rojos cap from the actual team store at Parque Fray Nano in Mexico City. In each case, I have documented the relevant search for the benefit of posterity — even if posterity has failed to show any real interest in my work.

Recently, the author has renewed his dumb search. What follows is the third installment of same.

To wit:

Big Ten 10 Basketball Indiana Hoosiers Nutmeg nwt (Link)
Style: Snapback
Time Left: 17 days, 11 hours
Cost: US $19.99 (Buy It Now)

Essential to the task of becoming an adult is learning to abide life’s myriad, ridiculous contradictions. At an institutional level, the Big Ten Conference presents an excellent model for doing that exact thing. For years, it was composed of 10 actual universities. Since 1990, however, the membership has slowly increased to 14 schools. And yet, despite these mathematical realities, the conference’s constituents have remained unflinching in their commitment to the Big Ten moniker. Accordingly, wearing this cap is less a means by which to exhibit one’s support for a handful of midwestern flagship universities and more to acknowledge publicly the inherent absurdity of existence.

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With all due respect to Carter Capps, it looks like we have our first major injury of spring training.

Jhonny Peralta suffered a left thumb injury on Saturday. Early indications are that he tore a ligament, could miss 2-3 months.

— Jenifer Langosch (@LangoschMLB) March 7, 2016

The Cardinals have a good team, and are looking to contend again this year, but they don’t have a lot of organization depth at shortstop, and were counting on Peralta to play everyday. Barring a future move, this injury likely pushes Jedd Gyorko from a utility role into the starting line-up, which isn’t a position the Cardinals wanted to find themselves in. Not only is Gyorko not really a shortstop, losing him off the bench would mean taking away Kolten Wong’s platoon partner, weakening the team against lefties even more than simply swapping out Peralta for Gyorko would suggest.

While the gap between Peralta and Wong at shortstop over a full season is probably only 2-3 wins, and it sounds like Peralta will only miss about half the season, this seems like the kind of injury that should encourage the Cardinals to make a move for outside help. For one, the Cardinals can’t really afford to just surrender a win, given the fight the NL Wild Card race is shaping up to be, not to mention how good the Cubs are likely to be this year. And this isn’t a team that is built to just say “let’s get them next year”, as veterans like Adam Wainwright, Matt Holliday, and Yadier Molina aren’t getting any younger. The Cardinals need to maximize their chances of winning in 2016, and running Gyorko out at shortstop for a few months is at odds with that goal.

Unfortunately for St. Louis, the injury occurred a week after Ian Desmond and Jimmy Rollins signed as free agents, so there’s no easy solution sitting around looking for a job. On the trade market, Erick Aybar would seem to be a potential fit, as the Braves don’t really need a walk-year shortstop during their rebuild, but they don’t have any internal replacements ready, so Atlanta might not be keen on giving up their only decent option. The Mets have infield depth, with Ruben Tejada and Wilmer Flores both set to serve as reserves this year, but they might not be interested in helping an NL contender stay in the race.

The Cardinals have a long history of finding hidden gems, but unless they know something about Gyorko’s defense that no one else does, this could be a pretty big blow. Talking the Braves out of Aybar seems like the best option available at the moment, at least from my perspective.

Right-handed Washington prospect Lucas Giolito recorded his spring debut this afternoon in Florida, producing a 3:0 strikeout-to-walk ratio in 2.0 innings while conceding no runs and just a single hit against the Miami ball club (box).

If certain, possibly British sources are to be trusted, the three strikeouts were facilitated, at least in some part, by means of Giolito’s breaking ball — a breaking ball described recently by former Mets GM Steve Phillips as “bowel-locking.”

Because Evil is both real and constantly among us, video footage of those particular curveballs isn’t available for public consumption. What is available, however, is the educational film above, excerpted from the right-hander’s August 14th start against Double-A Baltimore affiliate Bowie. Within that footage, one finds a specimen of Giolito’s curveball, both in real time and also provocative slow-motion.

The reader is advised to locate his or her keys before consuming said video, however, so that it might be easier to unlock his or her bowels after examining it.

Brian Reinhart is a stranger, even to himself. He’s also responsible for bringing this series of events to the author’s attention.

Position: Texas Rangers TrackMan Operators

Locations: Surprise, AZ; Adelanto, CA; Round Rock, TX; Spokane, WA

Description:
The TrackMan operator will be responsible for running TrackMan system, and one is desired for each Rangers affiliate. The position runs from April to mid-September for Adelanto, Round Rock and Surprise, and mid-June to mid-September for Spokane.

Responsibilities:

• Responsible for setting up rosters and tagging the information.
• Track/chart information for the entire game – monitoring the system and making any changes throughout the game (i.e. roster changes, defensive substitutions, etc.).
• Responsible for fixing any errors, uploading the game to the TrackMan system, and running data and reports for coaches/front office staff.

Qualifications:

• Must be able to work all home games.
• Previous experience with TrackMan System is preferred but not required.
• Must have strong attention to detail and ability to communicate well with others.
• Strong preference will be given to local applicants at each location.

Compensation:
This position is compensated.

To Apply:
Interested applicants can email their materials to abrenner@texasrangers.com. Please specify which location you are applying for.

Larry Walker is a great hitter.

He’s a great hitter at Olympic Stadium. He’s a great hitter at Coors. He’s a great hitter at Busch. He’s a great hitter at any ballpark named after a beer.

Whereas the average hitter might create 120 runs per 162 games at Coors, Walker would create 190. That’s +60%.

Whereas the average hitter might create 85 runs per 162 games in every non-Coors park, Larry Walker would create 110. That’s +30%.

When you evaluate Larry Walker, you have two choices:
1. Neutralize Larry Walker by giving him 268 plate appearances in each of the home ballparks of the 30 MLB teams. His 2501 PA at Coors? Now we only count about 11% of that. His 32 PA in Oakland? We have to figure out how he’d have done if he got 268 PA. And so on.

2. Take a league average hitter, and put him in the same playing conditions as Larry Walker. Walker had 2501 PA at Coors? Great, let’s count it all. But let’s compare him to a league average hitter who also got to bat 2501 times at Coors. He came to bat 32 times in Oakland? Then the league average hitter also came to bat 32 times.

So, what are the strengths of these two options. In Option 1, we don’t allow Larry Walker to take “unfair” advantage of a park he might be ideally suited for. Whereas most hitter would increase their runs created by 40% at Coors relative to a non-Coors park, Larry Walker increased his by 70%. Given that he got 2501 PA at Coors, Walker ends up shining more than he would otherwise. It’s like letting Mariano Rivera come in 1- and 2-run games, while letting Trevor Hoffman and Billy Wagner only enter blowouts. They are all suited to close games, but if only Mo gets to leverage that, he’ll be the one getting all the saves. Is that fair? I dunno.

In Option 2, we deal with what actually happened. We don’t have to play a game of what-if. We simply accept what the player did, and that he was able to leverage (or not leverage) his unique playing conditions. All we do is make the comparison level all those thousands of players who also played in those exact same conditions, at the same frequency as our player. Walker@Coors is compared to average player at Coors, and Mo in high leverage situations is compared to the average reliever in high leverage situations, and so on. Everyone gets to keep what they did.

So, how do you want to see it?

While much of what appears on the author’s desktop is both detestable and also capable of being detested, that’s less the case with the image above, itself recently captured and and edited and uploaded by that same author by means of his personal computer.

Depicted in that image are Toronto right-hander Drew Hutchison and Philadelphia batter J.P. Arencibia, the latter of whom one finds in the process of recording what the Phillies broadcast team has referred to optimistically as Arencibia’s “first spring-training home run.”

While such a course of events typically wouldn’t merit attention, it’s notable today on account of how the image has been made by possible by the first telecast available this spring by way of MLB.TV. Later today, it would appear, curious fans also have the opportunity to observe Cleveland and Cincinnati face off roughly 2000 miles away from the dirty, dirty shores of the Cuyahoga River. For the moment, however, Maikel Franco is batting for Philly and in the midst of a 1-1 count.

Collmenter came in relief in 32 games. Among the 210 relievers with at least 30 innings, he had the 6th lowest Leverage Index at a tiny 0.39. He also had the 23rd best performance as measured by change in Run Expectancy.

Only 4 of those 32 games did he enter the game when it really mattered. There was another 3 when it sort of mattered. The other 25 games were a smattering of complete blowouts to mostly didn’t matter.

When we’re handing out wins, especially in games that he had virtually no hand in participating because of the timing in which he came into the game, should Collmenter be like a tree in the forest with no one around?

Or do we give him credit for his performance even though it had no impact when it did happen?

On September 1, 2015, the Nationals and Cardinals played a game where the Nationals took a big lead, only to give most of it back almost immediately. The Nationals kept trying to hold on, until the end, when the Cardinals won the game on a 3-run HR.

Source: FanGraphs

Let’s look at that ninth inning. First up was Jason Heyward. He grounded out. That context-neutral run value of making an out is -0.25 runs (or -.027 wins). Making an out to start the inning with the bases empty is only worth -0.225 runs (or -.024 wins). Therefore, the base-out timing value of the out is +.025 runs (or +.003 wins). It looks like this:

-.027 wins: Heyward’s out
+.003 wins: low impact timing of out with bases empty

But we know more information. It was a 5-5 game to start the bottom of the 9th. This is a higher leverage situation than random. Heyward’s out actually reduced the chance of winning by .050 wins, not .024 wins. That is, the impact is felt twice as much as a random leadoff situation. So, there’s yet another .026 wins to account for. This is what it looks like:

-.027 wins: Heyward’s out
+.003 wins: low impact timing of out with bases empty
-.026 wins: high impact timing of out in 9th inning of tied game

The question to ask yourself (not to me, but to yourself), is how much do you want to credit Heyward for making an out in this situation: do you want to just credit him with a random out, because he was just plucked into this situation, or do you want to credit him with making an out as the leverage was lower impact (bases empty) or even high impact (9th inning of a tied game)? Is an out an out, or does the out depend on the situation?

Let’s continue. Yadier Molina also got an out. Going through the above machinations gives us this:

-.027 wins: Molina’s out
+.010 wins: low impact timing of out with bases empty
-.019 wins: high impact timing of out in 9th inning of tied game

Now the fun begins. Cody Stanley doubled.

+.081 wins: Stanley’s double
-.056 wins: low impact timing of double with two outs
+.043 wins: high impact timing of double in 9th inning of tied game

So, in a random situation, a double with two outs is not that valuable. It’s less valuable than a random walk. That’s why we have a huge -.056 win value to account for its low impact. But at the same time, this puts the winning run on base in the bottom of the 9th. This is enormously high impact. How you approach valuation will decide how you want to credit Stanley and his double.

Tommy Pham walked with first base open and winning runner already on base.

+.032 wins: Pham’s walk
-.020 wins: low impact timing of walk with 1st base open
-.009 wins: low impact timing of walk (run is useless)

Let’s pause here. The double put the winning run on base, and left 1B open. The walk is in fact practically useless. The win value changed by +.003 wins, which is pretty close to zero. The batter and pitcher know this, which is why we see a NEGATIVE impact of the walk in the 9th inning of a tied game, even though we are in a high leverage situation. This is unlike the double which had a huge POSITIVE impact. The entire sequencing of the situation matters. Given that the batter and pitcher are aware of the situations as they develop, the entire timing values noted above make perfect sense.

Finally, the HR by Brandon Moss.

+.150 wins: Moss’s HR
+.137 wins: high impact timing of HR with 2 runners on
+.114 wins: high impact timing of HR to win the game

In the end, the Cardinals went from a 61.4% chance of winning to 100%, adding +0.386 wins. Adding up the above, and we get:

+.209 wins: all the events in a random situation
+.074 wins: high/low impact timing for base-out situations
+.103 wins: high impact timing of inning/score (except walk)

So, how do you, the reader, want to evaluate each of these plays? How much do you want to assign to the batter (and pitcher) and how much do you just want to have some general “timing” buckets, not linked to any particular player?