Archive for Outside the Box

The 2016 Strike Zone and the Umpires Who Control It


One of the most-discussed issues in Major League Baseball is the consistency of the strike zone. The rule-book strike zone states “The STRIKE ZONE is that area over home plate the upper limit of which is a horizontal line at the midpoint between the top of the shoulders and the top of the uniform pants, and the lower level is a line at the hollow beneath the kneecap. The Strike Zone shall be determined from the batter’s stance as the batter is prepared to swing at a pitched ball.” After watching games throughout the regular season and playoffs, it is easy to realize this is not the strike zone that is called. Each umpire has tendencies and dictates his own strike zone and how he will call a game. With the rise of PITCHf/x and Trackman in the last few years, umpires have been increasingly monitored and judged for their accuracy and impartiality. For this reason, umpires are criticized for incorrect calls more than ever before and I believe are now trending towards enforcing the rule-book strike zone more than in years past.

The purpose of this research will be to do two things. First, I will focus on identifying overarching themes where I look at finding how umpires are adjusting to modern technology but also how the rule-book strike zone is not the strike zone we know. After this, I will dive into a few umpire-specific tendencies. The latter would be helpful to teams in preparing their advance reports by knowing how certain umpires call “their” strike zone dictated by situations in a game.


Using PITCHf/x downloaded through Baseball Savant, I have looked at major-league umpires since 2012 in regards to their accuracy in correctly labeling pitches, primarily strikes, and their tendencies dictated by specific situations. While the height of the strike zone is often influenced by the height of the batter, there are other factors to take into account such as the how the batter readies himself to swing at a pitch. Unfortunately, the information publicly available to conduct this research does not include the batter handedness, pitcher name, or measurements of individual strike-zone limits. For this reason, a stagnant strike zone serves our needs best. The height of the strike zone shall be known as 1.5 feet from the ground to 3.6 feet from the ground. This is the given strike zone of a batter while using the pitchRx package through RStudio when individual batter height is not included.

All PITCHf/x data is from the Catcher/Umpire perspective, having negative horizontal location to the left and positive to the right. The width of home plate is 17 inches, 8.5 inches to both sides where the middle of the plate represents 0 inches. After calculating the average diameter of a baseball at 2.91 inches, we add this to the width of the plate. Therefore our strike-zone width will be 17 + 5.82, or 22.82 inches. The limits we will then set are going to be -.951 to .951 feet (or 11.41/12 inches). Throughout the paper I will be referring to pitches that fall within the boundaries of our zone as “Actual Strikes” and pitches correctly identified as strikes within this zone as “Correctly Called Strikes.”

Called Strike Accuracy By Year

As Table 1 shows, correctly identifying strikes that fall in the parameters of the rule-book strike zone has risen substantially. While 2015 has a higher percentage of correctly called strikes, 2016 PITCHf/x data from Baseball Savant was incomplete, with 28 days’ worth of games unavailable at the time of this research. A rise of 5.90 percent correctly called strikes from 2012 to 2015 shows the rule-book strike zone is being more strictly enforced.


While this provides some information, we can also look into where strikes are correctly being called using binned zones. Understanding that the evolution of umpires over the last five years is taking place and trending toward correctly identifying strikes more today than in years past, we can analyze where, in the strike zone, strikes have been correctly labeled.

Called Strike Accuracy by Pitch Location

In Table 2, we can see a tendency among umpires. Strikes are called strikes more routinely over the middle of the plate and to the left (from umpire perspective). As I have mentioned before, the publicly available PITCHf/x data I used did not include batter handedness and I am unable to determine who is receiving the benefit or disadvantage of these calls. Presumably from previous research on the subject, lefties are having the away strike called more than their right-handed counterparts, explaining the separation between correctly identifying strikes in zones 11 and 13 versus 12 and 14.

Binned Strike Zone


While one may argue that there should not be strikes in these bordering zones, we consider any pitch that crosses any portion of the plate a strike. Due to our zone including the diameter of the baseball on both sides of the plate, the outer portion of the plate includes pitches where the majority of the ball is located in one of these zones.

Called Strike Accuracy by Individual Umpire

When gauging an umpire’s ability to correctly identify a rule-book strike, an 85.67% success rate sets the mark with Bill Miller, while Tim Tschida ranks at the bottom of this list, only calling 71.57% correctly. We can infer from Tables Three and Four along with Table One, that while umpires are calling strikes within the strike zone more often, they are still missing over 17% of these pitches. It is important to note that this information does not take into account incorrectly identifying pitches outside the rule-book strike zone as strikes, which when considering an umpire’s overall accuracy, should absolutely be taken into account.



Called Strike and Ball Accuracy by Count

One of the most influential factors in whether a taken pitch is called a strike or a ball is the count of the at-bat. We have all seen pitches in a 3-0 count substantially off of the plate called a strike, just as we have seen 0-2 pitches over the plate ruled balls. Table Five shows the correct percentage of strikes and balls by pitch count. While this shows that umpires are overwhelmingly more accurate at identifying strikes as strikes in a 3-0 count (91.06%) as compared to an 0-2 count (56.66%), we must acknowledge this only paints part of the picture. Umpires are conversely most likely to correctly labels balls in 0-2 (98.73%) counts and misidentify balls in 3-0 (90.32%) counts. I included their accuracy of correctly identifying both strikes and balls here as opposed to throughout the entire paper because we can clearly tell through this information that umpires are giving hitters the benefit of the doubt over pitchers. Umpires are far more likely overall to correctly identify a ball than a strike, as evidenced by the fact that there are no counts during which umpires correctly call less than 90% of balls.


The data in Table Five is corroborated by the visualizations in Figure One and Figure Two. These visualizations of the strike zone include pitches off of the plate and we can see that in a 3-0 count, a more substantial portion of the rule-book strike zone is called strikes while also incorrectly identifying balls as strikes. While in a 0-2 count, a smaller shaded area of the rule-book strike zone works with our findings that less strikes are identified correctly but more balls are correctly called.


Called Strike Accuracy by Pitch Type

The next area I looked at was whether pitch type significantly altered the accuracy of umpires. In order to do this, I grouped all variations of fastballs into “Fastball” and all other pitches into “Offspeed”, while omitting pitch outs and intentional balls. I was able to see how umpires fared in correctly identifying strikes by pitch type in Table Six.

Not surprisingly, we see Bill Miller near the top of the list with both Offspeed and Fastball accuracy. For umpires as a whole, the difference in accuracy between the two is not large (79.05% Offspeed accuracy vs. 78.91% Fastball strike accuracy). On the other hand, what may come as a surprise is the fact that eight of the top ten highest accuracies were for Offspeed pitches.

Called Strike Accuracy for Home and Away

One of the most-mentioned tendencies of referees or umpires in any sport is home-team favoritism. Whether a foul or no-foul call in basketball, in or out-of-bounds call in football, or a strike or ball ruling in baseball, many think that the home team receives more of an advantage than their visiting counterparts. Looking at top and bottom half of innings, away and home team respectively, we can identify trends and favoritism in major-league umpire strike zones.

While a difference of .62% accuracy may seem like a lot, especially in a sample size of over 650,000 total pitches, we can look at this on a game-by-game level to see the actual discrepancies. For simplicity’s sake, we can assume 162 games a season, making for roughly 11780 games played in our data set (this subtracts all games from the unavailable 2016 data). This leaves us with 23.03 Correctly Called Strikes out of 29.05 Actual Strikes for away teams per game, meaning that 6.02 strikes were not called. As for home teams, we have 22.04 Correctly Called Strikes a game with 28.02 as the Actual Strikes, averaging 5.98 missed strikes a game. By this measurement we can see that more hitter leniency was given to the away team than the home team.

During this time frame, while a higher percentage of strikes were judged correctly, hitters were given more leniency as the away team than the home team on a game-by-game basis.


Called Strike Likeliness in Specific Game Situation

Included in Table Eight are the three most and least likely umpires to call any non-fastball a strike below the vertical midpoint of our zone. I split the strike zone at 2.55 vertical feet and looked at any pitch (not necessarily within the zone) below that height. Here, we are not judging an umpire’s accuracy of correctly identifying pitches, but rather looking at where a certain umpire may call specific pitches. We can see that Doug Eddings is 5.34% more likely to call a strike on a non-fastball as compared to Carlos Torres.

While this does not paint the entire picture, we are able to see how their tendencies can play an important role in the game. Information like this may be valuable to a team in deciding how to pitch a specific batter, which reliever to bring into a game, or factor into being more patient or aggressive while at the plate.


External pressures and increased standards are undoubtable effects on umpire strike zones. As evidenced throughout this paper, strike zones are called smaller than the rule-book strike zone specifies. And while umpires are trending toward correctly identifying strikes, situations such as count and pitch type can affect their judgment.

While the system in place is not 100%, we must understand that these umpires are judging the fastest and most visually-deceptive pitches in the world and are the best at what they do. Major League Baseball must use modern technology to their advantage and provide the best training for umpires to achieve the goal of calling the rule-book strike zone. Another option, while more drastic and difficult to implement, may include adapting the definition of the rule-book strike zone, something that has not been changed since 1996.

The Least Interesting Player of 2016

Baseball is great! We all love baseball. That’s why we’re here. We love everything about it, but we especially love the players who stick out. You know, the ones who’ve done something we’ve never seen before, or the ones that make us think, “Wow, I didn’t know that could happen.” It’s fun to look at players who are especially good — or, let’s face it, especially bad — at some aspect of this game. They’re the most interesting part of this game we love.

But not everyone can be interesting. Some players are just plain uninteresting! Like this guy.

OMG taking a pitch? That’s boring. You’re boring everybody. Quit boring everyone!

You caught a routine fly ball? YAWN! Wake me when something interesting happens.

But it’s hopeless; nothing interesting will ever happen with Stephen Piscotty. I’m sure the two GIFs above have convinced you that he was the least interesting player in baseball last year. But, on the off-chance that you have some lingering doubts, we can quantify it. I’ve made a custom leaderboard of various statistics for all qualified batters in 2016. For each of these statistics, I computed the z-score and the square of the z-score. In this way, we can boil down how interesting each player was to one number — the sum of the squared z-scores. The idea is that if a player was interesting in even one of these statistics, they’d have a high number there. Here are the results:

Click through for an interactive version

I don’t need to tell you who the guy on the far right is. On the flip side, though, there are two data points on the left that stick out. The slightly higher of the two is Marcell Ozuna, with an interest score of 1.627. The one on the very far left is Stephen Piscotty, with an interest score of 0.997. That’s right — if you sum the squares of his z-scores, you don’t even get to 1! This is as boring and average as baseball players get.

Where the real fun begins, though, is when you start making scatter plots of these statistics against each other. I’ve made an interactive version where you can play around with making these yourself, but here are a few highlights:



ISO vs. wRC+

Pretty boring, right? But wait, there’s more! Let’s investigate a little further what went into his interest score. Remember how we summed his squared z-scores and got a value below 1? Well, let’s look at the individual components that went into that sum.

The Most Boring Table Ever
Statistic Squared z-score
LD% 0.108
GB% 0.002
PA 0.296
G 0.220
OPS 0.001
BB% 0.057
SLG 4.888e-05
WAR 0.007
BABIP 0.141
K% 0.103
IFFB% 0.0004
ISO 5.313e-05
FB% 0.007
wOBA 0.022
AVG 1.69e-29
wRC+ 0.025
OBP 0.006

Yes, you’re reading that right — where he stood out the most was in games played and plate appearances. Yay, we got to see that much more boring! Also, I think it is especially apt that his AVG was EXACTLY league average.

All right, time to step back and be serious for a second. As Brian Kenny is always reminding us, there is great value in being a league-average hitter. Piscotty was worth 2.8 WAR last year, just his second year in the league. He’s already a very valuable contributor to a very good team. Maybe it’s time we started noticing guys who do everything just as well as everyone else, and value their contributions too?

(Nah, I’m going to go back and pore over Barry Bonds’s early-2000s stats for the next few hours.)

All the code used to generate the data and visualizations for this post can be found on my GitHub.

dSCORE: Pitcher Evaluation by Stuff

Confession: fantasy baseball is life.

Second confession: the chance that I actually turn out to be a sabermetrician is <1%.

That being said, driven purely by competition and a need to have a leg up on the established vets in a 20-team, hyper-deep fantasy league, I had an idea to see if I could build a set of formulas that attempted to quantify a pitcher’s “true-talent level” by the performance of each pitch in his arsenal. Along with one of my buddies in the league who happens to be (much) better at numbers than yours truly, dSCORE was born.

dSCORE (“Dominance Score”) is designed as a luck-independent analysis (similar to FIP) — showing a pitcher might be overperforming/underperforming based on the quality of the pitches he throws. It analyzes each pitch at a pitcher’s disposal using outcome metrics (K-BB%, Hard/Soft%, contact metrics, swinging strikes, weighted pitch values), with each metric weighted by importance to success. For relievers, missing bats, limiting hard contact, and one to two premium pitches are better indicators of success; starting pitchers with a better overall arsenal plus contact and baserunner management tend to have more success. We designed dSCORE as a way to make early identification of possible high-leverage relievers or closers, as well as stripping out as much luck as possible to view a pitcher from as pure a talent point of view as possible.

We’ve finalized our evaluations of MLB relievers, so I’ll be going over those below. I’ll post our findings on starting pitchers as soon as we finish up that part — but you’ll be able to see the work in process in this Google Sheets link that also shows the finalized rankings for relievers.

Top Performing RP by Arsenal, 2016
Rank Name Team dSCORE
1 Aroldis Chapman Yankees 87
2 Andrew Miller Indians 86
3 Edwin Diaz Mariners 82
4 Carl Edwards Jr. Cubs 78
5 Dellin Betances Yankees 63
6 Ken Giles Astros 63
7 Zach Britton Orioles 61
8 Danny Duffy Royals 61
9 Kenley Jansen Dodgers 61
10 Seung Hwan Oh Cardinals 58
11 Luis Avilan Dodgers 57
12 Kelvin Herrera Royals 57
13 Pedro Strop Cubs 57
14 Grant Dayton Dodgers 52
15 Kyle Barraclough Marlins 50
16 Hector Neris Phillies 49
17 Christopher Devenski Astros 48
18 Boone Logan White Sox 46
19 Matt Bush Rangers 46
20 Luke Gregerson Astros 45
21 Roberto Osuna Blue Jays 44
22 Shawn Kelley Mariners 44
22 Alex Colome Rays 44
24 Bruce Rondon Tigers 43
25 Nate Jones White Sox 43

Any reliever list that’s headed up by Chapman and Miller should be on the right track. Danny Duffy shows up, even though he spent most of the summer in the starting rotation. I guess that shows just how good he was even in a starting role!

We had built the alpha version of this algorithm right as guys like Edwin Diaz and Carl Edwards Jr. were starting to get national helium as breakout talents. Even in our alpha version, they made the top 10, which was about as much of a proof-of-concept as could be asked for. Other possible impact guys identified include Grant Dayton (#14), Matt Bush (#19), Josh Smoker (#26), Dario Alvarez (#28), Michael Feliz (#29) and Pedro Baez (#30).

Since I led with the results, here’s how we got them. For relievers, we took these stats:

Set 1: K-BB%

Set 2: Hard%, Soft%

Set 3: Contact%, O-Contact%, Z-Contact%, SwStk%

Set 4: vPitch,

Set 5: wPitch Set 6: Pitch-X and Pitch-Z (where “Pitch” includes FA, FT, SL, CU, CH, FS for all of the above)

…and threw them in a weighting blender. I’ve already touched on the fact that relievers operate on a different set of ideal success indicators than starters, so for relievers we resolved on weights of 25% for Set 1, 10% for Set 2, 25% for Set 3, 10% for Set 4, 20% for set 5 and 10% for Set 6. Sum up the final weighted values, and you get each pitcher’s dSCORE. Before we weighted each arsenal, though, we compared each metric to the league mean, and gave it a numerical value based on how it stacked up to that mean. The higher the value, the better that pitch performed.

What the algorithm rolls out is an interesting, somewhat top-heavy curve that would be nice to paste in here if I could get media to upload, but I seem to be rather poor at life, so that didn’t happen — BUT it’s on the Sum tab in the link above. Adjusting the weightings obviously skews the results and therefore introduces a touch of bias, but it also has some interesting side effects when searching for players that are heavily affected by certain outcomes (e.g. someone that misses bats but the rest of the package is iffy). One last oddity/weakness we noticed was that pitchers with multiple plus-to-elite pitches got a boost in our rating system. The reason that could be an issue is guys like Kenley Jansen, who rely on a single dominant pitch, can get buried more than they deserve.

Maximizing the Minor Leagues

Throughout each level of the minor leagues, a lot of time and effort is devoted to travel. A more productive model would be for an entire level playing in one location. Spring training’s Grapefruit and Cactus Leagues are a great example. Like spring training, the goal of the minor leagues is to develop, not to win. In this system, players would have more time to work on strength, durability, and skill development. This system could be in effect until the prospect reaches Double-A. At that level, players could start assimilating themselves to playing ball all over the map. However, this is merely a pipe dream. The more realistic option to improving the minor leagues would be to raise each player’s salary.

In 2014, three ex-minor-league baseball players filed a lawsuit against Major League Baseball, commissioner Bud Selig and their former teams in U.S. District Court in California. Sports Illustrated attorney and sports law expert, Michael McCann, explained their case.

“The lawsuit portrays minor league players as members of the working poor, and that’s backed up by data. Most earn between $3,000 and $7,500 for a five-month season. As a point of comparison, fast food workers typically earn between $15,000 and $18,000 a year, or about two or three times what minor league players make. Some minor leaguers, particularly those with families, hold other jobs during the offseason and occasionally during the season. While the minimum salary in Major League Baseball is $500,000, many minor league players earn less than the federal poverty level, which is $11,490 for a single person and $23,550 for a family of four….

The three players suing baseball also stress that minor league salaries have effectively declined in recent decades. According to the complaint, while big league salaries have risen by more than 2,000 percent since 1976, minor league salaries have increased by just 75 percent during that time. When taking into account inflation, minor leaguers actually earn less than they did in 1976.”

Like many big corporations, MLB teams would never increase minor-league salary just because it is the right thing to do. What’s in it for them? Think about it like this.


At point A, when the average MiLB player has a wage set at W2, the player will take Q2 hours out of the day to work toward baseball. As you can see, there is room to improve, as point B is optimal. Accomplishing point B would mean increasing a player’s salary to W1. In turn, players could afford to take Q1 hours out of the day toward baseball. With most minor-league players needing to find work in the offseason or even during the baseball season, a raise in salary would give them the opportunity to be full-time baseball players. These prospects would spend more time mastering their craft, speeding up the developmental process.

With a season as long as 162 games, there is no telling how much depth could be needed in a given year. Just ask the Mets. That’s why it is important to maximize the development in a team’s farm system. At the end of the day, this is merely a marginal benefit. It will not take an organization’s farm system from worst to first. However, it only takes one player that unexpectedly steps up in September to alter a playoff race, proving worth to the investment.

The St. Louis Cardinals Have a Type

This series will cover various trends I’ve observed major-league baseball teams following. Some trends will be analytical while others will be more…”conceptual.” Trends may span a season, or even several, it doesn’t matter, I don’t want to limit myself out of the box. Ideally, I’d like to cover all 30 teams, but I also don’t want to expect too much of myself out of the box, either. After all, I don’t have Francisco Lindor’s smile to pull it off.

Image result for francisco lindor trips on bat gif

Maybe that kind of thinking is limiting — not the part about Lindor’s smile (though in a weird way it does tie in), but maybe I like to undertake something with the caveat that I might not follow through because of experience, mine or others (see Stevens, Sufjan — 50 states project). Or maybe I don’t want to underestimate the extent of my laziness. Or maybe I’m just a glass-half-empty kind of guy…

And maybe all of this relates to my face.

From Wikipedia: “Physiognomy is the assessment of a person’s character or personality from his or her outer appearance, especially the face.”

It’s no secret that we’re all judged on our outer appearance. Some studies have shown it even relates to how well we’re paid. A predisposition towards handsome exists in baseball, too. It’s in the old scouting maxim, “the good face,” which essentially is the baseball colloquialism for “hottie.” But it can also refer to the potential presence of naturally-elevated levels of testosterone, as a strong jaw and well-defined cheekbones are sometimes indicative of the hormone.

Hogwash! Right? Well, have you ever wondered what it would be like to look like Brad Pitt and thought about how differently people would react to you? Now, I’m not talking about a matter of right or wrong, but people would generally respond more positively to you, both socially and professionally, and that does have an impact on confidence, which plays a massive, albeit intangible role in a baseball player’s on-field success.

But, come on. With all the advanced methodology we have to evaluate players, isn’t the “The Good Face” adage a thing of the past? I’m sure it’s probably lost some of its weight in the player-evaluation process, but it hasn’t disappeared. In fact, in the evaluation process used by (arguably) the most successful team of this decade, it’s very much alive. In recent memory, there have been enough handsome doppelgangers in their mix to wonder if the “Cardinal way” isn’t some iron-clad philosophy the organization established to allow them to get the most out of their young players, but that it might just be a certain type of guy!

You’re at FanGraphs, and so I assume you’re a savvy individual and that you know a ruse when you read one, but I want to qualify this writing by saying that this proposition is roughly ~0.0000000000000001% serious. Okay, so essentially, there are six archetypes for Cardinals players’ faces.

  1. The Wain-Os


Pseudoscience says: “If you have an oval face shape, you always know the right things to say.”


2. The Kozmakazies


Pseudoscience says: “If you have a square-shaped face, you are gung-ho and a total go-getter.”


3. The DesCarpenSons


Pseudoscience says: “You value logic and you’re a really good thinker. Plus, you’re an awesome planner.”

4. The Lynnburger


5. The Ambiguous Pham


Pseudoscience says: “If you have a diamond face shape, you’re a control freak. You’re very detail-oriented…”

6. We Don’t Want No Scruggs


Pseudoscience says: See the Wain-Os

Yes, it helps they’re in the same uniform, and yes, I very obviously cherry-picked some of those, but aren’t you still a little floored? The variation here rivals the lack of distinguishability featured among the male contestants on the Bachelorette.

So isn’t this proof of old-school scouting at work? What gives with all the talk of the Cardinals’ cutting-edge front office — are they just masquerading with the hiring of NASA data analysts and organizational philosophies? Or have they truly married the new school and old school? Maybe there is something to building a roster of similar-looking players that prevents “fault lines” from forming.

Or maybe…

Think back to the hacking scandal of 2015. The Cardinals’ new Director of Scouting, Christopher Correa, hacked the Astros’ database for information on players regarding the draft, bonuses, and trade talks. Keep in mind, he was working for the Cardinals, not a brand-new expansion team; he could’ve hired anyone he wanted to work for him. He could’ve had his own NASA data analyst, just like Jeff Luhnow had done before him. I know that in the minds of these men, there’s a lot at stake, and so they look for any competitive advantage they can, but this scenario feels like it’s the smartest kid in class copying off the other smartest kid in class on a math test.

So what did Jeff Luhnow have access to that Correa didn’t?!

It was one file, actually. A file buried deep within the infrastructure of the Astros’ database. A file called “Stardust” (Yes, like in Rogue One). Allow me to explain.

This is daughter Luhnow. Her name and age are unknown (Jeff did not respond to my tweets), but my wife estimates her to be 19 in this photo. If we work off that number, she’s at least 20 now, and that means she’s probably been able to identify boys she thinks are cute for around 15 years, which lines up perfectly with when her dad was hired by the Cardinals in 2003.

Imagine it, “it’s easy if you try;” one day, a five-year-old daughter Luhnow wandered into her dad’s office and climbed up onto his lap while he was looking at some files of some players he was targeting to acquire. Mostly just talking to himself, Jeff explained the pros and cons of each player to his daughter and showed her their pictures. When he got to a young pitcher in the Atlanta Braves’ farm system, she put her hands to her mouth and giggled. “What’s so funny?” Jeff asked his suddenly-bashful daughter. Her face was nuzzled in her dad’s chest, so the words were a bit muffled, but Jeff heard them clear as day. “He’s cute,” she responded.

It was a strange moment for Jeff — he wrestled back the protective instincts welling up inside him, but as he looked at the picture of the lanky, young right-handed pitcher, he realized that she wasn’t wrong. Adam Wainwright was handsome in an awkward, President’s son kind of way.

While this was the deciding factor for Jeff, he was thrilled that he wouldn’t have to admit that to his bosses, because Wainwright was also a top-100 prospect. So the Cardinals sent J.D. Drew and Eli Marrero to the Braves for Wainwright, Jason Marquis, and Ray King (an admittedly motley crew).

Jeff remembered that moment and would, from time to time, call his daughter into his office and gauge her reactions to the players he’d show her. Eventually, however, he didn’t need to call her in anymore. Daughter Luhnow liked baseball, and liked looking at the pictures of the young men; it was like reading a Teen Beat magazine with her dad!

Before I go any further, I want to note that this is one of those conceptual pieces I referred to in the intro, and that the parts about daughter Luhnow are entirely fictitious. There are also no underlying misogynistic themes at play here. I believe a woman could run a major-league baseball team as well as any man — I just think the idea of a team as renowned and successful as the Cardinals being run on the lustful whims of a teenage girl is really funny.

So the way I see it, she had her own Excel spreadsheet where she could rate the features of potential acquisitions on the same 20 – 80 scale as scouts. She could comb through high-school, college, minor-league, and major-league rosters and highlight her favorite guys by coming up with an overall score.

This authoritative list, while completely undisclosed until now, has unwittingly been at the center of a couple of controversies. It is what ultimately drove the wedge between Walt Jocketty and the Cardinals, and also, as previously mentioned, is the holy grail that Christopher Correa was in search of when he hacked the Astros’ database — and what he is currently serving a 46-month jail sentence for.

About the moves that Correa made without the elusive “Stardust” file. He had an idea of her type of guy based on previous transactions, and he was able to make some quality, daughter-Luhnow-inspired acquisitions. Of course, that’s hardly a silver lining. Try explaining to your cell mate that you’re in prison for hacking into someone else’s computer for a list of cute, young men (some of which are still in high school!).

You get it. You’re on board. The Cardinals’ success has largely been driven by a teenager’s romantic fantasies. Okay, maybe not. Regardless, I still have a hard time telling the difference between Adam Wainwright and Michael Wacha and I want to see if you are any better. Here are eight pictures of the two Cardinal pitchers, four of each; in the comments section, please attempt to sequence these correctly, and that’s it. This is what happens in the doldrums of the offseason!



G-Beards v. W-Snappers: A New All-Star Event

A lot has been written about the youth movement in professional baseball. A bulge of pre-arb and arb-eligible studs are pushing out the hobbled gritty vets and reworking how the old ballgame is played, structured, and thought about. Aside from bullpen usage, this may be the biggest current trend in baseball, and the defining trait of the post-Moneyball, big-data, and steroid eras. The value and role remaining for baseball’s seniors is a question playing out on the field (Trea Turner) and in contract negotiations (Jose Bautista and Mike Napoli). But what if it was actually played out straight-up man versus child, craft versus skill, knee brace versus jock strap, once a year? A one-game exhibition between under-24s and over-34-year-olds. Graybeards versus Whippersnappers.

While the All-Star Game promises to be more entertaining now that Bud’s “This time it counts!” policy is no más, the majority of FanGraphs fans still likely prefer the weekend’s other event, the Futures Game. It’s a chance to see the abstract names and grades we’ve read about for so long on a real major-league diamond, showing what they can do against similar talent. A youth-versus-veteran competition would offer the same kind of spectacle. A chance to see how well the recently devalued old-timers, with their guile and years of experience, do against the heralded up-and-comers, with their loose swings and swagger. Who wouldn’t want to watch that? It would give players left off the All-Star roster but having respectable seasons a chance for publicity, and help create more generational fraternity among players in a way not based on locker-room hazing. It would be fun — there are not many avenues for inexperienced labor to directly challenge their seniors in any field of work — and I think it would also be surprisingly competitive.

Let’s imagine what this might have looked like last year in San Diego, using WAR leaders (100 PA or 10 innings pitched minimums) through the first half of the season for players 24 and under and 34 and older, not in the All-Star Game. For the actual selections, if this event ever took place, each team could send a player that fits each age bracket, or status quo could be maintained and fans could vote (in which case, I have a feeling Bartolo would start every year he’s not an All-Star).



(C) David Ross

(1B) Adrian Gonzalez

(2B) Ian Kinsler

(3B) Adrian Beltre

(SS) Jimmy Rollins

(OF) Nelson Cruz

(OF) Ichiro Suzuki

(OF) Curtis Granderson

(DH) Jose Bautista


(OF) Rajai Davis

(2B) Chase Utley

(2B) Aaron Hill

(C) Victor Martinez

(OF) Jayson Werth

(OF) Marlon Byrd

(1B) Mike Napoli

(3B) Juan Uribe

(OF) Matt Holliday

(1B) Albert Pujols

(OF) Ryan Raburn

(C) A.J. Ellis

(OF) Coco Crisp

(OF) Nori Aoki

(2B) Brandon Phillips

(C) A.J. Pierzynski


Rich Hill

Adam Wainwright

John Lackey

CC Sabathia

Colby Lewis

Jake Peavy

Hisashi Iwakuma

R.A. Dickey

James Shields

Jonathan Papelbon (worth the price of admission)

Francisco Rodriguez

Brad Ziegler

Joe Blanton

Oliver Perez

Jason Grilli

Koji Uehara




(C) Christian Bethancourt

(1B) Miguel Sano

(2B) Jose Ramirez

(3B) Nick Castellanos

(SS) Trevor Story

(OF) Christian Yelich

(OF) Gregory Polanco

(OF) Joc Pederson

(DH) Javier Baez


(2B) Jonathan Schoop

(3B) Maikel Franco

(2B) Rougned Odor

(SS) Tim Anderson

(2B) Jurickson Profar

(OF) Nomar Mazara

(OF) Michael Conforto

(OF) Max Kepler

(OF) Mallex Smith

(SS) Eugenio Suarez

(SS) Chris Owings

(OF) Byron Buxton

(1B) Tommy Joseph

(3B) Cheslor Cuthbert

(OF) Delino DeShields

(OF) Jorge Soler


Aaron Nola

Vince Velasquez

Carlos Martinez

Joe Ross

Lance McCullers

Michael Fulmer

Jon Gray

Robbie Ray

Carlos Rodon

Zach Davies

Matt Wisler

Julio Urias

Taijuan Walker

Blake Snell

Roberto Osuna


Those are pretty interesting lists of names. All-time greats like Ichiro and Pujols against great career starts like Odor and Story. The AL ROY (Fulmer), many former top prospects, and familiar names make up the pitchers. Almost all the elder statesmen have played in an All-Star Game before, and it’s likely many of the youngsters will get their chances soon. Some 2016 borderline All-Star snubs like Beltre, Kinsler, Yelich, Cruz, and Polanco would have had an opportunity to show what they can do in San Diego. Clearly, the lists show that shortstops don’t last as long and catchers take a while to mature. Youth is heavy on starters (suggesting they will either flame out or be converted to relievers) while age has more relievers sticking around, racking up WAR.

The W-Snappers’ position players edge the G-Beards in total WAR (26.7 to 19.2) and average wRC+ (103.8 to 98.04), but trail in rate stats (7.3 to 9.3 BB% and 23.4 to 18.2 K%) that tend to refine as players age. Counting stats show that under-24s lead in strength and speed (233 to 224 home runs and 96 to 78 stolen bases) despite having nearly 1000 fewer total plate appearances. Age wins in the “old-school” counting stats RBI (850 to 787) and runs (864 to 799). These tallies and plate appearances suggest that teams continue to use their veteran players more often and higher up the lineup than might be prudent. But the game would be a chance to see if savvy situational hitting by aged hitters, in fact, met the eye test. Despite these stats, it would be hard to bet against a lineup with Bautista, Beltre, Kinsler, and Cruz, but I do wonder if that is that only because I’ve been seeing those names for years? Surprisingly, both sides pull the ball and hit for hard contact at nearly the same rates (around 32 and 40% of the time respectively), although the younger players are luckier, with a 29-point higher BABIP (.317 to .288), likely due to their speed.

For pitchers, the two arsenals come in with nearly identical ERAs (3.9 for youth and 3.8 for age), BB/9 (around 3/9), and K/9 (around 9/9), but again the younger players have the edge in total WAR (21.9 to 15.8). Contact (77%), Zone (48%), Swing (46%), and Hard Contact (31%) rates are all uncannily similar across both teams. I suspect the similarities are because the sampling of player quality is roughly equivalent, but I had wondered if there would be more noticeable differences in how pitchers on opposite ends of the age spectrum were getting hitters out (nibbling and generating weak contact, for example).

To make the game more about the players, there would be an additional rule: player-managers for both sides. This would be a chance for managerial hopefuls like David Ross to audition and stir their dogged age-grades against the ravages of time. On the other side, young clubhouse leaders could emerge and rally their cohort against the stubborn establishment. Baseball is about rituals, and what is a more eternal ritual than coming-of-age ceremonies in which fathers initiate young men into adulthood, but not before a challenge of brawn? Imagine the storylines: brush-backs, pick-offs, and Ichiro beating out an infield single would all take on new meanings. Names would be made and stars would fade honorably into fatherly roles who could still show they had it.

Would players go for it? Probably not. They wouldn’t want to label themselves as old, and might see the game as a gimmicky sideshow to the weekend’s main attraction, where everyone would rather be playing. If it were going to work, it would need to be branded in a respectful way: MLB’s Mentorship Game (sponsored by The Boys and Girls Club of America!) between veterans and young guns. The Player’s Union would probably not like older players missing their chance to rest during the break, but it also might be enticing as an opportunity to demonstrate that both vets and youth have a place in the game, and that aging players should receive more contract interest and younger players should have more early-career leverage.

I highly doubt many emerging players would miss a chance to hang out with their elder heroes and show them up during All-Star weekend. So, the question is, what say you, Napoli and party — challenge accepted?

Running Into an Out as a Strategy

I tried to come up with a witty preamble to this but all I could come up with was a lame story about playing RBI Baseball 4 against my older brother. And unless you have mistakenly come to FanGraphs while trying to get to Farmers Almanac (no judgments, Google auto-complete can be weird sometimes) then you probably don’t care about that. So let’s dispense with the amusing introduction and get right to the question. (Or did I just subversively come up with a witty preamble by explaining how I did not have a witty preamble?!)


Runner on first with two out. 0-2 count.

Now anyone who is even slightly familiar with baseball will tell you that this is not a good situation for the offence. Those who are very familiar with baseball to the point that they read things like this post will probably even quote the run expectancy matrix to demonstrate how bad of a situation this is for the offence.

So, yeah, not looking good for the offence. The chance of scoring a run from that base/out state is 0.127. And that is without even accounting for the 0-2 count which obviously makes things worse. MLB as a whole slashed .155/.187/.237 with a 47.6 K% and a 10 wRC+ last year through two-out, 0-2 situations with runners on. In other words, the batter made the third out ~80% of the time. Even Mike Trout, who is Baseball Jesus, strikes out over half the time in 0-2 counts and is running a tOPS+ that is almost single digits. For all intents and purposes, the inning is likely over when it hits that situation.

But the team at the plate is not totally powerless. It can still decide how to end the inning, and they could do it in a way that gives them a more favourable outcome. Which brings me to the crux of this argument;

Why not have the guy on first just take off running?

Before the pitcher even comes set, just take off for second. Worst case, they tag him out and the inning ends (which was the most likely outcome anyway), but now the guy at the plate leads off the next inning in a fresh count, which is obviously a much more favourable scenario for a hitter. And best case, the defence screws up and the runner is now on second. Granted, that is an extreme outcome, and even two out and runner on second is still not a great scoring scenario. But referring back to the run expectancy matrix, it’s ~50% higher than when he was standing on first.

If the outcome of the scenario is almost overwhelmingly going to be an out, then you are not really giving away an out as much as you are just deciding who takes the out. If you have a good hitter at the plate, why have him continue to hit in what is a pretty futile situation, and waste one of his limited PAs, when you can reset the situation and give him what amounts to an extra PA by having the runner take the out instead?

Let’s look at Mike Trout’s career as an example since, well, since it’s fun to look at Mike Trout’s numbers.

No surprise, Mike Trout is a much, much, much better hitter overall than he is in 0-2 counts. Every hitter is. Now let’s also check back in with our friend, the run expectancy matrix.

So right off the bat (no pun intended), we see that the chances of scoring a run at the start of any inning are considerably better than scoring a run with two outs and a runner at first. Add in the fact that you have a very good hitter leading off in Trout and things have seemingly changed significantly for the better, simply by having your base-runner act like an 11-year-old exchange student on the base paths.

If Trout does anything to get on first (single, walk, HBP, dropped third strike, coming to the plate and performing a stand-up routine that is so good the opposing team just awards him first as a thank you, etc etc), now all of a sudden the chances of scoring a run in the inning have gone up to 0.416. Given that Mike Trout got on base nearly 45% of the time last year and is around 40% for his career, it seems like a fairly reasonable outcome. So by having your base-runner deliberately make an out to end the previous inning and saving Trout from doing so, you have gone from a situation where you had a .127 (or lower given the fact that the 0-2 count is not accounted for in the matrix) chance of scoring a run and your best hitter producing an out to a situation where you very likely have a 0.416 chance of scoring a run. And that does not even account for all the other things Trout might do new in this new PA. If he hits a lead-off double, your chances of scoring a run in the inning are now 0.614. If he hits a lead-off home run, your chances of scoring a run are….hold on, where is my calculator? Plus, you have also avoided what was highly likely an out for your best hitter and having to wait two or three innings for him to bat again.

Last year, MLB teams averaged 219 PAs where they had runners on and an 0-2 count. As stated above, in that situation the hitter wound up making the third out ~80% of the time. So that is ~200 innings that could have started with a different guy at the plate and ~200 outs at the plate that could theoretically have been something other than an out. How many innings would have been different by simply giving up the runner for the third out and letting the hitter lead off the next inning in a more favourable count? If you have a good hitter at the plate and he is down 0-2, it might be worthwhile strategy to just tell your base-runner to take off and let your hitter try again the next inning.

Or maybe I have had too much coffee today.

Hierarchical Clustering For Fun and Profit

Player comps! We all love them, and why not. It’s fun to hear how Kevin Maitan swings like a young Miguel Cabrera or how Hunter Pence runs like a rotary telephone thrown into a running clothes dryer. They’re fun and helpful, because if there’s a player we’ve never seen before, it gives us some idea of what they’re like.

When it comes to creating comps, there’s more than just the eye test. Chris Mitchell provides Mahalanobis comps for prospects, and Dave recently did something interesting to make a hydra-comp for Tim Raines. We’re going to proceed with my favorite method of unsupervised learning: hierarchical clustering.

Why hierarchical clustering? Well, for one thing, it just looks really cool:

That right there is a dendrogram showing a clustering of all player-seasons since the year 2000. “Leaf” nodes on the left side of the diagram represent the seasons, and the closer together, the more similar they are. To create such a thing you first need to define “features” — essentially the points of comparison we use when comparing players. For this, I’ve just used basic statistics any casual baseball fan knows: AVG, HR, K, BB, and SB. We could use something more advanced, but I don’t see the point — at least this way the results will be somewhat interpretable to anyone. Plus, these stats — while imperfect — give us the gist of a player’s game: how well they get on base, how well they hit for power, how well they control the strike zone, etc.

Now hierarchical clustering sounds complicated — and it is — but once we’ve made a custom leaderboard here at FanGraphs, we can cluster the data and display it in about 10 lines of Python code.

import pandas as pd
from scipy.cluster.hierarchy import linkage, dendrogram
# Read csv
df = pd.read_csv(r'leaders.csv')
# Keep only relevant columns
data_numeric = df[['AVG','HR','SO','BB','SB']]
# Create the linkage array and dendrogram
w2 = linkage(data_numeric,method='ward')
labels = tuple(df.apply(lambda x: '{0} {1}'.format(x[0], x[1]),axis=1))
d = dendrogram(w2,orientation='right',color_threshold = 300)

Let’s use this to create some player comps, shall we? First let’s dive in and see which player-seasons are most similar to Mike Trout’s 2016:

2016 Mike Trout Comps
Season Name AVG HR SO BB SB
2001 Bobby Abreu .289 31 137 106 36
2003 Bobby Abreu .300 20 126 109 22
2004 Bobby Abreu .301 30 116 127 40
2005 Bobby Abreu .286 24 134 117 31
2006 Bobby Abreu .297 15 138 124 30
2013 Shin-Soo Choo .285 21 133 112 20
2013 Mike Trout .323 27 136 110 33
2016 Mike Trout .315 29 137 116 30

Remember Bobby Abreu? He’s on the Hall of Fame ballot next year, and I’m not even sure he’ll get 5% of the vote. But man, take defense out of the equation, and he was Mike Trout before Mike Trout. The numbers are stunningly similar and a sharp reminder of just how unappreciated a career he had. Also Shin-Soo Choo is here.

So Abreu is on the short list of most underrated players this century, but for my money there is someone even more underrated, and it certainly pops out from this clustering. Take a look at the dendrogram above — do you see that thin gold-colored cluster? In there are some of the greatest offensive performances of the past 20 years. Barry Bonds’s peak is in there, along with Albert Pujols’s best seasons, and some Todd Helton seasons. But let’s see if any of these names jump out at you:

First of all, holy hell, Barry Bonds. Look at how far separated his 2001, 2002 and 2004 seasons are from anyone else’s, including these other great performances. But I digress — if you’re like me, this is the name that caught your eye:

Brian Giles’s Gold Seasons
Season Name AVG HR SO BB SB
2000 Brian Giles .315 35 69 114 6
2001 Brian Giles .309 37 67 90 13
2002 Brian Giles .298 38 74 135 15
2003 Brian Giles .299 20 58 105 4
2005 Brian Giles .301 15 64 119 13
2006 Brian Giles .263 14 60 104 9
2008 Brian Giles .306 12 52 87 2

Brian Giles had seven seasons that, according to this method at least, are among the very best this century. He had an elite combination of power, batting eye, and a little bit of speed that is very rarely seen. Yet he didn’t receive a single Hall of Fame vote, for various reasons (short career, small markets, crowded ballot, PED whispers, etc.) He’s my vote for most underrated player of the 2000s.

This is just one application of hierarchical clustering. I’m sure you can think of many more, and you can easily do it with the code above. Give it a shot if you’re bored one offseason day and looking for something to write about.

Gary Sanchez Should Bat Second

What do Mike Trout, Josh Donaldson, Dustin Pedroia, Corey Seager and Manny Machado all have in common? Besides the numerous accolades that they share between the Rookies of the Year, the Silver Sluggers, the MVP awards and the combined 16 All-Star appearances, they all share one less obvious trait: they have more career plate appearances batting second in the lineup than anywhere else. Gone are the days of your team’s best player batting third or fourth. The new normal is now MVP-caliber players batting second. It has worked for Pedroia and the Boston Red Sox, Machado and the Baltimore Orioles, Donaldson and the Toronto Blue Jays and Seager and the Los Angeles Dodgers. Not for nothing, but those teams all made the postseason last year with large contributions from their second-hole hitters AND Trout was the AL MVP for the second time in his career on a last-place Los Angeles Angels team. And as more teams continue to adopt this trend, the New York Yankees should also look to bump up their best hitter.

In an appearance the other week on a YES Network interview, GM Brian Cashman has stated that the Yankees have kicked the tires on splitting Brett Gardner and Jacoby Ellsbury in the lineup. This makes a lot of sense when looking at their game; they both rely on their ability to get on base and set the table more so than their ability to drive in runs. Additionally, both players have slowly, but noticeably, been in decline in recent seasons, primarily due to age and injury. Gardner has been the subject of trade rumors over the past few seasons and Ellsbury has been the ire of the New York media for largely failing to live up to the seven-year, $153-million deal he signed before the 2014 season. River Ave Blues has already had a look at how the Yankees would approach this situation and they have provided a solid solution, but they almost immediately toss out the idea of Gary Sanchez batting there for one reason or another, while Sanchez is most deserving of the promotion.

Sanchez has established himself as the Yankees’ most dominant hitter after bursting on the scene last year. The Yankees, their fans, and the nation all expect Sanchez to hit in the third spot in the lineup, a prestigious position considering the history of the franchise, but moving the young slugger to second would not only better suit the team, but would also play to his strengths. Sanchez, despite the short sample size of 231 plate appearances, has proved to be a pretty good fastball hitter. Of the 294 fastballs he has seen, he has connected for a .328 AVG and .781 SLG, and nine of his 20 home runs. Why does this matter? Traditionally, number-two hitters have seen more fastballs than elsewhere in the lineup, and to further cement his commitment to the fastball, per Brooks Baseball, Sanchez had an exit velocity of 94.3 MPH against the heater (Sanchez ranked in the top 10 in overall exit velocity last year). Young players are also traditionally late to adapt to major-league breaking pitches. Can you blame them when they’re up against this or this?

Secondly, it has been proven that two-hole hitters collect more plate appearances per season than the three through nine spots. This is not new information, but the exact number of plate appearances has been up for debate for years. Beyond the Box Score might’ve ended the debate while also examining how the two hole has changed, stating that “[e]ach drop in the batting order position decreases plate appearances by around 15-20 a year,” which might explain why MVPs Trout and Donaldson have made a living there over the past few seasons. An extra 10-20 plate appearances could mean an extra home run or two over the course of the season. Baseball is a game of inches, but it’s also a game of runs.

With a lineup bereft of veteran power and more intent on utilizing the “Baby Bombers,” as they’ve been so aptly named, moving Sanchez up to second could and should give the lineup a much-needed boost if the reliance on Greg Bird and Aaron Judge should go somehow awry. Veterans Matt Holliday, Chase Headley and Starlin Castro have had good seasons and impressive resumes, but they need to return to All-Star form to carry a team of youngsters and a questionable starting rotation. No one really expects Sanchez to produce at the same rate that he did last year, but perhaps a bump up would allow him to produce at an above-average level again.

wRC+ by Leverage: the Good, the Bad, and the Funky

So I got a little carried away with the new splits leaderboard when I was looking up some wRC+ data. I was curious about which players performed the best/worst in high-leverage situations and one thing led to another and it led me to looking at top performers across the three leverage situations (low, medium, and high). If you want to know more about how leverage is calculated there is an old article in The Hardball Times here.

I used the splits leaderboards to gather 2016 hitter data by leverage situation and I only included players who had a minimum of 20 PA per split. Once I gathered all the data I converted each player’s wRC+ by leverage situation to a percentile and calculated each player’s mean percentile rank along with the variation around the mean using standard deviation to produce the following plot.

The blue line is just a LOESS line showing the general trend of the data. What the line is telling us is that players on the extreme end of the percentile ranks also seem to have the lowest variation or, more simply put, good players seem to be consistently good and bad players seem to perform poorly across all leverage situations. Using that plot as my baseline, I started exploring the data to answer some question about player performances in 2016. I included the top 10 players in ordered tables going from from least interesting to most interesting, at least in my opinion. First, let’s look at the top performers from this year.

Players who ranked highest in wRC+ across all leverage situations
Leverage Rank
Name Low Medium High Mean Rank SD
Mike Trout 98 97 99 98 1
Freddie Freeman 97 88 94 93 4.6
Josh Donaldson 97 94 88 93 4.6
Anthony Rizzo 93 88 97 92.7 4.5
Joey Votto 96 98 84 92.7 7.6
David Ortiz 96 99 77 90.7 11.9
Matt Carpenter 91 82 94 89 6.2
Paul Goldschmidt 88 86 88 87.3 1.2
Tyler Naquin 93 81 87 87 6
Ryan Schimpf 80 86 93 86.3 6.5

Boring, Mike Trout leads the way as the top performer. Apparently it doesn’t matter when he comes up to the plate; he is going to smash the ball. But I’m not going to focus on Trout, as I’m not qualified to write about him and he’s above my pay grade, so let’s leave him to the professionals. Like I said before, least interesting first and hopefully it’ll get more exciting as we go. Here’s a fun fact to keep you going: In high-leverage situations among players with a minimum of 20 PA, Ryan Howard led the league in ISO with a 0.640 mark. Ryan Schimpf was second with an ISO of 0.542. And Howard did that with a 0.118 BABIP, too.

Second, let’s take a look at the worst performers of the season.

Players who rated as the worst performers across all leverage situations
Leverage Rank
Name Low Medium High Mean Rank SD
Yan Gomes 17 23 0 13.3 11.9
A.J. Pierzynski 17 21 9 15.7 6.1
J.B. Shuck 25 16 11 17.3 7.1
Nick Ahmed 15 35 3 17.7 16.2
Jake Marisnick 37 19 6 20.7 15.6
Ramon Flores 21 20 21 20.7 0.6
Gerardo Parra 33 29 1 21 17.4
Juan Uribe 19 38 11 22.7 13.9
Adeiny Hechavarria 20 34 15 23 9.8
Alex Rodriguez 19 30 22 23.7 5.7

After a pretty impressive career, although it also came with its fair share controversy, we see A-Rod make this list. And it doesn’t look like he is going to be playing again this year, which casts some doubt on whether he is going to make it to 700 career home runs (he’s currently at 696).  But more importantly, our poorest performer of 2016 looks to be Yan Gomes. I was inclined to say A.J. Pierzynski should actually be considered the poorest performer of the year since his standard deviation was about half of Gomes’, but then I noticed that Yan Gomes was in the 0th percentile in high-leverage situations — literally the worst. Not all-time worst, but still pretty bad! And I guess if you want to argue that the worst percentile should actually be 1, as in the 1st percentile, then you could make that argument, but the value was rounded to 0 when Yan Gomes registered a whopping -72 wRC+ in high-leverage situations. The second-worst was Gerardo Parra at a -59 wRC+; that’s a pretty significant gap between first and second. Fun-fact time: In high-leverage situations, Mike Zunino ran a 30.8% walk rate, although he also struck out 30.8% of the time too. Yasmani Grandal had a 30.4% walk rate to go with a much smaller 13% K%.

Everyone always seems to be looking for players who are on the extreme ends of the leaderboards, but let’s give some love to the unsung heroes of the world, the completely average performers! I wasn’t sure if I simply wanted to use mean percentile rank as a measure for averageness, so I decided to go with what I called Deviation in the table. Deviation is calculated by adding the standard deviations (SD) of a players percentile ranks to the Δ50 column. The Δ50 column is calculated as the absolute value of a players mean rank minus 50.

The most average performers of 2016 in wRC+
Leverage Rank
Name Low Medium High Rank SD Δ50 Deviation
Scooter Gennett 55 46 49 50 4.6 0 4.6
Ezequiel Carrera 46 44 51 47 3.6 3 6.6
Leonys Martin 44 54 47 48.3 5.1 1.7 6.8
Matt Duffy 41 49 49 46.3 4.6 3.7 8.3
Avisail Garcia 45 51 42 46 4.6 4 8.6
Howie Kendrick 46 59 52 52.3 6.5 2.3 8.8
Johnny Giavotella 40 44 42 42 2 8 10
Jason Castro 47 49 62 52.7 8.1 2.7 10.8
Jonathan Schoop 62 53 54 56.3 4.9 6.3 11.2
Brandon Phillips 55 48 63 55.3 7.5 5.3 12.8

And Scooter Gennett comes away as the most average performer of the season! He also ran a 0.149 ISO on the season and I think 0.150 is usually considered average. Look how wonderfully average these guys were; we should all take a minute to enjoy the little things in life. I realize this may not be the sexiest table, but it’s still interesting. You might not be getting a whole lot out of these guys over an entire season, but they are going to go up there and do average things whether you like it or not.

Two tables left — hopefully you’re still with me here. Let’s look at consistency. People always say consistency is key. I guess that’s good advice except when you’re on the terrible end on the spectrum.

Table looking at the most consistent performers based on percentile rank
across the 3 leverage situation (low, medium and high)
Leverage Rank
Name Low Medium High Mean Rank SD
Ramon Flores 21 20 21 20.7 0.6
Ivan De Jesus 32 32 33 32.3 0.6
Mike Trout 98 97 99 98 1
Paul Goldschmidt 88 86 88 87.3 1.2
Johnny Giavotella 40 44 42 42 2
Yunel Escobar 66 69 65 66.7 2.1
Hunter Pence 79 76 80 78.3 2.1
Wilson Ramos 81 80 85 82 2.6
Alexei Ramirez 26 32 28 28.7 3.1
Austin Jackson 43 38 37 39.3 3.2

Ramon Flores and Ivan De Jesus both had extremely consistent seasons; it’s just too bad they are on the wrong end of the spectrum. But I have to say Ramon Flores beats out Ivan De Jesus as he registered on average 12 percentile ranks poorer. In third we see Mike Trout showing incredible consistency while being the top performer in the league, followed closely by Paul Goldschmidt. It’s interesting see the top four players on this list from opposite ends of the spectrum, but the rest of this list bounces back and forth as well.

And here we are, the last one or as the title says “the Funky”. I found that volatility was the most interesting question, or which players showed the most boom or bust in 2016. Most of the players in this list performed best in low- and medium-leverage situations, often above the 90th percentiles.

Looking at players who showed the highest volatility based on percentile rank
across the 3 leverage situation (low, medium and high)
Leverage Rank
Name Low Medium High Mean Rank SD
Sandy Leon 96 84 2 60.7 51.2
David Peralta 95 29 1 41.7 48.3
Dansby Swanson 23 99 15 45.7 46.4
Yangervis Solarte 93 73 5 57 46.1
Mac Williamson 59 95 4 52.7 45.8
Alex Avila 36 99 12 49 44.9
Jarrod Saltalamacchia 41 9 97 49 44.5
Pedro Alvarez 86 85 9 60 44.2
Ryan Zimmerman 21 85 1 35.7 43.9
Kris Bryant 98 91 19 69.3 43.7

After perusing though the list, one of the most interesting names that jumps out should be Jarrod Saltalamacchia and his 97th percentile rank in high-leverage situations last year. And here’s another twist, would it surprise you to hear that in 2016 Miguel Cabrera was the least-clutch hitter among all Tigers qualified hitters? Check out the Tigers leaderboard here. But the 2016 volatility award goes to Sandy Leon, who absolutely mashed balls in low-leverage situations, was no slouch in medium-leverage spots, but dropped off the map in high-leverage situations. I have no idea how BABIP relates to wRC+, but with Sandy Leon it looks like his BABIP reflects what was happening in the different situations (0.434, 0.393 and 0.190). There is probably some combinations of descriptive stats that would explain some of the variance, and BABIP may very well be included, but I’m not going to go into that here.

Hope you enjoyed this. If anyone wants a copy of the R code I used to make the graph and tables, leave a comment below and I’ll pass it along. I ended up finding a pretty cool library to create html tables in R so you don’t have to mess around with formatting and manual inputs. As long as you’re willing to put a little work into understanding css you can basically customize the look of your tables.