Which is Better? A Ground Ball Pitcher or a Fly Ball Pitcher

It’s very likely that if you’ve spent any time at all reading sabermetric analysis that you’ve heard some mention of a pitcher’s batted ball profile. You might have seen a reference to a guy being a “ground ball machine” or an “extreme fly ball pitcher” and perhaps you wondered to yourself, “which is better?” Would a pitcher be better off as one or the other?

In reality, there’s no ideal batted ball distribution for a pitcher, just like there’s no perfect distribution for a hitter. Pitchers would love to never allow line drives and get tons of infield fly balls, but within the realm of possible outcomes, you can be successful as a ground ball pitcher or as a fly ball pitcher. One isn’t better than other, they’re just different.

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Considering High Leverage Performance and Clutch Hitting

Human beings love big moments. We have an innate attraction to crescendos, buzzer beaters, walk-offs, and those scenes in movies when people sprint through airport terminals. It matters to us in a very primal way what transpires when the chips are down. This is why RBI is a popular statistic and why so much attention is paid to stats like batting average with runners in scoring position. We believe that players who perform well in the big moments are the best players. There are probably all kinds of cognitive and psychological biases at play, but I think we can all agree that success in critical situations is more highly valued than success in general. This is as true in life as it is in baseball.

Yet there is also a lot of evidence that tells us to ignore these performances in baseball, or rather, to treat them just like any other performance. A home run with the game on the line is more important than one in a blow out, but it’s not really a reflection of the player being better or being clutch.

This is a controversial stance. Sabermetricians have been commenting on the false “clutch” narrative for many years and have received a great deal of push back. The alternative view is that certain players are able to rise to the occasion and that they know how to slow the game down and deliver in critical spots. Rather than taking a hard line on the subject rhetorically, instead I’d like to review a bit of the research done on clutch and provide some important questions to consider regarding clutch performance.

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What Do We Know About Catcher Defense?

We’ve seen some pretty revolutionary baseball research over the two decades, but until about three years ago our public estimations of catcher defense were pretty limited. We had some idea about which catchers were the best at catching base stealers, but blocking, framing, game calling, and the other nuances of the job were relative unknowns. We knew they were there, we could see them at work in individual situations, but we just didn’t have quality, public data to give us a clear pitcher of catcher defense. That’s starting to change, although we’re still a long way from home.

Over the last couple of seasons, pitch framing has become a popular topic of conversation in the game with teams like the Rays, Pirates, and others seemingly targeting quality framers. We have had new metrics and seen lots of articles considering the merits of those catchers who can steal extra strikes. It’s hard to say if it’s permeated the baseball world, or just the advanced metrics/blogger world, but framing is the new “it” asset. We even saw our own Dave Cameron place a high value on catcher defense on his 2014 NL MVP ballot.

Catcher defense can essentially be divided into five categories: normal fielding, pitch framing, blocking, game calling, and controlling the running game. In no area are we perfect, but there are some areas that we can evaluate better than others. Catcher defense is an evolving area of study and hot topic of conversation. Let’s briefly consider what we do and don’t know about the most indispensable position.*

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Shutdowns, Not Saves: The Logic and the Leaders from 2014

Who led the league in saves in 2014? Hopefully, you don’t know the answer off the top of your head. Saves aren’t a good measure of anything relating to player performance or talent and with so many things you could remember about the 2014 season, you probably don’t want to waste vital brain capacity on a random piece of trivia like who had the most saves.

The reason saves aren’t very useful is because the rule itself is not designed to provide much information. You can earn a save if you strikeout Miguel Cabrera, Victor Martinez, and J.D. Martinez in a one run game or you can earn a save if you allow five base runners against the bottom of the Padres’ order. You don’t earn a save if you preserve a tie, or if you preserve a lead in the 7th inning. Nearly everything about the rule is arbitrary, which leads you to find arbitrary results.

But the idea of something like a save is compelling for many people. There is a desire for a statistic that measures the number of a times a reliever comes in and pitches very well in an important spot. We can look at rate stats like ERA, FIP, or xFIP or cumulative numbers like RE24 or WAR, but it’s perfectly fine to want some sort of counting stat that tracks how many times a reliever slammed the door (or didn’t).

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Stats To Avoid: Runs Batted In (RBI)

Even the best statistics, things like wRC+, are imperfect. You can’t take wOBA as a perfect measure of truth or be certain that FIP is a perfect estimate of pitcher performance. In many cases, they may be the best we have, but we acknowledge the limitations. While it’s true that even our favorite metrics have flaws, that doesn’t mean that we should give equal considering to extremely flawed statistics.

This post will be the first in a series, scattered across the offseason months, that demonstrates the serious problems associated with some of the more popular traditional metrics. Many of you are well aware of these issues, but plenty of people are reading up on sabermetrics for the first time every day and our goal here is to create a comprehensive guide that helps everyone get the most out of everything we have to offer. Part of that puzzle is explaining why you might not want to look at things like batting average, RBI, and wins. Today, we’ll start with Runs Batted In (RBI).

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How To Use FanGraphs: Player Pages!

The mission of the FanGraphs Library is to make it easier for readers to understand and use our data and site. This means providing information about the statistics and principles we use, but it’s also a place to point out the various features of the site and how to get the most out of the metrics we offer. A couple of months ago, I wrote about our leaderboards and today I will discuss everything you can do on individual player pages.

We’ll be using Lorenzo Cain as an example because he’s the rising star of the moment. The pitcher pages are only different in the specific statistics they offer, but the basic format and set of features are the same.

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What Exactly is a Projection?

It’s always important to know exactly what question you’re asking. In baseball, one of the most difficult distinctions for many people is difference between how a player has performed and how the player is going to perform. It is very common to see analysis, even from well-versed fans of advanced metrics, that goes something like this.

“You want Player X up in this situation because he has a .380 wOBA against LHP this year.”

Even if the sample size is sufficient, that statement isn’t an ideal reflection of our expectations about the future. We frequently treat the recent past as an estimate of future skill even though we know that it isn’t. Certainly, the educated observer doesn’t need to be convinced that the ten most recent plate appearances aren’t useful information on their own, but even the last 600 PA aren’t what you want. What you want, when making a claim about the present or future, is a projection.

Now you might not always be asking a future-oriented question. If you want to decide who the best hitter in baseball was in 2014, you don’t need a projection. If you want to know who has thrown the most effective slider since 2012, you don’t need a projection. But if you want to know who is the best hitter in baseball right now or who is going to be a better signing next year, you want a projection.

A projection is a forecast about the future. It is certainly imperfect. It’s an estimate. Projecting a .400 wOBA doesn’t mean you make a $1,000 bet on that player running a .400 wOBA, it means that’s the best guess for how that player is going to perform. On average, some players will do better and some players will do worse. There’s error involved in the actual calculations, but the idea behind it is sound.

You want to make decisions about the future based on every single piece of relevant data and you want to weigh that data by its importance. Steamer projects Miguel Cabrera will have a .407 wOBA in 2015. What that means is that Steamer, based on everything it knows about Cabrera’s history and the way players typically age, we should expect a .407 wOBA. Steamer knows that Cabrera had a “down” year in 2014, but it also knows he had a great 2013 and that hitters of his caliber usually age in a certain way. It’s all built in. You don’t just care how a guy did last year or how he did in his career, you care about the entire body of work and the underlying factors that are driving it.

Think about it like the weather. You want to know if it’s going to rain today. How would you go about predicting whether or not it will rain? You would obviously pay some attention to the recent weather, but you would also look at historical weather patterns, and then you would look at the conditions in and around your area. It rained to your west last night: When that happens, how likely is it that the rain will come your way? There is a certain mix of pressure and air flow, what does that usually lead to? It’s all relevant information.

The same is true for baseball players. You care how Cabrera has hit for the last 600 PA. Those are super important data points, but they aren’t the only ones. You also care about the 600 PA before that. And before that. The older the data, the less important, but it never becomes useless. Additionally, you don’t just care about performance, you care about the underlying numbers.

If a player has a .400 wOBA with a.390 BABIP, you know most of their great season is predicated on getting lots more hits on balls in play than average. You wouldn’t automatically expect that .390 BABIP to continue, so you need to determine the typical BABIP regression for players of this type based on everything else you know about them.

You never want to make a decision based on a player’s simple past. You want to use that data to make a valid inference about the future and the process of doing so constitutes a projection. There are all sorts of different methods. Some are as simple as taking a couple years of data and weighing them by recency. Some like ZiPS, Steamer, Oliver, etc use much more advanced methodologies to estimate how well they think a player will perform using all sorts of information about that player and similar players of years past.

There is no ideal system, but the idea of projection is ideal. You care that the Royals won X number of games last month, but that doesn’t mean they’ll win X games this month. The last month is relevant, but it isn’t the whole story. Baseball is volatile and unpredictable any one sample of data is going to deviate from the true, underlying skill of a player. You want to do your best to make the best guess you can about their future and then use that to make decisions. That’s projection.

We like projections at FanGraphs. They’re useful for approximating current true talent levels and they help us predict which teams will be successful and which teams won’t. You could guess who was going to win the divisions based on the previous year’s player performances, but those players are going to perform differently this year and you want to account for that.

Many people are turned off by the idea of projection because projections seems like a black box. If you see a guy is hitting .380 wOBA this year but the projection says he’s a .340 wOBA hitter, you can’t easily internalize that a .340 wOBA hitter has produced a .380 wOBA to date. It’s human nature to assume the outcomes we observe are measures of truth, when in reality, they are influenced by randomness.

So when a stat-geek says they don’t want Player X to hit because they aren’t great, even though the player has a .350 wOBA during the last 400 PA, it doesn’t make sense. They have hit well, so they are good. But that isn’t exactly right. Their last 400 PA matter, but they don’t tell the whole story. A projection is trying to tell the whole story.

The systems aren’t perfect and the nature of the beast means they won’t get very many players exactly right, but they do a better job predicting the future than the last six weeks or six months of data will.

But it all comes down to the question. You might not care very much about predicting the future or approximating true talent. If you only care about past value, you can stick to the raw stats. But if you want to say something about how well a player is going to perform and what their true talent is, you want a projection. FanGraphs houses many of these each year and you can follow along, not only with the preseason numbers, but how they change based on the data of a new season.

Questions about projections? Ask them in the comments!

The Biggest ERA-FIP Differences of 2014

Fielding Independent Pitching (FIP) is one of the more prominently featured statistics on FanGraphs and one of the bedrocks of sabermetric analysis. We all know that FIP is an imperfect measure of pitcher performance because it assumes average results on all balls in play, but we also know that it does a better job isolating the individual pitcher’s performance than simply looking at their ERA or RA9 because it only looks at strikeouts, walks, home runs, and hit batters. It’s a very informative tool, but it’s a metric derived from a subset of results.

When a pitcher’s ERA is significantly different from their FIP, the standard credo is that they were lucky or unlucky, but there are genuine reasons why a pitcher might have results that are better or worse than their FIP. To illustrate this, let’s take a peak at the biggest FIP over and under-performers of 2014.

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Why It’s Always Better to Use Multiple Statistics

One of the most common questions I get when talking about advanced metrics with people who are new to the experience is “what’s the best stat for looking at X?” My standard response depends on the particular question, but I almost always drop the caveat that you should always be looking at multiple pieces of information rather than one single stat and I don’t think I’m alone in offering that advice.

As our metrics for evaluating baseball improve there’s a desire among many for the new stats to push the old stats out of the conversation. Now that we have wOBA, why would you ever use OBP? And then once you have access to wRC+, is wOBA even necessary anymore? If we have K%, isn’t K/9 completely useless?

In some cases, that’s a fine idea, but in many you would rather have access to as much information as possible because stats that don’t do very well on their own can still be informative in the context of other statistics. Wins Above Replacement (WAR) is the best single metric we have to determine a player’s complete value, but WAR only conveys the answer to a very specific question. If you want to know about how good a player is overall, WAR is great. If you want to know if he’s a power hitter or a player with a good eye, WAR doesn’t do very much.

The same is true for wRC+. You know a 150 wRC+ means someone has had a very good season, but you don’t know if he’s doing it with a high average, good patience, excellent power or some combination of them. We’re striving for better measures of performance but you can’t only look at one or two numbers because baseball is full of questions that require a variety of tools to evaluate.

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Calculating Position Player WAR, A Complete Example

One of the hallmark statistics available at FanGraphs is Wins Above Replacement (WAR) and we’ve just rolled out an updated Library entry that spells out the precise calculations in more detail than ever before. There’s always been a clear sense of the the kinds of things that go into our WAR calculation, but we’re never just dropped an equation in front of you and said, “Here!”

As of today, we’ve done that and I encourage you to go check out our basic primer on WAR and our detailed breakdown of how we calculate it for position players. If you’re a hands on learner, grab a pen and paper or spreadsheet and follow along. I’m going to walk you through a complete examples of how to calculate WAR for position players. Let’s use the 2013 version of Joey Votto as our exemplar.

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The Beginner’s Guide to Using Statistics Properly

We’ve spilled a great deal of virtual ink and audible podcasting words on the nature of Wins Above Replacement (WAR) and defensive metrics recently. Jeff Passan of Yahoo! Sports and many who responded to his critique of the current WAR calculation dug into the relative merits of the metric itself and how well we’ve estimated it to date. That’s a great conversation to have and Dave has done the heavy lifting on behalf of FanGraphs in that regard. I’d like to pivot and discuss a very important point about the use of statistics in baseball: Everything has flaws.

Every single statistic is wrong. Your eyes are wrong. It is all wrong. Nothing we have will provide you with perfect information or even truly accurate information with respect to the underlying variables about which you care. You don’t get to choose between flawed and not flawed statistics, you get to choose between useful and not useful statistics. More importantly, statistics become useful based on your awareness of the proper way to wield them.

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The Beginner’s Guide to Measuring Defense

There’s a decent chance you’ve arrived at this page without a serious desire to hear more about defensive statistics. Trust me, I understand your frustration and your fatigue. Defensive stats like Ultimate Zone Rating and Defensive Runs Saved are controversial in some circles because they are reasonably new and the underlying data is somewhat hidden from view. You hear words like “flawed,” “absurd,” and “subjective” surrounding them. You’re tired of it.

Yet I’d like to lay out why we have advanced defensive statistics and how they work in the abstract. You won’t get to the end of this post and decide that UZR has perfectly measured Alex Gordon‘s defense, but hopefully you will have a better appreciation for why we measure defense the way that we do.

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Learning to Speak Saber: Runs and Wins

One of the things people love about baseball is that the game is both very simple and very complicated all at once. Baseball is simple in that all you’re trying to do is score more runs than the other team during 162 finite, nine inning contests. You are trying to reach base and advance runners and you are trying to prevent the other team from doing the same. How you go about doing those things is where baseball gets complicated. Jeff Sullivan often refers to baseball as being “obnoxiously complicated,” which I find to be a fitting description.

Think of all of the different possible outcomes of every pitch and all of the different pitches and locations from which the pitcher can choose. The complicated part of baseball is what makes baseball interesting, but the simple part of baseball is where you need to start to get your head around sabermetrics and player evaluation. Baseball is about producing and preventing runs.

As a result of that simple reality, the heart of baseball analysis is determining what leads to run scoring and run prevention. Specifically, how many runs is each possible action worth? If a player hits a single, how much has that player just increased his team’s odds of scoring a run? If a fielder makes a nice running catch, how many runs has he prevented? We don’t actually care about hits and walks and double plays, we care about how those finite events contribute to the overall goal.

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Defensive Metrics, Their Flaws, and the Language of Writers

If you spent time hanging around the comments section of Dave’s Alex Gordon piece, you lurked in the shadow’s of his conversation with Jeff Passan on Twitter, or you’re one of those people who Twitter searches the word “FanGraphs,” you probably saw a decent amount of skepticism about single-season defensive metrics this week. People tossed around words like “flawed” and “absurd.”

The interesting part of the debate, for me at least, was that there was skepticism from both sides. The sabermetric elite dove into an esoteric debate about how to best incorporate defense into WAR and less analytically minded fans used Gordon passing Mike Trout in WAR as kindling for their “WAR is silly” crusade.

Dave’s piece does a nice job covering exactly what it means to say Alex Gordon leads position players in WAR, but the fact that Dave had to write that piece in the first place speaks to a problem we often run into when using advanced metrics. It’s a communication problem. Dave addresses it, but I’d like to expand on it here because it’s vitally important.

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ERA, FIP, and Answering the Right Question

One of the things baseball fans and analysts work very hard to do is isolate individual performance. At the end of a game, there is a final score that tells you how many runs each team scored. At a very basic level, that’s all that really matters. Baseball is a battle to score more runs than your opponent over the span of nine innings repeated 162 times. Yet analyzing the game requires more information than that because we want explanations. We want to know which players are good and which players aren’t so good. We care about how individual performance contributes to winning.

For pitchers, this is especially difficult because while pitchers have a huge impact on the number of runs they allow, they don’t have complete control. You can’t just look at the number of runs a pitcher allowed and say they were definitively responsible for those runs and call it a day. You aren’t isolating their performance and if you aren’t isolating individual performance you’re looking only at outcomes, and that’s not typically very interesting.

Every statistic, or really any analysis in general, should start with a question. On a basic level, the question we have is “How good is this pitcher?” which more specifically translates into “How effective is this pitcher at preventing runs?”

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Why We Care About BABIP

Batting Average on Balls in Play (BABIP) is actually a pretty tried and true part of the baseball vernacular. Sabermetricians may have given it a long name with a fun-sounding acronym, but the principle goes back as far as presidential first pitches and wooden bats. Everyone knows that bloop hits and seeing eye ground balls go for hits quite regularly and that screaming rockets get snatched out of the air by leaping defenders pretty often. You couldn’t find a baseball fan alive who would argue with you on that simple fact.

BABIP is really just the amalgamation of all of those screaming rockets and bouncing grounders. When a batter puts the ball in play, it either goes for a hit or it doesn’t. Sometimes it’s a clean single, sometimes the defender can’t quite reach it. It’s a game of inches and these things happen.

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How to Use FanGraphs: Leaderboards!

In addition to updated glossary entries and blog posts extolling the virtues of various sabermetric statistics and principles, the revitalized FanGraphs Library is also going to be a place where we highlight features available at the site that will allow you to get the most out of our data.

Below, you’ll find everything you ever wanted to know about the FanGraphs Leaderboards. If you’ve been a long-time reader who never misses a single post, a lot of this might be old news. If you’re anything short of that, there’s a good chance you’ll pick up a few tricks to get the most out of the site.

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wRC+ and Lessons of Context

This introduction is a setup. Don’t fall for it. I’m going to present you with two stat lines and ask you to silently compare them. Your job is going to be to determine which player had the better season at the plate. Remember, it’s a trick.

  • Player A: 697 PA, .372/.463/.698, .476 wOBA, 42 HR, 59 2B, 103 BB, 61 K
  • Player B: 716 PA, .323/.432/.557, .423 wOBA, 27 HR, 39 2B, 110 BB, 136 K

If I hadn’t primed you, it would be hard to suggest anything other than that Player A had the better season. He’s leading everything, except for a slight disadvantage in walk rate. Player A had the better season, right? It’s obvious. Even though I told you it was a trick, you’re still struggling to find a way to argue the opposing side. I’m telling you that Player B actually had the better season, but that’s because I have more information. I know a couple of important pieces of information that you don’t have and it makes a world of difference.

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wOBA As a Gateway Statistic

Despite all of the rhetoric and talk-radio bluster, sabermetric principles and statistics aren’t actually very complicated. It might take a sharp statistician or savvy programmer to derive perfect park factors, but it doesn’t take anything more than a curious mind to understand and apply the basics. In my time working to help spread these principles, one of the most common and useful questions I get is about which few statistics a person should learn when trying to get into the world of advanced stats.

On Wednesday during my chat I got such a question. Here’s how I responded:

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FanGraphs Library Stat Glossary

To find a particular statistic, use Ctr-F and type in the abbreviation or stat name that you are looking for.


OBP – On-Base Percentage
OPS – On-base Plus Slugging
OPS+ – On-base Plus Slugging Plus
wOBA – Weighted On-Base Average
wRAA – Weighted Runs Above Average
UBR – Ultimate Base Running
wRC – Weighted Runs Created
wRC+ – Weighted Runs Created Plus
BABIP – Batting Average on Ball In Play
ISO – Isolated Power
HR/FB – Home Runs per Fly Ball rate
Spd – Speed Score
GB% – Ground ball percentage
FB% – Fly ball percentage
LD% – Line drive percentate
K% – Stikeout rate
BB% – Walk rate
O-Swing% – Outside-the-zone swing rate
Z-Swing% – Inside-the-zone swing rate
Swing% – Swing rate
O-Contact% – Outside-the-zone contact percentage
Z-Contact% – Inside-the-zone contact percentage
Contact% – Contact percentage
Zone% – Percentage of pitches within the zone
F-Strike% – First-pitch strike percentage
SwStr% – Swinging Stike percentage
wFB – Fastball runs above average
wSL – Slider runs above average
wCT – Cutter runs above average
wCB – Curveball runs above average
wCH – Change-up runs above average
wSF – Split-finger fastball runs above average
wKN – Knuckleball runs above average
wFB/C – Fastball runs above average per 100 pitches
wSL/C- Slider runs above average per 100 pitches
wCT/C – Cutter runs above average per 100 pitches
wCB/C – Curveball runs above average per 100 pitches
wCH/C – Change-up runs above average per 100 pitches
wSF/C – Slit-fingered fastball runs above average per 100 pitches
wKN/C – Knuckleball runs above average per 100 pitches


rSB – Stolen Base Runs Saved runs above average
rGDP – Double Play Runs Saved runs above average
rARM – Outfield Arms Runs Saved runs above average
rGFP – Good Fielding Plays Runs Saved runs above average
rPM – Plus/Minus Runs Saved runs above average
DRS – Defensive Runs Saved runs above average
BIZ – Balls In Zone
OOZ – Balls Out Of Zone
RZR – Revised Zone Rating
CPP – Expected Catcher Passed Pitches
RPP – Catcher Blocked Pitches in runs above average
TZ – Total Zone
TZL – Total Zone with Location data
FSR – Fan Scouting Report
ARM – Outfield Arm runs above average
DPR – Double Play runs above average
RngR – Range runs above average
ErrR – Error runs above average
UZR – Ultimate Zone Rating
UZR/150 – Ultimate Zone Rating per 150 defensive games


ERA – Earned Run Average
WHIP – Walks and Hits per Innings Pitched
FIP – Fielding Independent Pitching
xFIP – Expected Fielding Independent Pitching
SIERA – Skill-Interactive ERA
tERA – True Runs Allowed
K/9 – Strikeout rate
BB/9 – Walk rate
K% – Strikeout percentage
BB% – Walk percentage
K/BB – Strikeout-to-Walk ratio
LD% – Line drive rate
GB% – Ground ball rate
FB% – Fly ball rate
HR/FB – Home runs per fly ball rate
BABIP – Batting Average on Balls In Play
LOB% – Left On Base percentage
ERA- – ERA Minus
FIP- FIP Minus
xFIP- – xFIP Minus
SD – Shutdowns
MD – Meltdowns
O-Swing% – Outside-the-zone swing rate
Z-Swing% – Inside-the-zone swing rate
Swing% – Swing rate
O-Contact% – Outside-the-zone contact percentage
Z-Contact% – Inside-the-zone contact percentage
Contact% – Contact percentage
Zone% – Percentage of pitches within the zone
F-Strike% – First-pitch strike percentage
SwStr% – Swinging Stike percentage
wFB – Fastball runs above average
wSL – Slider runs above average
wCT – Cutter runs above average
wCB – Curveball runs above average
wCH – Change-up runs above average
wSF – Split-finger fastball runs above average
wKN – Knuckleball runs above average
wFB/C - Fastball runs above average per 100 pitches
wSL/C- Slider runs above average per 100 pitches
wCT/C – Cutter runs above average per 100 pitches
wCB/C - Curveball runs above average per 100 pitches
wCH/C - Change-up runs above average per 100 pitches
wSF/C – Slit-fingered fastball runs above average per 100 pitches
wKN/C – Knuckleball runs above average per 100 pitches

Win Probability:

WPA – Win Probability Added
-WPA – Loss Advancement
+WPA – Win Advancement
RE24 – Run Above Average based on the 24 Base/Out States
REW – Wins Above Average based on the 24 Base/Out States
pLI – A player’s average LI for all game events
phLI – A batter’s average LI in only pinch hit events
PH – Pinch Hit Opportunities
gmLI – A pitcher’s average LI when he enters the game
inLI – A pitcher’s average LI at the start of each inning
exLI – A pitcher’s average LI when exiting the game
WPA/LI – Situational Wins
Clutch – How much better or worse a player does in high leverage situations than he would have done in a context neutral environment



Batting – Park Adjusted Runs Above Average based on wOBA
Base Running –  Base running runs above average, includes SB or CS
Fielding – Fielding Runs Above Average based on UZR (TZ before 2002)
Replacement – Replacement Runs set at 20 runs per 600 plate apperances
Positional – Positional Adjustment set at +12.5 for C, +7.5 for SS, +2.5 for 2B/3B/CF, -7.5 for RF/LF, -12.5 for 1B, -17.5 for DH
Fld + Pos
RAR – Runs Above Replacement (Batting + Fielding + Base Running + Replacement + Positional)
WAR – Wins Above Replacement


RA9-Wins – Wins Above Replacement calculated using Runs Allowed
BIP-Wins – BABIP wins above average
LOB-Wins – Sequencing in wins above average (calculated as the difference between RA9-Wins and WAR minus BIP-Wins)
FDP-Wins – BABIP and Sequencing wins above average, also the difference between RA9-Wins and WAR
RAR – Runs Above Replacement
WAR – Wins Above Replacement