Author Archive

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

New Library Section: Contract Details

While contractual details may not be sabermetric statistics or concepts, they can still be really confusing. I consider myself a pretty knowledgeable baseball fan, yet I still get baffled with details about player options and service time. Baseball is one of the more complicated sports in terms of rules, and so it only makes sense that the many transaction rules surrounding the game are just as intricate and tedious.

As a result, I’ve started a new hub over at the Library for contract details. You can find the hub underneath the “Sabermetric Principles” drop down tab, and I’ll be adding pages to it throughout the next week. At the moment, the first page up there is on Player Options. I also have planned articles on waivers, service time, and a few miscellaneous topics like the Rule 5 draft. If there are any other topics that you would like to see covered, please contact me either on Twitter or using the “Contact” link provided in the sidebar at the Library.

After the jump, you’ll find the write-up on player options that can now be found at the Library.

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The Curious Case of Ben Zobrist

This piece was originally written for a mainstream audience, yet I’ve never been able to find a good place for it. I think it’s a good example of how you can write sabermetric pieces without relying heavily on advanced statistics and without scaring away new readers. Enjoy.

There are some players in baseball that are chronically underappreciated by fans. These are the players who do not fit into any of our traditional molds: they are first basemen, but not power hitters; leadoff hitters, but not basestealers; bullpen aces, but not closers. Growing up following the game, we learn to expect certain things from specific players, and become baffled when a player does not fit in a specific mold. What to do with a clean-up hitter that only hits 20 homeruns, or a leadoff hitter that hits .260 and steals 4 bases? Both these players may still be valuable – the clean-up hitter could have hit 50 doubles and the leadoff hitter could have reached base more often than a .300 hitter – but our expectations blind us, leading us to view these players as inherently less valuable than others.

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Pitchers and Injuries: It Happens

When news broke on Wednesday of Adam Wainwright‘s season-ending injury, it obviously was quite distressing news for Cardinals fans. Not only was Wainwright the ace of the Cardinals’ pitching staff, but the Cardinals are projected to be thick in the race for the NL Central, making his contributions all the more valuable. While Wainwright isn’t costing the Cardinals much this season, the list of pitchers that will be competing to replace him isn’t anything to get excited about. If I were a Cardinals fan, I’d be watching this video over and over and over again, drowning my sorrows in fond memories and root beer.

But Wainwright’s injury isn’t traumatic only for Cardinals fans: no matter what team you root for, this news is frightening. Wainwright is a relatively young pitcher (entering his age 29 season) and he’s pitched 230 innings each of the previous two years. He’s been a perennial Cy Young contender, and never had significant arm issues before. If this sort of an injury can happen to him, well, who isn’t at risk?

This is probably old news for the majority of FanGraphs readers, but this point can’t be driven home often enough: pitchers are fickle creatures that are always at risk for an injury.

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Understanding Projections, “True Talent Level”, and Variability

This is the second in a series of posts about projections. The first part was about the methodology behind each projection system. In this section, we look at what projections are actually telling us.

If you’re new to projections and want to use them to, say, help with your fantasy team, it’s easy to make a common mistake: underestimating the built-in variability in projections. Many people – and I used to be among this group myself – view projections as hard and fast guesses at a player’s production this next season. Most people get into projections as a result of fantasy baseball, so this makes sense; we all want to know which player is going to hit 30 homeruns this next season and which will steal 40 bases. However, projections are actually measuring something different than a player’s expected production: they’re measuring a player’s true talent level.

This might seem like an arbitrary distinction, but trust me, it’s not. As we all know from our day-to-day lives, having a “true talent level” at a particular skill does not necessarily mean you’ll perform at that level every single time in the future. Our minds love to ignore variability and instead treat outcomes as solely talent-driven, but the world doesn’t work that way. Let’s consider a couple examples.

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Food Metaphors, Replacement Level Style

When writing my irreverent NotGraphs post on Casey Fossum, an interesting question popped into my head: how could I best explain the concept of a replacement level player using a food metaphor? In other words, is there a “replacement level” food? Not every baseball fan is a math nerd, but ALL sports fans love food. This is an indisputable truth, and means that food metaphors have the potential to be one of the most potent teaching instruments since these amazingly quirky mathematics videos.*

*Also, before you ask, this post is a direct reference to Fire Joe Morgan and their historic “Food Metaphors” tag, possibly the best thing that Ken Tremendous has ever created, ever. And yes, I’m a huge fan of “The Office”.

Before we get into the nitty gritty of finding the perfect food metaphor for replacement level, we need to know what replacement level is. In case you have forgotten (or don’t know), here’s Graham MacAree’s description of replacement level, as taken from our page in the Library:

We can define a replacement level player as one who costs no marginal resources to acquire. This is the type of player who would fill in for the starter in case of injuries, slumps, alien abductions, etc.

These are essentially the Triple-A filler players that can be found in every organization (and in copious amounts on the free agent list) every year. They cost next to nothing to acquire, can be found in massive quantities, and should only be used in case of emergency – at best, they make adequate bench players. They are, in short, the very base of major league baseball’s (triangular) talent distribution.

So with this in mind, what’s the ideal food to capture the essence of a replacement level player? Let’s take to the Twitter!

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The Projection Rundown: The Basics on Marcels, ZiPS, CAIRO, Oliver, and the Rest

Now that football season is over and baseball is once again close at hand, Projection Season is well underway. Fantasy players, analysts, bloggers, and plain ol’ fans – everyone turns to projections to help them this time of year. The Hot Stove has cooled down and Spring Training has just started, so really…what else is there to do?

With that in mind, I’ve got a handful of posts on projections in the works for the next week. This is the first one, and in it I deal with a basic question: what are the different projection systems available, and how are each of them calculated? In order to know how to properly use each projection, it’s always a good idea to understand what data is taken into account and how it is used. Remember: there is no one “gold standard” for projection systems. Each system will tell you something slightly different, so whenever trying to draw conclusions from projections, it’s best to use as many sources as possible.

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“Sabermetrics for Dummies”: Mainstream Media Style

Jason Collette and Tommy Rancel talking with J.B. Long from the Bright House Sports Network.

Rarely do you ever see a mainstream media outlet take the time to discuss sabermetric stats. Every now and then you’ll see a passing reference to WAR or FIP on ESPN, but the announcers have a maximum of 30 seconds to introduce the statistic, explain what it means, and make their point. These mentions are great for general awareness of sabermetric statistics, but do they actually educate anyone? They can make be a good introduction to a statistic and make someone curious to learn more – and don’t get me wrong, I love when mainstream news sources mention saber stats – but to truly educate someone about sabermetrics takes more than that.

Enter the Bright House Sports Network.  While Bright House is a major sports network in the Tampa Bay area, covering topics ranging from national sports stories to local high school teams, they’ve begun augmenting their baseball coverage with some sabermetric analysis. Jason Collette, Tommy Rancel, and R.J. Anderson – three premier Rays bloggers – contributed articles on the BHSN website during the later half of the 2010 season, using their analyses as a springboard for readers to become familiarized with advanced statistics.

And now, Bright House is taking it a step further: filming “Sabermetrics for Dummies” videos with Jason, Tommy, and reporter J.B. Long. This first video is a mere introduction to the series, but more videos will be released this week and the topics will include wOBA, BABIP, LOB%, WAR, IsoP, and FIP. These are extended videos, with the idea of explaining to viewers how the sabermetric stats are calculated and why they are useful.

Is it just me or is this rather unique? Has any other mainstream sports station done something similar? I’d love to hear examples of other media outlets doing similar projects (please share!), but at least to my knowledge, the Bright House Sports Network is ahead of the curve.

Left On Base Percentage (LOB%): A Video Explanation

Analyzing pitchers is one of the most difficult things to do in baseball (at least, in the “non-playing” category). Pitchers are notoriously fickle, and their performances can vary widely from start to start and year to year. They don’t follow a set aging curve like position players (who peak at ages 27-30), but improve and decline with no overarching pattern. Some pitchers are late-bloomers and don’t peak until their 30s (e.g. Randy Johnson), while others peak in their early 20s and never reach the same level again (e.g. Scott Kazmir).

Not to mention, when you try analyzing a pitcher’s results, there are so many variables in play. How much of a pitcher’s performance is his talent shining through, and how much is the defense, opposing team, umpire, catcher, and ballpark? With no discernible difference in his pitch movement, sequencing, or velocity, a pitcher may let up 8 runs in four innings during one start yet turn around and throw an 8 inning shutout his next time out. How much of that variance should we pin on the pitcher and how much is outside his control?

These are all difficult questions without any exact answer, which is why there are a large number of pitching statistics available here at FanGraphs. In order to see past those confounding variables and get a grasp on a pitcher’s true talent level, it’s best to look at a wide range of statistics instead of relying upon one as the be-all-end-all. ERA, FIP, tERA, xFIP, BABIP, LOB%, HR/FB – all these stats tell you something different and paint a more complete picture when used together.

And so, here’s a chance to learn a bit more about one of those statistics: Left On Base Percentage (LOB%). This video is courtesy of Bradley Woodrum from DRaysBay and Tom Tango from The Book Blog:

Heat Maps: What They Show, and Mistakes to Avoid

When David Appelman dropped his newest bomb on us the other day and announced that you could now find customizable heat maps here at FanGraphs, I think it’s safe to say that most of us saber-nerds had our minds blown. Personally, I’ve always admired the work that Dave Allen and other Pitch F/x gurus have done, yet being unskilled in the art of SQL and R, I figured this was a type of analysis that would always be beyond my abilities. Following in the footsteps of other FanGraphs updates, though, this analysis has now been democratized and made available to even the newest of saber newbies. You don’t have to know how to string together code or manipulate huge data sets: all you need is a mouse and a pointer finger.

But heat maps are like any other tool: before you can add them to your toolbox, you have to understand how to use them. Pitch F/x data can be a tricky thing to interpret, and many experienced saberists (myself included) have made mistakes because they didn’t know what they can and can’t do with that data. What exactly are heat maps? What do they show, and how should we use them? Let’s go exploring:

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What Is the FanGraphs Library, and How Do I Use It?

After Dave Cameron’s introduction yesterday, I wanted to take a brief minute to explain the purpose and goal of the Library for those that haven’t seen it before. And before I begin, thanks to everyone for the kind words yesterday about the Library.

The Saber Library was initially created around a year ago, in response to some criticism we’d received at DRaysBay. Many (most) of our columns there deal with advanced stats, like the work done here on FanGraphs, yet many readers were having a tough time accessing the information and learning about sabermetrics. We’d tried running a saber primer or two, yet those could never be comprehensive enough to cover everything that needed to be said. Readers wanted to learn, but didn’t have a place to go to do so.

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Statistic Percentile Charts

How awesome is this chart? It’s simple, easy to understand, and imparts a swath of information all at once. I had no idea what league-average Isolate Power (ISO) was until now, but bam, there it is. This, my friends, is a thing of beauty. Until I saw this chart, I had no idea I needed all this information, but now that I’ve seen it, I want more.

I suppose I shouldn’t be surprised: this work of genius was created by Lee Panas, the writer at Tiger Tales and the author of “Beyond Batting Average”, a concise, well written book geared toward introducing everyday baseball fans to sabermetric statistics and analysis. It’s a great book and I recommend it thoroughly (although in the spirit of full disclosure, I must admit that I have a soft spot for Lee: not only am I a fellow saber-ed nut, but like Lee, I’m forced to root for my favorite ball team from afar, stuck in the wintery hellscapes of New England).

But I’m not writing this article as a book review (you can find those elsewhere); I’m writing because of that beautiful graphic up above. One of the complaints I hear most frequently from saber-newbies is that while they want to use these new statistics, they have no idea if the numbers they’re seeing are good or bad. Is a .320 wOBA good? Exactly how bad is a -5 UZR? I know it’s bad, but is it only mildly bad or tear-your-eyes-out bad? And what, pray tell, does a 4.00 tERA mean? It’s one thing to understand the theory behind the statistic, but sometimes understanding its scale can be just as challenging.

And so, I’ve taken Lee’s lead and included similar charts on each of the statistic pages here in the Library. The league-average rates are all accurate, and I’ve estimated percentiles based on the scores of all batters with more than 400 PA and pitchers with more than 90 IP. These percentiles may not be 100% accurate in all instances, but they are close enough to work as estimates in order to provide context.

Thanks again to Lee for the inspiration. If you like the charts, go check out some of his work.

Suggestions and Questions

Now that the new version of the Saber Library is up and running, I’d like this to be more than just a reference site: I want this to be an area where people can come and have their questions about sabermetrics answered. If you ever have any questions about a sabermetric principle or statistic, please reach out to me on Twitter (@steveslow) or by emailing the Saber Library’s mailbox: saberlibrary at

I can’t promise that I’ll give you an in-depth answer to every question, but at the very least, I’ll do my best to direct you to the right place to find the information you’re seeking. Also, if I get enough questions, I’ll start a “mailbag” feature and answer some commonly asked questions in a weekly post. I’m no expert on sabermetrics – far from it – but we have many knowledgeable writers on staff here at FanGraphs, so if I don’t know the answer to a question, I’ll be sure to find someone else who does.

Also, feel free to contact me with the Saber Library’s email address if you notice any factual mistakes with the Library (like I said, I’m no expert), you have any suggestions for the site, or you have any cool content / links to pass along. There are many writers out there making saber primers, filming introductory videos, and creating educational visuals, and I want the Library to help promote those efforts.

In other words, if you have any questions, suggestions, or cool links, hit me up.

Welcome to the Library!

Really, the Saber Library should never have existed. It took way too much time to create, cost me out-of-pocket money, was tedious as hell, had no tangible reward, and took away time from what I really should have been doing: finding a job. It was a project that most people in their right minds would never contemplate doing. Thankfully, though, I’m stupid.

When combined with large doses of boredom and time, stupidity can be a dangerous thing: a perfect storm for mayhem. In the ninth grade, my best friend experienced a particularly brutal convergence of these three things in bio lab one day, and stuck tweezers in the electrical socket to “…see what would happen”. Sparky eventually escaped his legacy by doing something even more stupid (breaking his leg during a chess match),  but so far, I haven’t been so lucky. Instead, I got to re-edit this entire thing for all you here at FanGraphs. Am I a sucker or what?

Thankfully, though, there are lots of smart people out there doing smart things that’s I’ve been able to steal from. Bradley Woodrum’s introduction to sabermetrics videos? Swipe! Graham MacAree’s incredible Saber 101 series? Stolen. Visuals from Justin Bopp at Beyond the Boxscore? Really, this is all too easy. Everyone’s been very gracious and willing to share, which has turned this site into a regular hodgepodge of smart things contributed by smart people. Trust me, I’m just as surprised as everyone else.

The Saber Library is now almost a year old and it’s here at FanGraphs to stay. I’ll be updating pages as FanGraphs changes, adding new statistics while changing the information on old ones, and I hope to make this a living, breathing resource. Consider it your Hitchhiker’s Guide to navigating the universe of sabermetrics (see what I did there?). While the math may be confusing and the Book Blog may give you a headache, this site is here to tell you “DON’T PANIC!” Things are not as complicated as they seem, and you don’t need to understand multivariable calculus to understand sabermetrics.

In the Library, you’ll find individual pages for nearly every statistic housed here at FanGraphs, along with a selection of primers on important sabermetrics concepts. We’ll also be using this space as a separate blog on FanGraphs (á la NotGraphs and RotoGraphs) and we’ll be writing articles geared toward helping people understand statistics. This space will (likely) not be updated as often as the other FanGraphs blogs, but we’ll see how this space develops.

And so, enjoy! Tour through the Library, learn something new, and whatever you do, keep those tweezers in your medicine cabinet.