Archive for Glossary

Library Update: wOBA and wRC+

A couple of weeks ago, we announced a renewed commitment to the FanGraphs Library and promised regular updates to glossary entries and blog posts. You’ve probably noticed our weekly FanGraphs Q&A chats at 3 p.m. EST on Wednesdays, but the other changes to the library aren’t necessarily obvious on the site’s main page.

If you haven’t had a chance to check out the changes to the library, the entries on Weighted On-Base Average (wOBA) and Weighted Runs Created Plus (wRC+) have been updated and include more current information, a more thorough explanation of how and why you should know and use these stats, and for the first time at FanGraphs, information on how to actually calculate wRC+.

Additionally, you’ll find the library’s blog populated with a couple of posts discussing the importance of learning wOBA and wRC+.

If you’re looking for information on other statistics we offer, on how to make use of various FanGraphs features, or if you have related questions, check out the weekly chat, comment on this post or posts in the library blog or contact me on Twitter @NeilWeinberg44. We’ll roll out more educational posts and glossary updates each week, so be sure to check often.


A (Re)Introduction to the FanGraphs Library

Entering play on Thursday night, Kyle Seager owned a .274 batting average. Chris Johnson‘s average was a nearly identical .273. The two third basemen have played in a similar number of games and have come to the plate close to the same number of times. If you use batting average to evaluate these players’ seasons, you’d come to the conclusion that Seager and Johnson are essentially equivalent players this year.

They’re not. In fact, it’s very clear Seager is substantially better than Johnson. Let me rephrase that: It’s very clear Seager is better than Johnson — but only if you’re well-versed in the language of baseball statistics. If you know how to properly value walks, extra base power, baserunning and defense, the difference between Seager and Johnson is impossible to miss.

At FanGraphs, our writers use statistics and metrics like wOBA, wRC+, FIP and WAR to evaluate baseball players and teams. We provide those tools, and more, so others might conduct evaluations on their own. Want to know Miguel Cabrera‘s wOBA against lefties? You can find that on FanGraphs. But what if you don’t know what wOBA means, how it’s calculated or why you should care about it more than batting average?

You can find some of that information on FanGraphs. A well-motivated, self-starter could show up at the site, notice something called wOBA on the leaderboards, go to the glossary and figure out what it means and why it’s important. But it can be intimidating and challenging for people who are just starting out to make sense of everything we offer.

In an effort to make advanced statistics easier, and to understand and to better use the data and features available at FanGraphs, we’re relaunching and promoting the FanGraphs Library. There’s a lot of great information there already, but this revamped library is even better. There’s a steep learning curve, though, so I’ve been tasked with making things a bit simpler.

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FanGraphs Library Expansion

If you have dropped by the FanGraphs Library recently, you may have noticed that the place has a bit of a new look. Ben Duronio, Bradley Woodrum, and I have recently finished expanding the Library to include more information, so now there are individual subject headings — with multiple articles under each heading — on the following new areas:

PITCHF/x
Wins Above Replacement
Business

We’ve always had sections for Offensive Stats, Defensive Stats, Pitching Stats, Win Probability Stats, and Sabermetric Principles, but considering the importance of PITCHF/x and WAR, I’ve always been disappointed that we didn’t have fuller sections on each of them. Now we do, and with all the pages we wrote on each, you should be able to answer almost any question you have on either topic.

Want to know how Pitching WAR is calculated or read the explanation behind why we use FIP as the basis of pitcher evaluation? Curious about the concept of “replacement level”? Want to know what a certain PITCH F/x abbreviation represents? Or how to interpret certain charts or data? We have you covered.

We’ve also added a full section going in-depth on some of the more confusing Business aspects of baseball, like how service time, the luxury tax, and revenue sharing each work. It also includes a section detailing all the changes in the most recent CBA agreement.

As always, feel free to contact me on Twitter if you have any questions. Enjoy!


FanGraphs Library Update

Thanks to all your suggestions earlier this off-season, the FanGraphs Library has now been re-edited and rejuvinated in preparation for the 2012 season. Every stats page has been updated with new context tables — they are not year-specific anymore – and each page has been beefed up with more detailed and accurate information.

– Curious about what the formulas are for xFIP, wRAA, or SIERA? We have those, and many more.

– Wondering what the wOBA weights were for 1990, or maybe 1945? Or even 1900? Got it.

– Where can you find historical FIP constant values? We have those too.

– Need to find the league-average ISO back in 1956? Or league-average HR/FB rate in 2002? You can find links to historical, league-average leaderboards on every statistics page.

– Need a handy context chart for a specific stat that you can copy and paste into a blog article? The new context charts are easy to highlight and copy/paste from setting to setting.

There are more changes planned for the coming months — additions this time, not just edits — so stay tuned.


FanGraphs Glossary: The Winter Cleaning

As pointed out on Twitter today, there’s some good news for baseball fans today: the wait until Spring Training is officially half over. The middle of February is looking closer and closer now that January is a few days away, so before we know it, pitchers and catchers will begin their yearly migration down to the warmer climes. Our long, dark teatime of the soul is all but over.

But this is bittersweet news. Yes, the wait until Spring Training is almost over, but the coming month and a half is typically the slowest, most painful time of the offseason. The Winter Meetings have passed and baseball news has slowed down to a crawl, so there isn’t much to keep us baseball-philes content. This January promises to be more eventful than most, considering Prince Fielder is still on the market and there are multiple potential trades that may happen, but I’m not about to set my expectations too high.

Since things can get so slow this month, this is typically the time of the year when I update and re-edit the Sabermetric Library — a mid-winter cleaning, if you will. I haven’t begun dusting out the cobwebs yet, though, as I’d love to get input on what people would like to see this time around. And so…

  • Are these any pages in the Library you think badly need an edit? Is there anything you’d like to see added to any particular page?
  • Are there any new pages or articles you’d like to see added to the Library?
  • Any new links that I should be sure to include in the Library?

In short, if you have any ideas on how to improve the glossary here at FanGraphs and to make it more useful, please share! I’ll be spending the next month or so making edits and changes, and I welcome any ideas.


How Should We Measure Power?

What exactly is “power”? Is it the ability to hit home runs? Doubles? Triples? Should we consider how far a player hits a ball, or are we just concerned with the outcome? How would you define it?

If we were to try and define power from the ground up, obviously you’d have to start with home runs. Power hitters are guys that mash lots of home runs, right? When I think power, I think of players like Jose Bautista, Babe Ruth, Hank Aaron, and Barry Bonds. Home runs are so flashy, they steal the show.

But there’s more to power than a player’s raw home run total. You can’t completely ignore other extra base hits, which is why there are statistics like Slugging Percentage and Isolated Power. Slugging Percentage measures a player’s total bases and Isolated Power measures a player’s extra bases*, so both statistics count doubles and triples as well as home runs.

*Quick refresher course for everyone. Slugging Percentage = Total Bases / At Bats ; Isolated Power = Extra Bases / At Bats

Or if you prefer to think about it another way, Jose Bautista has a .330 ISO this season. That means he averages nearly one extra base every three at bats. 

Both these stats have the same problem, though: not all bases are created equal. If a player has accumulated 30 extra bases in 100 at bats, isn’t there a big difference if those extra bases were accumulated through 10 home runs versus 30 doubles ? Both players have the same Isolated Power, but which one has provided their team with more value through their power production?

Good question, I’m glad you asked.

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Why Strikeouts Stink

It’s long been a sabermetric truism that for hitters, strikeouts aren’t any worse than any other out — or at least, that strikeouts are much less harmful than is typically assumed. Strikeouts are slightly worse than outs on balls in play, since sometimes in play outs can advance or score a runner. But the difference between the two is minuscule, while fans tend to lampoon high strikeout hitters and overestimate the negative effects of strikeouts.

So the sabermetric truism has stuck: strikeouts aren’t that bad. Hitters can have high strikeout rates and still contribute loads of offensive value through their plate discipline. After all, the end goal is not making an out, right? It shouldn’t matter how a player does it, simply as long as they reach base at a high rate and avoid making outs.

But there’s a problem with this logic. While a player can be valuable even he strikes out frequently, strikeouts still decrease how often a player reaches base and can have an adverse effect on a player’s on-base percentage. They’re not as harmless as casual saberists typically assume.

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The Short and Simple SIERA Primer

We’ve had our five part series introducing everyone to FanGraphs’ newest stat, SIERA. Now, how about we simplify things and explain SIERA in 500 words?

The following is taken from the new FanGraphs Library page on SIERA, so it will always be available here whenever needed.

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Saber-Friendly Tip #3: On Decimals

If you’ve missed my earlier Saber-Friendly Tips, you can find them here.

As I have alluded to in the not-so-distant past, I feel like sabermetric writing should not be all the same. If you’re writing a piece that’s geared for other saberists — or for a very knowledgeable audience, like this one — then obviously very different rules apply than if you’re trying to cater your analysis to a broader audience. You can toss around multiple acronyms and discuss statistical concepts without much worry, while doing the same thing in other places could have you denigrated by your audience as a know-it-all, pompous jerkface.

We all love to poke fun at television announcers — whether at ESPN, MLBN, or elsewhere during game broadcasts — but they face a very difficult task: how do you give insightful analysis while still appealing to the wide range of different viewers out there? There are plenty of announcers out there that love stats and analysis (hello, David Cone!), and it’s no easy task to try and mesh those numbers into a game broadcast without scaring off all the viewers out there who don’t like math.

These same challenges apply to us saberists. What sort of an audience are we trying to reach, and how can we best do so? Today I want to suggest another way in which people can help make saber-stats easier to digest: rounding your numbers.

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On Research and Writing: The Growing Niches of the Saber-Sphere

I’m a little bit late following up on this, but I absolutely loved this quote from Tom Tango during a recent Baseball Prospectus Q&A:

Q: I like to flatter myself that I’m an ‘early adopter’ to the sabermetric perspective on the game, even though it’s been so many years since its introduction and uptake by those like yourself. Is sabermetrics already ‘mainstream’ in your mind, or how long do you think it will be til it is? What was / will be the tipping point to #2?

Tango: Sabermetrics will always be on the leading edge. There’s no need for it to be in the mainstream. If the mainstream wants to adopt, they know where to find us. If they want to ignore us, they can. We’re there to make sure they don’t misuse numbers, that’s all.

I hope [the tipping point] never happens, actually. You look over to your left and right to make sure that whoever wants to be part of the movement has the tools and knowledge to join in. There’s no sense in looking over your shoulder to make sure everyone comes along. They aren’t in a burning building they are trying to escape. They are on the beach, and they can decide if they want to come surfing with us or not. But I don’t need them to tell me that I’m drowning people with numbers. We’re giving out surfboards, and they can decide if they want one. And then we’ll be happy to make sure they don’t drown.

I couldn’t agree more, but I realize that might seem counterintuitive for those that have followed my recent Saber-Tips series here. A large part of my writing and work here seems geared at making sabermetrics more mainstream – or at least, more widely used – but that’s not my intention. Let me explain.

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Ultimate Base Running Primer

Base running linear weights or base running runs, or Ultimate Base Running (UBR), is similar to the outfield arm portion of UZR. Whatever credit (positive or negative) is given to an outfielder based on a runner hold, advance, or kill on a batted ball is also given in reverse to the runner (or runners). There are some plays that a runner is given credit (again plus or minus) for that do not involve an outfielder, such as being safe or out going from first to second on a ground ball to the infield, or advancing, remaining, or being thrown out going from second to third on a ground ball to SS or 3B.

Runs are awarded to base runners in the same way they are rewarded to outfielders on “arm” plays. The average run value in terms of the base/out state is subtracted from the actual run value (also in terms of the resultant base/out state) on a particular play where a base runner is involved. The result of the subtraction is the run value awarded to the base runner on that play.

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Saber-Friendly Tip #2: Talkin’ About Power

In case you missed the first article in this series — in which I talk about another way to look at BABIP — I’m trying to take a look at alternative ways to present sabermetric stats, in order to best represent them to an audience.

When you stop and think about it, despite the numerous baseball statistics out there, there are only a few limited ways of talking about a batter’s power. While there are a multitude of options when talking about plate discipline — On-Base Percentage, walk rate, outside swing rate, etc. — there are only a handful of widely available stats to use for power: the old standby, Slugging Percentage; a player’s raw total of homeruns or extra base hits; or the sabermetric alternative, Isolated Power.

So when you want to talk strictly about how powerful a player has been, which stat do you use? There are pluses and minuses to each of these stats, but do any of them necessarily stand out from the others? I’d argue no.

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Saber-Friendly Tip #1: The Linguistics of BABIP

Through some conversations with colleagues, I’ve recently had a bunch of thoughts floating around in my head about how to best present sabermetric stats to an audience. I posted some of these thoughts recently in an article, and I’m planning to continue listing tips every now and then. And of course, a bit thanks to Sky Kalkman’s old series at Beyond the Boxscore for the title inspiration.

Batting Average on Balls In Play (BABIP) is one of the mainstays of sabermetric analysis. In fact, I’d suggest it’s one of the most commonly used saber-stats; it’s important whether you’re talking about batters or pitchers, and it’s useful in explaining why players aren’t performing as you’d otherwise expect. If you’re trying to analyze a player and talk about how they will perform going forward, how can you not talk about BABIP?

But despite being such an important statistic, many people are initially skeptical of BABIP. What do you mean to tell me that batters don’t have control over where they hit the ball? Why should I believe that there isn’t a large amount of skill involved in BABIP? To say that there’s a large amount of variation and luck involved in BABIP (and therefore, batting average) seems counterintuitive to people. After all, many baseball fans grew up with the idea that hitting for a high average is very much a skill, not the product of skill and some luck.

So recently, I’ve started trying something a little bit different: presenting BABIP as a percentage. And so far, I think it’s helping.

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‘Stabilizing’ Statistics: Interpreting Early Season Results

As I’m sure many of you are aware, doing early season baseball analysis can be a difficult thing. It’s tempting for saberists to scream “Small sample size!” whenever someone makes a definitive statement about a player, and early season results should always be viewed with a heavy dose of skepticism. After all, it’s a heck of a long schedule: the season started over a month ago, but we’re still less than 20% of the way finished. With most players, we have years and year of data on them – whether in the majors or minors – so why should we trust their results over a mere 100 plate appearances? More data almost always leads to better predictions, so at this point in the season, trusting 2011 results over a player’s past history is a dangerous thing.

At the same time, completely ignoring 2011 results is a horrible idea too. Some players do make dramatic improvements in their game from year to year, and there are always players that age at a different rate than expected — young players that develop fast (or slow) and old players that age quickly (or slowly). Some of a player’s early season results might be the result of a slump or streak, but sometimes there’s also an underlying skill level change that’s tied in with that slump or streak.

So how do we untangle what’s random variation and what’s a skill level change? Scouting information is huge when evaluating players in small samples, but sadly, not many of us are scouts. But stats can still help; you just have to know where to look.

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Batter/Pitcher Splits Crib Sheet

I don’t know about everyone else, but it can be somewhat difficult for me to keep track of all the different splits that are worth remembering. We all know that batters typically fair better against opposite-handed pitchers, but sabermetric knowledge has now progressed to the point where that’s not the only thing to keep track of anymore. What about batted ball splits? Does this pitcher throw a dominant changeup, and if so, what are the platoon splits for changeups?  How large of a sample size do I need before I can make assumptions about a player’s platoon split? It can be a lot of knowledge to remember, but it’s all important information in case you want to analyze a managerial move or lineup.

So below the jump, you’ll find a crib sheet for understanding lefty-right, batted ball, and pitch platoon splits. If you have any questions, feel free to ask in the comments.

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How to Speak Sabermetrics to a Mainstream Audience

Alternate titles to this article: “How to NOT Look Like a Nerd” or “Convincing Your Friends You’re Right and They’re Wrong”.

As weird as it may sound, sabermetrics doesn’t need to be geeky. After all, saberists are simply trying to answer the same questions that everyday fans are trying to answer. How valuable is this player? How will certain players and teams perform in the future? Was this the correct managerial move or not? Sabermetrics is a new tool – a confusing tool to some people -but the questions are the same ones that fans have been asking for the last 80+ years.

But how do we present these new tools in a way that keeps mainstream fans from tuning out? How do you talk to your friends about sabermetrics without confusing them and looking like a nerd? It’s a tough balance to maintain, but I’ve found there are five guidelines that work well for me when talking with friends and writing articles.

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A Visual Look at wOBA

If you’re any sort of saberist, you should already know that Weighted On-Base Average (wOBA) is vastly superior to On-Base Plus Slugging (OPS) at measuring offensive value. While OPS is a mishmash statistic, throwing together OBP and SLG for kicks and giggles, wOBA was created based on research on the historical run values of events. It weighs all the different aspects of hitting in proportion to their actual, real-life value to a team’s offense.

But how exactly do these two statistics differ in assigning value to events? See for yourself:

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Regression and Albert Pujols’ Slump

If you haven’t taken a statistics class, regression can be rather tricky to grasp at first. It’s a word you’ll hear bantered about frequently on sabermetrically inclined websites, especially during the beginning of the season: “Oh, Albert Pujols is hitting .200, but it’s early so he’s bound to regress.” “Nick Hundley is slugging over .700, but that’s sure to regress.” This seems like a straightforward concept on the surface – good players that are underperforming are bound to improve, and over-performing scrubs will eventually cool down – but it leaves out an important piece of information: regress to what level?

The common mistake is to assume that if a good player has been underperforming, their “regression” will consist of them hitting .400 and bringing their overall line up to the level of their preseason projections. I like to call this the “overcorrection fallacy”, the belief that players will somehow compensate for their hot or cold performances by reverting to the other extreme going forward. While that may happen in select instances, it’s not what “regression” actually means. Instead, when someone says a player is likely to regress, they mean that the player should be expected to perform closer to their true talent level going forward.

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Plus/Minus & Runs Saved FAQ

Baseball Info Solutions has just released a more comprehensive FAQ on their fielding system, which we list on FanGraphs as DRS (and the various components that make up DRS).

It goes into details about how they make adjustments for various positions, ball hogging, home runs saved, the Green Monster, player positioning, etc….

Click to read the FAQ


The FanGraphs UZR Primer