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.

In and of themselves, options aren’t a confusing concept. The idea behind them is simple: to keep teams from hording minor league talent, and to provide minor leaguers more of a chance to reach the majors. If a minor league player is placed on a team’s 40-man roster – which must be done to protect that player from being selected in the Rule 5 draft – then they are given three option seasons. This means that if a team sends a minor-league player on their 40-man roster to the minors at any point during a season, they use one of that player’s options. After all three of a player’s options have been used, that minor-leaguer can no longer be freely sent to the minors – they must first be placed on waivers, giving other teams the chance to claim them.

The key word in the above section is “option seasons“. A team can call up and send down a prospect multiple times in the same season, yet they still only use one of that player’s options. In other words, this means that if a player doesn’t stick in the majors three years after being placed on a team’s 40-man roster, they have to either be kept on the 25-man roster or be placed on waivers before going to the minors again.

That’s the basics, now for some caveats and details.

- Players with fewer than five professional seasons will be given a fourth option year. This comes into play mostly with marginal players, as you need to be good enough to get added to a team’s 40-man roster at some point, but not good enough to reach the majors and stick within the next three season. Also, this typically affects players who sign major league contracts right after the draft.

-  An option isn’t used if a player is injured all year or they spend less than 20 days in the minors during the course of the season.

- Once a player is sent to the minors, they are must remain there for at least 10 days before being recalled (with the exception of if they need to return due to an injury). This is to prevent teams from bouncing one or two players up and down depending on the day of the week.

Links for Further Reading:

Options – Biz of Baseball

The Quick Rundown of Options – FanGraphs

Death, Taxes, and the Major League Waivers – Baseball Analysts

Understanding Option Years – River Ave. Blues

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.

Acquired in 2006 from the Astros in the Aubrey Huff trade, Ben Zobrist has never been a typical ballplayer. Zobrist started his time with the Rays as a minor-league shortstop with a weak bat, a good batting eye, and a decent glove – and yet, when he finally reached the majors for good in 2008, he had transformed into a unique player: a slick fielding, power hitting utility man. Between 2008 and 2009, “Zorilla” hit an extra base hit once every 9.6 plate appearances, tied for second-best on the Rays with Carlos Pena. He became one of the Rays’ most potent offensive players in 2009, hitting 27 homeruns and driving in 91 runs, yet he saw time at every defensive position (besides catcher) and every spot in the line-up. He finished eighth in the AL MVP voting that season, but there is an argument to be made that even then people were underrating Zobrist.

Zobrist has always been underrated because he derives a large part of his value from unconventional sources: plate discipline, baserunning, and defense. These are skills that are not normally given much attention by mainstream analysts, and they are easy for the average fan to miss when watching a game; however, by being good in all these areas, in 2009 Zobrist was debatably more valuable than any American League player outside of Joe Mauer.

Skeptics should consider Zobrist’s horrible 2010 season: his power evaporated, he hit just .238/.346/.353, and his batting average was under .200 each of the final three months of the season. Despite this drop off, Zobrist played nearly every day (151 games, tied with Evan Longoria for the most on the team) and the Rays considered him a key player on their team. How come? Again, plate discipline, baserunning, and defense.

With only 27 outs per game for a team, a hitter’s most important job is to not make an out. Hits (especially those of the extra base variety) are obviously good, but walks are also an effective means of reaching base and extending an inning. In 2010, despite his .237 batting average, Ben Zobrist walked 92 times and had an on-base percentage 21 points above league-average. While his power production was horrid, his on-base prowess was enough to make him (believe it or not) an average offensive player – and, remember, an average player is better than 50% of the league. In the majors, average has real value.

The Rays are notoriously aggressive on the base paths, and Zobrist’s baserunning proved both aggressive and effective: he stole 27 bases at a high success rate (89%), and he took an extra base (first to third, second to home, etc.) more often than league average. According to baserunning statistics reported by Baseball Prospectus, Zobrist was the 21^st best baserunner in baseball last season, better than Jose Reyes, Andrew McCutchen, and B.J. Upton.

And finally, we reach Zobrist’s pièce de résistance: his defense. Defense is an oft overlooked part of a player’s value, and even when it is considered, it is rarely given the weight it deserves. A player can add value to their team by hitting a double, but they could also add value by catching a ball in the gap, turning a double into an out. As an extreme example, what has more impact on a game: hitting a solo homerun or robbing a solo homerun? It’s a trick question, of course – both events have the same effect, giving that player’s team a one-run boost.

Zobrist excels at defense. Last season, he had a 1.000 fielding percentage in the outfield and a .985 fielding percentage at second base, and advanced defensive statistics all graded him as well above-average in both positions. Ultimate Zone Rating (UZR), the preeminent defensive statistic publicly available, takes into account a player’s range, arm strength, double-play ability, and error rate, and it rated Zobrist as contributing nine runs to the Rays in defensive value. That may not sound like a lot, but it was third best on the Rays behind only Crawford and Longoria, and it placed him as one of the top 25 best defensive players in the majors.

So despite his low batting average and lack of power, Ben Zobrist was a valuable player to the Rays in 2010. His skillset is so unusual, though, he was underrated by fans in 2010 – heck, he has been underrated every year of his career and he is likely going to be underrated in 2011. Keep this in mind the next time Zobrist takes a walk, steals a base, or makes a running catch; he may not be flashy, but the Rays are lucky to have him.

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.

Jeff Zimmerman’s been doing some ground-breaking work on injury projections for pitchers, and according to his numbers, even the most durable pitchers are still 30% likely to come down with an injury in any one season. This number might seem high at first – there are players like Roy Halladay and Greg Maddux (selection bias!) – but pitching is an unnatural act that pushes our bodies and shoulders literally to their breaking point. Our bodies simply weren’t built to hurl a 5 ounce ball over 90 MPH, not to mention add spin to the ball by twisting the arm. And this isn’t a just a matter of doing this once or twice: major league pitchers have thrown thousands of pitches before they even reach the majors.

As such, pitchers are treated with care and caution every step of the way. Teams rarely invest large chunks of money in pitchers; in fact, of the 26 players in major league history to receive a contract of $100M or greater, only 6 of these players have been pitchers.* “Joba Rules” are created to keep promising young pitchers from flaming out, while teams try to avoid the “Verducci Effect” by slowly increasing a pitcher’s innings total from season to season.

And yet despite all this caution, pitchers still flame out. Joba Chamberlain got injured despite the best efforts of the Yankees, and young pitchers fail so frequently that the common refrain is “There’s No Such Things As A Pitching Prospect” (TINSTAAPP). Pitchers don’t age the same way position players do: while position players typically peak at age 27-30 and slowly decline afterward, pitchers don’t follow any average path. Some peak at age 23 and rapidly fall of a cliff, while others are mediocre for their entire career before bursting out in their mid-30s.

Adam Wainwright’s injury stinks for Cardinals fans, but it should be heeded by other fanbases as a well. Giving large contracts to pitchers before they reach free agency is unnecessary; heck, if I were a General Manager, I’d be leery of giving any pitcher more than a four year contract. What would happen if your ace got injured? How confident are you that they’ll keep producing at this level? Could they go the way of Scott Kazmir or Mark Fidrych? These aren’t just questions Cardinals fans should be asking: they’re questions all baseball fans should keep in mind.

*The six pitchers to receive contracts greater than $100M? C.C. Sabathia, Johan Santana, Barry Zito, Mike Hampton, Cliff Lee, and Kevin Brown. Both Brown and Hampton had injury issues during the length of their contract, while Santana’s had recent issues as well. Not a great track record, no?

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.

Let’s start with something simple: flipping a coin. We’d expect that a normal coin would have a “true talent level” of landing heads 50% of the time, right? If you flipped that coin 100 times, though, it may be that you’d end up with 53 heads and 47 tails….or with 45 heads and 55 tails. You’d be most likely to end up with a result close to 50/50, but it’s no guarantee that things would end up precisely at the coin’s true talent level every single time.

If that’s not convincing enough (after all, there’s no “talent” involved in whether a coin comes up heads or not), consider basketball. Each of us has a specific true talent level for hitting free throws, some better than others. I’m horrible at basketball, so I’d place myself around a 30% talent level, meaning I’m likely to make around three baskets out of every ten. If I went out and shot five different sets of 10 free throws, though, I wouldn’t necessarily make three every single time; my scores may look something like 2, 4, 5, 3, 1. This variability doesn’t mean my true-talent level is wrong: it just means that there’s no guarantee we’ll perform exactly at our true talent level every time we perform. Sometimes we may over-perform, while others we may under-perform.

You’ll sometimes hear people refer to projections as “50th percentile projections”, which expresses exactly this concept: 50% of the time a player will over-perform their projection, while 50% of the time they’ll under-perform it. Players and teams are most likely to perform at a level close to their projection level, but that’s no guarantee. Variability for teams and players typically follows the normal distribution:

This is the statistical way of showing how likely a person (or team) is to perform close to their true talent level. Say we simulate this upcoming season 1,000 times with the Red Sox as a 94-win true talent level team. According to this graph, we’d expect the Sox to finish 68% of those seasons within one standard deviation of 94 wins, and 95% of them to fall within two standard deviations. “Standard deviation” simply refers to the predicted amount of variability with a team or player, with major league teams typically having a standard deviation of six games over a 162-game season.*

*Someone smarter than me, feel free to disagree and disprove this – that isn’t my calculation, so I’m not wed to it. I’d argue that standard deviation varies on a team-by-team basis, with some teams having a slightly tighter deviation and some having a slightly wider one, but as an average six games seems to make sense.

In other words, say we project David Wright to hit 20 homeruns this season. That projection isn’t saying that he’s going to hit exactly 20 homeruns, but instead that he’s 68% likely to hit within one standard deviation of 20 homeruns. With that in mind, Wright hitting either 15 or 25 homeruns wouldn’t necessarily prove the initial projection “wrong”: it just means that Wright’s season varied from his projection, and we can use that information to better project his true-talent level going forward.

How do we measure that variability? What’s the typical standard deviation for homeruns, batting average, wOBA, etc? Those questions I don’t have a specific answer to, as there are many conflicting issues in play. For one, projections are only best guesses at a player’s true talent level, and there are many things we can’t know or measure: the exact effect of injuries, exactly how a specific player will age, the exact amount of talent residing in a player. We can make good guesses based on historical trends and what we see on a day-to-day level, but “true talent level” is an ethereal concept and can’t be measured like you can a temperature. As such, projections will never be perfect.

Also, the standard deviation for specific statistics can be different depending on the player. Adam Dunn has almost exactly 40 homeruns for six seasons in a row, but there are also players like Jose Bautista and Ben Zobrist that have had wildly varying power numbers over the past few seasons. Each aspect of each projection therefore has a different level of confidence to it, and you have to be able to assess that confidence by looking through a player’s career and determining if you think that projection has a wide or tight standard deviation.

So the next time you start looking through projections, remember to take variability into account. Our minds love to eschew probability and uncertainty – why do you think casinos make such a killing? – but understanding this concept can keep you from drawing faulty conclusions from projections. Embrace uncertainty, and it might help you beat the house (or beat your friends at Fantasy).

*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!

This answer comes from Matt Bandi from Pirates Prospects, and I like it: rice is cheap, widely available, and by itself it’s about as bland and “meh” as possible. Of course, you can add gravy to it and turn rice into Albert Pujols, but gravy will do that to anything; it’s like the steroids of food.

A similar answer to the rice: instant potatoes are cheap, widely available, and an easy fall-back plan when all your attempts at cooking dinner go up in flames. Also, I just like the idea of envisioning Bartolo Colon as a potato (since he already is one).

I received a couple other ideas that I really like: cereal, toast, Spaghetti-O’s, and gruel. I think you easily could go with any of these when trying to describe the concept to someone, so it’s really a matter of personal preference.

My personal replacement level food of choice would have to be Twinkies. I know, I know…some people actually like Twinkies and think they taste good, but don’t some people also love David Eckstein and Casey Kotchman? Twinkies may look like food and smell like food, but if you ever look at their ingredient list, they’re most certainly not food. And while they may not be everyone’s go-to fall-back option as a snack, if there’s ever a nuclear war and we’re left trying to survive in an apocalyptic hellscape, Twinkies will likely be the only food option remaining. In my eyes, they are the ultimate replacement food.

So next time you’re sitting in a ballpark and the person next to you starts proclaiming that Willie Bloomquist is the bee’s knees, turn to him (or her) and explain how Bloomquist is really the baseball equivalent of a Twinkie. Or a potato. Whichever you choose, I’m sure Ken Tremendous will be smiling somewhere.

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.

You can also find this new information on the Library’s Projections page, so it’ll always be available there as a reference.

- Marcel – Developed by Tom Tiger, Marcel is a simple projection system that is still quite reliable. I’ll let Tango do the explaining:

“The Marcel the Monkey Forecasting System (or the Marcels for short) is the most advanced forecasting system ever conceived.  Not.  Actually, it is the most basic forecasting system you can have, that uses as little intelligence as possible. So, that’s the allusion to the monkey. It uses 3 years of MLB data, with the most recent data weighted heavier. It regresses towards the mean. And it has an age factor.”

Theoretically, projections that do more work than Marcels (like ZiPS, Bill James, CAIRO, Oliver, PECOTA) will be more accurate, but in the past, other systems have only added a small increase in accuracy. Even though it is very basic, the Marcel system is still quite accurate and serves as a good reference point when looking at other projections. 2011 Marcels projections can be found here and on FanGraphs.

- Bill James - Created by Baseball Info Solutions, the Bill James projections uses at most eight seasons of data per player, with a strong focus on the previous three. While the exact methodology is proprietary, the Bill James projections are based on past performance, age, home park, and expected playing time. His projections tend to be the most optimistic of all the major systems, especially with young players.

- ZiPS – The work of Dan Szymborski over at Baseball Think Factory, the ZiPS projections uses weighted averages of four years of data (three if a player is very old or very young), regresses pitchers based on DIPS theory and BABIP rates, and adjusts for aging by looking at similar players and their aging trends. It’s an effective projection system, and is displayed at FanGraphs for off-season and in-season projections.

- Oliver – This system was created by Brian Cartwright and is available over at The Hardball Times. It’s a comparatively simple projection system – using weighted averages of the past three seasons of data, and adjusting for aging and regression – but it calculates its major league equivalencies (MLEs) in a different way than most systems, taking the raw numbers and adjusting them based on park and league. Since most projection systems simply try to adjust for the transition between each minor-league level, Oliver’s projections are better when showing how young players will perform at the major league level. This is also the only projection system to include a fielding and WAR component.

- CAIRO – A system developed by the folks at Revenge of the RLYW, the CAIRO system starts with a basic Marcel projection model, but then includes minor league statistics, adjusts for park and league effects, adjusts the aging curve depending upon the statistic, takes age and position into account when regressing a player’s performance, and uses four years of data instead of three. These projections are then put into the Diamond Mind simulator, and team projections are estimated using the results of 50,000 simulations. 2011 projections can be found here.

- Fans – During the off-season following the 2009 season, FanGraphs began the the Fan projections, which rely upon a “wisdom of the crowds” approach at evaluating a player. Fans are asked to fill out ballots on various players, ranking how they expect those players to perform in the upcoming season. Ballots are they compiled and averaged for each player, giving us their Fan projection.  These projections are normally quite optimistic, but in some cases they can add real value about players that may follow an unusual career path. They’re also a good way to estimate a player’s potential playing time, which is a variable that most projection systems struggle with.

- PECOTA – Developed by Nate Silver and Baseball Prospectus, PECOTA is one of the more complicated projection models, using a player’s statistics and historical statistics of similar ballplayers to arrive at a projection. Colin Wyers has done work in recent years to improve PECOTA’s accuracy, and a stripped-down version of PECOTA has been shown to be as effective as the Marcels projection system (implying that the full PECOTA would be slightly more accurate). PECOTA also does projections on a team level and creates a list of comparable historical players for each projection. You can find PECOTA at the Baseball Prospectus website.

- CHONE – Developed by Sean Smith, this system used four years of data for hitters and three years for pitchers. It adjusted for park, league, and aging effects, and it also uses batted ball data and minor league statistics. CHONE was widely considered one of the most accurate projection system, but it is no longer available to the public.

For more on the accuracy of each projection system, I recommend reading Tom Tango’s recent study.

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.

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:

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:

Heat Maps are rather intuitive: quite simply, they’re a strikezone plot that shows how often a pitcher throws a pitch in a certain location. To use David Appelman’s example, if you want to know where Mariano Rivera throws his cutter against lefties (or righties), this is the tool for you. These specific heat maps won’t tell you anything about a pitch’s movement, velocity, or effectiveness – they’re strictly plots of pitch location – but that doesn’t mean they’re without their uses. By looking at heat maps for pitchers, you can learn how a pitcher’s repertoire varies between lefties and righties, where in the zone they attack lefties and righties (and with what pitches), and if they hit the corners or leave many pitches over the plate.

When you look one of these heat maps, you’re looking at the strike zone from the catcher’s perspective. This is a detail that confuses many people, since we’re used to watching baseball from the pitcher’s perspective, but this is how all Pitch F/x charts are set up by default. If you need a visual to understand, here’s my (very) rough attempt:

(I know, I know, I have mad Paint skills. This is years of practice, folks.)

Now that you know what Heat Maps are and what they show you, let’s discuss some common analytical mistakes to avoid:

- Pitch Classifications: As Dave Allen told me, “The graphs are only as good as the pitch classifications.” There are hundreds of thousands pitches thrown over the course of a baseball season, and therefore there are hundreds of thousands of lines of data that need to be classified and organized. The people at MLB Advanced Media (MLBAM) are responsible for creating the algorithm that sorts pitches – this is a cutter, this is a fastball, this is a slider, etc. – and that algorithm has changed and improved over the years.

Due to these changes in the algorithm, you should be very careful when looking at a pitcher’s heat maps over time. While it may look like a pitcher has dramatically changed his pitch selection and location over the past few years, that’s likely the result of his pitches being reclassified. For example, look at Mariano Rivera’s fastballs and cutters over time. It looks like Rivera used to throw few cutters and lots of fastballs, but we all know that’s not true: Rivera has always predominantly thrown a cutter. Those cutters were just classified as fastballs in the past.

- Sample Sizes: As with all statistics or graphs, you should always be careful about drawing conclusions from a small amount of data. This shouldn’t be a concern with starting pitchers, who throw a large amount of innings and pitches each season, but I’d hesitate before using these heat maps to draw conclusions about pitchers that only had a few starts in one season or pitched a small number of innings in relief. These heat maps are good at showing us large, overarching trends, and it’s difficult to have a “trend” if you’ve only pitched a small number of innings.

- Over-Smoothing: Creating a perfect heat map that displays useful data is more an art form than a science. Since David Appelman gave us all control over the definition and color scheme of the heat maps, you can make the exact same data look a variety of ways. For example, here are a number of different ways to look at Rivera’s 2010 cutter usage versus lefties:

Each map is displaying the exact same data, yet each one looks vastly different and tells you different information. You want to make the data look pretty by smoothing out the heat map and making the color gradient flow, but at the same time you don’t want to over-smooth your map and remove all valuable information, like in the first two charts on the top row. It takes time to find the happy medium – the one that looks best while also still remaining true to the underlying data.

- Over-Stating Results: Remember, these heat maps will only show you information on pitch location – NOT on pitch movement, velocity, or effectiveness. They can tell you which pitches a pitcher throws against each hand and where those pitches are normally located, but you shouldn’t use these charts to make exaggerated claims. If you’re trying to understand why a pitcher has been effective or ineffective in certain situations, it’s best to look not just at these heat maps, but also at pitching statistics and all the Pitch F/x charts available. Pitchers are notoriously tricky to properly evaluate, so if you think you’ve found a simple solution/answer to a pitcher’s problems, you’re probably wrong.

These heat maps can be a great evaluative help when used properly, and if nothing else, they’re a heck of a lot of fun to look at. Keep all these above caveats in mind if you try using them for analysis, but if you’re just looking to have some fun, enjoy!

For more information, check out the new Heat Maps page in the FanGraphs 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.

And so, being naive and stupid, I decided to spend the time putting together a comprehensive learning resource where saber newbies could learn practical knowledge about the statistics and concepts that undergird sabermetrics. It was an ambitious project, but I received help from a couple of my fellow writers at DRaysBay and input from living legend Graham MacAree. I made the pages easily linkable, so writers wouldn’t need to re-explain a statistic every time they used it in an article, but could link to the Library and direct their readers there for more information (like, say, wOBA).

FanGraphs has long needed an updated glossary, so moving the Library here was a no-brainer. I made a number of changes when moving it over – updating content, adding percentile charts, and incorporating more content from elsewhere in the saber-sphere – but its general purpose is still the same: the FanGraphs Library is meant to be used as a learning tool. I provide a concise description of every statistic, document the most important facts to know about each stat, and link to resources to expand your knowledge even further. I hope to continue expanding the Library in the future, and to update pages continuously as need be.

Some links may have gone on the fritz as we transferred everything, but all old links to the Library should still work. The old saberlibrary.com address is now being redirected to the new site, so if you are a writer and want to continue (or start) linking to the Library in your articles, you can still use our old URLs to make it easier for you. I think they’re pretty intuitive, but you can decide for yourself:

wOBA
FIP
UZR
WAR

You can access the FanGraphs Library by clicking on the “Glossary” button above. When you arrive at the Library main page, you’ll see the biggest new feature of the site: a blog! I’ll be writing content over at the FanGraphs Library page, focusing primarily on saber education. I plan to highlight work done around the saber-sphere (so pass along any links if you find something good), while also writing original pieces geared toward helping people understand sabermetrics better. It’s a new space and I want the content to best help you readers, so please send me feedback along the way.

In short, if you want to learn about sabermetrics, make your articles more accessible for new readers, or teach a friend why batting average isn’t the be-all-end-all, come check out the FanGraphs Library. No Dewey Decimal system required.