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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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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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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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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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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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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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!

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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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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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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