The Beginner’s Guide To Plate Discipline

At its heart, baseball is a battle to control the strike zone. There are plenty of other things going on, but the origin of the action is over the plate. Good hitters make good decisions about when to swing and when to take and good pitchers attempt to negatively impact that decision-making process. As the importance of walks and working counts became clear over the last generation, hitters who knew the zone and pitchers who could generate swinging strikes became very popular.

Throughout history, batters have been judged by their results. Things like batting average and RBI have given way to wOBA and WAR, but in general the average fan cares about the outcomes rather than the process. Plate discipline numbers are inherently process based. You don’t get credit in the box score for taking a pitch just off the plate, but taking a pitch just off the plate is probably going to help you do things that lead to runs, like walking and getting good pitches to hit.

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The Difference Between Range and Positioning

Perhaps one of the biggest objections people have with the current state of defensive metrics is that the stats don’t account for the starting position of the defender. Shift plays are excluded from the calculations, but when a center fielder plays in 20 feet, the system doesn’t know that he’s starting from a different spot than the average center fielder, which could obviously lead to some imprecise accounting.

This is true for every position except pitchers and catchers, as the starting location of the fielder influences the probability they will make a play, independent of anything they do from the moment the ball is pitched. If you start out of position, even if you run at top speed and take a perfect route, you might not be able to offset the initial disadvantage of not being in the right spot to begin with. This creates problems, but there’s a lot of nuance to these problems that are worth discussing, even as we get closer to having StatCast and rendering the discussing irrelevant (we hope!).

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How To Use FanGraphs: Depth Charts

In addition to the daily analysis and normal statistical offerings, FanGraphs has added some pretty useful and powerful features over the last couple of years. Anchoring a lot of those features are the Depth Charts, which in addition to providing information on their own, power the playoff odds and projected standings we host on the site.

The Depth Charts are pretty simple in theory. They blend together two of the leading projection systems (Steamer and ZiPS) and then scale those projections to our expectations about playing time. The Depth Charts are updated constantly to provide the most up-to-date snapshot possible for the current state of a team, league, or position. You can think of the Depth Charts as the baseline projections for the entire site, as they are the input for the projected standings, playoff odds, and game odds.

As far as the basic Depth Charts are concerned, there are essentially three different views. You can look at a team’s Depth Chart, you can look at Depth Charts by position, and you can look at the summary data of both of those at one. To generate each the charts, we take a 50/50 mix of Steamer and ZiPS for the rate stats and then our staff manually allocates playing time based on what we expect teams to do with their lineups and injury histories.

Steamer and ZiPS update nightly throughout the season and our playing time estimates change every 15 minutes (if necessary). If a player gets hurt, we update their playing time. If a player gets moved to the pen or changes positions, we update the Depth Charts. Also, the Depth Charts are showing what we expect to happen for the rest of the season, not the stat line we expect them to end the season with.

As always, when you’re dealing with constantly updating information, there are occasionally bugs. If you see something that looks obviously wrong, it’s likely just a database error that will resolve itself once the system updates in a few minutes.

As far as viewing options, you can look at the Depth Charts in team view, in position view, or in summary view. In team view, you get a breakdown of a single team by position, meaning on the Blue Jays page there’s a box for catchers, first basemen, etc with the expectation that each position for each team will receive 700 PA per season. Obviously that will vary a bit, but it’s a good rule in general. Each team also has a box for all positional players and all pitchers, as well as a box on the right that shows you where they stand overall.

In position view, you can look every team’s Depth Chart at any one position. For example, here is the page for catchers. This allows you to compare positions around the league and see which group of backstops is most valuable. Obviously these rankings are based on the projection systems and our playing time estimates, so if you believe playing time will shake out differently that we do, you might expect to see a different overall ranking.

Finally, this handy grid collapses those two views into one. You can’t see all of the players in that view, but it puts together each team’s expected WAR at each position so that you can quickly compare how teams and positions stack up against each other.

The Depth Charts are very useful for a couple of reasons. First, they blend two projection systems together without you having to do any of the work, and that’s helpful because aggregate projections are better than any one system. Second, playing time is controlled by humans. While projection systems are much better at forecasting performance than people, projection systems aren’t very good at figuring out how much playing time a player is actually going to get. Finally, the Depth Charts gather a lot of information in one place. We’ve had projections on the site for years, but having them built into the system like this allows you to make a lot of comparisons and see where teams are strong or weak.

So as you get back into the swing of things this season, the Depth Chart pages will be a valuable resource if you want to look into the future. Obviously, the charts are only as good as their inputs, but if you care at all about the inputs, the way the data is presented is really helpful.

The Beginner’s Guide to Sample Size

A baseball season is the amalgamation of a lot of little events. Each pitch fits into a plate appearance which fits into an inning which fits into a game which fits into a series which fits into a season. That’s a lot of little data points flowing into an overall end result. We care a lot about which players will have good seasons and careers. It matters to us that we can distinguish between good players and bad players, but doing so requires that we understand which chunks of data are meaningful and which aren’t.

Enter sample size. You’ve heard this phrase plenty over the last few years when talking about baseball statistics and it’s usually a conversation ended rather than a conversation started. Someone cites a stat and then another person says it doesn’t matter because the sample size is too small. What does that mean and how should we properly think about sample size in baseball?

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Interpreting Playoff Odds and Projected Standings

As you might have noticed, our playoff odds and projected standings are now up and running for the 2015 season. If you’re a regular FanGraphs reader, or intend to be this year, you’re going to see a decent amount about the various numerical expectations we post on the site. While these odds and standings are a lot of fun and a great tool for taking stock of the league, it’s also pretty easy to misunderstand or use them improperly.

Before I run through the proper way to read the odds and standings, I want to provide a brief overview of how we arrive at the numbers you see on the site.

Our player projections are based on the FanGraphs Depth Charts which are generated by giving equal weight to Steamer and ZiPS (two projection systems) and then manually estimating playing time. Then based on the depth charts, we simulate the season 10,000 times and report the results as playoff odds and projected standings. We also host a Season to Date model and Coin Flip model which project the season based on the current year’s stats (instead of projections) or a 50/50 chance at winning each game, respectively.

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The Beginners Guide to the Positional Adjustment

Getting newcomers on board with Wins Above Replacement has a number of challenges, but the way we measure and evaluate defense is typically one of the biggest sticking points. Getting an open-minded person to believe in wOBA instead of average and RBI isn’t that difficult. Getting someone to accept that there’s more to base running than the number of stolen bases is pretty easy. Convincing them that it’s useful to compare players to replacement level is a bit harder, but nothing really compares to the questions people have about defense.

There’s good reason for this. Again, a thoughtful person can see the flaws in using errors or fielding percentage, but it’s harder to sell the merits of runs saved metrics for a number of reasons. If you want a little more information on how we measure defense and why we do it that way, check out our beginner’s guide to measuring defense. Today, we’re going to consider a corollary to the actual measurement of defense which is the positional adjustment.

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Stats to Avoid: Batting Average

Batting average is the most recognizable statistic in the game. It might be the most famous statistic in sports and it’s probably up there with Gross Domestic Product (GDP) among the most popular statistics about anything anywhere on the planet. Even people who don’t like or watch baseball understand what batting average means. Just like how you know a singer is famous because your mother knows who they are, you know batting average is huge because you never have to explain it to anyone.

Which is why it’s so difficult to remove it from our vernacular. Batting average is built into the language of the sport, but it’s simply not a useful statistic and if you want to analyze a player properly, it’s something you don’t want to pay close attention to at all.

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The Beginner’s Guide to Replacement Level

Like any good acronym, the letters in WAR each stand for something. The “W” stands for wins, which is something with which we’re all pretty familiar. The “A” stands for above, which is just an adjoining word, but the “R” stands for replacement which is a place where newcomers sometimes get lost. What is replacement level, why does it matter, and how do you calculate it? If WAR compares players to replacement level, to understand WAR we need to understand R.

Let’s start from the beginning. Replacement level is simply the level of production you could get from a player that would cost you nothing but the league minimum salary to acquire. Minor league free agents, quad-A players, you get the idea. The concept is pretty tidy. These are the players that are freely available and if five of your MLB level players came down with the flu, you could go out and acquire replacement level players without really giving up anything you value other than their union mandated payday.

In other words, if you had no one on your roster on April 1st and just needed to populate a team, you’re generally signing replacement level players.

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The Beginner’s Guide to Understanding Trade Value

If you’re not someone who comes up with trade proposals, you’re someone who reacts to trade proposals. It’s one of the great baseball fan parlor games. They’re everywhere. They populate our chats, they dominate Twitter, and they even sneak into real live interpersonal communication. Would the Nationals trade Strasburg? What could they get? Who would they want? These are all very interesting questions, and while most trade ideas disappear into the ether, plenty do come to fruition.

We talk a lot about trade value on FanGraphs because a lot of our writers care about the roster construction aspect of the game. Certainly we cover what happens between the lines, but there’s a lot of interest among our readers regarding how those players happened to wind up on the teams in question.

Every summer, Dave Cameron runs a trade value series where he ranks players based on his reading of the baseball landscape. Jonah Keri has a similar series at Grantland every winter. This is a topic that generates lots of interest, so this post is going to lay out the variables you should consider when pondering what a player is worth to the rest of the league.

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The Beginner’s Guide To Understanding Park Factors

One of the things that makes baseball interesting is that none of the playing fields are the same. In the NHL, NBA, and NFL there are certain things that might make certain stadiums feel different than one another, but the measurements of each are the same. In baseball, the bases are all 90 feet apart and the mound is at regulation length, but the fences vary by distance and height. You can travel to all 30 parks and never see the same same dimensions twice, but that also poses a problem when trying to evaluate the game because there’s an additional variable influencing the outcome of every plate appearance.

If we want to properly evaluate players and teams we need to have some way of adjusting for the fact that every park is different. More specifically, each park plays differently for reasons beyond the outfield dimensions. If you pitch at Coors Field in an identical manner to identical hitters as you pitch to at AT&T Park, your results will be different due to the ballpark. We want to try to control for this when we create statistics, so we apply something called a park factor to even out the differences.

These park factors are imperfect for a variety of reasons, but what they’re after is on the money. The parks influence the game and we want to strip that out of our evaluation of individuals.

How Parks Vary

It’s not just the dimensions. The dimensions matter, obviously, but deep fences don’t automatically make a pitcher’s park and short porches don’t always favor hitters. In addition to the dimensions, the weather matters, the air density/quality matters, and topology of the surrounding area matters. The ball tends to travel better in warm air and thin air, and the surrounding buildings and ballpark structures can influence how well the ball carries.

Petco Park, for example, has a marine layer that doesn’t let the ball fly. You probably know that Denver is way above sea level, making the Coors Field air thin and ripe for plenty of carry. Beyond that, the arrangement of the stands can influence how well the ball flies and the average temperature certainly affects the game play.

So while “big” and “pitcher’s park” are often used synonymously, there is more to it than that.

The Noble Goal

If you had the power to do so, you’d want to know how every single plate appearance would play out in all 30 MLB parks. If it turned into a single in the park of interest and then went for a single in 25 other parks, an out in three, and a double in one, you’d have a good sense of the way the parks played. The park that allowed the double would be a hitter’s park and the ones that created outs would be more pitcher friendly. But unfortunately, we don’t have that kind of data.

We want to know how parks influence each moment of the game, but we simply don’t have granular enough data to really get there. A ball hit at 15 degrees directly over the shortstop while traveling at 93 miles per hour will travel how far and land where? That’s basically what we want to know for every possible angle and velocity, but we just don’t have the data and we don’t have it for every type of weather in every park.

Instead, we have to settle for approximations.

Park Factors, As They Are

There are many different park factors out there. We have some. Baseball-Reference has different ones. Stat Corner has more. Individuals create some. It goes on and on. We use 5-year regressed park factors and you can dive into our method here.

At the end of whatever process you choose, you wind up with a number that communicates how much more offense is produced in that park than you would expect to be produced in an average one, and when we display them on the site, we cut them in half so that you can more easily apply them to player statistics.

A league average park factor is set to 100 and a 105 park factor means that park produces run scoring that is 10% higher than average (halved so 110 becomes 105 in 81 games). We also provide park factors for each type of hit and batted ball, and for handedness, although we use the general ones when making park corrections.

For example, if a player has a .340 wOBA, but their home park is hitter friendly, they we need to adjust their wOBA down as a result. We don’t calculate a wOBA+, but some do. Instead, we jump over to wRC+ for our park adjusted offensive metric. This stat, among other things, applies a park adjustment to the player’s batting line. Stats like ERA- do this as well, and pretty much any time you see a +/- stat, it’s park adjusted.

And our park factors are applied with the additive method, meaning that we’re essentially adding or subtracting a little production based on how much offense is affected by the park in our estimate, but remember that we only apply half of the full park factor because a player only plays at home for half their games. We assume the rest are played in a pretty average setting.

What Park Factors Get “Wrong”

As I said before, park factors aren’t perfect for a variety of reasons. They do a nice job on average, but in specific cases they fail to properly capture the nuances of the game. For example, Target Field is actually a slightly above average park for hitters. It’s on par with Yankee Stadium in fact, despite the much different dimensions. However, if you’re talking specifically about left-handed home run power, Target Field is a desert and Yankee Stadium is an oasis.

The problem with park factors as they stand right now is that while we’re trying to adjust for the run environment, the run environment is difficult to capture is a single number. Lefties and righties experience the world differently, but so do ground ball/fly ball guys and guys with speed and guys without.

It’s safe to say that AT&T Park is a bad place to hit and Coors Field is a good place to hit, but parks don’t affect every player evenly and our park factors sort of assume that they do.

In the future, you could imagine a world in which we could know what the average outcome of a batted ball might be (i.e. the average outcome across all 30 parks of that swing is .25 singles, .15 doubles and so on) so that we can compare the observed outcome to the expected outcome, but we aren’t there yet.

Where That Leaves Us

This isn’t to say you should ignore park factors. The park factors we have and use are much better than pretending all 30 parks play evenly, but you have to be aware that in some cases the numbers we use aren’t going to make the right corrections. For example, a right-handed hitter who spends 81 games at PNC Park is going to hit fewer HR than if he played at Great American Ballpark on average, but if it’s a righty who happens to have more power the other way that to his pull side, the PNC park factor is actually going to overcompensate.

It’s a tricky business and one that requires caution. You really just need to be careful and to look closely if you think something looks funny. The parks play differently and we need to pay attention to that, but we also have a long way to go before our estimates are perfect and we can say for sure exactly how much of a boost or deduction is necessary.

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

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

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

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Considering High Leverage Performance and Clutch Hitting

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

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

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

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What Do We Know About Catcher Defense?

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

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

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

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

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

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

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

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Stats To Avoid: Runs Batted In (RBI)

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

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

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How To Use FanGraphs: Player Pages!

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

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

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What Exactly is a Projection?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Questions about projections? Ask them in the comments!

The Biggest ERA-FIP Differences of 2014

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

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

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Why It’s Always Better to Use Multiple Statistics

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

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

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

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

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Calculating Position Player WAR, A Complete Example

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

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

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