The World Series ended just over a week ago, but the offseason is already in full swing. Free agents are free to sign with any club they wish and we’ve even had our first significant trade. The MLB offseason is a little slower to develop than some of the other major sports, but there is plenty to follow from the start. One of the first steps in the offseason journey is the extension and acceptance or decline of the Qualifying Offer (QO). The qualifying offer is a pretty simple concept that comes along with some relatively important consequences.

It works like this. Teams who are losing free agents are able to offer those free agents a one-year contract which the players can choose to accept or reject. If the player rejects the contract and signs with another team, the team who lost the player gets an extra (between the first and second rounds) draft pick the following June and the team signing the player loses their first round pick the following year. Because this is baseball, there are a number of nuances to that description.

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Baseball analysts are frequently wrong. Everyone who writes for this website picked the Nationals to win the NL East, for example. We also split between Detroit and Cleveland for the AL Central, with no votes for the Royals. Predicting baseball is difficult because it’s a game with many variables and lots of randomness. It is probably very unlikely that a world class chess player would lose to a novice in any one game, but it’s especially unlikely they’d lose more often than not over 100 games. In baseball, there are so many things impacting single games, and there are 2,430 games, so predicting a full season is especially challenging.

And as has been noted in a lot of places, we didn’t do a great job predicting the 2015 season. The Rangers, Astros, Blue Jays, Royals, and Mets weren’t exactly consensus playoff picks. Whoops!

This has led to plenty of push back against sites like ours and serves as a criticism of the work we do when it comes to predicting the game. Presumably, if we can’t accurately predict which teams will be good and bad, you might not want to put a ton of stock in what we’re saying. Surely, no reasonable person would hold anyone to a standard of perfection, but whiffing often can be a sign of a flawed process.

I’m not going to litigate exactly where our projections may or not be flawed in this post, but rather, I want to separate out two very different components of overall wrongness. In fact, there are essentially two ways in which our overall estimates of the league can be incorrect and you should understand the forces at play when determining how much stock to put into the work done here, and at other sites like Baseball Prospectus.

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Unfortunately, if you are a major league front office employee, this is not a presentation of ground-breaking new research regarding the prediction of pitcher meltdowns that will save you innumerable frustrations. Rather, this post provides a summary of some of the basic factors that go into the decision to pull a starting pitcher. If you’re new to the game or are just starting to pay attention to sabermetrics, it’s likely that you haven’t really ever had a run down of the different decisions a manager needs to make when plotting out their mid- to late-inning choices.

The conventional wisdom is generally about two things, fatigue (usually in terms of pitch count) and effectiveness (usually in terms of a stat line or recent hitter performance). A pitcher will get yanked after 100-115 pitches unless they are absolutely dealing or a pitcher will get yanked if they’re getting hit around a lot. Over the first seven or eight innings, that’s typically the mindset of many. Of course, there’s the obnoxious “save situation” problem that arises in the ninth inning, but we’ll leave that for another day.

But in general, while fatigue and effectiveness are good variables, the decision to pull a starting pitcher is multi-dimensional. Let’s consider some of the factors in more depth.

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Every statistic is an answer to a question. “How often does a batter reach base?” is answered by On-Base Percentage. “How many extra bases does a hitter average per at bat?” leads us to Isolated Power. A statistic is only as good as it’s generating question and if you’re asking a silly question, the statistic may give you a silly answer. Stats like pitcher wins, saves, and RBI all answer questions, but they don’t really answer questions we really want to know the answer to.

RBI, for example, tells you how many times a batter has had their hit, walk, or sacrifice fly lead directly to a runner crossing the plate. On the surface, this may seem like a useful statistic as a measure of run production. But you soon realize that RBI is reliant on the number of opportunities each player has to drive in runs. Coming to the plate with a man on first and coming to the plate with a man on third are not the same type of RBI opportunity, even if the batter hits a single in both situations.

In other words, RBI is a very crude context-dependent statistic. Generally, RBI isn’t very useful because it doesn’t provide you with a lot of information about individual player’s role in the production of a run. If they have a lot of RBI, did they have a ton of opportunities? Did they cash in on a large percentage of their opportunities? You don’t really know. But the fact that RBI doesn’t provide much insight does not mean that context-dependent stats aren’t valuable when designed properly. Essentially, context-neutral and context-dependent stats are both useful, but they are simply answering different questions.

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Batting Average on Balls in Play (BABIP) is one of the most commonly cited statistics in sabermetric analysis, and it’s role in mainstream coverage of the sport is growing as well. BABIP is a measure of how often “balls in play,” or non-home run batted balls, fall for hits. It’s an easy statistic to understand, but it’s not always the easiest statistic to use properly.

The problem occurs when people focus too heavily on one of the three main drivers of BABIP, which are player quality, defense, and luck. Most of the discussion surrounding BABIP is on the amount of luck that is involved. For some people, BABIP is simply a measure of how lucky or unlucky a player is getting over a period of time. But in reality, that is only part of the equation. Certain hitters consistently produce higher BABIP than others, and the presence of a good defense behind a pitcher can absolutely suppress their BABIP even before we consider the role of luck in the process.

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You’ve probably had a chance to peruse our leaderboards and player pages, and hopefully you’ve had a chance to check out our posts about getting the most out of the leaderboards and player pages. Another thing you might have seen on the site, or being shared on the internet, is our live win probability graph. It looks like this:

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There are a lot of reasons you might have arrived at FanGraphs. Perhaps you’re here for the articles or you’re just trying to find a detailed fantasy baseball game, but there’s a good chance that our various statistics are a big part of the draw for you. We host a lot of numbers and there’s a lot you can do with them if you know where to look. Last year, I put together a primer on how to use the FanGraphs Leaderboards to aid readers in their efforts to manage the information we provide.

If you’re new to the site, that’s a great place to start, but if you’re somewhere between newbie and expert, this post might help you get the most out of what we have to offer. When you’re thinking about baseball, there are a lot of questions you might want to answer. How do these two players compare? How does this player measure up historically? How rare is this particular thing?

Today, we’re going to use Bryce Harper’s exciting 2015 season to explore some of the features available at FanGraphs. This isn’t an exhaustive run down of the tools, simply an explanation of some of the more useful ones that don’t get enough recognition. If you’re reading this in the future, the screen grabs for 2015 are current through July 18, 2015, but the links will update automatically with new data.

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While there’s rightfully plenty of focus on the events on the field, teams and fans are also interested in getting the right players onto the roster in the first place. This is why there’s so much focus on free agency, the trade deadline, and the draft. Games are won and lost on the field, but it’s a whole lot easier to win if you’ve assembled a good roster. As a result, we spend a lot of time evaluating roster moves. We care about how well teams are using their resources to assemble a team. One of the important concepts to understand when evaluating these moves is service time.

Service time is exactly what it sounds like; the number of years and days of major league service a player has in their career. Typically, it’s written as Year.Days, so we would express a player with four years and one hundred and fifteen days of service time as 4.115. You earn a day of service time for every day you are on the 25-man roster or the major league disabled list during the regular season. If you’re called up on June 22 and you’re sent down after June 28, you’ve earned seven days of MLB service. Your team doesn’t have to play a game for you to accrue a service day.

There are usually about 183 days in an MLB season, but a player can only earn a maximum of 172 days per year. That means if you’re on the roster for 178 days, you earn 172 days. If you’re on the roster for 183 days, you also earn 172 days. Not surprisingly, 172 days of service is equal to one year of service.

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The currency of baseball is wins. The ultimate goal is to win enough games to make the postseason and then win enough games in the postseason to win a World Series. For that reason, we care a lot about what leads to wins and losses, and outscoring your opponent is the only path to victory. This is all pretty obvious, but if we unpack it we stumble on to some pretty important realizations.

Before we go anything further, this post stays at 30,000 and serves as an introduction to Pythagorean Record and Base Runs. I won’t be going into the details of the exact formulas, but rather why these statistics are useful when looking at the team level. If you’re already well-versed in the various expected records, there probably isn’t a lot of new information below.

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You’re likely aware that there are different versions of Wins Above Replacement (WAR) housed here, at Baseball-Reference, and at Baseball Prospectus (called WARP). For a lot of people, this makes the statistic confusing because it seems like there shouldn’t be multiple ways to calculate something with the same name. To the credit of the critics, somewhere along the way we should have agreed on a way to make it easier to communicate which statistic is which that’s a little more clear than fWAR, rWAR, and WARP, but that’s not the focus of the discussion today.

When it comes to WAR for position players, the differences among the models are less philosophical and more technical. The sites use different defensive components, different base running stats, and a few other differences in the same vein, but the overall approach is pretty much equivalent. The inputs are different, but the different WARs agree on what should be measured. When it comes to pitching, it gets more complicated because what should be measured becomes the debate itself. This article doesn’t intend to tell you which WAR is best, but rather to walk through the decisions that one needs to make when evaluating a pitcher’s value.

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