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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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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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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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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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.

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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