Archive for April, 2015

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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