My last article introduced the concept of estimating a pitcher’s ERA using a simulation called SERA. As I pointed out throughout the article, SERA was strictly an estimator, not a predictor. That is, a pitcher’s SERA in one season wouldn’t do a great job predicting that pitcher’s ERA the next season. It’s more similar to FIP than it is to xFIP; descriptive rather than predictive.

But what if we want to create a simulator that predicts ERA for the future instead of just estimating what the ERA should’ve been? Some things are going to need to be changed — not just the code for the simulation, but also the inputs.

Read the rest of this entry »

Pitchers are expected to lose command after Tommy John surgeries. Prolific base stealers coming back from hamstring injuries are expected to take it slow for a week or two before regularly getting the green light on the basepaths. A broken finger for a slugger is blamed for the loss of power; a blister for a pitcher might mean a loss of feel on their breaking ball. What is not well publicized, however, is how a player recovers from and reacts to returning from a concussion. For an injury that has been talked about in the media so often in the last few years, we know very little about the actual long-term, statistical impacts that concussions have on players that experience them.

Players often talk about being “in a fog” for some time after suffering a concussion – often even after they return to play. The act of hitting is a mechanism that involves identifying, reacting, and deciding on a course of action within half a second. With that in mind, I wondered: do concussions change the quality of a batter’s eye and discipline at the plate? Do brain injuries add milliseconds to those individual steps? Even though each injury is different, do varying lengths of disabled list stints due to concussions change a player’s performance on the field after they return? The most direct route to answering those questions might be studying the impact of concussions on strikeout and walk rates.

Read the rest of this entry »

ERA, probably the single most cited reference for evaluating the performance of a pitcher, comes with a lot of problems. Neil does a good job outlining why in this FanGraphs Library entry. Over the last decade, plenty of research has cast a light on the variables within ERA that often have very little to do with the pitcher himself.

But what is the best way to use fielding-independent stats to estimate ERA? FIP is probably the most popular metric of this ilk, using only strikeouts, walks, hit batters, and home runs to create a linear equation that can be scaled to look like an expected ERA. Then there’s xFIP, which is based off the idea that pitchers have very little control over their HR/FB rate; to account for this, it estimates the amount of home runs that a pitcher should have allowed by multiplying their fly balls allowed by the league average HR/FB rate.

For many people, however, these are too simple. FIP more or less ignores all balls in play completely; xFIP treats all fly balls equally. Neither one correctly accounts for the effects that any ball in play can have; we know that the wOBA on line drives is much higher than the wOBA on pop ups, but we don’t see that reflected in many ERA estimators. The estimators we use also are fully linear, and may break down at the extreme ends; FIP tells us that a pitcher who strikes out every batter should have an ERA around -5.70, which is, well you know, not going to happen.

Read the rest of this entry »

In September of 2013, Billy Hamilton made his MLB debut. As a pinch runner. It’s wonderful to see Billy Hamilton be a pinch runner. A few days before his 20th birthday, Tim Raines made his MLB debut as a pinch runner, and all six games in his September callup was as a pinch runner. He played 15 games the next season, 7 of which was as a pinch runner. (The season after that, 1981, is when he established his star.)

I see nothing but positive about this kind of development. The only reason we see it in September is because of the loosening of the roster rules. Now, rather than expanding the rosters during the season, or declaring a 15- or 20-man roster on a game-by-game basis, what would happen if we changed the substitution rules?

Say you bring in Hamilton to pinch-run for the catcher. At the end of the half-inning, the manager currently has two choices: (a) choose whether to bring in a new catcher and knock Hamilton out of the game or (b) double-switch Hamilton into the game, with a new catcher. In either case, the original catcher is knocked out of the game. But, what if we add a third option: (c) allow the manager to keep the original catcher while knocking Hamilton out of the game. The substitution still knocks one player out of the game, but now the manager gets to decide which of the players involved gets the boot.

And you can extend that to pinch hitting as well. And if you do that, you can also do away with the DH. You can now bring in a pinch hitter for your starting pitcher in the second inning, without knocking out the pitcher (but you do lose the PH): You simply let the PH hit, and then that guy is out of the game, while the pitcher remains. And the same applies for a defensive substitution: at the end of the half-inning, you decide which of the two players is knocked out of the game.

You still have to worry about your bench and when to allow your pitcher to bat or not. But, you now give the manager a bit more flexibility. And if he wants to have a speedster on the roster, without thinking he’s burning through two roster spots (i.e., knocking out both the original catcher and Hamilton), he’s only burning through one.

What we have here is a rule that:
a. has no roster impact (you always lose a player)
b. is unobtrusive (no one is really going to notice anything)
c. allows the DH to disappear from the rulebook
d. gives the manager great flexibility

Downside? You tell me.

UPDATE: Based on one or two comments, I don’t think I was clear enough: at the end of the half inning, the manager chooses which player he loses for the game. In the above situation, if he goes for option c, he’d knock Hamilton out for the game, and the original catcher is allowed to stay. The same applies for a fielding sub: Hamilton could have come in as a defensive sub in the 8th inning in CF. At the end of the half-inning, the manager decides whether to knock Hamilton out of the game, or knock the guy he had replaced out of the game. There is NO “re-entry”. Someone will get knocked out of the game, just like it is today.

Defensive statistics have been open to debate since they were first created. This back and forth probably will continue on for years to come, even with some new technologies offering the promise of better data.  One limitation with giving individual players values for their defensive metrics is positioning. The player’s coaches may have them completely out of position for a seemingly routine play and zone based metrics are going to downgrade the player because they didn’t make the play. While it may be impossible to know the correct player position before each play, the chances of a defender making a play knowing their initial position can be estimated with Inside Edge’s fielding data. By using their Plays Made information, I will add another stat to the defensive mix: Plays Made Ratio.

The concept is fairly simple. Inside Edge provides FanGraphs with the number of plays a defender should make given a range of possible chances. Inside Edge watches each play multiple times and grades the difficulty of the play. Here is their explanation for how they collect the data.

Inside Edge’s baseball experts include many former professional and college players. Every play is carefully reviewed, often more than once. It is not uncommon for IE scouts to review certain plays together in order to reach a consensus on the defensive play rating. IE also performs a thorough post game scrubbing process before the data is made official.

Read the rest of this entry »

There is one pitcher out there that throws his changeup over 30% of the time and calls it a ‘heavy sinker.’ Alex Cobb aside, though, we traditionally lump the changeup in with the slider, the curve, the splitter — it’s not a fastball.

And yet, in some really important ways that go beyond movement and leak into usage, the change works like a sinker. In a league where strikeouts rule, the change actually has a strikeout problem.

Read the rest of this entry »

A little while ago, I wrote an article for the Community Research blog about projecting plate appearances before they happen based on the batter and the pitcher. It was pretty well received (which was nice, because I put some serious work into that thing), and apparently it was good enough for Dave Cameron to foolishly kindly decide to call me up to the big leagues.

If you read through the comments there (or if you left a comment!) you probably realized that no, the Outcome Machine — as the tool was dubbed — was not perfect. There were flaws in the way I conducted my research, and some of the assertions I made probably weren’t 100% true. So in this article, I am going to follow up on that first one and hopefully remedy any errors. Those include:

Read the rest of this entry »

It’s easy to see that baseball has changed over the last couple of decades. Walks, strikeouts, homers, and stolen bases have all seen their ups and downs, and we’re currently experiencing a valley in terms of offense. Games are longer. There’s instant replay.

But there’s evidence that players are getting similarly better and worse at these things — the distribution hasn’t changed, the graph has just been shifted. It’s possible that the relative value of certain events in baseball as a whole could still be about the same. A stolen base’s relationship to a win could be unchanged if the distribution of stolen bases is similar, and there are just fewer of them.

Is that what you find when you look back empirically? If you relate strikeouts, walks, stolen bases, and home runs to winning, is that relationship steady over these turbulent times?

Read the rest of this entry »

Madison Bumgarner pitched quite a bit this past season. Including the regular and post season, he threw a total of 4,074 pitches, which wasn’t even the season’s top total; James Shields bested him by throwing six more, for a total of 4,080 pitches in 2014, not including spring training. So with all of the pitches thrown this season (and one month less of rest), how should we expect these two to produce next season? Let’s look at some comparable pitchers.

Read the rest of this entry »

Void of any analysis, this post is!

Full of fun GIFs, also, this post is!

Because baseball is still just a game. Despite all the number-crunching, data-mining, spreadsheet-making, question-asking, answer-seeking, conclusion-drawing and soul-sucking we do here at FanGraphs, it’s important every once in a while to just sit back and soak up what it is that keeps us coming back and makes baseball so fun and interesting: Weird things happening all the time. And dingers. One must always remember to appreciate the dingers.

We’re about halfway through the 2014 season now (!), so it’s time for everyone to start doing best first-half this’ and worst first-half thats. Or, in this case, the most extreme first-half homers.
Read the rest of this entry »