Going back to Henry Chadwick’s invention of the box score in the 1850s, statistical summaries have been integral to telling stories about baseball. In the latter half of the 19th century, box scores were a way to explain the narrative of a game to an eager public without TV or photographs, in a time when the only access to sports was at the stadium. Now we are bombarded with a multitude of avenues with which to enjoy baseball, but the role of data is fundamentally the same.
To illustrate my point, consider the following scenario: Let’s say you come to me and ask how the game went yesterday because you didn’t get the chance to see it. I could say something like, “The Royals beat the A’s, 9-8.” It might be a factual statement, but it wouldn’t be an especially interesting one. A better way to tell the story would be to explain who played, who scored the runs, and when—the fundamental components of a box score. A still better summary of the game might highlight some of the unexpected happenings, like the way that the Royals exposed Jon Lester’s inability to stop the running game to the tune of seven steals. A yet more rich description of it might wrap the occurrences of the game up into historical narratives and longer-term trends, noting for example that despite nearly matching the single season record for innings caught (and presumably suffering under the burden of tremendous fatigue), Sal Perez was able to knock in the walkoff single in the bottom of the 12th inning. All of these details come from data, and help to transform the rote happenings of sport into a story worth listening to.
When a prospect's movement tells us more than his ranking alone does.
In 2007, Reid Brignac was a sure thing. Lacking only elite defensive skills, hounds such as BP’s own Kevin Goldstein praised Brignac’s bat with the most effusive verbiage. With an average ranking of 25th on a collection of top prospect lists, the only question regarding Brignac was at which position he’d be posting an All-Star batting line.
As any recent student of baseball will know, this was not to last. The next year, his ranking had fallen to 32nd, then 89th the following year. Brignac’s offense collapsed somehow, and never recovered. A quick perusal of Brignac’s player comments reads like the arc of a cannonball: soaring upward toward stardom, lingering for a moment as a high-end, blue-chip prospect, and then suddenly crashing downward into irrelevance. After having amassed an unimpressive -0.8 WARP in lifetime value, Brignac was last seen signing a minor-league contract with Marlins.
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These scenarios suggest that the value of prospects is a complex and contentious topic, even within baseball’s front offices. Admittedly, I am notthefirst and will not be the last to approach this question. But the quandary of prospect valuation is worth reexamination both because of its complexity and importance.
People love to talk about the mood of a franchise, or the collective feeling of its fanbase. Are they dispirited, optimistic? Ecstatic following a World Series win, or broken after an agonizing walkoff loss? For the most part, we leave it to the beat writers to gauge mood (which is not necessarily a bad thing), without any kind of backing for their proclamations (which might be a bad thing).
Hypothetically, fans are a reservoir of great wisdom (collectively, although perhaps not individually). So tapping into the mood of a fanbase could be more than interesting, it could be useful. But, beyond inquiring with potentially biased observers, there was little we could do to objectively or quantitatively measure a fanbase’s mood.
Of all of the recent advances in sabermetrics, catcher framing is likely the one with the greatest impact on our view of the game. Already, it has begun to affect our notions of player value, giving us a new respect for defensively-minded backstops. But even beyond that, our new knowledge of framing re-allocates a huge amount of the value we had implicitly assigned to the pitcher.
Catcher framing, and its effect on the game, are also difficult to measure rigorously. Fortunately, we are lucky enough to have Jonathan Judge, Harry Pavlidis, and Dan Brooks on the case. Their most recent article provided a precise framework for measuring much of the value due to catcher framing.
PITCHf/x data is able to make a significant contribution to injury prediction.
Injuries, I think we can all agree, are a deplorable scourge on baseball. They remove our favorite players indiscriminately from the field, or ruin their effectiveness. They can take teams that are great on paper and reduce them to smoldering piles of ash (see the Texas Rangers, 2014). Even though injuries appear random, the result of bad luck and stochastic variation, they are (to a limited extent) predictable. Based on prior research, it turns out that commonsense factors can predispose position players to injury.
In particular, my last foray into the subject found that age as well as the number of days missed in the past three seasons could provide a reliable prediction into how many days each position player would miss in the coming year. However, the accuracy of these predictions was modest, and required extensive information from prior years. It would be desirable to make further improvements upon these injury predictions, but in the absence of other possible sources of information, prospects seemed slim.
Can we improve on PECOTA's forecast for a hitter just by looking at which pitches the opposing catcher called?
The fastball is the meat and potatoes of the batter-pitcher contest. Variations in fastball velocity and movement explain a lot of the differences between pitchers, and a good heater can set up a whole arsenal of other pitches to boot. Fastballs are the most commonly thrown pitch by a wide margin, and so they determine to a great extent the results of any given matchup.
It’s no surprise then that pitchers tend to vary how much they use their fastballs on a hitter-by-hitter basis. Some hitters see fastballs rarely, others overwhelmingly, and the difference between hitters tells us something about their power (as well as their proficiency against fastballs). Being that they are the main offering of most pitchers, fastballs are the easiest to tee off against, and so they are thrown more rarely against powerful hitters.
Can the uncertainty in a player's projections be projected?
There are two important aspects of prediction. The first concerns the accuracy of the prediction—that is, how close a prediction is to the actual, observed result. The second is uncertainty, which is how sure a forecaster is about his or her projection. These issues are fundamental forecasting concepts, and similarly apply to predictions of the weather, the stock market, or the outcome of tomorrow’s ballgame. At present, only one of these facets of a prediction gets much attention in the world of baseball projections, and that is accuracy. Accuracy is measured by the absolute error, which defines how close, on average, a forecast is to the actual, observed result. Projectionists struggle primarily to minimize this number.
The under-examined facet of prediction that we will address in this article is the uncertainty. Whereas we know that predictions tend to be accurate to within a hundred or so points of OPS, we would also like to know whether we are more or less likely to be wrong on certain players. The uncertainty is often treated as a second-order concern because it is usually more difficult to estimate. However, as we show, it is possible to predict ahead of time which players’ forecasts are more uncertain than others. This concept is important because certain teams may prefer high versus low-risk players—a team with high win expectations (90+ wins) might prefer to reduce risk, whereas a middle-of-the-road team (80-85 wins) would presumably seek risk in order to “get lucky” and reach the postseason.
In what direction are voting totals trending for marginal candidates, and are steroids actually to blame?
Every year, the Hall of Fame vote brings a great deal of vitriol to baseball. With each year’s ballot, we are confronted by the specter of the steroid era, always a sore subject. But even neglecting the steroid era candidates, the BBWAA voters manage to produce a handful of idiotic ballots, defended with harebrained rationales, sometimes leading to obvious omissions.
It would be easy to pin the Hall’s recent mismanagement solely on the steroid issue, but the problems do not stop there. There’s a clear backlog of players that’s been developing for more than ten years, leaving deserving stars (with no steroid evidence against them) like Tim Raines and Curt Schilling on the outside. The situation is especially dire for pitchers, where the voters seem to rely upon outdated benchmarks like 300 wins, which even the best modern pitchers simply cannot hope to reach. This failure is through no fault of their own—pitcher usage patterns and injuries have changed the game. Clinging to the old milestones has the effect of artificially increasing the standards for induction, so that only the most inner-circle, obvious Hall players can make it.
The “fastball hitter” is one of the oft-repeated archetypes in baseball. This is the notion of a hitter who can strike fastballs just fine, but struggles to deal with the unpredictability of a breaking ball. A simple search of Baseball Prospectus’ archives reveals 64 results for “fastball hitter”; Google, surveying the entirety of the internet, pulls down more than 10,000. Like many of baseball’s finest tropes, the fastball hitter has even been enshrined in cinema lore.
Beyond movie characters and conventional wisdom, it seems plausible that some batters might have more difficulty recognizing or mentally adjusting for the break of a curve, for instance. Curves and sliders, in particular, possess not only the capacity to slice through the air horizontally, but also are often said to create visual illusions in the mind of hitters.
The error spectrum of projections shows the limitations of analysis, or the progress we can still make.
It’s around the time that projection engines are being tweaked, updated, and improved, in anticipation of the release of new predictions for the coming year. At Baseball Prospectus, Rob McQuown is hard at work ironing out the kinks for this year’s release of PECOTA. Given the present focus on predictions, the time is ripe for a retrospective look at how the projections fared last year.
There’s no better source for a large-scale comparison of projection algorithms than Will Larson’s Baseball Projection Project, which I will use for this article. Larson’s page houses the old predictions of as many different sources as he can get his hands on, including methods as diverse as Steamer, the Fan Projections a FanGraphs, and venerable old Marcel. It’s a rich storehouse of information concerning the ways in which we can fail to foresee baseball.
Constructing a leaderboard that passes the smell test.
I’ve recentlywritten about the role and value of plate discipline in hitting. I concluded the last article with the takeaway that plate discipline, while undoubtedly important in hitting, was not fully separable from the other attributes of a hitter. In searching for a complete per-pitch way of evaluating hitters, we have to account for the entire package of skills, because all of the skills interact with each other. So we have to go back to basics.
Despite being athletically impossible, hitting is theoretically simple. Every hitter is confronted on every pitch with a choice: to swing, or to take. A take is valuable when the pitch is likely to be a ball; depending on the count, you can get a walk, or at least advance the count in a favorable direction. If you swing here, you both lose the benefit of the called ball, and also risk whiffing on the pitch or making weak contact. On the other hand, when the pitch is thrown over the middle of the plate and is thus likely to be called a strike, the better choice is to do your best to make contact. If you take, you get a strike, and lose the opportunity of a hittable pitch.