Breaking down the debate about sabermetrics between Brian Kenny and Hawk Harrelson.
I don’t know how we got to this point, but the long-awaited grudge match between White Sox color commentator Hawk Harrelson and MLB Network broadcaster Brian Kenny (with occasional contributions from Harold Reynolds) took place last night. Everyone was polite, nobody got sent to the hospital, and Hawk launched a thousand indignant tweets. You can see the whole thing through the miracle of YouTube, if you have ten minutes to spare for Hawk to say five minutes’ worth of sentences twice:
Does public criticism of Starlin Castro and Anthony Rizzo help the Cubs?
Robert W. Chambers was one of the more successful authors in what may well have been the heyday of written fiction in America at the turn of the previous century, and he’s an interesting example of how writers were far less constrained to a single genre back then. During his lifetime, Chambers was mostly known (and read) for his romantic fiction, which produced several bestsellers. He also wrote war stories and historical fiction, as well as a handful of illustrated children’s books.
Nowadays, to the extent he’s remembered at all, it’s for his contributions to the field of horror. His best-remembered work is a collection of short stories called “The King In Yellow,” which contains several stories about a play titled (yes) “The King In Yellow.” Chambers only ever quotes from the first act, which characters describe as banal and innocent. The second act, however, is so terrifying and horrible (and so filled with awful truths) that it drives those who read its text or see it performed utterly insane. Chambers never reveals the contents of the second act in full, only hinting at its contents obliquely:
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Announcing the arrival of the 2013 PECOTA percentiles.
PECOTA percentiles are now available to subscribers.
Those of you new to BP, or to PECOTA, might wonder why we publish percentiles in addition to the weighted-mean projections for players, which we’ve already released. The answer is that forecasting is an inexact science; the future is not exactly what you'd call certain. The percentiles allow us to put a range of outcomes around a single-point forecast, to illustrate how uncertain the forecast is and what range of outcomes are most likely.
Asking questions about PECOTA's projections, and explaining what the system thinks is in store for Bryce Harper.
When the PECOTA spreadsheet appears, one of the first things people do is pick out the players projected to make the greatest gains or suffer the largest declines. Then the questions start: Why does PECOTA like/dislike so-and-so so much? Is there a problem with the projections? Or is the system just picking up on something I’m not seeing?
Behind the scenes, the BP staff goes through the same thought process. Before we publish the projections, we approach PECOTA’s output with a skeptical eye, on the lookout for anything that could be a bug. But even after we’re satisfied with the spreadsheet and release it to our subscribers, PECOTA retains the capacity to surprise.
Before we get underway, some notes. PECOTA does not hate your favorite team. PECOTA is a collection of algorithms, written in computer code and run by an unfeeling machine. It cannot hate, or love. It can do only what it is told to do, nothing more or less.
BP begins to roll out its projections and fantasy tools for the 2013 season.
Welcome to the initial launch of this year’s PECOTA forecasts. We hope you find them enlightening, useful, and predictive.
Let’s start with the business aspects of things. In order to access the PECOTA forecasts, you need to be a subscriber to Baseball Prospectus. Monthly subscribers will have access to certain PECOTA features but will not have access to downloads like the PECOTA spreadsheets. The best value we offer is a yearly subscription, which not only gives you access to the full PECOTA product offering, but also unrestricted access to our extensive prospect coverage, R.J. Anderson’s Transaction Analysis, in-depth analysis from the likes of Ben Lindbergh, Sam Miller, and more, and the latest in baseball research from the likes of Russell Carleton and myself. If you feel you can pass on that, we offer our lower-priced Fantasy subscription, which give you full access to the PECOTA products and all fantasy-focused articles on the site.
Writers didn't want to induct anybody into the Hall of Fame this year, a decision with no small consequences.
The writers struck out looking. They were lobbed a fat pitch over the heart of the plate and they failed to even take a swing at it. Defenders will note, correctly, that it isn’t the ninth inning. But it was the last at-bat of the eighth, and they face an exceedingly difficult challenge in coming back to win this thing.
The biggest takeaway is that there is a sizable contingent of voters who will refuse to vote for any player, no matter how qualified, if there’s the barest taint of steroids on him, up to and including “playing the majority of his career after 1993.” Many will cast this as a referendum on Bonds and Clemens, two of the sports’ greatest stars who ended up in legal hot water over the use of performance-enhancing drugs. But a litany of deserving players, including Biggio, Bagwell, Piazza, and others, have been punished too, with little more than hearsay to incriminate them. This was a well stocked ballot, filled with newcomers with impressive resumes and a handful of players (like Raines and Trammell) who have been sadly overlooked. It’s easy for even a seasoned analyst to find himself having to trim his list to meet the 10-player limit established by the voting process.
There was some chatter on Twitter this morning about park factors, and Marc Normandin made the point that all of the most common park-adjusted offensive stats out there (TAv, OPS+, wRC+) use "generic" run-based park factors, not component park factors. (Baseball Prospectus does have component park factors which we use in PECOTA, and we use those to generate our run-based park factors, but we use run-based park factors in TAv.) Marc wondered if using one-size-fits-all rather than the component factors might lead to inaccuracies—after all, we know different parks affect different types of hitters in different ways, and our park adjustment methods here don't account for that. (Marc isn't the first to raise this point, by the way.) So why do we all do it this way?