Even though Encarnacion is on the “wrong” side of 30, he’s done nothing but improve his most valuable asset—his power hitting. In the 2015 season, he hit .277/.372/.557 with a TAv of .324 and 39 home runs, up from .268/.354/.547, .310 TAv and 34 a year earlier. He might be just another weapon in a fearsome Blue Jays lineup, but he'd be good enough to be the best hitter in a championship-quality offense.
A lot has changed in Toronto since their playoff run ended in October. Alex Anthopoulos has moved on to warmer places, David Price is taking the mound for Boston, and Ben Revere is suiting up for the Nats. The Jays have also made significant changes to the pitching staff, adding some desperately needed depth. While those additions are nice, they’re mostly window dressing, as the pitching is still only decent. Thankfully, the Jays only need mediocre pitching, because their hitters straight up mash.
The Blue Jays were a magnificent offensive team in 2015. We know it. You know it. Sam Dyson definitely knows it. But I don’t think many of us realized just how good Toronto’s hitters were last year. In addition to leading baseball in runs scored, the Jays led the league in HR, BB, SB%, TAv, and most other offensive statistics that you can think of. Basically, the offense could do it all. In order to fully visualize that, look no further than this fantastic tweet from Gideon Turk, which charts OBP against SLG.
Did this year's IBA voters really prefer the player we said they preferred in a tight race?
I love crowdsourcing projects. I love them because they take virtually no effort to set up, and yet we get a huge amount of information out of them. They work because the voters are the ones doing all the work, and whatever biases they may have get cancelled out, so that we're left with a reasonable view of the perceived truth. That's if everyone is playing fair. Sometimes, the voters try to game the system.
Start the bandwagon: The next criminally underrated HOF candidate is today's criminally underrated superstar.
I might be a little biased, but I think that if there’s something that last week’s Hall of Fame results needed, it was more inductees named Russell. With Russell Branyan not eligible for election (and in legal trouble), things have been looking kinda bleak. But something else happened in last week’s results that gives me hope. Other than that guy who’s going in with a backwards cap, catcher Mike Piazza finally got his spot in the Hall of Fame.
Why do these two teams like each other's pitchers so much?
The A’s sold Arnold Leon on Tuesday, a fairly forgettable transaction involving a fairly forgettable (I’m so sorry, Leon family) pitcher. Few things are as unremarkable as the A’s letting another team have a marginal talent in exchange for some much-needed cash. I might not have taken note of this deal at all, but for the identity of the team taking on Leon: the Blue Jays. That makes this move stand out to me a little bit, for a simple reason: the Jays and A’s sure seem to like one another’s pitchers.
How a mystery that began with R.A. Dickey ended with a new, more precise way of measuring catcher performance.
Recently, we overhauled our approach to how we evaluate passed balls and wild pitches here at Baseball Prospectus. It started innocently enough, as an attempt to make our data better-behaved, but progressed to a gradual recognition that we—and as far as we can tell, plenty of others—have been taking the wrong approach to these events for quite some time. Today, we’ll talk about what we’ve learned, and how our models are much the better for it.
It’s no secret that some catchers are better at blocking pitches than others. Yadier Molina seems to be pretty good at it, and Mike Zunino does not. But raw wild pitch and passed ball numbers can be unfair. The catcher, after all, is not the one throwing the pitch, and some pitching staffs are wilder than others, particularly if those pitchers like to throw certain pitches in certain places. The sabermetric community’s longstanding skepticism of official scoring has also led to the practice of combining passed balls and wild pitches for modeling purposes, even though the former are judged by the scorer to be the catcher’s fault, and the latter to be the fault of the pitcher.
As with all things sabermetric, the means of adjustment for these factors have become more sophisticated over time. At the simplest level, we could simply trust the official scorer, and assume the other factors largely balance out. A more sophisticated approach is the “With or Without You” method, which grades a catcher based on how he does without certain pitchers, or how pitchers do without various catchers. Going one step beyond, researchers have tried to identify relevant factors driving passed balls and wild pitches, incorporated them into models of “likely” passed ball/wild pitches versus “actual” such events, and then grading a catcher on the difference. FanGraphs has adopted a model created by Bojan Koprivica as the basis for its Runs per Passed Pitches (RPP) metric. (The parameters of that model appear to be proprietary, although Bojan does describe the relevant aspects). Finally, we unveiled our own blocking model last year, called “RPM WOWY”: a combination of PBWP likelihood, as determined by PitchInfo, followed by a WOWY assignment of credit among catchers and pitchers.
When we began incorporating mixed models into our catcher metrics earlier this year, we converted our catcher blocking model over as well, since it made sense to have all our catcher metrics on the same basic method. And so, throughout the 2015 season, we combined all of what we now call “errant pitches” into a linear mixed model, specified as follows in the R programming environment:
The model was, frankly, a bit of an afterthought. We were focused more on converting RPM WOWY into our new framework rather than thinking it over from scratch. The log transformation of errant pitch probability (the former “RPM,” and the “prob” in the new blocking model) was added as much to assist convergence as anything else.