And Now for Something Completely Different

Today we're going to talk about pop-up rate and Z-contact, their inverse correlation to BABIP and their effect on DIPS projections.

Ha! Just kidding! I mean, that's what we'll be discussing , but you won't know it. What you will know is that we've engaged in a new and fascinating discussion that might help you win your fantasy baseball league.

Ha! If anything in this blog were actually fascinating, Braindrizzling would be positively thrumming with annoying but remunerative pop-up ads and flashing banners. Far more likely, you'll find it mildly relevant if you manage to read to the end.

First things first: this blog post owes everything to Dave Cameron and Voros McCracken. It is a recapitulation of this fine post by Cameron at Fangraphs.

Fourth things second: there will be no math and you won't be quizzed on it next week. However, there will be a wee bit of trailblazing that will help us understand yet a skooch better how pitchers succeed or fail.

Second things third: the abridged version of the back story. Sabermetricians, led by McCracken in '99, realized that pitchers can control Ks, BBs, HBP and HRs, and not much else. Fielding, ballpark and luck are much more relevant than pitcher aptitude to whether a grounder punches through for a single or gets gobbled up by the shortstop for a 6-4-3. Their BABIP-against, that is, the batting average on all those other balls put into play, tends to be random. Sort of. Hurlers with high BABIP one year often have low ones the next and vice versa. Not all and not always, but more or less. Void where prohibited by law.

Except: flyball pitchers give up fewer safeties than worm killers. On the other hand, those fly ball hits more often fall in as doubles and triples and those four hoppers through the infield rarely push the hitter past first.

So when the SABR dudes attempt to project pitching ERAs, they don't look at a pitcher's previous ERA but at the constituent parts -- HR, K, BB, HBP -- and assume an average BABIP going forward. These projections have bested projections using just previous ERA by a significant and consistent amount over the last decade, particularly as further refinements -- like ground ball and fly ball proclivities -- have helped salt the projections.

In English, if you want a prediction of Johnny Cueto's ERA in 2013 because you're considering making a bid on him in your fantasy league, his 2012 ERA of 2.78 says GO! But his constituent parts whisper FLUKE! They project a 3.74 ERA in 2013, assuming a league-average BABIP.

And now for something completely different: Suppose Dave Cameron tells you that he has a formula that's NEW AND IMPROVED! It can account 15% better for the batting average against a pitcher on balls put in play. That means he can get you a better idea of the pitcher's ERA and WHIP, and give you a leg up on the innumerate competition. Has Dave Cameron ever lied to you before?

In a nutshell, what Cameron found is that pitchers who induce a lot of pop-ups and who get batters to swing and miss on more pitches in the strike zone, reduce their BABIPs. It's partly why consistently good pitchers, like Justin Verlander and Jared Weaver, post consistently low BABIPs. In other words, we've found another way that BABIP is not entirely random for pitchers. (It's not random at all for batters, as anyone who's watched Ichiro knows intuitively.)

If Cameron's conclusion is supported by more research -- because this is how science advances -- pitching projections will get 15% better. How big a deal is that? Five advances of 15% each double the accuracy of projections. (Trust me on the math; I stayed in a Holiday Inn near MIT.) But even without four more Eureka moments among the seamheads, you can look up a pitcher's pop-up rate last year and squeeze out an edge on the competition in your fantasy league.

The naysayers will still neigh because advances of these stripe -- small and complicated -- are hard to see. The projections still won't identify the lucky dogs, the breakthroughs and the cliff divers. They won't be able to predict which pitcher tamed his control tiger, altered his mechanics and added 4 mph to his heater, or just figured something out. Truth is, luck is still way too big a factor in baseball to overcome with science. But if science can help you win your fantasy league you probably want to listen to the scientists.

When Models Look Extra Ugly

Democrats are predictably excoriating Paul Ryan these days for his "attack" on Medicare. His voucher plan for the government-sponsored health care insurance for the elderly (and the not-nearly elderly; you can join at 65) diminishes benefits going forward.

Democrats are comparing Ryan's plan to perfection, or to the current system. Neither, of course, is an apt comparison. Medicare is rushing headlong off the financial cliff without reform, and threatening to take the entire American economy with it. Relative to insolvency, which is the ultimate fate of the program under everyone else's plan (i.e., no plan at all), Ryan's proposal is a revelation.

A similar debate is shaking the Sabermetric community these days in the wake of a pair of disturbing arithmetic developments. But the conclusion is the same: the measure of a new idea isn't in comparison to perfection, but to the old paradigm.

First, some context. Ever since Voros McCracken unveiled the idea in 1999 that pitchers have limited control over what happens once their pitch is put into the field of play -- a conclusion now much more nuanced and qualified -- seamheads have been whipping up concoctions to strip luck and defense out of pitching results to determine how well pitchers are actually pitching.

One of the many formulae brewed by the stat wizards is FIP -- fielding independent pitching. Without excavating spreadsheets filled with higher math, it can be described this way: given the number of batters a pitcher has fanned, walked, hit with pitches and allowed to homer, how many runs should we expect to score against him given average defense and ballpark?

In July, Reds closer Aroldis Chapman broke FIP (in the words of CBS Sports). He faced 52 batters in 14 1/3 innings and whiffed 31 of them. He allowed just six hits, two walks, a HBP and nary a run, nailing down 13 saves. Batters posted a desultory .122/.173/.143 line against him.  In other words, Chapman was Superman for a 30-day period. (He's been no slouch under other moons.)

Putting those numbers through the meat grinder yielded a FIP for Chapman of -0.99. That is, Chapman could be expected, given his performance, to yield minus-one run per nine innings. Certifiably nuts.

Others have noted odd sightings in Baseball-Reference.com's WAR (Wins Against Replacement) calculations. WAR attempts to review all of a player's performance and all of the context and measure him against a Triple-A replacement at his position. In mid-August, Cubs keystoner Darwin Barney had a higher WAR than Brewers slugger Ryan Braun. Just for context, Barney is hitting .268/.309/.386, or about 12% below the MLB average. Braun, at .301/.380/.526, is 53% above average. Braun is also superior in the baserunning department and has grounded into fewer double plays. Moreover, Wrigley Field and Miller Park are about equally kind to hitters, and both batters suffer equally in their inability to bat against their own team's woeful pitching staff.

What WAR sees that we don't is defense. It credits Barney for defense double in value of any other second-sacker's, while Braun's left-field stylings more resemble the staccato flight of a pigeon. Subjectively, there appears to be a grain of truth here, but clearly not enough that anyone in Wisconsin would trade Braun for every Barney ever known, including the purple dinosaur. WAR is suffering convulsions and has been put on bed rest, at the very least.

These two developments have led some to observe FIP and WAR's death throes. The projection models have failed and it's time to put them out of their misery. But in the words of that great philosopher Quick Draw McGraw, "Now hold on there just a doggone minute, Baba Looie."

No model is perfection, not even Adriana Lima. Clearly outlier performances like Chapman's make mincemeat of statistical models. As I've mentioned before, quantitative analysis of defense is just in the bloom of its youth, beholden to bursts of impaired judgment. I rarely rely on WAR or WARP and pay much more attention to offensive valuations, leavened by a general sense of a player's glovework.

Nonetheless, FIP is an extremely useful tool, and though the value it applies to Chapman's performance is nonsense, it's not really wrong. FIP says Chapman was virtually unhittable, and by golly, he was. Only 18 batters out of 52 could even put the ball in play.

The real question then is, are these models less imperfect than the old tools? WAR certainly measures something bigger and more relevant than Triple Crown stats do, but whether it measures relative positional value better than Triple Crown stats measure hitting performance would require some sort of study. There's no doubt that FIP tells us much more about a pitcher than W-L and ERA; it's been shown to be vastly better at projecting performance.

All of which presents the same moral that every other development in quantitative analysis has: the state of the art is improving, but will never be perfect. After all, they measure the performances of people.