A Theory on Russell Martin’s Framing Numbers

Projection systems tend to look at reality a whole lot more soberly than us humans, who can fall madly in love with a player on the basis of aesthetic appeal alone. That’s why most offseason columns here at FanGraphs reviewing free-agent acquisitions tend to damper down instead of ramp up excitement.

So it is a meaningful testament to Russell Martin’s skills that, upon being signed by the Toronto Blue Jays to a five-year, $82M contract as a 31-year-old catcher — i.e. after Martin has already sustained several lifetimes of knee-shredding, cup-checking abuse in baseball’s most brutal position — the deal was graded positively in these pages by Mr. Sullivan.

As Jeff notes in the piece, not only is Martin’s bat an improvement over Toronto’s primary catcher last season, Dioner Navarro, but Martin has also provided the equivalent of the value of an above-average player via his pitch-framing skills alone. (And these skills are not presently included while calculating WAR here at FanGraphs.) This, while Navarro tends to provide negative value to his team, as one of the league’s least-proficient framers.

Even more impressively for Martin: he has annually sustained this framing excellence even while bouncing from team to team, with none of the long-term security of franchise cornerstones Buster Posey or Yadier Molina. After two-year stints with both the New York Yankees (2011-12) and Pittsburgh Pirates (2013-14), the Blue Jays are Martin’s fourth team since just 2010, his last year with the Los Angeles Dodgers, and he has been an excellent framer at every stop.

Using framing data from StatCorner, we see that Martin has had a consistently low percentage of pitches in the zone called for balls (zBall%), a consistently high percentage of pitches from outside the zone called for strikes (oStr%), and a meaningful amount of runs saved above average (RAA) each season. His league rank in RAA is annually superb, especially considering that Martin is not an offensive liability like plenty of the catchers above him:

Year Team zBall% oStr% RAA Rank
2010 Dodgers 13.4 8.7 9.7 11
2011 Yankees 12.3 8.9 28.7 3
2012 Yankees 12.3 9.1 24.1 2
2013 Pirates 12.1 8.3 17 6
2014 Pirates 11.8 8.7 11.7 10

So how bizarre, then, that Martin ranks 56th among catchers in RAA thus far this season, actually providing negative value as a framer, sitting in the neighborhood of notoriously poor framers Jarrod Saltalamacchia, A.J. Ellis, and Kurt Suzuki in the rankings.

What’s interesting about Martin’s struggles this season is that his zBall% is at 12.0%, or right in line with his excellent rates of the past five seasons. And it should be noted: Martin’s zBall% is considerably better than the 2015 rates of catchers like Saltalamacchia (14.0%), Ellis (17.6%), Suzuki (14.2%), or Navarro (23.1%). When there is a pitch thrown inside the strike zone, Martin is still elite at not letting them get away.

It’s Martin’s oStr% that has taken a considerable tumble, down to 6.1% after holding steady around 8% since 2010. In 2015, Martin has been stealing strikes at virtually the same rate as Saltalamacchia (5.7%), Ellis (7.1%), and Suzuki (6.9%). (Navarro is at an incredible 1.0%, but I think the point holds true.) This year’s elite framers are stealing strikes at considerably higher rates, like Chris Iannetta (10.5%), Yasmani Grandal (10.1%), or the Pirates’ own Martin replacement, Francisco Cervelli (11.9%).

Does a difference of two percentage points really turn an elite framer into a framer that’s costing his team value? Well, yeah. Now, a difference of two hundredths of a point in batting average — like from .240 to .260 — hardly has any influence on a hitter’s overall value. That’s because a hitter is getting, at the very uppermost, 700 plate appearances a year, meaning that a change by those two hundredths is affecting, at the most, the outcome of 14 plate appearances on the whole season. Catchers, meanwhile, are receiving thousands of pitches a year, with the league’s most prolific one or two catchers getting around 10,000 pitches caught (that is: pitches that actually make it into the catcher’s glove). Martin is already at 2,264 pitches caught this season: by losing two points of his stolen strikes, that means he’s already lost 45 strikes that he usually secures for his pitchers. Hey, that’s a few good innings of nothing but balls that Martin has typically converted into nothing but strikes! If Martin gets around his full-season average of 8,000 pitches received, that’s a difference of 160 pitches — more than a whole game of could-be strikes that are going down as balls. Expressed in terms of total pitch count like this, it’s a lot clearer (at least for me) how framing really does add up to full wins and losses.

So what’s Martin’s deal? A few days before the season started, FanGraphs alumnus August Fagerstrom wrote on how the Blue Jays were going to experiment by having Martin catch knuckleballer R.A. Dickey. Well, that experiment lasted three whole Dickey starts, with Dickey’s personal hombre Josh Thole catching his last four starts.

Did these three starts do in Martin’s numbers? Using Baseball Savant’s PitchF/X tool, one finds that Dickey threw exactly 300 pitches over those three starts, with 203 of them making their way to Martin’s glove. Martin successfully stole 15 strikes, or 7.3% of the eligible pitches — above his season average.

So it wasn’t even catching the only active knuckleballer that threw Martin off-course. What about the other Blue Jays pitchers? The following table is not totally precise, as many of the relievers have pitched a small handful of innings to Navarro and/or Thole. These are all numbers for the full 2015 season except in the case of Mark Buehrle, whose two season-opening starts pitching to Navarro have been excluded:

Pitcher Innings Eligible Pitches oStr% BB/9
Drew Hutchison 36.1 400 9.5 3.22
Aaron Sanchez 32.1 384 4.9 6.96
Daniel Norris 23.1 303 8.2 4.63
Mark Buehrle 27 227 13.2 2.08
Marco Estrada 20.1 217 7.3 3.1
Roberto Osuna 18.1 182 3.2 2.45
Jeff Francis 12 153 6.5 3.75
Miguel Castro 12.1 135 4.4 4.38
Aaron Loup 13.2 124 3.2 1.32
Liam Hendriks 13 114 7 1.38
Brett Cecil 10 107 2.8 3.6

The correlation isn’t perfect, but I think it’s pretty strong: the Blue Jays have a lot of pretty wild pitchers, and Martin & Co. are having a hard time stealing strikes for them. As a staff, the Blue Jays were projected for the highest BB/9 in the preseason, and they’re at third in the league so far, trailing only the Colorado Rockies and Philadelphia Phillies. As Zachary Levine wrote at Just a Bit Outside in mid-April, the Blue Jays have a phenomenally young pitching staff, which makes the wildness understandable.

So here’s my theory: Martin has not, suddenly, lost his considerable gifts as a pitch-framer. Rather, when he sets up the target on the edge of the strike zone, hoping to steal a pitch on the fuzzy edge of the strike zone, this young staff is unable to hit the target, making it difficult to establish a convincing frame. You could almost blame the whole decline just on Sanchez. Usually we only talk about catchers when we talk about framing, but perhaps there’s a bit more teamwork at play when it comes to stealing strikes.

We hoped you liked reading A Theory on Russell Martin’s Framing Numbers by Miles Wray!

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Miles Wray contributes sports commentary to McSweeney's Internet Tendency, Ploughshares, The Classical and Hardwood Paroxysm. Follow him on Twitter @mileswray or email him here.

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everdiso
Member
everdiso

BP’s pitch framing numbers have Martin as a very good pitch framer this year, like in every other year.

http://www.baseballprospectus.com/sortable/index.php?cid=1819124

vivalajeter
Guest
vivalajeter

What’s the difference in methodology? Yadier Molina is -5.7 at BP and +2.6 at SC. That’s a pretty big swing.

On a separate note, do these numbers just look at pitches out of the zone and see how many times they’re called a strike? So if it’s a pitch in the dirt, or a foot wide of the plate, it’s treated the same as a pitch an inch off the plate? If so, I would think the pitcher has a pretty big impact on these numbers. With a very wild pitcher, no catcher will have good framing numbers.

everdiso
Member
everdiso

BP introduced a new, insanely comprehensive methodology this year. I was duly impressed, at least at the stuff I could understand:

http://www.baseballprospectus.com/article.php?articleid=25514

Joe Durant
Guest
Joe Durant

Statcorner: “The catcher is simply used as a grouping point. There’s no attempt to control for the pitchers, the umpires, the counts, or anything other than which side the hitter stood on.”

BP: “This new model allows simultaneous consideration of pitcher, catcher, batter, umpire, PITCHf/x, and other data for each taken pitch over the course of a season, and by controlling for each of their respective contributions will predict how many called strikes above (or below) average each such participant was worth during a particular season.”

Not sure why you’d even use StatCorner, frankly.

Joe Durant
Guest
Joe Durant

And I don’t really see this mentioned much, but there seems to be a lot of errors in pitchf/x. Not sure I trust the system to be accurate enough for this sort of thing.

everdiso
Member
everdiso

That was my first impression. BP’s new system seems to be a quantam leap in pitch framing statistics. But I’m waiting for some smarter stats guys to tell me why I’m wrong.

pitnick
Guest
pitnick

Interesting. So we should probably see these numbers start to agree more as the year goes on, I’d think.

Andy
Guest
Andy

BP’s system looks at out of zone called strikes and in zone called balls. But these are compared to the league wide % for every pitch location, much as defensive stats are based on buckets of varying difficulty. So if a wild pitcher throws a ball well out of the zone, that location will have a very low % of called strikes, and the catcher will lose very little value if the ball is called a ball.

Most of the pf value comes from pitches that are on the edges of the zone, where they are called strikes an appreciable % of the time. When it’s called a strike, a catcher gets significant value, and when it’s called a ball, he loses significant value.

I think the main way a wild pitcher could hurt pf is if most of his pitches were either clearly in or clearly out of the zone, not giving the catcher a lot of opportunities at the edges.

everdiso
Member
everdiso

P.S. the amazing thing about BP’s numbers, in short, is that they take into account the strike/pitch influence of all four of the catcher, pitcher, hitter, and umpire.

Cool WHIP
Member
Cool WHIP

Right, that’s the beauty of machine learning