Editor’s Note: This article was originally printed in The Hardball Times Baseball Annual 2017. You can purchase a copy of it here.

Here’s a little exercise for you. Which teams would you consider to have forward-thinking, stat-friendly front offices? Go ahead, list them off. I’ll even play along.

I don’t know where you start. The Dodgers, obviously. And the Cubs, of course. You have to make sure to fold in the Rays and the A’s. The Yankees have a massive analytics department. The Mariners took a step forward with Jerry Dipoto. The Phillies have changed under Matt Klentak. The Twins are looking to get more analytical under their new management. Cleveland has to count, and so do the Astros, and the Cardinals, and…

It goes on. Every front office has some sort of analytics department. Even the Royals! Especially the Royals. Major league baseball, overall, has gotten smarter. It’s no longer enough to just hire some smart guy in a suit. Smart guys in suits are everywhere. Any hiring has to be considered in context.

In a sense, analytics doomed analytics. At first, they seemed powerful. You could find market inefficiencies everywhere. And so early adopters were able to benefit. But then came the later adopters, and then came the last adopters. Numbers are no less powerful than they used to be, but there’s less relative power to be wielded when everyone’s trying. A given front office now would have a massive advantage over an organization run by a bunch of stubborn throwbacks, but such organizations are so near to extinct they’re almost hypothetical. So the comparison doesn’t have any meaning.

That’s the larger trend. That might be the trend, as major league baseball goes. The sabermetric movement has been fully embraced. But I mostly want to talk to you about a similar trend on a much smaller scale. It’s not the easiest thing to prove, but it’s plenty easy to discuss, and I feel it works as a legitimate theory. As analytics arguably doomed analytics, pitch framing seems likely to doom pitch framing.

Pitch-framing analysis has been moved to the back burner. It’s a product of the PITCHf/x system, and it was deeply exciting, but many have moved on from PITCHf/x to Statcast, because it’s the shiny new thing, and it’s impossibly informative. “Exit velocity” and “launch angle” have turned into commonplace terms overnight. We’ve already learned so much about hitters. We’ve already learned so much about pitchers. And, importantly, we’re beginning to learn about defenders. Statcast is going to get us places.

But pitch framing didn’t go away when the attention did. And, if I can give you some background: Pitch-framing statistics represented a major breakthrough. They were made possible by PITCHf/x around 2008 and 2009, and the greatest public efforts were made by Mike Fast, Max Marchi, Matthew Carruth and Dan Turkenkopf. Maybe you’ve been reading baseball analysis for a few years. Maybe longer. But, maybe not so long. It was groundbreaking stuff. What the numbers indicated was that pitch framing, or pitch receiving, could be worth dozens of runs in a season, by preserving or stealing strikes. More, the different methods mostly agreed with one another, and they tended to show year-to-year sustainability. There was signal, which meant there was talent.

The closest José Molina ever came to being a superstar was on the internet. On the internet, he was the face of pitch-framing statistics. According to numbers from Baseball Prospectus, the way Molina caught was worth 36 extra runs in 2008. The next year, it was worth another 19, and then that went up to 24 in 2010. Pitch framing was specifically cited as a reason why Molina wound up with the Rays. Molina always had a talent, but, at last, its real value could be quantified.

Not that Molina was the only catcher who stood out. The numbers have also celebrated guys like Russell Martin, David Ross, Jonathan Lucroy and Francisco Cervelli. On the other side, when there are players who are good at something, there have to be players who are less good. The opposite of Jose Molina was Ryan Doumit. Unlike Molina, Doumit could hit. But, as it turns out, Doumit couldn’t catch. His framing in 2008 cost his team an unfathomable 63 runs. The next year, in less time, he cost his team 29 runs. Then 24. Doumit was a defensive negative. The impact was worse than one would’ve imagined.

All right, let’s pause. Think about what was happening when the ball started to roll. It was being demonstrated, for the first time, that there were real differences in value among catchers, just based on how they caught. That actually flew in the face of prevailing sabermetric consensus at the time. Evidence mounted as the teams continued to shift toward being more number-savvy. Just in theory, what do you think would happen?

You’d think smarter teams would start to seek out good receivers. You’d think, additionally, that smarter teams would attempt to develop good receivers. That’s not as easy as just flipping a switch—that requires a lot of video work, to see what catching techniques help and what catching techniques hurt. But, just in general, you’d think pitch framing would be more heavily prioritized. What happens when something gets prioritized by an increasing number of teams?

At first, there were inefficiencies to be taken advantage of. The Rays, for example, got Molina for cheap. Some teams were skeptical of the framing data. Yet, over time, most teams have been turned into believers. Any executive or coach you talk to would agree that catchers have some degree of influence over the strike zone. Now we have well-established numbers for that ability. It’s not anything to be ignored. And when every team wants to get better at something, something that was only recently discovered, you look for the laggards to catch up to the leaders.

Every team wants a good-receiving catcher. Every pitching staff wants a good-receiving catcher. Every organization wants to develop good-receiving catchers. And it even feels like something a player can just learn. If a hitter is going to adjust to hit for more power, odds are, that same hitter is also going to strike out more often. There should be no such drawback with attempting to improve how a catcher catches. It’s just learning appropriate technique. Bad-receiving catchers might be helped. Then you’d have a catcher who just made himself better. No downside.

A Hardball Times Update by Rachael McDaniel Goodbye for now.

Stories have gotten out to the public. There were articles written about the Astros trying to improve the way Jason Castro caught. There were similar articles written about the Angels and Chris Iannetta and Hank Conger, and there were articles written about the Padres and Nick Hundley. There have been more such stories, and there have been countless additional stories that just weren’t or haven’t yet been written. Teams continue to work on this. The A’s have had a system implemented in earnest for about the last 18 months. No team wants to settle for a Doumit-type catcher anymore.

We might as well get to some actual evidence. To this point, it’s all been theory. The theory is that, over time, gaps between teams and catchers should narrow. Bad-receiving catchers should start to disappear, raising the baseline. The best catchers, after all, can’t improve much, but the worst ones sure can.

The most advanced numbers out there come from Baseball Prospectus, so that’s what I’ve used here. Using its data, I calculated for each catcher framing runs above or below average per 7,000 opportunities, going back to 2008—7,000 being roughly the number of opportunities a starting catcher will have in any given season. In that way, it’s like UZR/150. I narrowed to only catcher-seasons with at least 2,000 framing opportunities, and in the plot below, you can see each season’s standard deviations within the catcher pool.

There remains, of course, an existing gap between the best and the worst catchers. This is all still very new, as a statistical measurement. But it’s worth noting that the two smallest standard deviations have come in the last two seasons. A smaller standard deviation means a lesser spread. It supports the idea that the gap is gradually getting smaller.

As an alternative plot, here’s the year-to-year average of the top 10 framing catchers, in terms of runs above average per 7,000 chances.

Similarly, you see a drop, with the two lowest averages coming in the last two years. In 2011, the top 10 catchers were, on average, 30 runs better than the mean. In each of the last two years, the average has been a hair above 20. It doesn’t make too much sense that the best-receiving catchers would be getting worse. The other explanation would have to be that the average catcher is just improving. Just for fun, here’s the top and bottom 10 for the 2016 season:

To mix it up, here’s something on the team scale, now using information from FanGraphs. It’s pretty easy to calculate, for a team’s season, the difference between the number of strikes and the expected number of strikes. Obviously, there would be a relationship between this measure and pitch-framing ability. The more extra strikes a team gets, the better the receivers, and vice versa. I ran the numbers for every team’s season since 2008, and here are the year-to-year standard deviations.

This is fairly striking. The standard deviation in 2011 was 219 strikes. By 2016, that number dropped to 122, or 56 percent of what it had been. The two lowest standard deviations have come in the last two years. Going back to 2011, the difference between the best team and the worst team was 964 strikes. Last year, the difference was 437 strikes.

That’s still a difference. That’s still a pretty substantial and significant difference. But the differences have gotten smaller, which is what we would expect. Should the trend continue, the differences will get smaller still. Perhaps not smoothly, perhaps not year after year after year, but in the big picture the gap should get smaller. Evidence suggests more teams are on board with pitch-framing ability, and as that happens, the best framers have less of a relative edge.

Over the course of the nine seasons we have this data for, things have changed quite a bit. We can see just how much they’ve changed in this table of year-to-year differences for each team:

ADJUSTED EXPECTED STRIKE DIFFERENCE, 2008-2016 Team 2008 2009 2010 2011 2012 2013 2014 2015 2016 Total Brewers 72 89 377 482 452 445 406 243 151 2,716 Braves 376 501 333 440 343 57 -189 -250 -102 1,510 Cardinals 330 276 265 176 175 116 -131 15 25 1,245 Diamondbacks 102 267 80 224 199 79 306 -24 -35 1,197 Yankees 306 68 -221 210 243 278 220 22 69 1,194 Reds 434 247 278 138 208 -4 -34 -108 -64 1,096 Giants -109 84 17 252 130 -38 159 101 145 741 Astros -13 -126 168 87 32 -1 123 133 178 580 Nationals -25 -16 200 298 138 25 -12 -79 -137 391 Phillies 162 200 75 -34 117 28 -42 -107 -68 330 Orioles 22 192 81 28 -61 -77 99 -20 46 310 Angels 42 -5 51 149 29 -11 80 -53 4 286 Blue Jays 182 -68 28 -19 -29 85 -181 -12 155 140 Padres -68 -143 -4 -232 40 146 310 118 -36 132 Cubs -93 -82 97 -7 -19 -129 -115 180 203 35 Red Sox -16 -122 -219 26 75 81 90 -40 41 -84 Dodgers 244 68 -55 -202 -213 -43 -103 62 111 -130 Rays -232 -6 -134 -205 179 163 89 53 -47 -139 White Sox -107 -31 -64 -51 -122 -28 -45 267 -199 -380 Mets -89 -125 -159 -147 111 -140 -2 36 79 -437 Royals 99 57 -46 -170 -142 -99 -101 -97 -67 -565 Pirates -463 -353 2 -202 -319 187 135 303 101 -607 Athletics 22 -132 -99 -168 -117 41 -66 -169 -131 -819 Rangers -269 45 -154 -138 -135 -131 -150 75 6 -851 Twins 5 56 10 85 -220 -346 -361 -94 -124 -990 Rockies -131 -13 -60 -146 -188 -148 -220 -219 64 -1,060 Mariners -234 -318 -315 -157 -312 -132 233 86 -97 -1,248 Tigers -180 -346 -123 42 -105 29 -160 -276 -137 -1,256 Cleveland -54 -205 -284 -482 -298 -135 -62 -86 -56 -1,662 Marlins -316 -61 -124 -277 -189 -298 -276 -60 -77 -1,677

Quality pitch framing isn’t going to be rendered irrelevant, certainly not as long as there are humans responsible for calling balls and strikes. But remember the example of a hypothetical old-school, backward front office. That front office might get savaged in the market today, but that front office also doesn’t really exist. Likewise, a truly bad pitch framer would score horribly now. The average only gets better and better. But there just won’t be many truly bad pitch framers. When everyone is a good receiver, no one is a good receiver.

As long as there are umpires, and not robots, there will be a gap. We will never see a day when every single catcher in baseball is identically good at catching pitches. It is a skill, and only so much can be taught. Yes, anyone can improve his forearm strength. Yes, anyone can improve his wrist strength, and the way that he positions himself behind the plate. These are fundamentals. But there’s an instinct component. There’s anticipating where pitches are going to go. Taller catchers will be better up in the zone; smaller catchers will be better down in the zone. It’s not realistic to believe the average draft pick can be coached into becoming another José Molina. Some of that ability was innate.

But some of it wasn’t. Some of that ability can be coached. Maybe even much of that ability. And now that that ability can be recognized, it could end up selected for, in drafts and internationally. Teams are running out of excuses for having lousy receivers, and eventually, it stands to reason any advantages are going to be slight. Maybe the gap between the best and worst catchers gets trimmed to 20 runs. Maybe it gets trimmed to 10. It’s nothing that will happen overnight, but it looks like the process is already underway.

Pitch-framing numbers answered questions we didn’t know we had. A new statistical category emerged out of nowhere, and before long, the research proved its own worth. That was the birth of the revolution. Every team now wants good-receiving catchers. Every team, additionally, wants to develop more good-receiving catchers. The market is going to end up flooded with good-receiving catchers. By then we’ll no longer recognize them as good-receiving catchers. Pitch-framing is sufficiently important that baseball teams will prioritize it right into insignificance.

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