In my last article on these here internet pages, I attempted to create the best conceivably possible player by taking the most valuable seasons in baseball history and putting them all together into one awesome player. The results were fairly ridiculous. Can we get equally ridiculous results from the greatest age-seasons from pitchers? I’m going to make a couple of tweeks to my methodology from last time to tailor it more towards pitchers, who, at the extremes, seem to have more peculiar career arcs.

• No pre-1900 seasons

I included this rule for position players because I’m not too familiar with the era. I’m including it for pitchers because they weren’t doing anything like what modern day pitchers, or even pitchers in the 1920s were doing. I mean, in 1889, John Clarkson of the Boston Beaneaters pitched 620 innings with a 2.73 ERA. His FIP-WAR was 10.9 while his RA9-WAR was 19.7. That is 4.7 WAR better than Babe Ruth’s best season.

• No seasons with less than 50 IP

This doesn’t really apply all that much, unless you want me to include Joe Nuxhall’s two-thirds of an inning in 1944 when he was 15. He allowed 5 runs on 2 hits and 5 walks.

• No duplicates, take player’s best season

This wouldn’t be as much fun if it were just young Dwight Gooden and old Randy Johnson

As for which version of WAR I’m going to use, FIP-based WAR seems to be less prone to wild fluctuations, particularly with old-timey players. The gradual increase in strikeouts over the past century-plus seems to have balanced out the gradual decline in innings, so that the ridiculous seasons of today are similar in value to the ridiculous seasons of yesteryear. With all that said, let’s get to it and create a Ridiculous Moon Wizard Pitcher.

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Weighted On-Base Percentage (wOBA) is typically considered to be the best measure of offensive ability and effect on runs scored among other rate statistics such as batting average, slugging percentage, and on-base percentage. 89.8% of a team’s runs scored correlates to wOBA between 2005–2015. I decided to look at a team’s performance, measured by how many runs they scored in a season, against the amount of runs wOBA predicted* they would have scored.  (wOBA Expected Runs was calculated based on a linear regression model with runs modeled as wOBA. The adjusted r-squared value of R~wOBA is .898)

Generally, the results are what you would expect. Teams deviate from their wOBA Expected Runs, but the 50% of the teams (between the 25th and 75th percentile of the observations) range between -17.49 and 16.9 runs from their wOBA Expected Runs.

The outliers even fall within the uncorrelated portion of the relationship between runs scored and wOBA. As stated above, wOBA explains 89.8% of runs between 2005 and 2015. At the far right of the graph is the 2008 Minnesota Twins, who scored 829 runs against their 756 wOBA Expected Runs. The difference, 73 runs, is less than the 10% of runs that is theoretically not explained by wOBA. At the far left of the graph is the 2005 Arizona Diamondbacks who scored 696 runs against their expected amount of 756. Again, this 60-run differential falls within the 10% gap we would expect.

The mean difference of runs scored from the wOBA Expected Runs Scored is minuscule (.003 runs) and the standard deviation from that mean is 24.9 runs. This all strengthens wOBA’s position as the best offensive run predictor.

What does this all have to do with Don Mattingly and the Dodgers? The graphs below show each team’s runs scored below or above their wOBA Expected Runs Scored. You’ll see that teams fall within the standard deviation of runs scored less wOBA Expected Runs (-25.93–24.87), with some exceptions. The exceptions that fall outside of that range generally do not display a tendency for extreme over- or under-performance of their wOBA Expected Runs in consecutive seasons; however one team does stand out.

The 2013–2015 Dodgers consistently under-performed their wOBA Expected Runs, with the following differences in the respective seasons from 2013–2015: -51, -33, and -58 runs. To put this in context, only 8 of 330 of teams, or roughly 2%, that took the field between 2005–2015 under-performed their wOBA Expected Runs by more than two standard deviations (-49.8). The 2013 and 2015 Los Angeles Dodgers were two of those teams. No other franchise appears on the list twice, much less twice within three seasons.

In Mattingly’s first two seasons with the Dodgers (2011 and 2012) the results were standard, with a -6 and +12 runs to wOBA Expected Runs differential, but when the Dodgers came under new ownership and started spending to bring in new players things changed. The team got better but their performance in relation to what they were doing got worse.

A glance at the graphs above will show that teams have under-performed their expectations, but never this badly for a three-year stretch. There is luck and there are trends, and the Dodgers are a trend of under-performance. Does this mean Don Mattingly is a bad manager? Maybe. Does it mean that Mattingly was a bad fit for this Dodgers team as constructed? Probably.

It could all be on the hitters; it could all be bad luck, but those seem unlikely. The 2013–2015 Dodgers are the worst offensive under-achievers in the last decade. The results suggest that Mattingly was unable to shuffle a cast of talented and enigmatic hitters into the right order to produce the best sequencing of results. Alternatively, the other narrative is that Mattingly was handed a group of talented and enigmatic hitters that couldn’t execute situational hitting and hit inconsistently. Either way, the Dodgers cost themselves a lot of wins through one, or a combination of the two narratives. The team lost 5, 3, and 5 wins each year, compared to if they met their wOBA Expected Runs, as calculated using the Runs per Win for 2013–2015.

This doesn’t necessarily bode poorly for Mattingly in Miami. The Marlins don’t have the benefit of a deep and talented bench. They are a fairly straight-forward offensive team that should allow Mattingly to write-up consistent lineups so long as the team remains healthy. This is not to say the Marlins will out-perform the Dodgers. It is to say that the Marlins may perform closer to how we would expect them to perform.

However, if the problem did not lie with Mattingly, but instead the Dodgers’ roster, than things do bode poorly for the Dodgers. It will be interesting to see if Dave Roberts can unlock something Mattingly could not; or whether the players are to blame; or whether Los Angeles must wait for Gabe Kapler, baseball’s philosopher-king, to set the runs free.

Yes. Probably. If you’re in a hurry, you can now go do whatever you should be doing instead of reading about no-longer-prospecty baseball players. But if you’re not in a hurry, know this: there’s a chance he can survive, if only a small chance.

Kyle Drabek is rowing against a mighty tide that seeks to dash what’s left of his career against the jagged rocks. The former Phillies first-round pick (don’t fret, Ruben-haters — the really good guys in this draft were already gone) had a mediocre minor league career followed by a wretched major league one. Over 177 injury-plagued innings, Drabek has been a TTO arsonist: 6.1 K/9, 5.7 BB/9, and 1.2 HR/9. His career ERA/FIP is 5.27/5.42. When hitters die and go to heaven, they face him every night. And yet the Arizona Diamondbacks recently signed him to a minor league deal. What might they be thinking?

Drabek has been through Tommy John surgery twice, in 2007 and again in 2012. Here’s a list of repeat TJ offenders — you can sort on “Back to playing” to see who, well, made it back to playing. It’s largely a grim list, but there are some pitchers who came back to perform decently. As you can see from perusing the list, and this post, the vast majority of those are relievers.

Drabek’s major league performance so far suggests little more should be expected of him. In the expansion era there have been 73 starting pitchers who “achieved” a FIP of 5+ in at least 150 innings before age 28. Here they are — Drabek checks in at #27.  The name that jumps out most prominently on this list is Joe Nathan:

Through age 27         IP        ERA        FIP         K/9          BB/9          HR/9

Nathan                       187       4.61       5.72          5.6            5.2               1.4

Drabek                       177       5.27        5.42         6.1             5.7               1.2

Entering his age-28 season, Nathan was a failed starter with one shoulder arthroscope to his credit. That year the Giants converted him into reliever, and after season’s end converted him into A.J. Pierzynski. Nathan went on to rack up the eighth-most saves in MLB history, which is a pretty fair achievement even if you aren’t into saves.

How did he do it? Unfortunately, pitch-by-pitch data isn’t available back to the years (1999-2000) when Nathan did most of his damage as a starter. But a look at Nathan’s pitch values nevertheless suggests some clues. One big clue in particular: his slider was devastating. By fleeing to the bullpen, Nathan probably was able to add a little heat to the slider, and perhaps able to throw it more often. Hitters would only see him once as a reliever, but this may not have been a huge factor, since hitters were beating Nathan like a drum as soon as the cute guest-PA-announcer-kid finished shouting “play ball!” Get to him a third time, though, and it was like walking to the plate with a plutonium-corked bat.

One can imagine that being in the bullpen enabled Nathan to add some velo and subtract the amount of times he threw his weaker pitches. Salomon Torres presents a similar profile: a failed starter in the 1990s, Torres disappeared off the baseball earth for a few years before resurfacing with the Pirates as an effective reliever in 2002. Torres’ out pitches were the slider and splitter; he featured both as a reliever evolving toward the latter as he aged.

Justin Miller also made the transition from awful starter to solid reliever. Like the others, he did not wholly abandon a pitch when moving to the pen, but he placed greater emphasis on his — you guessed it — excellent slider, at the expense of his not-so-excellent fastball. Miller didn’t add much velocity in becoming a reliever; it seems to be the change in pitch selection that helped him turn the corner.

These career paths might be helpful signs for Drabek, but in at least two senses they aren’t: unlike the other guys mentioned above, Drabek lacks a carrying pitch. Nathan had an excellent slider, as did Miller and (at times) Torres, even when the rest of their pitches were failing them. Drabek’s pitches are all below average, so he appears to lack a safe base from which to make his bullpen transition (although to be fair, all of Drabek’s pitching stats suffer from the pain and embarrassment of small sample size). By the numbers, the curve is the least bad of his offerings; perhaps focusing on becoming a fastball-curve guy would benefit his development.

Focusing on the curve brings us to Drabek’s second problem: TJ survivors appear to struggle with breaking pitches. Throwing more curves at this stage of his career may be the last thing Drabek can (or is willing) to do. It’s not impossible: Jason Isringhausen leaned heavily on the curve as he remade himself into an elite closer. But there aren’t a lot of examples here.

Perhaps Drabek can develop his changeup, his second-least-bad pitch. It appears that, following his surgery, he tried to emphasize his cutter, a pitch that hasn’t been kind to him yet, but an approach that did help The Beard to become fearworthy. Perhaps a superior pitching coach could help Drabek, but here’s who the Snakes just hired.

Despite these difficulties, the Diamondbacks nevertheless have an incentive to pick through the Drabekian rubble to see if they can salvage any value. Even with their spiffy new TV deal the D’Backs will always be no better than second-tier in terms of resources and attendance, especially problematic with the Dodgers juggernaut in the same division. Finding cheap pitching hand-me-downs will enable the organization to invest elsewhere (as it is already doing with the lineup).

Given both Drabek’s limited major league success and his limited major league appearances, deciding how to reconstruct him may be even more difficult than such projects usually are. Drabek doesn’t have huge platoon splits, and while for now that means both southbats and northbats will feast on his pitches, over the longer haul it may mean that he could be useful as a swing man or multi-inning reliever.

The Diamondbacks have had success in re-imaging double-TJ survivor Daniel Hudson as a reliever, but he had already had much more success as a starter than any of the other pitchers mentioned in this post (except Tommy John himself). Indeed, the Snakes may move Hudson back to the rotation next year. But perhaps the work with Hudson has given the organization some clues for how to deal with a much more challenging project.

I’m rooting for Drabek, but I’m taking the under.

In the middle of June, I wrote an article looking at batted-ball data. Specifically, I grouped players into tiers based on their hard-hit percentage and looked at the statistics accumulated by the players in each group, then identified the outliers. This is a look back at that article to see if we can learn anything.

To start with, the following charts show a comparison of the correlation of other metrics to the different strengths of batted balls hit. I did this in the middle of June and will compare that chart to one I created using statistics for the entire season. In June, I used a cut-off of 150 plate appearances through June 14. This was right around the 60 game mark of the season. There were 236 players. At the end of the season, I used 350 plate appearances as the cut-off, which consisted of 249 players.

Noticeable here is the strengthening of the correlation for the power statistics with hard-hit percentage as more data came in. The three stats dealing the most with power—ISO, HR/FB, and slugging percentage—all saw an increase in their correlation with hard-hit percentage. This is true down the column until you get to batting average and BABIP, which showed a weaker correlation over a full season than over the first two and a half months. While ISO, HR/FB, and SLG all correlate with hard-hit percentage at .70 or above, batting average and BABIP are down around 0.10, and LD% is at .06.

In the June article, I separated the players into groups based on their hard-hit percentage. As you would expect, the players who hit the ball hard a higher percentage of the time were more productive hitters. Here is the breakdown again, first the chart through June 14, then the full-season chart.

Remember, these aren’t necessarily the same players within tiers in both tables. Some players could have moved from one tier to another as the season went on and more players qualified overall for the full season. The way to look at this is to go down the columns to see how the average statistics for each group change as hard-hit percentage goes down. It’s easy to see that the groups of players in the higher ranges of hard-hit percentage are more productive than the groups of players in lower ranges of hard-hit percentage. The players in the upper tier, with a hard-hit percentage of 35% and above, hit more fly balls, had more of those fly balls go over the fence, had a higher batting average, slugging percentage, and isolated slugging. Roughly 85% of these hitters had a wRC+ at 100 or better. The least productive tier was the group of players with a hard-hit percentage at 24% or below. A small number of these players were able to be league average or better hitters.

The numbers from June are similar to the numbers for the full-season. As hard-hit percentage goes up, offensive production goes up and the percentage of players who are above-average hitters (by wRC+) goes up. A similar trend emerges for ISO, fly-ball percentage, HR/FB%, and slugging percentage.

The interesting players to me are the ones in the minority among their group of hitters. Through June 14, there were seven players in the top tier who had a hard-hit percentage greater than 35%, but with a sub 100 wRC+. These players consistently hit the ball hard but were still below-average hitters. Considering how often they hit the ball hard, I expected these players to improve and more closely match the rest of the group from this point forward. Theoretically, these are the guys with upside based on their hard-hit percentage. At least, this was my hypothesis. How did these players do over the rest of the season?

The seven players who hit the ball hard a high percentage of the time but who had a wRC+ below 100 through June 14 are shown below. The following chart shows the performance of these seven hitters before and after June 14.

*note—to determine the wRC+ of the group, I just did a weighted average based on each player’s plate appearances. The other numbers are precise totals for the group.

These players did improve as a group, with their composite batting line going from .237/.292/.387 to .252/.305/.455. They improved even though their BABIP dropped from .289 to .286. The big increase was in their power. They hit more fly balls and had more fly balls go for home runs. Their ISO increased from .151 to .203 and their wRC+ went from 86 to 106.

Two of these players had fewer than 60 plate appearances after June 14, so they aren’t very helpful to us. Of the remaining five players, two stayed close to the level they had established by June 14 and the other three showed strong improvement. Here is a closer look at these players:

Jorge Soler was essentially the same hitter before and after June 14, right down to an identical 96 wRC+. His BABIP dropped from a sky-high .383 to a still very good .339, but he also struck out less often and his hard-hit percentage dropped from 39.5% to 32.3%. His hard-hit percentages in both portions of the season suggest he should have hit better than he did, but his low fly-ball percentage limited his power. Over the course of the whole season, Soler had a hard-hit percentage of 35.9%. That puts him in the top tier. The players in this tier of hitters had an average fly-ball percentage of 38%. Soler’s fly-ball percentage was 29.8%, which corresponds with the players on the lowest tier of hard-hit percentage, those players below 24%. Basically, Soler hit the ball hard as often as guys like Adrian Gonzalez, Bryan Braun, and Yoenis Cespedes, but hit the ball in the air as often as Gregor Blanco and Alcides Escobar. While he hits the ball hard with regularity, he doesn’t hit enough fly balls to take advantage of his hard-hit percentage.

Like Soler, Jay Bruce’s overall production did not improve. His wRC+ dropped slightly, from 96 to 90 even though he maintained a high hard-hit percentage. The shape of his production changed, though. He hit for much more power, with an ISO that was .040 higher after June 14 than before, but a corresponding drop in walk rate torpedoed his on-base percentage. The overall effect was going from hitting .212/.324/.394 through June 14 to .234/.277/.457 after June 14. Jay Bruce is a mystery. He had a top-tier hard-hit percentage and hit the ball in the air frequently enough, but his production didn’t compare to the other players with similar profiles.

Mark Trumbo was one of three players in this group who did improve a significant amount. Trumbo hit .242/.276/.445 through June 14 and .276/.333/.451 after. His wRC+ increased from 93 to 119 even though his hard-hit percentage dropped from 35.2% to 31.7%. The biggest change for Trumbo was an increase in BABIP from .280 to .337 and an increase in walk rate from 4.5% to 8.0%.

Both Will Middlebrooks and Matt Adams did not have enough plate appearances after June 14 to tell us much of anything.

Steve Pearce improved his wRC+ from 79 through June 14 to 106 from June 15 on even though his hard-hit percentage cratered from 35.6% to 25.4%. His BABIP was nearly the same. His walk rate and strikeout rate changed very little. He didn’t improve his on-base percentage by much. The big difference was an increase in slugging percentage from .365 to .471 with a corresponding increase in ISO from .153 to .248. He did this by greatly increasing the number of balls he hit in the air. His fly-ball rate through June 14 was 39%. After, it was 53%. That seems like a drastic change to me, so I wonder if Pearce made the decision to go all out for power by hitting fly balls as often as he could.

The final guy on this list was the greatest success story of this group, Matt Kemp. Kemp was terrible in the first part of the season. When I initially wrote about batted-ball data on June 14, Kemp was hitting .249/.289/.340 even though his hard-hit percentage of 35.8% was in the upper tier of hitters. From June 15 on, Kemp hit .270/.328/.519 with a hard-hit percentage of 45.5%. He hit fly balls at a higher rate (31% to 39%) and more of those fly balls left the yard (3.4% HR/FB% to 20.6% HR/FB%). Kemp’s ISO improved from .091 to .242 and his wRC+ went from 78 to 133.

This is a small group of players, so it is not an in-depth study. Also, two of this group of seven players didn’t have enough plate appearances to be meaningful. Of the remaining five players, three did significantly improve, while the other two continued their subpar ways.

The other group of hitters that interested me was the group of nine that had a wRC+ greater than 100 despite a hard-hit percentage below 24% through June 14. These players were somehow able to be above-average hitters despite carrying such a low hard-hit percentage.

The following chart shows these nine players (out of a group of 44) who had hard-hit percentages below 24% but with a greater than 100 wRC+. The top chart shows what they did through June 14 and the bottom chart shows what they did from June 15 on. My hypothesis was that these players would hit worse because their low hard-hit percentage would not let them sustain their above 100 wRC+.

As a group, these nine hitters went from hitting .313/.366/.404 through June 14 to .271/.315/.386 after June 14. They saw their combined wRC+ drop from 117 to 91. Only three of these nine hitters continued to have a wRC+ over 100 from June 15 on. The glaring change in BABIP from .353 to .303 for the group is likely a main culprit in their diminished production. They also walked less often and struck out more often.

Nori Aoki was the leader in wRC+ among this group of hitters on June 14^th. Had he been able to sustain that for a full season, it would have been a career year. Unfortunately, he suffered a broken leg when he was hit by a pitch from Carlos Frias about a week later and wasn’t the same hitter when he came back. He also dealt with concussion issues and didn’t play after September 3. He was much worse after June 14 but injuries were obviously a big factor.

Jacoby Ellsbury was already on the DL with a knee injury at the time I wrote the original article. He missed close to seven weeks in May, June, and July and really struggled upon his return. His hard-hit percentage was just slightly lower than it had been before but his BABIP plummeted from .379 to .261 and his walk rate dropped significantly also (11.2% to 4.8%). Like Aoki, injuries were probably a big factor in Ellsbury’s diminished production.

Jose Iglesias also dealt with an injury, like Aoki and Ellsbury, but his was in September and cause him to miss the last month of the season. He had already declined from a 125 wRC+ through June 14 to an 80 wRC+ from that point forward. His BABIP dropped from .367 to .302 despite an increase in hard-hit percentage from 13.7% to 17.9%. Even with that increase, a 17.9% hard hit percentage is ridiculously low. With a hard-hit percentage that low, I wouldn’t expect Iglesias to be anywhere close to a league-average hitter going forward.

Billy Burns had the lowest hard-hit percentage (13.6%) of any qualified hitter over the entire season and the highest soft-hit percentage (30.5%). He rode a .366 BABIP to a well above average 120 wRC+ through June 14. From that point forward, his wRC+ was 97, with a BABIP of .328. Over the whole season, Burns had a 102 wRC+ despite such a low hard-hit percentage. Like Iglesias, I wouldn’t expect Burns to be league average as a hitter next year either.

Salvador Perez and Jace Peterson both increased their hard-hit percentage but still saw a drop in their wRC+ by a significant amount. Perez had fewer fly balls leave the yard (15.2% HR/FB% to 10.6% HR/FB%) and his already mediocre .292 BABIP dropped to a less-than mediocre .257. Peterson had a 106 wRC+ and .339 BABIP on June 14, with a hard-hit percentage of 23.8%. From that point forward, his hard-hit percentage was an improved 27.6%, but his BABIP was .266 and he had a 63 wRC+.

Yunel Escobar and Ian Kinsler were the only two players among this group of nine who saw an increase in wRC+ after June 14. They also greatly increased their hard-hit percentage. Yunel’s hard-hit percentage went from 23.9% to 30.4%. Kinsler’s increased from 22.1% to 28.6%. Both of these hitters were below their career rate of hard-hit balls as of June 14 and hit closer to their career marks from that point forward, which was likely a factor in their improved production.

Dee Gordon joined Escobar and Kinsler in maintaining a wRC+ over 100, but he did see a drop from 118 to 109. His BABIP through June 14 was a ridiculous .418. From that point forward, it was a silly .357. His hard-hit percentage barely changed at all (17.7% to 17.5%). Gordon has had a very low hard-hit percentage every year of his career. His production is very dependent on a high BABIP. In the three seasons when he’s had a BABIP of .345 or higher, his wRC+ was 94, 101, and 113. In the two seasons when he had a BABIP below .300, his wRC+ was 58 and 73.

Overall, just two of these hitters had an improved wRC+ after June 14 and both of those hitters also increased their hard-hit percentage. A third hitter, Dee Gordon, had a worse wRC+ after June 14 but was still an above-average hitter (109 wRC+). The other six hitters in this group were significantly worse after June 14.

This is a look at individual outliers and there are factors beyond hard-hit percentage that come into play, but I do think hard-hit percentage can help us when analyzing a player’s production during the season.

In the world of sports, the catcher position is kind of weird. Catchers start each play out of bounds, facing a different direction than their teammates. On a more micro level, baseball’s most important in-bounds/out-of-bounds determination, the strike zone, isn’t static as it is other sports; it’s determined on every pitch, and the catcher has a role in making that determination. In a non-contact sport, they’re covered in protective armor. Those of us with lousy knees are in awe just of their ability to do all that squatting.

Catcher is also the only position on modern rosters where there is planned redundancy. Thirteen-man pitching staffs have more or less eliminated platoon tandems, but catching tandems persist. The Pirates don’t carry an extra center fielder to give Andrew McCutchen a rest in the second game of doubleheaders. The Nationals don’t have a second right fielder to play instead of Bryce Harper on day games after night games. The Yankees don’t roster a spare third baseman in case Chase Headley gets hurt or tossed from a game. But every team has to have two catchers, and each of them sees a decent amount of playing time. (The catchers for those three teams in particular, as we’ll see.)

Further, the impact of a catcher injury can be significant. A disabled catcher’s replacement could well be unfamiliar with his pitching staff and opposing hitters’ tendencies, impairing his ability to call a game. He may not know the league’s umpires and their interpretation of the strike zone. He might not be up to speed on his infielders’ shifting tendencies and how that may affect pitch selection and location. And his presence probably means extra work, and extra fatigue, for the team’s other catcher. Just ask a Boston Red Sox fan about the importance of healthy catchers. (That’s a rhetorical suggestion. I don’t recommend actually doing that, unless you want to hear a long exposition about the importance of good free agent signings, a reliable starting rotation, offensive production from your first basemen, keeping your closer and second baseman off the DL…really, it can go on for a while.)

This summer, while attending a Phillies-Pirates game during which the Pirates used both of their catchers, my friend wondered whether the Pirates had the skinniest catchers in the league. (Francisco Cervelli is listed at 6’1″, 205, Chris Stewart 6’4″, 210). While actually putting in the time to figure this out (the answer appears to be “yes,” if you can trust listed heights and weights), I noticed that Cervelli and Stewart had caught all but 17.1 innings for the Pirates in 2015. This was in August, but it remained the case for the entire year. Cervelli caught 1099.2 innings and Stewart 372.2. Combined, that represented 98.8% of all the Pirates’ defensive innings last year. This struck me as notable, as the Pirates had lost the durable Russell Martin to free agency over the winter, replacing him with Cervelli, who’d never played more than 93 major league games in a season previously. The Pirates are famous for their use of analytics, including monitoring player health, with an eye toward injury prevention. Maybe that’s working. Or maybe they’ve figured out something with skinny catchers. Either way, I wondered whether the Pirates’ tandem represented something unusual.

To check, I looked at every team’s catchers since the 1969 start of divisional play. Using this year’s Pirates as my model, I looked for teams for which the top two catchers caught 98.5% or more of all innings. Last year, the average team’s catchers caught 1,446 innings, so I was looking for teams for whom top two catchers were on the field for all but 21.2 innings, on average.

It turns out the Pirates weren’t unique. Brian McCann and JR Murphy caught every inning for the Yankees this year. Wilson Ramos and Jose Lobaton caught all but nine innings for the Nationals. Carlos Ruiz and Cameron Rupp were behind the plate for all but 18 innings for the Phillies. That’s about typical. Since 1969, there have been 240 teams whose top two catchers caught at least 98.5% of all innings during the season, or a little over five per year (closer to four and a half if you exclude strike-shortened seasons).

But totals don’t tell the whole story, since baseball’s expanded from 24 teams in 1969 to 26 beginning in 1977, 28 beginning in 1993, and 30 beginning in 1998. The graph below shows the percentage of teams, per season, with two catchers handling 98.5% or more of the workload. The overall average is 18.6%. There’s a very slight downward trend to the line–the slope is -0.03% (yes, I got the decimals right)–meaning that catchers have been becoming a little less durable over the years, but almost imperceptibly so. (I was tempted to say “a little less durable or managers are giving them more rest,” but other than the occasional Kyle Schwarber, who primarily plays another position but can catch in a pinch, teams just don’t carry three catchers any more, so rest for one catcher in a tandem means playing time for the other.)

(The outlier on the high side is 1994, when there were only 117 games played.)

Teams for which two catchers caught 98.5% or more of innings won, on average, 85 games during the non-strike-shortened seasons. That’s not super impressive, considering the selection bias inherent in this sort of analysis. Specifically, teams with two catchers handling virtually all of the time behind the plate are teams that not only avoid catcher injuries, but also have two catchers good enough that they’d want to have them there all year, contributing to overall team success. In 2014, for example, the Red Sox had three catchers with over 400 defensive innings, in part because none of them could hit: A.J. Pierzynski (540 innings, 71 wRC+), Christian Vazquez (458.1 innings, 70 wRC+), and David Ross (418.1 innings, 71 wRC+). (See, I told you not to ask a Red Sox fan about catchers.)

Still, 85 wins is decent, four games better than .500–that’s the Angels this year. Of the 213 teams for which two catchers caught 98.5% or more of innings in non-strike-shortened years, 77, or 36%, won 90 or more, which is generally good enough to get you into the postseason these days. So there’s certainly an advantage to getting all the work out of two catchers.

So has anybody cracked the code on keeping their two catchers healthy? I looked for teams that had three or more seasons in a row with two catchers handling 98.5% more of innings. If teams have a secret sauce, they should show up on this list with regularity:

Nope. The closest thing there is the Yankees, who had streaks with Thurman Munson in the 1970s and Jose Posada around the turn of the century. The only other teams to appear more than once are the Johnny Bench Reds and two iterations of the Pirates, over a decade apart and 30 years ago. There’s nothing in this table suggestive that it’s a matter of skill, rather than luck, to keep two catchers on the field all season. Specifically, these teams generally had an All-Star caliber No. 1 catcher who avoided injury with various guys in the backup role. That’s about it. No team has cornered the market on that formula.

So maybe that’s making the criteria too tough. Maybe I should be looking just at back-to-back 98.5%-plus inning performances. Given that, on average, 18.6% of teams had two catchers with 98.5% or more innings caught since 1969, random chance suggests that a team with two dominant catchers has about a one-in-five chance of repeating the following year, like flipping a coin that comes up heads 18.6% of the time. A rate of repeat significantly above that could indicate skill rather than luck. Of the 236 teams, 1969-2014, that had two catchers with 98.5% or more innings caught, 60 repeated the following year, or 25%. That’s not a statistically significant difference (using an N-1 chi-square test, if you were wondering). In other words, there’s no reason to believe that a durable catcher tandem is a matter of anything but good fortune.

So feel good about keeping your two catchers healthy this year, Yankees, Nationals, Pirates and Phillies. Especially the Pirates (111 wRC+) and Yankees (104 wRC+), who got above-average offensive performance from their catchers as well. (The 69 wRC+ Phillies and 62 wRC+ Nationals catchers were among the worst in baseball.) Just don’t assume you’ll be able to keep those two guys on the field all of 2016 as well.