“What’s to get tired from? This isn’t like football or basketball. Even if you play 100 games in the outfield, you handle only six or eight balls a game. What can wear you out? It’s hard to get physically tired in baseball, unless you pitch or catch.”

Ron Fairly

“Get up. Get down. Get up again. Get down. Come up throwing. Take the chest protector off. Take the shin guards off. Hit. Put them back on. Go back behind the plate and repeat the process. Catching just breaks a man down, inning by inning, game by game, year by year.”

Reggie Jackson

“Catchers are the most beat-up, bruised, broken, knurled players on the field. They are the only athletes I know who can stick their hands out straight and point behind them.”

-Ron Luciano

“By the end of the season, I feel like a used car.”

Bob Brenly

Baseball pundits often observe that the sport’s extended schedule more closely resembles a marathon than a sprint, but at least one subset of its players would be hard-pressed to compete in a race of any distance. Beginning in Little League, catchers are selected for their girth, arm strength, and tolerance for pain and discomfort, with scant consideration accorded to fleetness of foot or offensive ability. As a reward for their continued success, armor-laden backstops gain the privilege of squatting in the path of an ever-accelerating baseball barrage, absorbing an array of major and minor collisions in the process; to add insult to injury, their duties extend even to periodic plods down the line, designed to intercept wayward tosses from their more nimble, unencumbered teammates.

For the masked men whom Roger Angell once fondly described as “bulky” and “unclean,” a day at the park requires feats of endurance matched only by those of their battery mates. Methods of quantifying pitchers’ fatigue and overuse have been available for some time, leading to drastic reductions in starters’ workloads, but regular catchers have had only the occasional day game off after a night game to reassure them that their sacrifices have not gone unnoticed. While the more subtle effects of catcher fatigue merit neither an acronym nor a dedicated scoring system, they do deserve a brief investigation.

I recently found myself re-watching a TwinsYankees contest from April of 2006 (a clear indication that I need a new hobby), a rather mundane affair highlighted (for me, at least) by the spectacle of a vintage Rondell White oh-fer. In an effort to avoid dead air during a lengthy at-bat, YES Network play-by-play man Michael Kay observed that an approaching offday, followed by a Randy Johnson start (at the time, the sole purview of second-stringer Kelly Stinnett), would grant the hard-working Jorge Posada a two-day respite. The late Bobby Murcer, Kay’s partner in the booth, responded, “Joe [Torre] has always said, from a former catcher, that as much rest as I can get my everyday catcher, the better off we all will be in August and September.”

Had the tactically challenged manager truly wished to put his maxim to the test, he could have given Posada the rest of the season off, swapping his slugging for the replacement-level stylings of Stinnett and Sal Fasano, who served sequentially as Posada’s understudies in 2006. Such a move would have met with the overwhelming approval of Sal’s Pals, but would have left the Yankees several wins worse off in September. Of course, Torre didn’t intend for his assertion to be interpreted literally; after all, Posada had averaged 142 games played over his prior six seasons, topping out at 151 in 2000, and would go on to appear in 143 in 2006. What Torre sought, and like every other manager, continues to seek (with varying degrees of success), was the happy medium between overuse and inadequate production: the point at which the diminished production of a weary starter ceases to trump the performance of a fresh, but less gifted stand-in-or, to express the goal in another way, the point at which the harmful effects of increased exposure to the anemic bat of a backup might be negated by improved performance from a reinvigorated starter in a slightly more limited role.

Before we consider the macro ramifications of heavy catching workloads, we should first pause to examine their short-term effects. Thanks to some creative querying by Bil Burke, we can gauge the impact of consecutive games behind the plate on offensive production. The following graph reveals the rate at which offense declines as “consecutive games caught” streaks lengthen, incorporating data from the primary catchers of every team since 1974:


“Day one” signifies any game caught after a break from catching of any duration, whether it be a team offday (or days), an individual rest day, or even a game played at another defensive position; each subsequent interval on the X-axis represents a game caught without a break of any kind. Each data point on the graph corresponds to the weighted average of each of the catchers’ performances on the day in question, a sample which includes almost 130,000 at-bats on day one, and gradually shrinks to just over 5,000 by day eight. The Y-axis denotes the weighted average of the differential between each player’s “daily” OPS+ (in other words, his performance(s) on the “consecutive day” in question), and his full-season OPS+. This method allows us to avoid two potential sources of distortion: first, the fluctuating run-scoring environments inherent in a 35-season sample, and second, the fact that the sample of catchers grows stronger offensively with each day added to the “consecutive games caught” streak. The catchers in the “day one” sample posted a weighted seasonal OPS+ of 95, but by day eight, that figure rises to 103, suggesting that managers do show a tendency to leave their better-hitting catchers in the lineup for longer stretches.

With those explanations out of the way, we can analyze the results. As one might expect, rested catchers outperform their seasonal baselines very slightly, beginning to decline only on day three, and remaining above their mean production levels until day four. Tom Tango wrote an article for the most recent Hardball Times Annual in which he compared catchers’ performances with and without rest, and found little difference between the two. However, Tango’s study incorporated older data (as we’ll see, recent developments in the game may have affected fatigue patterns), excluded catchers over 30 (youth may act as a palliative), and divided plate appearances into those with “no rest” and those “with one or two days’ rest,” placing plate appearances on (for example) the second consecutive day of action in the same category as those that took place on any other subsequent day (and weighting the former more heavily, since they occur far more frequently). The graph above suggests that catching for eight consecutive days depresses production on the order of approximately 10 points of OPS+ (with the losses split almost equally between patience and power), with the steepest decline occurring between days five and six. That’s roughly equivalent to the difference between last year’s models of Russell Martin and Dioner Navarro, or, alternatively, Navarro and Brian Schneider.

Of course, this graph should not be interpreted to mean that every catcher should expect to lose an identical portion of performance with each consecutive game caught; like Tango’s positional adjustments, this model might serve as a generalization for a large pool of players, while failing to apply to each individual player within that pool. Still, it’s reasonable to expect a catcher to lose roughly this amount of production in the absence of other information, since any one backstop might never accrue enough repetitions to demonstrate his true ability to withstand unusually heavy workloads. This is an area in which an attentive coaching staff’s familiarity with a player’s physical gifts and shortcomings could pay dividends.

After the eighth day, beyond which most catchers fear to trudge, the differential line begins to fluctuate wildly, reflecting not only the increasingly meaningless sample sizes involved, but a possible selection bias, in that the players asked to undergo such tests of endurance might represent the select few capable of performing such feats without suffering drastic declines. By far the longest streak in our sample was Steve Yeager‘s epic 29-game odyssey from August 21 to September 17, 1975, which included both ends of an August 23 doubleheader. Hardly an offensive asset under the best of circumstances, Yeager went 2-for-21 at the tail end of the streak, finishing with a .174/.255/.293 September line.

Catchers come in all ages, sizes, shapes, and degrees of relation to Mrs. Molina; we shouldn’t assume that the young and old, short and tall, and chunky and less chunky all grow weary at the same rate. A slightly more granular examination might yield insights that would be lost in a glance at the group picture. Since particularly tall and heavy catchers tend to migrate toward the outfield, first base, or DH (if not the golf course) as they age, due both to the necessity of quick footwork behind the plate at higher levels, and the accumulated strain of years spent crouching without the aid of porcelain props, it might be instructive to determine whether their relocations are preceded by steeper short-term fatigue-related declines. Unfortunately, the state of our height and weight data (or anyone else’s, for that matter) is such that relying on it for an exercise of this nature might raise more questions than it would answer. However, with closet Guerreros and Tejadas somewhat scarcer than players who swear by the Media Guide Diet, we can segregate our catchers on the basis of age with a greater degree of confidence.


Since the creation of this graph necessitated a bifurcation of our original data set, I abbreviated the X-axis, but the display does indicate that once they begin, older catchers’ short-term declines outstrip those of their junior brethren. As Nate Silver observed a few years ago, the same holds true for catchers’ careers relative to those of players at other positions; backstops tend to reach their peaks late, and leave them behind early.

In another article from the same period, Nate wrote that, “Being a catcher is more taxing than it used to be, and there’s less room for error-and catchers are paying the price with their offense,” citing the fact that “Pitchers are throwing more pitches than they used to, fewer of those pitches are being hit into play, and the pitches are more likely to be thrown faster or with funkier movement (e.g., split-finger fastballs).” Superior training, conditioning, and equipment might partially obscure the effects of these developments, but let’s see whether any differences appear when we restrict our inquiry to the initial and final six years of our sample:


Take this graph with a cellar of salt, since with only six seasons of data, any apparent patterns could be little more than mirages, even with our focus narrowed to seven consecutive days. If the modern group’s precipitous decline on days six and seven (in contrast to the comparatively static performance exhibited by the older crop of backstops) does signify a trend of recent vintage, however, it could have meaningful implications for catcher usage. The graph below reveals why:


Over the last six seasons, the thirty teams’ primary catchers (defined by most innings caught) have posted a weighted-average OPS+ of 98, while their alternates (they of the second-most innings caught) have collectively recorded a paltry 81 OPS+. Only last year did the gap amount to little more than the 10 points of OPS+ that separate the typical weary starter from his rested self, which likely represents nothing more than a one-year quirk in the numbers. It should be pointed out that the backup catcher’s schedule of sporadic starts might not lend itself to success at the plate; perhaps these backups would find more success in a more regular role. But it’s also possible that a full-time gig would only further expose their weaknesses; the current arrangement grants managers the freedom to pursue favorable matchups for their weak-hitting reserves, an advantage reflected in the fact that non-switch-hitting backup catchers faced pitchers of opposite handedness in 41 percent of PA last season, compared to 37 percent for non-switch-hitting starters.

In either case, with a “fatigue gap” of 10 points of OPS+, and a “backup gap” of equal or greater value, managers may be justified in leaning heavily on their starters, accepting their fading performance as a necessary evil. However, if the current gulf hews closer to 25 points of OPS+, as the “Era and Catcher Fatigue” graph might suggest, the equation changes considerably. Needless to say, since few teams’ circumstances mirror the “typical” model exactly, much of this balancing act must be decided on a case-by-case basis. As one of the premier offensive catching talents of his era, Posada represents an extreme case, especially since the Yankees have consistently failed to back him with capable substitutes (such as the DiamondbacksMiguel Montero, or the MetsRamon Castro, who would merit starting roles on a number of teams). Last year’s Angels, who divided their catching duties between Jeff Mathis and Mike Napoli, with the less productive Mathis gaining a greater share of the playing time, exemplify the opposite extreme.

Offensive performance tells only part of the story of a catcher’s overall production; in fact, defense often receives top billing in the enumeration of many backstops’ strengths. Ironically, catcher defense has also proven the most resistant to quantification, largely because of the difficulty of separating the catcher’s contributions from those of the pitchers with whom he works. One might expect backup catchers to recoup some of their shortfalls at the plate while crouching behind it, reflecting the timeless truth of Nichols’ Law of Catcher Defense, but some recent research suggests that this may not be the case.

Brian Cartwright recently attempted to quantify catchers’ successes at controlling the running game and minimizing the incidence of wild pitches and passed balls, utilizing a WOWY (With or Without You) approach. Using Brian’s data for the last six years, which he was kind enough to send me, I found that when prorated over a typical full season, the average starter’s performance actually trumped that of the average backup in both areas by narrow margins of between one and two runs. Certain backups fit the “defensive specialist” mold, but it appears that most managers wisely elect to bestow the bulk of the playing time on well-rounded candidates. Theoretically, it might be possible to quantify the erosion of defensive skills as a function of playing time; some research (scroll down) has already suggested an inverse correlation between the two, which could provide further incentive for managers to rest their starters. The impact of game-calling, if any, has thus far eluded detection, but presumably would not be affected by physical fatigue to the same extent as other components of catcher defense.

Before we move on, let’s see how players at other positions have handled consecutive days of action, once again drawing upon data from 1974-2008:


Players at every position seem to suffer some fatigue-related short-term decline, though catchers exhibit the most linear descent. First basemen, situated at the opposite end of the defensive spectrum, manifest no sign of a drop-off until their sharp fall on day eight, which may be nothing more than a blip, given that they rebound to post a differential of zero on day nine (not pictured). Counterintuitively, designated hitters display a fatigue pattern similar to that of the catchers, despite their freedom from defensive duties. In 2007, the authors of The Book identified (but left unexplained) a so-called “DH penalty,” which adjusts for the fact that players who alternate between playing in the field and serving as the DH tend to perform worse in the latter capacity. It’s possible that both the penalty and the fatigue curve reflect some psychological byproduct of prolonged pine-riding, but there are more likely explanations for designated hitters’ susceptibility to fatigue-designated hitters tend to be older (teams’ primary DHs averaged 32.5 years of age in 2008, compared to 29.2 for catchers), relatively unathletic, and/or nursing an injury which prevents them from playing the field, all factors that could explain their superficially surprising tendency to tire.

Now that we’ve examined the short-term effects of heavy catching workloads, we can widen the scope of our inquiry to encompass a whole season. First, let’s take a look at the monthly breakdown of plate appearances, for catching seasons since 1974 with a minimum of 300 PA:


Because the average number of games on the schedule varies slightly between months (most clearly reflected in the relatively low March/April totals), this graph doesn’t offer quite as clear a picture as we might have hoped, but it still suggests two things: first, that the availability of the entire pool of catchers doesn’t seem to suffer very significantly in September/October, and second, that the hardest-working groups, represented by the top two lines, don’t sacrifice a greater proportion of their playing time late in the year than the catching leisure classes. This tells us nothing about the quality of the players’ production in that playing time; for that, we must look to the final graph on our program for today:


A CARFAX report on Bob Brenly reveals a September/October lemon with an OPS+ of 80 relative to his career totals, suggesting that he might have been wise to take Obi-Wan’s advice and send himself back to the factory for repairs each August. Brenly’s late-season collapses aren’t typical of all catchers, though we do see some decline from March-July rates in August-October-except for the anomalous September/October increase from the members of the 600+ PA group, whom one might expect to suffer the most severe downturn. By way of explanation, I’d offer the fact that catchers are a highly selective group to begin with; those who prove incapable of sustaining an acceptable level of performance throughout an entire season never make it to the majors, and as a result, don’t appear in this sample (for similar reasons, we see a relatively narrow range of defensive talent among backstops, despite the specialized skills required). The select few who manage to surpass the 600-PA threshold (an average of two catchers in each of the past 35 seasons) represent the most purely refined of the iron men.

One other oddity that deserves explanation is the scarcity of negative differentials; since no individual player can exceed his seasonal OPS+ in every month of the season, it seems strange that a group of players should prove capable of doing so collectively. The probable explanation lies in the fact that weighting the data by playing time skews the results toward the catchers who played the most in each month; that the weighted results are mostly positive suggests that the contributions of those particularly active players lean in that direction, which in turn implies either that more regular playing time translates to superior performance, or, more likely, that hot-hitting catchers receive more playing time than their slumping position-mates.

Last year, Tango wrote that “just by becoming a catcher, an average player will lose 20 runs of production. However, someone has to be the catcher.” This statement might come as scant consolation to the players saddled with the dirty jobs, but their teams’ fortunes often mirror their own. More work remains to be done in this area; it would be helpful to know whether long consecutive-games streaks or heavy-workload seasons increase the likelihood of injury or hasten career decline, as well as the effect of interspersing games at catcher with games at DH or elsewhere in the field (the inquiring minds of Miguel Olivo and Russell Martin, respectively, want to know). There’s a reason why only one position merits a dedicated backup-the next time you drag a lawn chair outside to serve as your wiffle-ball backstop, remember the men who play lawn chair for a living.

Thanks to Bil Burke and others for research assistance.

Ben Lindbergh is an intern with Baseball Prospectus and a student at Georgetown University. You can contact Ben by clicking here.

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Ben (and Bil Burke), kudos.

I look at the first graph, (D_OPS+)-(F_OPS+) vs. Consecutive Days Caught "Catcher Fatigue Curve," and I wonder if the Red Sox knew the shape of this graph over half a decade back when they decided to give Tim Wakefield a personal catcher. While one can look at it and see a linear decline from two to eight days' catching, with an outlier at six days, I see a discontinuity between five and six days. Catchers decline little by little over five days, but they lose a lot between the fifth and sixth day. Given that few other MLB catchers approached Jason Varitek's hitting ability until 2008, Boston would've wanted to use him most of the time, but giving Tim Wakefield a personal catcher offered Varitek an excuse to avoid the most challenging catching assignment endured by Boston catchers, as well as offering him "guaranteed" rest opportunities often enough to avoid serious fatigue-related effects. The "Era and Catcher Fatigue" graph makes the decline after day five seem even more pronounced.

Limiting catchers to no more than four consecutive games, as well as the traditional rules regarding catchers working neither both ends of a doubleheader nor day games following night games, seem to be good ways of limiting lost effectiveness due to fatigue. One doesn't have to use the backup catcher as a "personal catcher" to reach that goal, but it does offer what seems to be an appropriate amount of rest.

Excellent study - more please
this was REALLY great!!!
This is one of the best things I've seen on BP in the >5 years I've been a regular reader.
Well, this is one of the best comments I've seen on the >2 articles I've written. Remember to adjust for context, though--not having a deadline tends to inflate word/graph counts.

Timo Seppa wrote an article about goalie fatigue that went up on Puck Prospectus yesterday--there are some really interesting parallels to the catcher situation:

Anyone who hasn't done so already should check that out.
Outstanding article. Thanks for the plug!
Jayhawks comment makes me wonder ... I know we've seen it said that Greg Maddux lost wins by not having Javy Lopez catch him, but was Maddux helping the rest of the team by enforcing rest for Lopez? Do Glavine and Smoltz owe Maddux more than just a couple skins?
Thanks for a thoughtful and well-done article!

Woody Studenmund
Ben, outstanding work. Can you somehow derive from this, "Lindbergh's Law of Catcher Fatigue and Rest Patterns," which states that if the everyday catcher's YTD OPS is "x", and the backup catcher's OPS is "y", then the everyday catcher should be given a day off every "z" number of days?

It would seem that there should be a measurable (and perhaps predictable) tipping point where the everyday catcher's performance declines enough due to fatigue that it's the right move to give him a day off, because the effects of the fatigue offset the offensive decline the team suffers by playing the backup instead.

That's actually exactly what I set out to determine, but I'm not sure there's really any hard-and-fast law to be derived. It's hard to say (or at least, it's hard for me to say) exactly how much of the effect is real, and how much is sample size, and there's likely a fair amount of variability in how catchers handle the strain, even though they've all been selected (in part) for their ability to withstand it.

Of course, if anyone else wants to try their hand at naming something after me, I'm all for it.
Like everyone else, I enjoyed reading this article. However, the "Multi-Positional Fatigue Comparison" chart is a bit alarming to me. Taken at face value, it suggests that the DH should be receiving the same pattern of rest as the catcher. The paragraph following the chart offers a few potential explanations for why such a pattern of rest may be appropriate for the DH.

However, I look at the way the DH line wiggles up and down like crazy as a function of consecutive days played, and I look at how the 1B line drops like a rock at Day 8 (and then returns to normal at Day 9), and I wonder if we are seeing statistically significant trends in the data. It would be nice to know (1) the sample size in each bin (we got a little bit of this information for catchers, but not enough for this particular chart) and (2) the rms variation or confidence interval associated with the data in each bin (for example, do catchers on Day 8 have similar OPS+ differentials -- i.e. a very tight distribution; or is there a wide range of OPS+ differential for catchers on Day 8?). I think this analysis would also be useful for the catcher OPS+ differential by month chart as well.
I would think the overall sample size would be quite good for days 1 to 7 or so, but it seems logical that sample size driven anomalies like the 1B-Day8 can happen when you start looking further than that simply because most schedules will have an off day come along in there to restart the counter for everyone.

Also, with respect to what Will Carroll said, I actually had the EXACT same thought while reading the article. I had always thought of Maddux's insistence to have O'Brien catch for him as a minor detriment, but maybe it was actually a blessing in disguise.
Ben, this is absolutely fantastic. A whole book could be written on this topic, but you do an excellent job giving a broad overview. I wanted the article to keep going!

It would be really interesting to look at a case study for a team with its catching situation in flux at the beginning of the offseason - for instance, of all the scenarios the Astros were presented with, did Ivan Rodriguez/Humberto Quintero present the best? And if so, how should their playing time be allocated in order to maximize their contributions (if any...)? A team could almost have a full-time staff member just working on this problem. If they could milk an extra win or two out of the position as a result it could even be worth the salary.

In terms of the broader perspective you take I have one more big question, which is how catchers getting *regular* rest (versus a single, extended rest) fare near the end of the season. Maybe this just means a different version of your final graph, but ignoring players with DL stints? It's tricky to tease out, but it would be amazingly helpful. For instance, the Cubs would not only want to know how many days to go before resting Geovany Soto for the sake of the individual games (you answer this very well), but also how often he should be rested for the sake of September/October. Perhaps a team like the Cubs - with a fairly wide margin for error in the regular season - would be better off playing him only 2 days out of 3 and letting him pinch-hit in high-leverage situations on the third days? Or perhaps if they clinched early they would be best off keeping him out of the catchers' box from that moment until the postseason begins, with occasional PH/1B ABs to keep sharp? Lots of sample size problems here (not least Soto's own brief career), but interesting to think about.

Anyway, again, really nice job. You've gotten me thinking and analyzing the way the BP team does at its best.
The quirkiness of the firstbasemen's data makes me wonder about the validity of the data. Correlation is not causality, and if the correlation data is wonky, we don't have anything very solid.

However, if the data is valid, I wonder if age is the more underlying cause rather than the physical demands of the position. The graph comparing the older catchers to the younger catchers possibly shows to what degree age is the key element here. Firstbasemen and DHs tend to be older than average players as are catchers. Their declines after several consecutive days of playing is consistent with the key lessen here that older players needing more frequent rest.
woops: "need more frequent rest".
Wow ... One of the best articles I have ever read at BP ... right up there with the VORP analysis by draft position a few years back ...
In addition to the highly selective 600+ PA group, another (lesser) explanation for catchers' performance spike in September could be the decreasing temperatures.

As the only players on the field to wear full body armor, catchers are more susceptible to the extreme high temperatures of July and August, and they are likely to benefit the most from energy saved through the decrease in temperatures in September/October.

It wouldn't explain all the bounce-back, but some of it.
Great analysis. I would welcome your response to a slightly different, but related, issue. At my university, both the baseball and softball teams have a schedule in which only one midweek game is scheduled, but play doubleheaders on each weekend day. I run scoreboard for softball and have become concerned for the catcher there. She has caught every inning of every game as well as warming up the starting pitcher for every game. Would you predict a decline for her as the season proceeds? In any event, last year she hit .360 during the first two months and then proceeded to go hitless in the last 7 games (including the league tournament). Of course, this could be simply a chance blip. But I wonder...
Richard Ellis
Very nice article Ben. Thanks for the link and glad to help out.

When I was rating cathcer's throwing for FanGraphs, one of the things I looked at but didn't publish yet was how the CS% changed with age. I noticed that backup catchers didn't seem to suffer the same decline in CS% in their 30's that the starters did. I need to work this out to a full study, but I suspect that this effect probably correlates better with career innings caught rather than age, much as Ben has shown here with a day to day effect on the catcher's offense.