The Overrated Value of Catcher’s Throwing Arms

If you are familiar with my previous studies on the battery, I have often struggled with preconceived notions regarding the relationship between the pitcher, the catcher and the running game. I have previously concluded that it is the pitcher who has more influence on the caught stealing percentage of the battery than the catcher. In addition, I’ve concluded that it is the pitcher who has more of an impact on the passed ball. Meanwhile, in box scores and in broadcast booths all around the country we continue to reward caught stealing and responsibility for the passed ball to the catcher. Fact is, there are many variables at play and as a result, there is a battery effect that must be considered.

In my continuing study on the relationship between the pitcher and the catcher, this article addresses one specific area of the battery effect and will question the conventional wisdom that the catcher’s arm is the determining factor in the outcome of a would be base stealer.

While there are many variables in play, for today we will solely look at timing of the battery and the past success of the battery, the pitcher, and the catcher in controlling the running game.

There is no question that having a catcher with a great arm has great value in the battery dynamic, but is that enough to constitute a battery combination that will prevent the running game? It comes down to timing on both sides of the rubber, but whose speed, or lack thereof has greater influence on the outcome? Because timing data for each respective attempted steal is not information that is readily available, the following is the methodology that I painstakingly undertook in order to give credence to, or refute “conventional wisdom” that a caught stealing rests with the catcher’s arm.


I will quote what I have previously written in my prelude to this study — which you can find here:

Between the 2011 and 2012 seasons there has been 7757 stolen base attempts — specifically from first to second base. 5641 of these have resulted in a stolen base — 2116 of these have been caught stealing. Our population will act as all the stolen base attempts from first to second base during this time period — primarily because that is how far MLB.TV goes back. I cannot go through all 7757 stolen base attempts, so for the sake of my sanity, instead we will create a representative sample of the population. First, I have pulled all the stolen base attempts via retrosheet, and created two different groups — stolen bases and caught stealing. From those stratified samples, I randomly selected 50 stolen bases and 50 caught stealing and combined them into one large “representative sample”. Now, the number of stolen bases to caught stealing is not proportional to the population, but we want to get a good feel for the distinction between both — so it will serve us well enough.

Using the above methodology, I timed all the individual attempts on a frame-by-frame basis (40 fps), and split each attempt from a pitcher’s first movement to the time where the catcher caught the ball. Then, I timed how long it took between the catch and transfer to the point where the ball was caught at second base. For statistical purposes, the sample was taken from the population –all 7757 stolen base attempts from 2011-2012 – at random. Therefore, the sample is more or less a representative sampling of stolen base attempts from first to second base. I’d hate to bore you with any more technicalities, so let’s move on to the fun stuff.

Timing the Battery

The goal of this study is to answer whose timing is more indicative of the outcome – the pitcher or the catcher?

From our sample, below is a table and a chart of the catcher’s “Pop Time” — the time from catching the ball to the ball reaching the infielder’s glove at second base:

Disclaimer: I have chosen not to include outliers in the below correlation calculations, but they will remain in the charts and tables below. Reason being, given the small sample size we are working with, small outliers have a disproportionate influence. In doing so, we are keeping this analysis statistically sound.

Pop Time (s) CS% SBA
1.6/1.7 43.56% 25
1.8/1.9 51.00% 58
2/2.1/2.2 53.00% 17

chart_5 (2)

chart_7 (1)

What is important to note from this chart is that a catcher’s pop time has a correlation of 0.01 — which is strange considering that we would expect to see is a negative correlation which would suggest that a catcher with a smaller pop time would be more likely to throw a runner out. Here when binning similar times in the chart we see that generally a larger time equaled a larger CS%, very surprising. In this representative sample as a whole, a catcher’s time did not have a strong relationship with the CS%, going against conventional wisdom and suggesting that there are other variables influencing the outcome of a stolen base attempt. Perhaps it comes from the other side of the rubber?

To explore further, let’s take a gander at pitcher release times and their relationship with the CS%, with the same sample.

Release Time (s) CS% SBA
0.9/1.2/1.3 67.07% 15
1.4/1.5 59.00% 37
1.6/1.7 41.00% 39
1.8/1.9/2 22.00% 9

chart_4 (1)


chart_8 (1)


In contrast to the catcher’s chart, you can see this faux probability distribution is skewed left towards the smaller times. What does that mean? Well, that a smaller time for a pitcher’s release equals a greater probability that a stolen base attempt ends up in failure. A pitcher that releases the ball in a shorter amount of time  — with a slide-step perhaps — he is giving his catcher a better shot at throwing out the runner. The correlation backs up our point as it sits at - 0.88 — a strong negative correlation. So, in short, if a pitcher like Wide Miley is on the mound with a low release time (a 1.2 s release time) there is a far greater chance that a stolen base attempt will result in a caught stealing. Conversely, if you have Tim Lincecum (a 2.0 s release time) on the mound, you better have a catcher with an exceptional “pop-time” to have any chance.

However, do pitchers that throw harder have a better chance of throwing out the runner? Or is the advantage simply in the pitcher’s quickness to the plate?

Well, because we have the overall time of the pitcher from release to the plate all we have to do is subtract the time the ball spent in the air after release. In doing so, we find the time it took each to release the ball — we will call this “move time”.

When we calculate the “move time”, its relationship to velocity of the pitch is very weak at 12%. However, a relationship of the “move time” and the CS% sits at -90%, while velocity and CS% correlates at a -4% clip. This means you can have a hard thrower like Chapman, or a soft tosser like Mark Buerhle on the mound, and despite one throwing harder than the other, the success will be dependent on who releases the ball first, not who gets it quicker to the plate once the ball is released.  

So what about the overall time of the battery? How does overall time affect the probability of caught stealing in this setting? The following is the table and chart of the overall battery time and the corresponding CS%.

Battery Time (s) CS% SBA
2.8/3/3.1 85.71% 7
3.2/3.3 52.78% 36
3.4/3.5 48.72% 39
3.6/3.7/3.8 33.33% 18

chart_6 (1)



So here we have another distribution that is more or less skewed left towards a smaller time where a lower time equals generally a better CS%. Up until 3.5, we see a gradual descent; all totaled, we see a negative correlation of -0.81. From this chart we know that from 2.8 to 3.2 seconds you will generally see a caught stealing, and between 3.3 to 3.6 seconds you will see variations.

As expected, timing of the overall battery naturally has a lot to do with the probability of a caught stealing. However, our study suggests that one of the two battery mates has more of an effect on the overall outcome than the other. Given that we already know that a pitcher’s release time has a strong negative correlation with the CS%, we would expect to see that a pitcher’s time would have a strong positive correlation with the overall time of the battery — which would mean smaller release time, smaller time of the battery.

Running the numbers substantiates this conclusion; a correlation of 80% between release time and overall time of the battery. Conversely, a catcher’s pop time has little impact on the overall time of the battery, with a 38% correlation.

Another variable to consider is reputation; if a catcher’s arm is inconsequential in the terms of the overall time of the battery, will reputation keep base-runners on their toes? While this is not a timing variable, it is important to consider for our study since we are questioning the value of a catcher’s arm within the battery dynamic.

Past Success/Reputation 

To quantify “reputation” of the battery and its components I will implement my metric (bBRS) that estimates the number of runs saved in limiting the running game.

As expected, when a catcher with a high bBRS is behind the plate it is more likely his reputation will limit the number of stolen base attempts — a – 0.21 correlation compared to a 0.30 positive correlation for pitchers. While the relationships are not as strong here, the catcher is the one with the slight edge. This makes sense to me. When you have Yadier Molina behind the plate, a runner is less likely to steal in fear of being caught, so there will be less stolen base attempts against someone of his caliber.

But what happens when we compare past successes of the catcher and pitcher respectively versus the past success of the battery as a pair? Do we expect to see that the past success of the battery will be highly influenced by the pitcher, the catcher, or both?

In order to answer this question, I binned the sample into CS and SB, then compared pitcher bBRS to battery bBRS. For the CS bin the correlation was 88%, and for SB it was 80%. Meanwhile when comparing the catcher bBRS to the battery on both CS and SB the relationship was much different.  For CS there was a 57% correlation and for SB there was a measly 13%.

In other words, a pitcher who had past success maintaining the running game had more of an impact on the battery’s ability to throw out runners. This leads us to surmise that the pitcher’s success of holding runners on has more influence on CS% than a catcher’s past success in throwing runners out. In general, these findings back up what I previously found in larger samples where the pitcher had more to do with most battery outcomes than the catcher. For instance, in 2013 a pitcher’s bBRS correlated to the battery’s bBRS at 70%, while a catcher’s bBRS correlated at a 30% clip.

In short, when it comes to the timing variables within the running game and the reputation of the battery mates, our study refutes the conventional wisdom that the catcher’s arm is primarily responsible for caught stealing. While there are other lurking variables at play — like pitch location and handedness of the batter — surface value says that a pitcher’s quickness to the plate is a whole lot more influential than a catcher’s arm in the battery dynamic. Said lurking variables will be topics for future installments and will help us dive deeper into assigning credit to one of the two battery mates. When it comes down to the timing variable, the need for speed is on the pitcher’s side of the rubber.

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Max Weinstein is a teenage baseball analyst. He has written for Fangraphs, The Hardball Times, and Beyond the Box Score. Connect with him on Twitter @MaxWeinstein21 or email him here.

58 Responses to “The Overrated Value of Catcher’s Throwing Arms”

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  1. Max, really like this. Two ideas to think about. Couldn’t you run a model with SB/CS as the dependent variable and pitcher and catcher pop times as separate independent variables? Should give you a better idea about how to assign “credit.” You could also model this as a two stage process, with reputation determining the probability that a runner attempts a steal and the pop time predicting whether or not that attempt is successful.

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  2. Anon says:

    Correct me if my math is off but the fact that pitch speed doesn’t matter is not surprising since the difference in time to the plate is measured on the order of thousandths of a second, right?

    100 MPH = 528800 ft/sec = .00000189107 sec/ft x 55 ft(let’s say) = .000104009 seconds

    85 MPH = 48800 ft/sec = .000002228164 sec/ft x 55 = .000122549 secs


    Of course if the 85 MPH pitch is a slider inthe dirt that would affect the catcher ‘s pop time but the speed alone would have no impact,

    BTW the above underscores just how tough hitting is when a difference of 2/10,000 of a second can really screw a hitter up by messing with his timing.

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    • Anon says:

      Oops, that should be 448800 not 48800. The rest of it is correct

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    • Anon says:

      . . . .and of course it’s hundred thousandths of a second not tn thousandths. POint is the same though

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    • OOPS is right says:

      per Hour not second. off by a factor of 3600. Plus the release speed not equal to average speed.

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    • Luke says:

      Man, really? Baseball would be pretty ridiculous if pitchers were capable of throwing a ball that got to home plate in a 10000th of a second!

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    • Rex Manning Day says:

      100 mph = 528,000 ft/hr = 8,800 ft/min = 146.67 ft/sec = .38 seconds to travel 55 feet
      70 mph = 102.67 fps = .54 seconds to travel 55 feet

      Nobody throws a pitch that averages 100 mph all the way home, though, so:
      90 mph = 132 fps = .42 second to travel 55 feet.

      So the difference between 70 and 90 mph is about 120 milliseconds.

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    • section223 says:

      If one train departs the station at 7:59:59.6256 on a relatively straight line going 100 MPH to its’ destination that is 55′ away and a second train departs the station at 7:59:59.5588 on a curved line going 85 MPH to its’ destination that is also 55′ away, which one arrives first?

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    • Anon says:

      Yeah that was a pretty bad math fail – I feel like an idiot. Sorry.

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  3. Dan says:

    Awesome work, Max. It’s unfortunate that it will take another 10-20 years for this knowledge, coupled with the pitch receiving information, to trickle down into the lower minor leagues and scouting world. Pop time is everything for an emerging professional catcher’s chances of sticking behind the plate.

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    • Richard says:

      Eric has a great rebuttal below.

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    • CircleChange11 says:


      I’m pretty sure the advent and rapid large scale adoption of the slide step by RHP’s, leading to drastically fewer stolen bases indicates that teams understand what leads to stolen bases.

      The goal by pitchers is not the caught stealing, but the lack of attempt.

      Pop time is very important, because it’s important to have.

      However, when guys rush they tend to not be accurate …. so it’s the combination of pop time + accuracy.

      That would be like saying being able to throw 90+ mph is everything for a SS. Sure, unless you air-mail it every time.

      We’re looking at pop times for major league catchers. If those time were a level or two higher, then we might see some dramatic stolen bases figures. We can’t say “pop time at the major league level” (where everyone is pretty darn good) and somehow trickle that down to the minor leagues.

      If you want to say “among the top 1%, pop time variations are not all that significant”, then that’s one thing.

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  4. Benjammer says:

    Maybe catcher throw accuracy is something to look at? I know Molina more often than not throws a perfect strike to the SS/2B covering 2nd.

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    • Kruegere says:

      You *know* that, huh?

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    • Leo says:

      This is a great point. My anecdotal observation of tag plays generally (not only stolen base plays) is that the accuracy of the throw matters at least as much as its speed. There are many, many plays where the throw and the runner arrive at nearly the same time but the play isn’t close because the defender has to reach for the ball.

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    • randplaty says:

      Agreed, I would think accuracy would be the single most determinative factor because if its an inaccurate throw, there’s no play at all.

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  5. t ball says:

    Good stuff. All that taken into account, I’d still like a catcher to have a good arm, though.

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  6. TKay says:

    Might a catcher’s throw accuracy be significant? Is it possible that the slower pop times correlate with more accurate, less frantic throws, and result in more CS as a result?

    Really fascinating stuff. I believe AJ Burnett is one of the worst offenders in the game.

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  7. Nick says:

    The catcher is able to see that the runner is breaking for second before he receives the ball. That means that he’s able to trade speed for accuracy if the runner gets a good jump, or take his time to make a good throw if the runner gets a bad jump. When a base is stolen on a quick pop time, it may have been that the catcher never really had a chance.

    In addition, if the runner knows that a catcher with good pop time is behind the plate, he will steal less often, only attempting a steal when the other variables (speed of the pitcher’s delivery, the pitcher’s pickoff move, the count) are in his favor.

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  8. David Hess says:

    Could this result stem from a scenario where base runners respect hard throwing catchers but not fast releasing pitchers?

    In other words, runners are more careful against hard throwing catchers, so only the best runners attempt to steal. In theory, every catcher could have the same CS% because their throw speed correlates with the quality of rubbers attempting to steal against them.

    On the other hand, pitchers may not have the same reputation effects. So bad runners still try to steal against good pitchers, and the quality of the pitcher determines the CS%.

    This would mean that for any given runner, the chance of a successful attempt against a battery would depend on both pitcher and catcher. But the probability of making an attempt in the first place mainly depends on the catcher, skewing the sample.

    Kind of like how the fatality rate of climbing Everest is probably not as much higher than the fatality rate of climbing some random mountain as you’d expect, given the difference in difficulty.

    Our maybe you addressed this, and I missed it when skimming.

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    • Eric says:

      Also, it’s possible that MLB has just already imposed such a huge selection on catchers with good arms (+ pop time / etc) that it’s not that the arm doesn’t matter, but that you’re already at the point of diminishing returns when you’re looking at MLB-level catchers.

      Put another way – it doesn’t matter what pitcher is throwing, if I got behind a plate there’s zero chance I’d throw someone out, because my arm would be so terrible it wouldn’t matter. But catchers who are that bad will be weeded out by the time you’re in the high minors, and all you’re left with are the best arms anyway. So once you’re at MLB, you’re looking at the difference between “great” and “really great”.

      That’s not to say that the overall point is wrong – in fact, it goes hand in hand with the pitcher being more responsible at the MLB level, since that’s not a skill that’s traditionally used to screen pitchers. But it doesn’t mean that teams should necessarily start relaxing their criteria for catchers, though it would suggest that the reputations at the MLB level (and especially getting higher contracts based upon those reputations) aren’t useful.

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      • I would love to compare the pop time of a random sample of minor league catcher’s to the time’s of this sample to prove if there really is a selection bias towards only picking good arms. My gut says arm is not the only thing that differentiates them since a myriad of variables go into which catchers make the majors and which do not. In other words a catcher with a below average arm can make the majors due to his bat — i.e. Jesus Montero, among many others.

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    • CircleChange11 says:

      Base runners know who they steal on, and it’s the pitcher.

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  9. Tim says:

    Wide Miley was what middle school bullies called our little chubby friend.

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  10. isavage30 says:

    I would think a problem with the data would be the speed of the runner as a variable. The times where the catcher is taking longer, may be cases where it’s a slower runner attempting to steal, and the catcher knows he doesn’t need to rush the throw, and the runner is more likely to be thrown out. The ones that show a quicker catcher pop time are probably weighted towards base-stealers who are faster and/or got better jumps and are just going to be harder to throw out.

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  11. Richard says:

    An obvious point (which I didn’t see pointed out in the original article) is that a starting catcher will start roughly 4 times as many games as a starting pitcher and (assuming 6 IP per start for a starter), be involved in 6 times as many steal attempts as any starting pitcher. Thus, even if the pitcher is twice as important as the catcher in any steal attempt, a starting catcher would still be 3 times more important than any starter when it comes to controlling the running game over the course of a season. Thus, logically, teams would still care about the arms of catchers much more than they do how well a ptitcher is at keeping runners from stealing bases.

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    • While this is true on a player to player comparative basis — when we compare the ability of a catcher to a pitcher — it is besides the point I am trying to make. When you have a stolen base attempt, there is always a pitcher and a catcher, a single battery involved. In the scope of the battery the pitcher has more influence in that dynamic.

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      • Richard says:

        OK, that’s cool. One thing to keep in mind, though, is that if you mess around with how well a pitcher keeps a runner from successfully stealing, you may make him more ineffective in more important ways (after all, his main job is getting the guy at the plate out). However, there’s really no downside to a catcher who is good to throwing out runners.

        So from a practical perspective (which includes the greater number of times a starting catcher is involved in a steal attempt than any pitcher), for an organization to focus on the catcher part of the running game makes sense.

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  12. Sam says:

    Tommy Hanson pitching = safe every time

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  13. pft says:

    I was hoping to read something on throw time by C since an exceptional arm could possibly compensate for slow popup time/release time by 0.1/0.2 sec. Accuracy is also another important variable, since less accurate throws require a longer time to tag the runner.

    Another variable is the pitcher varying times to the plate to keep the runner off balance.

    Of those 100 cases, umps probably git the call wrong 20 times, so how did you handle that? Some fielders do a better job of getting the call.

    Good article though, which tends to confirm what we have been told. SB are not all on the catcher, shutting them down is on the P primarily, then C, and the coaches and fielders, and you need some help from the umps to get it right.

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  14. Marco says:

    If this is true, we should be able to see very different CS% results for different members of a pitching staff with the same catcher, yes?

    Max, have you researched this?

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  15. aascd says:

    Really great stuff. Why did you choose to only consider steals of 2nd?

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  16. Andy says:

    “Between the 2011 and 2012 seasons there has [sic] been 7757 stolen base attempts — specifically from first to second base. 5641 of these have resulted in a stolen base — 2116 of these have been caught stealing.”

    That’s about a 72% success rate. I believe that sabermetric analysis says a 2/3 success rate is break-even? That if you get caught more often than that, you are costing your team runs?

    Do managers know this and incorporate it into their game planning? As in, I think the runner has at least a 2/3 chance of stealing successfully, so I will send him. I think probably not, as well-known studies show that people fear making decisions that lead to losing something more than they want to make decisions that lead to gaining something. IOW, they tend to overestimate the downside of risks. I think managers are like this, and would not send a runner unless they thought the probability of success were much greater.

    I think the fact that the success rate is somewhat more than 2/3 reflects this. But I would expect that if managers thought the odds of success were 72%, they still wouldn’t send the runner. So why isn’t the % even higher? Maybe some of it is the runner making the decision, and overestimating his own ability to steal?

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  17. Andy says:

    Actually, almost 73% success rate. Point still holds. I think managers, too, must be overestimating the runner’s chances of success, though it works out for them.

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  18. Andy says:

    An illustration of overestimating risks: Ruth’s famous CS to end the 1926 Series. It appears it was the right move. Without stealing, he needed two singles to score, whereas if he had stolen successfully, one single would have brought him home. The odds of even a fairly slow Ruth stealing, about 50%, were greater than a second hitter behind him hitting safely. Yet even today Ruth seems considered by most to have made a poor decision.

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  19. Brian Cartwright says:

    I do believe the catcher should have more of an impact. Perhaps this was too small a sample, or as pointed out above, the catcher’s accuracy is also a factor.

    What I would like to look at is the size of the variance of caught stealing rates between pitchers compared to that between catchers.

    Here’s an anecdote: From 2010-13, AJ Burnett has allowed 39 SB and 7 CS (.15), an attempt every 32 batters faced, with Russell Martin catching, while 90 SB 5 CS (.05) an attempt every 19 batters with all other catchers (Cervelli 28-2, Barajas 37-0, Posada 11-3, McKenry 8-0, Moeller 3-0, Fryer 3-0)

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    • I’m thinking catcher’s accuracy and location of the pitch are the two most important variables for the catcher. My previous research has always shown the catcher to have less of an influence than the pitcher in regards to battery CS%. The link I posted above from Beyond the Box Score looks into how a catcher affected the pitcher when together and isolated without.

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      • If you all trust me enough, I am ready to go through the entire sample and measure the accuracy as objective as possible to answer that question. Whether you believe the accuracy of my measurements will probably be another, though. Any suggestions?

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      • Brian Cartwright says:

        I also believe the pitcher is more important, but we’re looking for the split 60/40? 70/30?

        Historically I’ve seen pitchers who can totally shut down a running game. In 1969-70 Steve Blass went over 220 innings without allowing a steal, I believe only 2 CS. Blass remembered that it was Tommy Agee who broke the streak. Humbly, he wanted to give the credit to his catchers who were above average, but my WOWY analysis took that into consideration, Blass was the 2nd hardest RHSP in the Retrosheet era to steal on, behind Jim Perry (although Johnny Cueto is likely joining them).

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        • In your WOWY analysis, how do you weight the results? This is half the motive for my research because I have a problem how WOWY assumes one or the other is responsible.

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        • Brian Cartwright says:

          The pitcher study was not published, but was a companion piece to the catcher study that was published here at FG (go to my author link).

          With WOWY there is a question of the time span of the control group (without) and how to weight older seasons. At the time I did those earlier studies I saw a choice of either current season or career, but recently I concluded that it could be modeled the same as a projection. For each pitcher, create a projection based on their stats minus one catcher. So Wainwright in 2010-2012 WITHOUT Molina compared to 2013 WITH Molina, weighting the past season data progressively less in the same manner that pitching projections are done.

          The WOWY attempts to give the true talent of each partner independent of the talent of all of the various partners. This does not assume responsibility. What I propose for that is comparing the the variance of pitcher’s true talents to the variance of catcher’s true talents. If, for example, all the pitchers have a variance of .30 in CS% and catchers have a variance of .20, there is more difference between pitchers than there is between catchers, therefor pitchers have a greater degree of control. From anecdotal evidence I’d say that there are pitchers who can totally shut down a running game while others can be ran wild on, but catchers do not vary to such a wide degree.

          In this case it might be 30/(30+20)=60% pitchers 40% catchers, but I’m not sure at this moment that’s a valid method. Might be, but need to verify.

          Also, to previous comments, in studying the results we see that catchers do have some amount of control, it’s a matter of how much. The micro analysis you did, looking at inputs, is a search for why they have whatever degree of control. I would suggest this article shows that pop time of the catcher is not predictive of the catcher’s CS% (it’s something else), not that the catcher has no effect on CS%.

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      • BritDawg says:

        It’s hard to reconcile the limited role you have observed for catcher’s in your study with what Indians fans witnessed last season with Yan Gomes and Carlos Santana. They pitched an almost identical number of innings with markedly different success rates in throwing runners out.

        Gomes likely does have a better pop time than Santana, but more significantly he’s almost certainly delivering far more accurate throws.

        Is there not a possibility that some of the catchers with super-fast pop times are simply rushing their throws and missing caught stealing opportunities due to loss of accuracy? A catcher who sacrifices a fraction of pop time to, for example, set his feet properly (like Gomes appears to do) will have far more success than one who simply strives for the fastest pop time.

        That would explain why your study seems to suggest that very fast pop times don’t correlate throwing runners out – the catcher’s contribution hinges largely on the accuracy of the throw, and super-fast pop times may not be conducive to achieving that accuracy.

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        • BritDawg says:

          Typo sorry – ‘pitched an almost identical’ should have read ‘caught an almost identical’.

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  20. aweb says:

    Could the hit and run be messing up the numbers? An intentionally poor jump on a hit and run, often by a slower runner, will often mean the catcher will take a slower pop time (can’t interfere with the swing), but still have a great shot at the runner. I’ve always wanted a “CS – manager” category for these.

    Removing hit and runs might be tricky, but I can think of a few proxies, such as just looking at the top 50-100 players in terms of SB attempts *(rarely do managers h&r with top basestealers by my observation). Overall CS% will likely go down, but these are the “important” guys to look at, the ones that teams are actively trying to prevent from stealing.

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  21. Mr Punch says:

    I’m pretty sure teams are not far off in their understanding of how important a catcher’s arm is. There are, after all, quite a few starting catchers who don’t throw all that well. But I suspect that sabermetrically inclined fans may not be so realistic – simply because percent caught stealing was until recently the main available stat for catchers’ fielding. (That’s why recent work on pitch framing is so valuable.) Even advanced defensive metrics have not worked well for catchers and first basemen, whose main fielding role is catching thrown rather than batted balls.

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  22. Peter Jensen says:

    Max -Please forgive me if you have addressed this earlier. Did you remove all steal attempts on pitchouts or separated them in a special group for your research?

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  23. td32 says:

    A catcher with a “pop time” of 1.6 seconds sounds quite extraordinary. In fact, I’ve never heard of it. Can you post a link to the video?

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  24. bh192012 says:

    Does the handedness of the pitcher matter? If so, is it accounted for? I’d guess it’s easier to steal (get a bigger lead?) 2nd vs a right handed pitcher.

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  25. james wilson says:

    You should sample only right handed pitchers and battery times. Lefties who are not slide stepping have all the time in the world to go through their motion before they pass the balk point. The running game is entirely differently managed from the left side. No one gets caught stealing on a 3.8 from the right side without a piano on his back.

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