In 2009, the Atlanta Braves as a team stole 58 bases and were caught 26 times, for a total of 84 stolen-base attempts. By some comparison, the Tampa Bay Rays stole a league-leading 194 bases (against 61 times caught stealing), meaning that the Rays *successfully* stole more than twice as many bases as the Braves *attempted* to steal. Because the manager is (generally) the one who gives the signal to steal or not to steal, by extension, we can assume that Rays manager Joe Maddon is an aggressive manager who "likes to run," while eternal Braves manager Bobby Cox is a more conservative gent. Or can we?

In the next few weeks, I will seek to profile the mind of the manager. I’m not out to evaluate managers and to figure out how much they affect their teams (yet). Instead, I’d rather take a look *inside* the mind of the manager to see how he operates. The reason is simple. Baseball can be a game of brute force, but it’s at its best when it’s a game of move and counter-move. The manager is the driving force behind the strategy that a team employs. To know his leanings is to be better able to predict what’s coming next. To know what’s coming next is to have a strategic advantage. I propose that we can create an effective psychological profile of a major-league manager by looking closely at his behavior.

To my knowledge, there are very few manager metrics out there. Even the manager stats kept here at Baseball Prospectus focus mostly on the idea of abusing starters and having to deal with bad bullpens. But how does a manager think? Are there some managers who are more aggressive than others? Do some like to tinker more than others? Can we quantify these differences? I believe that the answer is yes.

Most readers of Baseball Prospectus can quickly pick out the inherent problem in evaluating a manager. He can only work with the talent he’s given. If a manager has a bunch of fast guys who are always on base, we might expect him to try for more stolen bases than a manager who has been given a bunch of slowpokes who are rarely on first base to begin with. If you had a roster filled with Lou Brock, Rickey Henderson, Davey Lopes, Vince Coleman, and Kenny Lofton, you’d probably call for a few stolen bases too, even if you were overall reluctant to push the "run" button.

How do you get past this problem? We need some basis for comparison. It’s hard to know what another manager would do in the same situation, but it is possible to generate a good guess.

**Warning: Gory methodological detail alert **

First, let’s isolate some situations that *might* call for a stolen base. I took all instances from 2003-2009 in which a team had a runner on first and in which second base was un-occupied. Yes, this does eliminate any thefts of third (and home), and any double steals, but the majority of stolen base attempts are from first to second. I coded all of these events as yes (1) or no (0) as to whether a stolen base attempt was made. Whether the runner was safe was irrelevant (for now).

Since I have a binary outcome, I used a binary logit regression to predict the odds that a situation would have a SB attempt made in it. For those unfamiliar with the technique, since the outcome is binary, rather than continuous, the statistical program attempts to fit an equation that predicts how the independent variables will affect the chances of the dependent variable being "yes" vs. "no." So, it might say that, given this set of circumstances, the model believes that there is a 10 percent chance of the manager sending the runner.

As predictors, I used the inning (SB attempts tend to happen less in the middle of a game) as a categorical variable, with everything in the ninth and beyond grouped together. I also input the score differential (up by two? Down by three? Tied?), with everything beyond six runs grouped together. I also included the number of outs. For technical reasons, I also set these as categorical variables.

For speed, I used my own home brewed speed scores (I detailed my methodology for calculating those here), entered continuously. If you ever plan to do your own research, don’t use my speed scores. They’re a pain to calculate. I only used them because they’re mine, and I happened to have them handy (and because they’re *slightly* better than the classic Bill James formula.)

I asked my trusty laptop to save the chances that the runner would go for each situation. The resulting model tells me, given this set of circumstances (game state, speed of the runner), what the *average* manager in this sample would have done. I can then compare what each manager actually did to what the league-average prediction would have been for him. And I did. I created a simple ratio of actual SB attempts to predicted SB attempts.

**The results **

So, in 2009, who really was the most aggressive manager when it comes to stealing bases? Ladies and gentlemen… Bob Geren? Geren sent 166 percent of what a league-average manager would have done, outpacing Ozzie Guillen, who was in second place. What’s interesting to note is that Guillen, whose White Sox stole 113 bases (against 49 CS), was rated as more aggressive than Joe Maddon (third place), despite calling for 95 fewer stolen bases than Maddon. Guillen had a slower team to work with, while Maddon had Carl Crawford and B.J. Upton. The model corrects for this bias and shows Guillen to be the aggressive manager that his reputation suggests he is.

On the other side of the coin, Jim Leyland was the most reluctant to try to steal, followed by Don Wakamatsu and Fredi Gonzalez, again relative to what the league would be likely to do in the situations those men faced. What about Bobby Cox? Actually, Cox rated on the *aggressive* side, sending 108 percent of what the league average model would have expected of him. Cox’s Braves were one of the slower teams in MLB in 2009.

Five Most Aggressive Managers Five Most Conservative ManagersBob Geren 166% of expectation Jim Leyland 70% of expectation Ozzie Guillen 143% Don Wakamatsu 74% Joe Maddon 136% Fredi Gonzalez 81% Mike Scioscia 134% A.J. Hinch 82% Clint Hurdle 126% Ken Macha 83%

I also looked at whether this ratio showed any year-to-year consistency. Do managers keep the same level of aggressiveness from year to year? To test this, I used one of my favorite techniques, the AR(1) intra-class correlation. It’s somewhat like the year-to-year correlation, but it enables the inclusion of more than just two time points. It can be read, however, like any old correlation. Over the seven years in the study, the ICC was a nifty .538. (Sounds like a website.) So, managers are moderately consistent over time in how aggressive they are in ordering the stolen base.

**Where to go from here**

This work is part one of several. In the next few weeks, I’ll be looking at various things that managers *actually* do, whether their players actually successfully carry out their orders. Eventually, I’ll attempt to distill it down to a few dimensions of behavior on which we can rate the managers. Stay tuned. This ought to be fun.

*Russell A. Carleton, the writer formerly known as 'Pizza Cutter,' is a contributor to Baseball Prospectus. He can be reached here.*

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Track the percentage of successful steals at the same time and you get a measure of how effective each manager was in managing baserunners. In fact, you'd end up with a great chart showing aggressive/passive on one axis and successful/unsuccessful on the other.

Thanks

Managers listed in order of aggressiveness, with percentage of model expectation.

B. Geren 1.66

O. Guillen 1.43

J. Maddon 1.36

M. Scioscia 1.34

C. Hurdle 1.26

T. Francona 1.21

C. Cooper 1.19

J. Riggleman 1.18

J. Tracy 1.15

E. Wedge 1.14

B. Melvin 1.12

J. Torre 1.10

B. Black 1.08

B. Cox 1.08

R. Washington 1.06

T. Hillman 1.04

D. Baker 1.04

J. Manuel 1.03

D. Tremblay 1.03

J. Russell 1.02

M. Acta 1.01

J. Girardi .99

T. LaRussa .98

L. Piniella .97

B. Bochy .94

C. Manuel .94

R. Gardenhire .89

C. Gaston .86

K. Macha .83

A.J. Hinch .82

F. Gonzalez .81

D. Wakamatsu .74

J. Leyland .70

I'd upload the chart, but that doesn't seem to be an option here..

Think of ICC like year-to-year. If I only had five observations per year, then I'd probably get a lot of random variation and so not a lot of consistency within managers over the years. My choice of inclusion cutoff was somewhat arbitrary, but based more on the realities of what we're observing. We look at managers based on the season-to-season level, so I evaluated them as such.

Do you mean managers with 5 SB opportunities or 5 managers per year? I am talking about the former, of course, when I am talking about sample size. The number of observations will NOT affect the correlations, only the standard error.

You always say, "Think of an ICC as like a y-t-y correlation." But, as I originally said, the magnitude of a y-t-y correlation specifically depends on the number of "opportunities" in each year and without knowing that number, it means nothing. If I regress OBP on OBP from one year to the next, and I only include players with 100 or less PA each year, I might get a correlation of .25. If I only include players with PA greater than 400, I might get .60. So just saying, "My y-t-y 'r' for OBP was .5" means nothing unless I know the number of PA per year in my sample. (It is also nice to know the number of players or "observations" as that will help me to figure my standard error around the correlation.)

So if I have bunch of players in a bunch of years, and you tell me the ICC for OBP, again, that means nothing to me unless I know the range or distribution of PA in the sample, right?

Maybe I have it wrong. Maybe the ICC is sort of a combination of "r," as when we do a y-t-y "r" and the underling sample size. For example, if you have a bunch of players with samples of 400 PA and you do an ICC for OBP and you have a bunch of players with samples of only 100 PA, will you come up with the same ICC?

In this particular case, there are two different questions that one can ask. One is, "How reliable is this stat year to year?" (which I chose to ask, .538) The other is "How many PA/BF/opps does it take before this stat becomes reliable?" I haven't run that one yet.

Steals are best leveraged when you have hitters coming up who are good at driving in runners from scoring position, but not so good at driving them in from first - high BA, low ISO.

I did an article for the Idol competition that showed how league rates of steal attempts varied inversely with the overall performance of the league's batters.

http://www.baseballprospectus.com/article.php?articleid=9091

Just another thing to complicate your model!

Second comment: I've had the impression that first year managers call for more steals on average than other managers. Is this true?

Third comment: Is there a tendency for some managers to steal more but only with their fastest players while other managers order my steal attempts throughout the line-up? I've thought that Ozzie Guillen steals a lot with just certain players for example but that Mike Scioscia will steal with guys throughout the line-up. Can you identify (or disprove) that tendency with your statistics?

Fourth comment: Is there a point of diminishing returns where ordering too many steals leads to lower SB% success? I would guess maybe, but it'd be an awfully weak relationship.

Excellent research, by the way.

I'd love to see Adjusted Green Light Rate (or whatever far more catchy name you wanted to give it) incorporated into BP's panoply of statistical reports as soon as it's invented. Having already put together the code to calculate the metric on your own computer, it shouldn't be so onerous to translate (or have a DBA translate?) it for use on the site, right?

To take another example, wouldn't it be great to see JAWS be a part of the site's statistical reports? It'd be fun to monitor a JAWS list to see exactly the date that Joe Mauer becomes a Hall of Famer. (JAWS is of course trivial to calculate on one's own, but who wants to check in every day to grab the most updated WARP scores and redo that calculation? That's what the statistical reports' update scripts are for, surely.)