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Good players make good coaches? Leading to a discussion of confidence-building in statistical analyses

Andrew Oswald sent me this paper by Amanda Goodall, Lawrence Kahn, and himself, called “Why Do Leaders Matter? The Role of Expert Knowledge.” Here’s the abstract:

Why do some leaders succeed while others fail? This question is important, but its complexity makes it hard to study systematically. We draw on a setting where there are well-defined objectives, small teams of workers, and exact measures of leaders characteristics and organizational performance. We show that a strong predictor of a leader’s success in year T is that person’s own level of attainment, in the underlying activity, in approximately year T-20. Our data come from 15,000 professional basketball games and reveal that former star players make the best coaches. This expert knowledge effect is large.

My first thought upon seeing this paper was: What about Isiah Thomas? But a glance through reveals that their data end at 2004, before Isiah took up his Knicks coaching job.

More seriously, Goodall et al.’s findings seem to contradict the conventional wisdom in baseball that the best managers are the mediocre or ok players such as Earl Weaver and Casey Stengel rather than the superstars such as Ted Williams and Ty Cobb. I’d be interested to hear what the authors think about this.

Scatterplot, please! It’s not just about an eye-catching result; it’s about building confidence in your findings

I won’t bother to give my comments on the tables and graphs (except to note that the figures are hard to read for many reasons, starting with the fact that these are bar graphs with lower bounds at 0.4 (?), 0.6 (??), etc.).

What I will say, though, is that I’d like to see a scatterplot, with a dot for each coach/team (four different colors for the four categories of coaches), plotting total winning percentage (on the y-axis) vs. winning percentage in the year or two before the coach joined the team (on the x-axis). This is the usual before-after graph, which can then be embellished with 4 regression lines in the colors corresponding to the four groups of coaches.

When reading such an analysis, I really, really want to see the main patterns in the data. Otherwise I really have to take the results on trust. This is related to my larger point about confidence building.

5 Comments

  1. ZBicyclist says:

    So were university presidents great scholars T-20 years ago? ;)

  2. Tom says:

    well since coaches are not randomly assigned to teams it seems fairly difficult to isolate a coach effect from selection on unobservables.

  3. Amanda Goodall says:

    Our basket ball study was motivated by previous work looking at university presidents – and yes, the data suggest that better scholars make better presidents of research universities. This work is forthcoming in a Princeton book and is on my website if anyone is interested.

  4. Steve Sailer says:

    One issue is that ex-superstar players generally don't work their way up the coaching ladder by starting out as an assistant coach at South Dakota State or wherever. They either jump in right at the big league level (often at the team of their choice) or play golf and do a little TV.

    Perhaps they avoid coaching lousy teams because they can afford to stay out of bad situations?

  5. Steve Sailer says:

    One issue is that ex-superstar players generally don’t work their way up the coaching ladder by starting out as an assistant coach at South Dakota State or wherever. They either jump in right at the big league level (often at the team of their choice) or play golf and do a little TV.

    Perhaps they avoid coaching lousy teams because they can afford to stay out of bad situations?

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