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Does Recruiting Work like Craps?

Let's say that you are a budding entrepreneur. The development's finished, but now the task is to actually market and sell the damn thing. How then would you go about identifying your potential customer base? On one level, there's the process of directly finding leads, and building a personal relationship with them through sustained efforts that may or may not pay off at some undetermined point in the future.

Then there's advertising, whether as a substitute or supplement to traditional business networking. Maybe you just want to reach the broadest possible audience. Maybe you have a specific group in mind and set any search parameters accordingly. Are you familiar with the concept of micro-targeting? The idea is to filter segments of the population via characteristics and behavior, so as to create a more personal (and presumably or based off proven correlations, more persuasive) message.  Republican political operative Karl Rove's use of the practice in the run-up to the 2004 election drew plenty of media attention.

In 2004 the Bush team identified which Web sites its potential voters visited and which cable channels they watched. It spent its money accordingly, advertising on specialty cable outlets such as the Golf Channel and ESPN, whose audiences tilt Republican. In this way, Rove could reach out to potential Republican voters who lived in otherwise heavily Democratic neighborhoods, and who would once have been missed in get-out-the-vote efforts based on neighborhood or party registration alone. When the campaign learned that the sitcom Will & Grace was wildly popular with younger Republican and swing voters, especially young women, it larded the series with its commercials—473 of them in all.

Ad buyers are increasingly favoring these sort of approaches, unquestionably aided by people's increasing willingness to disclose any number of personal details without compensation.

If you're wondering why the past few paragraphs are on a college football website, bear with me for a moment. Coaches use some of the methods briefly described above when recruiting. Inherent to the idea of having 85 players on scholarship at any one time is that a certain percentage of them will either hit or bust. That also happens when evaluating personnel in professional sports leagues, or probably in any field. There are reasons and causes with explanatory power, but my intuition is that on some level any search process is a crapshoot (or, probably closer to darts if you think about the skill involved). If that is correct, then I'm curious as to whether resources spent on scouting and evaluating talent could be allocated more efficiently.

A few years back Andy Staples from Sports Illustrated had an intern input every DI football recruit into Google Maps. If you look at the New Jersey '04-'05 map (for some reason the '06-'08 one isn't displaying for me correctly), it's very similar to a map of New Jersey population density. Unsurprisingly, New Jerseyans tend to bunch up near the Parkway and Turnpike. This goes right back then to the intuition from the preceding paragraph in a slightly different form: given X number of athletes, a certain percentage of them will be able to play high level college football. Sorry Teterboro, but the odds are simply better in Newark.

That's not to suggest that this is solely a question of population density, considering how demographics may play a role as well. Earlier last week I was wasting way too much time looking at the New York Times's new Census map applet, and started thinking about how the new data from the Census Bureau's annual American Community Survey (the official 2010 data should be out soon enough) could have implications for some enterprising staffs looking for a slight edge.

The idea being that you download, say, the newest unweighted population sample count and median income tables (or whatever criteria you want), and plug the numbers into a spreadsheet or database to find locales that meet your chosen conditions. That way a staff could identify the working class areas with the highest population density, and adjust resource allocation accordingly. A lot of the findings would probably be blatantly obvious, but maybe something interesting would pop up every once in a while. Would it be that much of a surprise if many coaches already used an implicit, less-precise variant of this suggestion?

This would probably be a waste of time for a traditional power that can largely pick and choose their recruits, but it could be potentially fruitful for other programs that don't have large natural talent bases (i.e., in the great plains), but otherwise any other recruiting inclinations would probably depend on the current makeup of their coaching staff. Hayden Fry recruited New Jersey because it was available and was a good fit for his assistants at Iowa. Most programs recruit at least one of the college football talent hotbeds of California, Texas, and Florida. All other factors being equal, given a distant geographic location where should attention be directed towards?

Not only could this method help give an answer in that example, but could also be useful for identifying sleepers (after controlling for socio-economic factors) in any area. Certain municipalities or regions could be under-producing, and not generating the amount of prospects that would normally be expected. Perhaps a local coach is at fault. Maybe there's something in the water. The model obviously cannot answer those questions, but I think this aspect of recruiting isn't really examined enough and hope that my clumsy paragraphs can serve as inspiration to spur some further discussion on the topic.