CanonFootball

The CIA, the NFL, and Using Data Driven Models to Make Good Predictions

via tequila hardness

via tequila hardness

The difficulties associated with intelligence analysis are often attributed to the inadequacy of available information. Thus the US Intelligence Community invests heavily in improved intelligence collection systems while managers of analysis lament the comparatively small sums devoted to enhancing analytical resources, improving analytical methods, or gaining better understanding of the cognitive processes involved in making analytical judgments. This chapter questions the often-implicit assumption that lack of information is the principal obstacle to accurate intelligence judgments.1
If you think about it, the CIA analyst2 and NFL GM are engaged in a similar task: analyzing new information about disparate variables (the athleticism and production of college players), then projecting how the addition of specific units of observation (the players) to a complex system (an NFL team) will affect relationships within a larger ecosystem (the team’s standing within the NFL). This is a flawed process. If you’re a fan of an NFL team, I’m sure you can identify many player evaluations and draft picks that went awry for you team.3 But can the process be improved?
  1. emphasis added  (back)
  2. Looking at you, Jack Ryan  (back)
  3. Millen Alert  (back)

Subscribe to the best value in fantasy sports

You're all out of free reads for now and subscribing is the only way to make sure you don't ever miss an article.

By James Todd | @spidr2ybanana | Archive

No Comment

Leave a reply