This article argues that while analytics have become a major influence in NFL decision-making, many teams and commentators are misusing the data. Using examples like the Minnesota Vikings’ failed fourth-down decision that analytics had supported, the author explains that models such as ESPN’s simulate outcomes using historical league-wide data. The problem is that these models often apply group-level data to individual situations without accounting for specific team strengths, weaknesses, or in-game factors like injuries and performance trends. In other words, analytics may predict what “an average NFL team” should do, but not what that particular team should do in that moment.
The author outlines three main flaws in how analytics are applied. First, NFL models rely too much on generalized situational data rather than the unique attributes of each team. Second, teams misunderstand how probabilities work over time, confusing average outcomes across many games with the likelihood of success in a single attempt. Third, inter-team statistics (league averages) are less useful than intra-team ones (data from the team itself). The author concludes that while analytics can enhance decision-making, they need to be used with context and a clear understanding of their limits, not as one-size-fits-all solutions.
