The Daily Coach - The NFL Draft and Shoshin
The NFL Draft and ShoshinThe success rate of drafting talent is about the same as when teams relied on the Sporting News during the 1950s.
The NFL Draft is a little more than one week away — and teams are spending millions and millions of dollars to make great picks that will transform their organizations. But they often fall well short. As with any decision-making, model bias plays a huge part in making poor choices. Additionally, overconfidence in our abilities based on past experiences also leads to mistakes. The success rate of drafting talent is about the same as when teams relied on the Sporting News during the 1950s to randomly select players. Why hasn’t the forecasting of talent improved with the advancement of technology? Seventeen years ago, economists Richard Thaler and Cade Massey set out to study the history of the draft. They analyzed where different players were chosen and then compared the order to the players’ later performance. Thaler and Massey discovered that despite the time and money that football teams devoted to the draft, they weren’t very effective at predicting who would be the best. They concluded that there is only a 52 percent chance that a player picked first is better than a player picked at the same position later. In essence, NFL teams would be as effective with a coin toss as they are pouring millions of dollars into scouting. Perhaps one of the reasons for the lack of advancement is a Zen Buddhism concept called Shoshin. Shoshin is a belief that:
NFL teams believe they’re experts. They continue with the same processes they always have, never examining different possibilities or expressing fear they might be wrong. They won’t go against conventional wisdom, despite the fact conventional wisdom has proven to not be effective. Teams never adopt a beginner’s mind or apply Shoshin to their outdated unsuccessful methods. They fail in large part to do the following three things:
Teams have worked hard to improve their decision-making processes, but maybe it’s time to look for more possibilities in the data collection process. This might be what really helps drive better decisions.
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