The earliest uses of analytics in sports began in the 1950s and 1960s, although it was very much on the margins of the game. The modern era of sports analytics started in the early 2000s.

When the first proponents of using analytics to make decisions in professional sports found the courage to offer their ideas, they were not only ignored, but they were also the subject of derisive remarks like, "You are not a football person" or "You have never played the game." I won't compare it exactly to the first part of the movie, "Revenge of the Nerds", but it is not that far away from reality. Sports strategies were the domain of former athletes and "geeks with spreadsheets" were not looked upon kindly.

So what happened to change the status quo? Desperation happened. Billy Beane, of "Moneyball" fame, was the general manager of the Oakland A's baseball team in the early 2000s. Given the inordinate imbalance of spending resources between the big spending and small spending teams, Beane had to find an inefficiency in the player evaluation market because if he pursued the players the big spending teams also coveted, the Oakland A's would always get the bottom players in that barrel. He noticed that certain metrics, like on-base percentage, were undervalued by the market, while other metrics, such as batting average, were overvalued. Beane felt he had nothing to lose with this novel approach, now commonly referred to as Moneyball, and the ownership of the A's gave him the autonomy to pursue his theory.

From 2000 to 2003, the low-payroll A's made Major League Baseball's playoffs each year, an impressive feat even though they never made it past the division series round. The A's success started to wane, especially after a 2006 playoff appearance where the A's finally won a playoff series. What can explain the drop off?

The drop off occurred as other early adopter teams began using similar analytical methods in putting their teams together. The early adopters saw the success of the A's and were the first on the bandwagon seeking any competitive advantage they could gain. One of the most notable early adopters was Theo Epstein who expanded the use of analytics beyond player evaluation to other areas of strategy and decision making. The result: Epstein, as GM, won World Series titles with the Boston Red Sox in 2004 and 2007 (their previous World Series title was in 1918) and then moved to the Chicago Cubs, leading their efforts as they won the World Series in 2016 (their first since 1908!).

At this point, the success achieved through analytics was undeniable, and the use of analytics became mainstream. In the mid 2010s, every team had at least one analytics analyst on staff. Nowadays, every team has a full department of people dedicated to finding and applying analytics insights in all aspects of the team's operation, from player evaluation, to positioning players on the field, to other in-game strategy decisions, to training and nutrition regimes.

The same patterns of adoption are evident in professional football, basketball, and other sports. In fact, this lifecycle is not specific to professional sports. We see this pattern wherever new ideas fight for traction and acceptance against the conventional wisdom. For those who are familiar with Geoffrey Moore's book, Crossing the Chasm, the concept of an adoption curve is similar although the book is focused on product adoption and the development of markets for new products.

As change agents, it seems like the various stages we may face on the road to adoption are:

• Derision

• Desperation

• Early Success Leading to Early Adoption

• Mainstream Adoption after Undeniable Benefit Emerges

Going back to the example of baseball, the pendulum can swing too far in the new direction. Baseball became so focused on analytics and optimization that the new strategies made the game of baseball excruciating to watch. This led Major League Baseball to institute new rule changes in 2023 in an attempt to correct the over-optimization taking place.

Maybe we should add two additional stages on the road to adoption:

• Overcompensation

• Re-adjustment

The bottom line is that by understanding the lifecycle of idea adoption, change agents can look for the best opportunities to implement new ideas based on where we are on the adoption lifecycle.

Turning to the world of product development, where would you say we are in the agile adoption lifecycle? What is the next idea to move product development forward? For my take on where we need to focus on next, check out my recent blog article on the topic.