Analytics and experimentation are famous in sports. A perfect example of this is Moneyball, the popular book by Michael Lewis about Billy Beane, General Manager of the Oakland Athletics, who uses statistical analysis to assemble and optimize the ideal winning team.
Recently we have begun seeing this statistics-based approach in other sports, such as basketball. Brad Stevens, the new head coach for the Boston Celtics, has been known to be numbers-driven. In fact, when he was coaching for Butler University, he was credited as the first-ever college basketball coach to hire a basketball analyst and take a statistics-based approach. His winning record at Butler made him the most-successful third-year college basketball coach ever by making it to the Final Four championship game and losing by only two points to Duke.
Marketing experimentation is very similar to this Moneyball approach, except that instead of player statistics, we rely on digital analytics in the form of visits, bounce rate, conversion rates, heatmaps, and more. In fact, as digital marketers we’re lucky that we can test concurrent changes to our sites and quickly find results, as opposed to basketball, which needs to order tests sequentially.
Let’s explore the analytics-based approach to basketball that Brad Stevens is now building with the Celtics before we get to how this can help you.
On July 9, Brad Stevens hired Drew Cannon (who was also at Butler) as a Basketball Operations Analyst for the Celtics. At Butler, Drew developed numerous reports on player substitutions that maximized the team’s performance, and studied which team practice drills led to better game play. In addition, he developed hundreds of statistics and ways to look at game performance, on and off the court.
To truly understand the culture and performance mindset that Brad is building using Analytics, we have to look back at his time as a coach at Butler University. Before the 2014 NBA season even begins, Brad is sure to pour over previous game tapes, meet with individual players, and develop models on how to measure team performance. He will also likely look at all of his players’ shooting percentages -- not only in the open court, but also all around the parquet and at the three-point and free-throw lines. In addition, some of the key statistics he will likely look at include player efficiency, time on court, injuries, and how each of these metrics change depending on who is on the court. After pouring over all of these statistics, along with his coaching staff, he will put together an optimized lineup and continue testing on how to improve.
Experimentation and testing is largely the same – that is, as digital marketers and analysts, we come up with a hypothesis based on a number of inputs – including input from customers and web analytics -- and then test that theory.
So what can we learn from this approach to sports analytics as it applies to testing and experimentation?