A Progress Report On Data And Analytics In Professional Sports

The late New York writer Roger Angell dubbed baseball “The Summer Game,” a sport shaped by illustrious figures of the past including Honus Wagner, Ty Cobb, Satchel Paige, Dizzy Dean, and Josh Gibson. As we head into the 2022 MLB playoff season in just a few weeks, this is a fitting moment to reflect on how that American past that began after the Civil War — the first professional baseball team, the Cincinnati Red Stockings, was founded in the year 1869 – has changed in many ways through the use of modern data and analysis, as will other professional sports teams. I wrote about similar transformations of 19th Century companies in other industries — Levi’s in retail and JP Morgan Chase in banking — through data and analytics.

Baseball’s rise in popularity accelerated in the decades of the 1920s and 1930s. The New York Yankees made their World Series debut in 1921 and had appeared in eleven World Series by the late 1930s, led by stars like Babe Ruth, who set the single-season home run record in 1927. By the 1960s, other professional had Sports leagues began to compete with Major League Baseball for attention, particularly the NFL, NBA, and NHL in professional football, basketball, and hockey. The advent of new technologies and advances in computing power made it possible to collect data and metrics for in-depth statistical analysis of professional sports teams and athletes.

The dawn of a new data-driven era in professional sports was brought to widespread public attention with the publication of Moneyball: The Art of Winning an Unfair Game by Michael Lewis in 2003. money ball told the story of how the Oakland Athletics, under General Manager Billy Beane, used data and analysis to build a competitive baseball team on a tight budget. The book later became a 2011 film starring Brad Pitt as the data science-driven Billy Beane.

The premise of money ball was that the collective wisdom of baseball insiders, which included players, managers, coaches, scouts and the front office, was outdated and relics of a 19th-century view of the game. money ball argued that data and analysis could be used to develop modern metrics, allowing companies to set up a team to compete against well-funded, big market teams like the New York Yankees and Los Angeles Dodgers.

Zack Scott is a passionate advocate for the use of data and analytics in professional sports. Scott spent 18 seasons with the Boston Red Sox as a consultant and then in leadership positions, including vice president of baseball research and development and executive vice president and assistant general manager. Scott directly contributed to the Boston Red Sox winning 4 World Series Championships during his tenure. Scott also served one season as senior vice president and acting general manager for the New York Mets.

Today, Scott is focused on helping organizations across all professional sports leagues develop and implement data and technology strategies to improve on-field performance. Through his company, Four Rings Sports Solutions, Scott works with sports owners and executives to build sports businesses that drive innovation and sustain success. Scott’s professional focus has been on the baseball operations side, which includes player acquisition, player development, and in-game strategy, as opposed to business operations, which focuses on ticketing, attendance, and fan retention, among other things. He now brings that experience to other professional sports leagues, including working with the NHL.

Scott comments, “The growth in baseball’s application of data and analytics over the past 20 years has been extraordinary, both in terms of investment levels and the use of quantitative metrics. We’ve grown from 10,000 data points to 10 billion data points in that time.” He points out that the average number of dedicated data analysts and software engineers for leadership teams has grown to over 18 full-time equivalents, with the Tampa Bay Rays being an example of a team which is at the top with 39 dedicated professionals. Scott also sees it as a role model for other professional sports leagues.

Professional sports teams create predictive models using the latest biomechanical data, supported by camera footage. Some of the data most commonly collected and used by professional sports teams today includes tracking data captured through video, GPS data, wearable devices, biomechanical devices, and motion capture, used to measure variables such as club speed and pitch speed in of the MLB are used. Integrating data and analytics into sports operations has allowed coaches and scouts to focus on higher-level activities like defensive positioning.

Scott believes MLB has been ahead of other professional sports leagues for a number of reasons. Scott describes baseball as “a precision sport rather than a contact sport” and the largest professional sports league in terms of affiliate operations with its minor-league farm teams. However, other leagues are trying to catch up. I wrote last year about the National Football League’s (NFL) effort to hire a chief data officer for the first time in a major professional sports league.

The key to integrating data and analytics into professional sports teams, Scott says, is the ability to measure outcomes that demonstrate the value of investments in data and analytics resources and solutions. A metric based on scores on the field, represented by wins. However, teams need to consider the “lag effect”, which means that results do not always appear overnight but are more often realized over time.

Scott notes that it’s not uncommon for it to take 1-3 years for a professional sports team to see the results of their investments in talent and talent development. What applies to professional sports also applies to companies in many industries. However, just as companies are held accountable for annual and quarterly results, professional sports teams are held accountable for their performance each season.

As shown in money ball, the use of data and analytics has encountered headwinds and resistance over the years, largely due to the cultural shifts involved. According to Scott, “The use of data and analytics has meant that previous practices have been compromised. We had to address concerns about a greater reliance on data and analytics. We’re dealing with people.” In the end, Scott notes that using data and analytics to improve performance means professional sports teams need to learn to think differently. So here’s to the crazies, the misfits and rebels who see things differently. What applies in business life also applies in professional sports.

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