How Sports Analytics Drives Decision-Making

Introduction

            Although sports analytics has existed for decades, it was not widely embraced until 2002, when Billy Beane pioneered its use during his time as the general manager of the Oakland Athletics. However, with the advent of AWS and other analytical tools, sports analytics has exploded with no signs of slowing down. It is used at every stage of the season for a multitude of reasons, and with unprecedented stat-tracking, sports analytics will become increasingly useful toward informing decisions. This post will discuss what sports analytics is, how often different sports use it, what tools can be used for it, and how it drives decision-making. This post will also explain how people who are not involved with sports organizations can view and use the results of sports analytics.

What Is Sports Analytics?

            Datacamp.com defines sports analytics as the study of athletic performance and business health to optimize the processes and success of a sports organization: https://www.datacamp.com/blog/sports-analytics-how-different-sports-use-data-analysis 

It is used to provide sports organizations competitive advantages by drawing conclusions from historical data. According to Datacamp.com, there are two components to sports analytics: on-field analytics, which looks at data pertaining to gameplay, and off-field analytics, which looks at the business side of sports operations. By using sports analytics, sports organizations can improve gameplay and profitability and figure out ways to reduce costs.

Why Is Sports Analytics Useful?

            Sports analytics is useful because it gives organizations competitive advantages. It is used in strategizing at different points in a game. Coaches use sports analytics to substitute players at different points in a game. Soccer teams in particular use sports analytics for goalie changes and substitutions. For example, in one game, Chelsea manager Thomas Tuchel substituted his goalkeeper for Kepa Arrizabalga because Arrizabalga was better late-game. As a result, Chelsea won the penalty shootout thanks in part to two saves by Arrizabalga. NBA teams have also embraced sports analytics to track players’ energy levels and to substitute players early-game and late-game. MLB teams extensively use their own analytics called sabermetrics (more on that later) to decide when and if pitching changes should occur and if a steal should be attempted. Arguably more importantly, sports analytics was the driving force for the proliferation of the 3-point shot, as the fear of the opportunity cost has largely subsided with increased knowledge of the benefits.

            Sports analytics is also useful in increasing revenue. This includes analyzing revenue from concession stands, ticket sales, merchandise, and fan engagement. The Houston Astros used sports analytics to figure out how to convert single-game ticket owners into season-ticket holders. It can be used to determine what brands of food and drinks are selling better than others, how to set ticket prices, and what merchandise to sell. Most importantly, jersey sales, which comprise a billion-dollar market on their own, can be aided by sports analytics since the data can predict what players’ jerseys will be the most popular. Online purchases make off-field analytics even easier because the data is stored once the purchase is made (provided no refunds are issued). In summary, using sports analytics drives decision-making by helping organizations promote player safety, decide what merchandise to sell (or not sell), and determine the optimal in-game decisions for any situations.

Sabermetrics

            Sabermetrics, which is a special term for analytics used in baseball, earns its own section because it wass the progenitor of sports analytics in general. The term derives from the acronym SABR, which stands for Society for American Baseball Research. Its use was pioneered in the 1980s by Bill James, but it did not become popular until 2002, when Oakland Athletics general manager Billy Beane used it to help the team scout talent. This story was the basis for Michael Lewis’ book Moneyball, which was adapted into a movie starring Brad Pitt as Billy Beane. Sabermetrics was instrumental in creating the on-base plus slugging (OPS) statistic, as the traditional method of calculating batting averages (dividing the number of hits by the total number of at-bats) was ineffective: it ignored the other ways to reach a base besides a hit and gave all hits equal weight. Slugging percentage improves upon this calculation by factoring in the number of bases advanced on a hit. To calculate the slugging percentage, the number of total bases on all hits is divided by the number of at-bats. OPS has since become a powerful method of predicting player performance and comparing players.

            An example of sabermetrics improving pitching predictions is WHIP (walks plus hits per innings pitched). WHIP was developed to predict a pitcher’s performance independent of his fielders’ ability and is calculated by dividing the sum of walks and hits by innings pitched. Although WHIP is not completely defense-independent, it is a good indicator of how many times a pitcher will allow a batter to reach a base. Voros McCracken developed defense-independent sabermetrics in 1999 and it corrected many errors in sabermetrics by using more accurate formulas. McCracken’s work gave rise to the development and usage of other defense-independent statistics such as ERA (earned run average), component ERA (which assigns a weight to each of the recorded components of a pitcher’s performance), peripheral ERA, which accounts for differences in field dimensions (baseball fields’ dimensions are not standardized), and BABIP (batting average on balls in play). BABIP is a measurement of how many pitches result in hits, excluding home runs. Using and improving sabermetrics helps evaluate player performance, comparing players, evaluating talents, and even pitching changes during games.

Conclusion

            Although sports analytics only became popular in 2002, its importance to sports organizations cannot be overstated. Sports analytics is crucial to every aspect of sports games, from the draft to championship decisions, as well as off-field decisions such as stadium concessions and merchandise. The burgeoning industry will only reinforce sports analytics’ importance as teams vie for the best talent and front offices, but the ever-expanding suite of tools will make the process easier and more accessible. The principles that govern sports analytics are universally applicable in decision-making and predictive analysis.