Insights: Sports Analytics

When there is a significant volume of data and associated risks to analyze, an actuary is typically an appropriate fit for the task at hand. One area of focus on the rise, relevant to those with an actuarial background, is sports analytics. While sports analytics isn’t new, it’s a booming field that’s becoming increasingly sophisticated, as reports show. For instance, the application of modeling advances in sports analytics is, I assert, very actuarial.

Early sports analytics

In the U.S., sports analytics began in baseball with the crunching of relevant metrics. As an example, in 1921, engineer Earnshaw Cook began dabbling in baseball statistics. His first statistical study of baseball set out to prove that baseball icon Ty Cobb was a better hitter than slugger Babe Ruth. You’ll have to check out the book summarizing Cook’s 1964 research, “Percentage Baseball,” for the results.

Actuary involvement

How is sports analytics relevant to actuaries? Actuaries specialize in analyzing, predicting and ameliorating risk. Risk in sports can be done by examining the odds of how a given offensive player is likely to perform against a given defensive player, for example. Another sports risk analysis could be forecasting the odds of one team performing better than another. There are many scenarios and variables to examine.

One of many market forces driving data demand is the current surge in sports betting, which is driving attention to sports analytics as bettors can use model outputs to inform betting decisions.¹

Since the federal ban on sports betting was lifted in 2018, steady state-by-state legalization has taken hold, with, according to reports, 38 of 50 states allowing sports betting in some form (via online mobile betting or physical retail sportsbooks). Since legalization, U.S. states have generated record-setting handle and tax revenue, reflecting strong market growth and adoption, which in turn presents increased opportunities for analytics, as reports show.

Increasingly, professional sports teams that haven’t yet hired internal analytics staff are hiring analytics professionals. An actuary could be an appropriate fit for some of these positions.

There is a robust job posting pipeline for analytics and data roles in sports on employment platforms, including positions with professional teams, leagues, media companies and analytics services. For example, job listing site Indeed had 1,600-plus “Professional Sports Analytics” jobs across analytics, strategy and business insights roles as of January 2026.

Further, as the U.S. Bureau of Labor Statistics reports, relevant data-oriented occupations are projected to grow much faster than average employment (34% growth from 2024 to 2034), with 82,500 new jobs expected.

Examples of sports analytics

Sports analytics is a growing industry worldwide. In this discussion, we focus on the North American market for sports analytics in professional sports. The most popular professional sports in this market include football, basketball, baseball and hockey.

Let’s delve into professional baseball. There are 30 Major League Baseball (MLB) teams that all compete against each other, each playing 162 games during the regular season. Statistics are calculated for every player on each of these 30 teams. Statistics are also compiled on each team. There are 121 official statistical categories in baseball, with 72 of them being “standard,” and the remaining 49 being considered “advanced.”

In addition to the standard and advanced stats, MLB tracks 32 statistics it calls “Statcast,” including Arm Strength (ARM), Catcher Framing and Launch Angle (LA). Figure 1 lists MLB’s standard statistical categories that may be of interest for illustrative purposes.

Figure 1. MLB standard statistics

Source: MLB glossary of statistics

When stats are calculated and predicted for each player across the 30 MLB teams, this yields a significant volume of analytics. These metrics are then collapsed to the MLB team level for further analysis.

Another important function for sports analysts is on the business side, e.g., predicting revenue generated by fans through a variety of sources, including ticket sales, advertising deals and much more.

In addition to professional teams hiring sports analytics specialists, the overarching professional sports organizations, such as the MLB, also employ analysts.

Some typical analytics-related jobs in sports include:

• Player performance analyst
• Business intelligence analyst
• Team strategy analyst
• Data scientist
• Data visualization analyst
• Business data analyst
• Sports market researcher

Skills related to sport analytics

A background in mathematics, working with data and numbers, is important. Employers may seek those with degrees in mathematics, statistics or computer science. It’s not mandatory in every case. If one has other degrees but has gained the analytical skills needed, that could also be sufficient to be hired by a sports organization. A passion for sports is also helpful. Knowledge of a given sport can be key.

Growth in the industry

Data shows that the compound annual growth rate in the North American sports analytics market is about 20%. Several factors are influencing this growth. As we previously mentioned, legalized sports betting is one factor. Another major factor is the significant volume of data, and application of various models to these data.

Predictive modeling generated by large language models (LLMs), is increasingly used by sports teams and organizations (as with any advanced modeling, governance, validation and bias management remain key considerations). And data from wearable devices is increasingly used in analytics. Putting all of this together, teams can apply these analytics to potentially improve team performance. In addition, a team may use analytics in an attempt to improve overall profitability.

In closing

The relevance of actuarial education and training is very applicable to sports analytics. Given the industry’s current and expected growth, combined with the natural fit between actuaries and sports analytics, I believe this is a field that may be worth exploring for actuaries interested in data-driven decision-making.

Statements of fact and opinions expressed herein are those of the individual authors and are not necessarily those of the Society of Actuaries or the respective authors’ employers.

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