Winning with data: professional sports analytics

Data science has a wide variety of uses, from prediction of future events, to analyzing patterns. Because of this, it has tremendous use in professional sports to help teams examine their own performance or study the behaviors of their opponents. Keep reading to learn more about the uses for data science in sports, what the state of data science in sports is, and to see what some job postings for these roles can look like. 

Impact of data science on team performance 

Data analytics has revolutionized athletic team performance in the 21st century (see the movie Moneyball for a great example). You don’t have to look far to see an example—just across the river, the Boston Celtics (the city’s professional basketball team) are showcasing the power of data analytics in honing their craft. 

They use data to answer a key question: Which types of scoring attempts—such as three-pointers or layups—are statistically more likely to succeed? They also look at shots with a lower success rate and find ways to push their opponents into taking those riskier shots. By analyzing data for actionable insights, they can adjust strategies during the game. 

While the example above highlights data analytics uses in game strategy, there are other uses off the court. Another professional basketball team, the Orlando Magic, utilized data analytics to assist them with preparing for the NBA Draft (an event in which teams select players from college or overseas). 

Director of Data Analytics, David Bencs, credits AI-fueled data analytics platforms with helping the Magic to gain a competitive advantage over competitors. By leveraging AI-driven analytics, the Magic can go beyond traditional scouting methods, uncovering patterns and insights that might otherwise be missed. 

In soccer (or football, as it’s known globally), data analytics is just as important—if not more so—than in the NBA. 75% of clubs in the Premier League (England’s top professional football league) employ in-house data analysis teams, with data indicating that top teams are employing more individuals in their analysis departments than their less successful counterparts. Those top teams are able to gain actionable insights that directly translate to strategy, mitigating the risk of a counter-attack, for instance. 

Data science roles in sports analytics  

Even with widespread use, it’s crucial that analytics teams are effectively utilized and supported. One such way of supporting the team is understanding the different types of data science roles. In the world of sports analytics, data engineers, data scientists, and data analysts are the three primary functions, each ensuring that different parts of the analysis pipeline are functioning.  

Scientists and engineers are vital for the backend, with engineers focused on gathering large datasets, while scientists create models that can lead to actionable insights.  Analysts are more outward facing, delivering insights to stakeholders in the club, such as coaches, managers, and team executives. 

What sports analytics jobs look like 

To see what this work looks like in practice, here are two real-world examples of sports analytics roles—one at the internship level and another as a full-time position. Note: Some of the text has been changed to protect the anonymity of the two positions.  

First, let’s look at some of the highlighted job responsibilities for an internship-level Data Engineer: 

1. Support and monitor ETL (Extract, Transform, Load) processes and data pipelines to inject new data sources into our systems. 

2. Examine data quality, identify new data sources, and create data hygiene reports. 

3. Assist in developing data models and optimizing data infrastructure for better performance. 

4. Support data analysts and ML specialists, and document pipeline and dashboard setups. 

5. Document data flows and maintain source control policies. 

This position’s description focuses on insuring that the data pipeline is functional, focusing on infrastructure, and achieving high quality data. 

Second is a professional staff posting for a Quantitative Analyst: 

1. This position engages with all parts of the modern front office, including player evaluation, game preparation, resource allocation, sports science, and player development. 

2. Applicants should have the quantitative skills to analyze complex problems and the technical ability to implement their ideas effectively. 

3. A good candidate will be able to harness data to draw insights and improve decision-making. 

4. Proficient with data management and analysis in statistical software (R, Python, SQL) 

5. We expect applicants to have a good foundation in statistical modeling. 

In comparison to the Data Engineer internship, the Quantitative Analyst works to provide actionable insights to the team across a variety of avenues (game preparation, player development, resource allocation, etc.). There is less of a focus on getting the data into a workable condition, although the posting still highlights the need for statistical modeling and software skills.  

Setting your application apart 

Data science has carved out a role in sports, but it’s not a gold rush. While teams are using analytics to improve strategy, scouting, and player development, the number of people trying to break into the field often outpaces the number of available jobs. 

For those looking to stand out, a strong technical foundation isn’t always enough—passion for the sport itself can be that key difference. Teams want analysts who understand the game, not just the numbers. Whether it’s engineering pipelines, building statistical models, or presenting findings to coaches and executives, the work is there. But the best candidates are the ones who can connect data to what actually happens on the field. 

Interested in discussing what this could look like, or strategies for getting into the sports analytics field? Schedule an appointment with CAPD to receive one-on-one support with exploring career options or pursuing a path in sports analytics.