Leveraging Data to Win the Game in Sports Analytics
A Closer Look at the Expanding Role of Data Analytics in Sports.
The world of sports, like any industry, requires strategic, data-informed decision-making. However, unlike others, the sports industry is built on direct competition, where decisions have an immediate effect on outcomes, making data all the more indispensable.
As data collection access, and technology advance, the field of sports analytics is growing larger and more relevant every day. For sports enthusiasts with a knack for data science, the expanding industry offers a wide variety of career opportunities. Data professionals play an important role in almost every sports industry need. By modeling, analyzing, and managing data using advanced analytics, math and statistics skills, data experts may help teams select ideal players, develop training regimens, analyze historical data and game trends, create game strategies, forecast game outcomes through predictive models, and more.
Having graduated from Georgia Tech’s Online Master of Science in Analytics (OMS Analytics) program, a number of Georgia Tech alumni have found their way into sports analytics, elevating the game with cutting-edge skills and a vision for innovation. They recently shared their top advice for navigating this dynamic field.
What Is Sports Analytics?
Sports analytics is the practice of using sports data — everything from player tracking and biometrics to play-by-play stats and opponent tendencies — to answer strategic questions and make smarter decisions across an organization. Rather than relying solely on intuition or highlight reels, coaches and front offices or even marketing campaigns (and the teams behind them) turn raw information into valuable insights that can:
- Enhance player performance
- Reduce injuries
- Scout talent
- Optimize lineups
- Elevate fan engagement and experiences
At its core, sports analytics blends statistics, technology, and sport-specific knowledge to find patterns that humans might miss in real time. This helps instill teams with a competitive edge on and off the field.
Getting Started in Sports Analytics
Formal education can give you a strong analytical backbone for synthesizing statistical data. Programs like Georgia Tech’s Online Master of Science in Analytics blend statistics, computing, and business problem-solving — the same mix teams and front offices look for when hiring data analysts. Working through applied projects and using real datasets, students learn how to frame a sports question, select the right statistical models, and communicate insights to nontechnical stakeholders.
Beyond coursework, employers still want to see what you’ve actually done. That’s where experience (even informal experience) matters. Game-log analyses, player-tracking visualizations, video analysis, win-probability models for your favorite team, or a mini scouting tool you built in Python all show that you can take raw data and turn it into something a coach or manager could use. Collegiate student clubs and analytics competitions as well as volunteer work with local sports organizations are also great places to get real data and stakeholder feedback.
Finally, make professional development part of your routine. For example:
- Attend sports analytics conferences or virtual meetups.
- Follow team analysts on social media.
- Stay current on tools the industry actually uses (e.g., Python, R, SQL, version control, basic business intelligence dashboards).
Earning stackable certificates or microcredentials in data visualization, artificial intelligence and machine learning, or database management can signal to hiring managers that you’re serious about staying current. The goal is to show a pattern: You’re continually learning and plugged into the field, and you know how to apply analytics to real sports questions.
“In sports, the competition drives innovation in our analytics,” says Bryan Hayes, director of baseball development with the New York Mets and 2021 graduate.
“When I’m competing against another team’s sports analytics, the results happen on the field.”
To prepare for a career in sports analytics, it’s also beneficial to bolster your resume by taking a personal initiative to develop your skills. Think about what interests you, find a problem to solve, and develop your own proof of concept.
“The best resumes that we get aren’t just ‘I graduated with this,’” explains Hayes. “They’re ‘I graduated with this, and here’s what I applied to this massive dataset I found online,’ for example.”
Don’t worry about perfecting the next ground-breaking innovation. Start small with what you know and gradually explore new spaces, taking every failure as another step closer to expertise. Plenty of data is publicly available online, which Saiem Gilani, director of data science and engineering for the Houston Rockets and 2020 graduate, consolidated into Sports Dataverse. Even more data can be found by networking with various experts in the field. When you’ve completed your project, publish it to sites such as GitHub to receive feedback for further improvement.
“Treat yourself like an organization that never stops recruiting, never stops scouting, never stops networking with smart people,” Gilani advises.
Rather than waiting for someone else to give you an assignment, push yourself to grow. If you can demonstrate that you know how to leverage data effectively before you’re hired, you’ll stand out among other applicants.
Skills for Thriving in the Sports Analytics Industry
Data requires an astute mind to interpret and apply effectively. Raw data can be misleading, and it’s up to the data specialist to decipher it within the full context, weighing the numbers against other variables, known facts, and human intuition to uncover the most accurate finding.
“We all know that being comfortable with data means being comfortable with uncertainty,” says Cory Jez, another 2020 graduate who is now a professional sports executive and consultant.
“But organizationally, we need to be thinking about how we include that information in a holistic, well-integrated decision-making process.”
Most new, actionable insights are found at the intersection of a disagreement between big data and human intuition, but skilled analysis is vital to ensure that the new insight is worth basing a new strategy on.
For this, communication is essential. Every sports team or organization consists of many different people with varying perspectives and areas of expertise. To arrive at the best strategy, everyone needs to be able to gain a deeper understanding of the data and provide their perspectives. Context is important, and sports teams rely on skilled data experts to communicate it clearly to all their stakeholders for well-rounded, strategic collaboration.
While hard skills in data/statistical analysis and working knowledge of relevant technologies like Python, SQL, and JavaScript are invaluable to a career in sports analytics, the foundation of a strong skillset is a competitive desire to win. Just like the athletes on the field, the data experts informing decisions behind the scenes must be completely devoted to the team’s performance and common objectives.
Emerging Trends and Opportunities in Sports Analytics
Data is plentiful, and it is only becoming more so. Often, data for a single game must be split across several computers and takes significant time and storage to digest. Working with such large amounts of data is difficult, and as the quantity grows and techniques evolve, professionals will need to maintain and develop their skills in data management.
There is also an opportunity for teams to invest more in the improvement of their own players by integrating data more closely with player development. By adding data analytics to their academic expertise, teams can elevate their development strategy, specializing more accurately for each player, and pinpointing the most crucial needs and best practices for improvement.
Investing more data in player development provides an opportunity for elevating player acquisition, as well. With more data surrounding development, teams can better tailor their acquisitions to select the players best aligned with their team's specific needs and goals.
“If you can get acquiring and developing to work together, that’s the goal that we’re trying to solve,” says Hayes. “I think that is where sports analytics is going.”
Data Holds the Future
If you're interested in pursuing sports analytics, keep an open mind and a watchful eye for new opportunities with surrounding technologies. Amid the rise of sports analytics, the industry is acquiring new methods and processes, creating more opportunities for those who are willing to keep up.
“It's not just data science anymore,” says Jez. “There are also all the surrounding technologies – data engineering, front-end development, DevOps, and user experience – things that are not as commonly spoken about in the analytics space. And from observing the hiring trends in the space, data engineers and front-end developers are just as in demand as data scientists in this space.”
The power of data holds the future for sports. Find out more about Georgia Tech's Online Masters of Science in Analytics (OMS Analytics) program or click to view the full panel discussion on sports analytics.
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