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Sports Analytics Jobs (NOW HIRING)

AEG

Research Engineer (AI + Sports)

Pittsburgh, PA · On-site

YinzCam is seeking exceptional Research Engineers to lead the development of AI-driven video analysis and game analytics systems that power next-generation fan experiences in professional sports.

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Sports Analytics information

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$64.5K

$125.3K

$179K

How much do sports analytics jobs pay per year?

As of Sep 2, 2026, the average yearly pay for sports analytics in the United States is $125,326.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,000.00 and $149,000.00 per year, depending on experience, location, and employer.

What is sports analytics?

Sports analytics is a field of applied statistics that uses past performance data to provide a competitive advantage to a team or individual player. By using data, an analyst can make suggestions on strategies for a given game, monitor the performance of a player, and provide quick analysis of potentially record-setting activities. Sports analytics focuses on two areas: on-field and off-field. The on-field analysis helps improve the tactics and fitness of players, while the off-field analysis focuses on the business and merchandising aspect of sports. Sports analytics is also used in sports gambling to help casinos and similar companies decide on which odds to offer to customers.

What is sports analytics?

Sports analytics refers to the use of data and statistical methods to analyze athletic performance, strategies, and business operations within the sports industry. Professionals in this field collect and interpret data to help teams make informed decisions about player recruitment, game tactics, injury prevention, and fan engagement. The insights gained from sports analytics can provide a competitive edge and drive improvements both on and off the field. With advances in technology, the scope of sports analytics has expanded to include machine learning, video analysis, and wearable devices.

What are the key skills and qualifications needed to thrive as a sports analyst, and why are they important?

To thrive as a Sports Analyst, you need strong statistical analysis skills, a solid understanding of sports rules and strategies, and often a degree in statistics, data science, or a related field. Familiarity with analytics software such as R, Python, SQL, and sports-specific databases is typically expected. Attention to detail, critical thinking, and effective communication help interpret complex data and present actionable insights to coaches and teams. These capabilities are vital for making data-driven decisions that improve team performance and competitive edge.

How does a sports analytics professional typically collaborate with coaches and athletes to influence game strategies?

Sports analytics professionals work closely with coaches and athletes by providing data-driven insights that inform tactical decisions and player development. They often translate complex statistical findings into actionable recommendations, such as identifying strengths and weaknesses or optimizing lineups. Effective collaboration requires strong communication skills and the ability to tailor analyses to the team's goals, ensuring that data supports real-time decision-making and long-term strategy. Regular meetings, presentations, and feedback sessions are common to ensure alignment between analytics staff and on-field personnel.

What is the difference between Sports Analytics vs Sports Data Analyst?

AspectSports AnalyticsSports Data Analyst
CredentialsDegree in statistics, data science, sports managementDegree in statistics, data science, sports management
Work EnvironmentResearch, modeling, strategic planning in sports organizationsData collection, analysis, reporting for teams and organizations
Industry UsageUsed for performance optimization, game strategy, player evaluationUsed for data reporting, insights, and performance tracking

Sports Analytics and Sports Data Analysts share similar educational backgrounds and work environments, focusing on data-driven decision making in sports. While Sports Analytics often involves developing models and strategic insights, Sports Data Analysts primarily focus on collecting and reporting data. Both roles are essential in sports organizations, but Sports Analytics tends to have a broader scope in strategic planning and predictive modeling.

How do I get into sports analytics?

To pursue a career in sports analytics, develop strong skills in statistics, data analysis, and programming languages such as Python or R. Gaining experience through internships, building a portfolio of projects, and understanding sports data sources can improve your prospects; a background in sports management or related fields is also beneficial.

What are careers in sports analytics?

Careers in sports analytics involve analyzing data to evaluate player performance, team strategies, and game outcomes. Professionals typically use statistical software, programming languages like Python or R, and possess strong knowledge of sports and data analysis techniques. Common roles include sports analyst, data scientist, and performance analyst, often requiring a background in statistics, computer science, or related fields.

Will sports analytics jobs be replaced by AI?

Sports analytics jobs involve analyzing data to inform team strategies and player performance, requiring skills in statistics, programming, and domain knowledge. While AI tools can automate data processing and generate insights, human expertise remains essential for interpreting results and making strategic decisions, so these roles are likely to evolve rather than be fully replaced.
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What states have the most Sports Analytics jobs?

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Sports Analytics jobs near you
Infographic showing various Sports Analytics job openings in the United States as of August 2026, with employment types broken down into 1% Internship, 94% Full Time, 3% Part Time, and 2% Contract. Highlights an 79% Physical, 6% Hybrid, and 15% Remote job distribution, with an average salary of $125,326 per year, or $60.3 per hour.

Quantitative Trading Internship - 2027

Dime Line Trading

Chicago, IL

Internship

Re-posted 10 days ago

Job description

 
The internship at Dime Line is a 10-week opportunity with our team of quants, data scientists and traders, available all seasons of the year.

Dime Line Trading develops algorithms to trade all major US sports. Dime Line was founded in 2020 by veterans of the financial trading and sports betting industries, who started their careers in the sports gambling space before entering the financial industry, where they individually built successful trading teams at leading firms.

WHAT YOU'LL DO:
You will have the opportunity to work with our team while receiving feedback and mentorship from our tight-knit, collaborative employees in various roles. This is an opportunity to get your feet wet within the trading industry while utilizing algorithmic, statistical, and engineering concepts applied toward the sports betting industry. There are a number of different types of opportunities within Dime Line spanning quantitative research, algorithmic trading models, live trading, and data science. Interns will have the opportunity to work within multiple fields across multiple sports. We retain a startup culture, which means that interns will be working on production projects alongside the rest of the team.

SKILLS YOU'LL NEED:
Ability to work in a fast-paced environment and handle multiple demands at once
Interest in building solutions, solving problems, and understanding how things work
Interest in sports, sports analytics / sabermetrics - please let us know about your interest or work in sports analytics!

Hands-on experience and a high level of proficiency in one or more of the following:
- Statistical modeling, especially predictive modeling in Python/R
- Python development on Linux platform
- Building algorithmic trading models in financial markets, prediction markets or similar
- Quantitative sports gambling or daily fantasy sports

It's great to see:
- Past internship or job experience in a trading, quantitative or engineering role
- Advanced coursework in statistics, optimization, operations research, and computer science

Apply