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Sports Analytics Jobs (NOW HIRING)

Rapsodo

General Application (US)

Saint Louis, MO · On-site

Rapsodo Inc. is a sports analytics company that uses computer vision and machine learning to help all athletes maximize their performance. Our proprietary technology applications range from helping ...

Rapsodo

General Application (US)

Saint Louis, MO · On-site

Rapsodo Inc. is a sports analytics company that uses computer vision and machine learning to help all athletes maximize their performance. Our proprietary technology applications range from helping ...

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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 Aug 12, 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.

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 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.

How do I get into sports analytics?

To pursue a career in sports analytics, develop strong skills in statistics, data analysis, and programming languages like 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 athletic performance, game data, and team strategies using statistical tools and software. Common roles include sports data analyst, performance analyst, and data scientist, often requiring skills in programming, statistics, and knowledge of sports. These positions are typically found with professional teams, sports organizations, or media outlets and may require a bachelor's degree in a related field.

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.

What degree is needed for sports analytics?

A bachelor's degree in fields such as sports management, statistics, data science, computer science, or a related discipline is typically required for sports analytics roles. Advanced positions may prefer or require a master's degree or higher, along with skills in programming, data analysis, and familiarity with analytics tools like R or Python.

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 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.

More about Sports Analytics jobs
What cities are hiring for Sports Analytics jobs? Cities with the most Sports Analytics job openings:
What are the most commonly searched types of Sports Analytics jobs? The most popular types of Sports Analytics jobs are:
What states have the most Sports Analytics jobs? States with the most job openings for Sports Analytics jobs include:
What job categories do people searching Sports Analytics jobs look for? The top searched job categories for Sports Analytics jobs are:
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, 92% Full Time, 3% Part Time, and 4% Contract. Highlights an 78% Physical, 7% Hybrid, and 15% Remote job distribution, with an average salary of $125,326 per year, or $60.3 per hour.

Research Engineer (AI + Sports)

YinzCam Inc.

Pittsburgh, PA • On-site

Full-time

Posted 17 days ago

Job description

Description
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. This is a rare opportunity to conduct publishable research while building products that reach millions of fans in real time.
You'll work at the cutting edge of computer vision and machine learning applied to sports, collaborating with leading academic researchers at Carnegie Mellon University while taking your innovations from prototype to production. This role demands both research rigor and product sensibility. We value publication records and engineering excellence equally. This is a full-time, onsite position based in Pittsburgh, PA.
You will be at the forefront of establishing a new, in-house AI Research Lab within YinzCam, and working with multiple sports teams, leagues, and venues to apply AI to the fan experience and to business operations.
CORE RESPONSIBILITIES.
Video Analysis & Computer Vision
  • Design and develop AI systems for real-time video understanding of live sporting events (player detection, action recognition, spatial analysis, etc.)
  • Build robust computer vision pipelines that handle challenging real-world footage (lighting, occlusion, multiple camera angles)
  • Explore novel architectures and techniques in modern CV to solve sports-specific problems

Large-Scale Game Analytics
  • Develop AI systems to extract, aggregate, and interpret game data at scale across multiple sports, teams, and seasons
  • Create spatial and temporal analytics frameworks that surface actionable insights from video and sensor data
  • Build analytics platforms that scale from single games to league-wide deployments

AI-Powered Fan Experiences
  • Translate video understanding and analytics into engaging, intuitive experiences for millions of fans
  • Collaborate on product features that leverage AI (real-time highlights, personalized stats, interactive visualizations, etc.)
  • Ensure research outputs move through the full product development lifecycle

CORE GOALS.
  1. Publish Your Work: We intend to publish the work coming out of these research projects. Papers will be published in top-tier CV/ML venues and presented at conferences.
  2. Bridge Academia & Industry: Work directly with Prof. Priya Narasimhan (Carnegie Mellon University) and her research team to translate academic innovations into applied systems. Mentor CMU students, collaborate on research projects, and shape the next generation of sports AI researchers.
  3. From Research to Product: Own the path from prototype to production. You'll participate in design reviews, handle real-world deployment challenges, and see your ideas impact actual fan experiences at scale.

CORE REQUIREMENTS.
  • PhD in Computer Vision, Machine Learning, Computer Science, or a closely related field
  • Strong publication track record in top-tier venues (CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, etc.)
  • Deep expertise in modern computer vision techniques: neural networks, object detection, semantic/instance segmentation, action recognition, optical flow, pose estimation, or related areas
  • Proficiency in ML frameworks (PyTorch, TensorFlow) and modern deep learning practices
  • Strong software engineering fundamentals: Python, Java, AWS, SQL, Redshift, version control, testing, CI/CD
  • Demonstrated ability to implement complex systems end-to-end
  • Background in sports analytics, sports tech, or applied computer vision (industry, research, or both)
  • Genuine enthusiasm for sports and AI
  • Genuine enthusiasm for going beyond book learning, and to have ideas go into large-scale production

HOW TO APPLY
Please submit:
  1. Your CV (with publication list) and research statement.
  2. A cover letter describing your research interests and why you're excited about this opportunity
  3. Links to your top 2-3 publications hat best represent your work

Apply