2027 Sports Analytics Careers: Skills, Education, Salary & Job Outlook
Co-Founder and Chief Data Scientist
Choosing a sports analytics career means deciding whether you want to combine sports knowledge with data, statistics, programming, and communication. The work is not just about loving sports or building models; it is about helping coaches, athletes, executives, media teams, and business leaders make better decisions from evidence.
The field can be competitive because many candidates bring either sports passion or technical skill, but fewer can translate complex analysis into practical recommendations under pressure. That combination is what makes sports analytics valuable. The career path often includes formal education, portfolio projects, internships, and steady skill-building in tools such as SQL, Python, R, visualization platforms, and statistical modeling.
This guide explains what Sports Analytics Specialists do, the skills and credentials that matter, how earnings and job outlook can vary, and what trade-offs to expect in the work environment. It also helps you compare advancement options and related careers so you can decide whether sports analytics is the right long-term direction for you.
Available labor-market signals point to meaningful opportunity, including projected job growth of 13% over the coming decade and median salaries around $61,000. Salary estimates can differ by source, job title, employer type, and whether the role is closer to team performance, business intelligence, media analytics, or data science. Use the figures in this guide as benchmarks, not guarantees.
Key Things You Should Know About Sports Analytics Career
- The typical earning potential for a sports analytics specialist in the United States is around $125,326 annually, with most salaries ranging from $100,000 to $149,000 and top performers earning up to $165,000.
- This field is growing strongly, with employment in entertainment and sports occupations expected to increase by 6% through 2033, faster than the average for all occupations.
- A bachelor's degree in areas like statistics, mathematics, computer science, sports management, or business analytics is usually needed to start a career in sports analytics.
- Success depends on skills in data analysis, statistical analysis, data visualization, and programming languages such as R, Python, and SQL, along with strong communication, interpersonal abilities, stamina, and a strategic understanding of sports.
- Career growth often moves from entry-level analyst to senior analyst, then lead analyst or director of analytics, with possibilities to reach executive roles like vice president of analytics in sports organizations or media companies.
What do Sports Analytics Specialists do?
Sports Analytics Specialists collect, clean, analyze, and explain sports-related data so organizations can make better decisions. Their work may influence player evaluation, injury prevention, game strategy, training plans, ticketing, fan engagement, media production, or betting and fantasy sports products, depending on the employer.
At the team level, the role often supports coaches, scouts, strength staff, medical teams, and front-office leaders. In media, technology, or business settings, the same skills may be used to build audience insights, automate reporting, create predictive tools, or explain performance trends to viewers and customers.
A day in the life of Sports Analytics Specialists
A typical day blends technical work with communication. A specialist might start by checking data feeds from games, tracking systems, wearable devices, or public databases. They may then clean errors, run models, update dashboards, and prepare a short briefing for coaches or decision-makers.
The pace changes with the sports calendar. Game days and trade deadlines can require fast analysis and late hours. Off-seasons may involve deeper research, model improvement, database maintenance, and long-term planning. The most effective specialists do not simply deliver spreadsheets; they turn findings into recommendations that non-technical stakeholders can act on quickly.
Common types of sports analytics work
| Area | Typical focus | Who uses the analysis |
| Performance analytics | Player efficiency, tactics, training load, matchups, development trends | Coaches, athletes, performance staff |
| Scouting and recruitment analytics | Player valuation, draft models, roster construction, opponent comparison | Scouts, general managers, front offices |
| Health and injury analytics | Risk indicators, workload monitoring, return-to-play patterns | Medical teams, strength staff, coaches |
| Business analytics | Ticket sales, sponsorship performance, fan behavior, pricing, marketing | Executives, revenue teams, marketing departments |
| Media and product analytics | Broadcast insights, content performance, fantasy tools, betting models | Media companies, tech firms, product teams |
What are the key responsibilities of Sports Analytics Specialists?
The core responsibility of a Sports Analytics Specialist is to convert raw data into decisions that improve performance, operations, or strategy. The job requires accuracy, speed, context, and the ability to explain why a finding matters.
- Collect and organize data from live games, historical records, video systems, wearable technologies, scouting reports, and business platforms.
- Clean, validate, and standardize datasets so coaches, analysts, and executives are not making decisions from incomplete or misleading information.
- Analyze player, team, opponent, fan, or revenue data using statistical methods, machine learning, and domain-specific performance metrics.
- Build and maintain databases, dashboards, reports, and predictive models for outcomes such as player performance, injury risk, lineup decisions, or game results.
- Create visualizations and written summaries that make complex findings clear to non-technical users.
- Collaborate with coaches, scouts, medical staff, front-office leaders, media teams, or business departments to align analysis with real organizational goals.
- Track whether recommendations are working and refine models as new data becomes available.
The most challenging vs. the most rewarding tasks
The hardest part of the job is often not the math. It is earning trust. Coaches, scouts, players, and executives may be skeptical when a model contradicts experience or intuition. A strong analyst must explain assumptions, limitations, and confidence levels without overstating what the data can prove.
The most rewarding part is seeing analysis change a real decision. That might mean identifying an undervalued player, adjusting training loads to reduce risk, improving a matchup strategy, or helping a business team understand fan behavior more clearly. The impact is visible, especially when recommendations contribute to improved performance or better organizational planning.
Common mistakes to avoid
- Focusing on model complexity over usefulness: A simple, trusted insight is often more valuable than an advanced model no one uses.
- Ignoring sport context: Data must be interpreted alongside rules, roles, tactics, injuries, schedules, and coaching philosophy.
- Overpromising certainty: Sports outcomes are noisy. Good analysts communicate probability, not false guarantees.
- Building reports without a decision in mind: Every dashboard or model should answer a practical question.
For students who need a faster route into an undergraduate program, an online accelerated bachelor's can help build the academic foundation for roles that require data, business, or sports management training.
What are the key skills for Sports Analytics Specialists?
Sports analytics requires a balanced skill set: technical ability to work with data, sports knowledge to ask the right questions, and communication skills to influence decisions. Employers generally look for evidence that you can solve real problems, not just complete coursework.
Core hard skills
- Data analysis: Interpreting large volumes of player, team, game, fan, or business data to identify patterns and decision points.
- SQL: Querying databases, joining datasets, filtering records, and preparing clean data for analysis.
- Data visualization: Building dashboards, charts, and reports that make findings easy to understand and act on.
- Programming in Python or R: Automating data workflows, running statistical models, building predictive tools, and integrating multiple data sources.
- Statistics: Understanding distributions, regression, probability, sampling, uncertainty, and model evaluation.
- Sports domain knowledge: Knowing the rules, tactics, performance indicators, and decision cycles of the sport or business area you support.
Essential soft skills
- Critical thinking
- Complex problem solving
- Active listening
- Judgment and decision making
- Clear written and verbal communication
- Collaboration with technical and non-technical teams
- Comfort receiving feedback from coaches, executives, athletes, or clients
The one overlooked skill that separates the good from the great
Business acumen is often the difference between an analyst who produces interesting findings and one who changes decisions. In sports organizations, recommendations must fit budgets, roster constraints, coaching philosophy, player development timelines, media goals, sponsorship priorities, or fan engagement strategies.
For example, an analyst may identify a promising player trend. A stronger analyst can also explain whether that trend matters enough to affect roster construction, salary allocation, player development resources, or marketing value. That broader view helps prevent decisions based only on isolated metrics.
How to prove your skills to employers
- Create a portfolio using public sports datasets and explain your process, not just your final charts.
- Show before-and-after examples: the question, data source, method, limitations, and recommendation.
- Use GitHub, a personal website, or professional networking platforms to make projects easy to review.
- Practice writing short executive summaries for non-technical readers.
- Demonstrate that you can work with messy, incomplete data rather than only polished classroom datasets.
Advanced study can be useful for candidates who want deeper research, modeling, or leadership roles. Those considering doctoral-level education may compare easiest PhD degree programs, but a PhD is not required for many sports analytics roles.
Breaking into sports analytics is easier to manage when you treat it as a sequence of skill-building and proof-building steps. Employers want to see that you can work with data, understand the sports context, communicate clearly, and operate in a competitive environment.
- Build foundational education: Start with coursework in statistics, data science, computer science, business, sports management, economics, or a related field. A sports-specific program can help, but a strong quantitative degree with sports projects can also be competitive.
- Develop analytical and technical skills: Learn SQL, Python or R, spreadsheet modeling, statistics, and visualization tools. Focus on repeatable workflows: data collection, cleaning, analysis, validation, and presentation.
- Build a sports analytics portfolio: Analyze public datasets, create dashboards, write scouting-style reports, or test predictive models. Make each project decision-focused rather than purely descriptive.
- Gain practical experience: Look for internships, student manager roles, volunteer analytics projects, research assistantships, athletic department work, media projects, or freelance analysis. Experience inside a sports setting helps you understand pace, communication, and stakeholder needs.
- Obtain professional credentials or certifications: Use certifications to validate specific tools or methods, especially if your degree is not directly related to analytics. Certifications work best when paired with a portfolio and practical experience.
- Network strategically: Connect with analysts, coaches, alumni, professors, and sports business professionals. Ask specific questions, request feedback on projects, and attend industry events when possible.
- Pursue specialization and advancement: Decide whether you are more interested in performance, scouting, health, business operations, media, technology, betting, or fan engagement. Specialization helps you target roles more effectively.
What to prioritize first
If you are early in the process, prioritize SQL, statistics, one programming language, and a small portfolio. If you already have technical skills, focus on sports context and communication. If you already work in sports, strengthen data skills and start documenting measurable projects.
The most successful candidates usually do not wait for permission to start. They build visible work, ask better questions, and show that they can translate analysis into decisions.
The most common educational route for a Sports Analytics Specialist is a Bachelor of Science (B.S.) degree in Sports Analytics, Business Administration with a Sports Analytics major, or Sports Management with a concentration in Sports Analytics. Related degrees in Data Science, Statistics, Computer Science, Economics, Mathematics, or Sports Science may also be acceptable when paired with relevant projects or experience.
Employers typically care less about the exact title of the degree and more about whether you can handle data, understand the sports environment, and communicate usable insights. Internships, practicum placements, capstone projects, and applied research can be especially valuable because they show that you have worked on real or realistic problems.
Essential certifications often include the Certified Sports Analytics Professional (CSAP) from the International Institute for Analytics and the SAS Certified Specialist: Base Programming Using SAS 9.4 from the SAS Institute. Tool-specific credentials can help, but they should support—not replace—a strong project portfolio.
Most undergraduate programs require practical experience through internships or practicum placements with sports organizations, complemented by capstone projects or senior seminars. Formal residency or supervised hour requirements are rare; instead, hands-on learning is typically built into coursework and field experiences.
Are advanced degrees or niche certifications worth the investment?
Advanced degrees such as a Master of Science in Sports Analytics, Sports Management, or Data Science can help candidates pursue more technical, specialized, or leadership-focused roles. They may be especially useful for people targeting data science-heavy positions, research teams, consulting firms, professional franchises, or analytics management.
These programs generally require 1-2 years of full-time study and significant financial investment, so the decision should be tied to a clear career goal. If you can already build models, communicate insights, and gain experience through internships or entry-level roles, work experience may produce a stronger return than immediately pursuing graduate school.
| Credential path | Best for | Main trade-off |
| Bachelor's degree | Entry-level analytics, business, scouting support, or operations roles | May need internships and portfolio work to stand out |
| Master's degree | Advanced analytics, research, management, or specialized technical roles | Costs time and money before or during career progression |
| Certification | Proving tool-specific or method-specific competency | Rarely enough by itself without applied projects |
| Portfolio and experience | Demonstrating practical ability to solve sports problems | Requires self-direction and feedback from knowledgeable reviewers |
For a broader comparison of degree options with strong career returns, including pathways that may support sports analytics, review the most profitable bachelor's degrees.
The average salary for sports analytics stands at $125,326 annually, or about $60.25 per hour. This figure represents the median pay across the United States as of August 2025 and can serve as one benchmark for evaluating the financial potential of the field.
The sports analytics salary range from entry level to senior level is broad. Reported compensation starts near $100,000 for entry-level roles at the 25th percentile and rises to $165,000 for senior-level positions at the 90th percentile. Mid-career professionals commonly earn around $149,000.
These figures should be interpreted carefully. Sports analytics job titles vary widely. A role with a professional team, a media company, a betting technology firm, a university athletic department, or a business intelligence group inside a sports organization may have different salary structures, expectations, and advancement paths.
Factors that influence pay
- Employer type: Major leagues, high-profile teams, technology firms, and specialized analytics companies may pay differently than smaller organizations or college programs.
- Location: Salaries can reflect regional cost of living and the concentration of sports, media, and technology employers.
- Technical depth: Candidates with strong SQL, Python, R, machine learning, data engineering, and visualization skills may qualify for higher-paying roles.
- Sports domain expertise: Analysts who understand the sport, decision cycles, and stakeholder needs can be more valuable than candidates with only general data skills.
- Experience and responsibility: Senior analysts, managers, directors, and specialists who influence high-stakes decisions generally have stronger earning potential.
How to evaluate a salary offer
Look beyond the base salary. Consider expected hours, travel, game-day obligations, benefits, bonuses, contract stability, relocation costs, access to proprietary tools, mentorship, and whether the role builds skills that transfer to other industries. A lower-paying first role may still be valuable if it gives you credible experience and a strong network.
Employment in entertainment and sports occupations, including sports analytics specialist roles, is projected to grow 6% through 2033, which is much faster than the average for all other occupations. The outlook is supported by the expanding use of data in athlete performance, team strategy, fan engagement, media, sports technology, and business operations.
Demand is not limited to professional teams. Colleges, youth development programs, media companies, betting and fantasy platforms, sports technology vendors, health and performance organizations, and women’s leagues are also using analytics more actively. This broadening market can create more entry points, though competition for team-based roles remains strong.
The key factors shaping the future outlook
- AI and machine learning: Organizations are using more advanced modeling to support scouting, performance prediction, fan engagement, and operational planning.
- Wearable sensors and tracking systems: More data is available on movement, workload, biomechanics, and training response.
- Real-time analytics: Teams and media companies increasingly need fast insights during games, broadcasts, and live events.
- Expansion into new markets: eSports, women's leagues, college sports, and youth development programs are creating additional analytics use cases.
- Business-side adoption: Ticketing, sponsorship, pricing, marketing, and fan experience teams rely on analytics to guide revenue decisions.
How to stay competitive
To remain relevant, build skills that transfer across employers: database work, statistical reasoning, programming, visualization, communication, and project management. Avoid becoming dependent on one proprietary tool or one narrow metric. The strongest candidates can explain methods, adapt to new data sources, and connect findings to decisions.
Students comparing flexible education options may want to apply free to accredited online colleges as they look for programs that support analytics, statistics, business, or sports management preparation.
Sports Analytics Specialists usually work in fast-moving, deadline-driven environments. Many are based in team facilities, athletic departments, front offices, media headquarters, consulting firms, or technology companies. Some tasks can be completed remotely, but fully remote roles are less common when the job requires close collaboration with coaches, scouts, athletes, executives, or live-event staff.
A common question is whether sports analysts work weekends. Often, yes. Schedules follow the sports calendar, so evenings, weekends, holidays, travel periods, and game days may be part of the job. Typical weekly hours range from 50 to 66, especially during active seasons, tournament periods, drafts, trade windows, or major events.
What the workplace is like
- Team and league environments: High pressure, close contact with decision-makers, and direct connection to performance outcomes.
- College athletic departments: Broader responsibilities, possible work across multiple sports, and close integration with coaches and student-athletes.
- Media and broadcasting: Fast turnaround, audience-friendly storytelling, and emphasis on visual explanations.
- Sports technology firms: Product development, model building, client support, and scalable analytics tools.
- Business operations: Revenue analytics, fan behavior, marketing performance, sponsorship, and pricing strategy.
Is the lifestyle a good fit?
This career can be exciting if you enjoy live sports, rapid problem-solving, and collaboration with competitive people. It can be difficult if you need predictable hours, low-pressure deadlines, or a clear separation between work and game schedules. Before committing, talk to professionals in the specific setting you want to enter because lifestyle can vary significantly by employer and sport.
A Sports Analytics Specialist career can be highly rewarding for people who enjoy data, competition, and practical decision-making. It also comes with pressure, irregular hours, and a competitive job market. The right fit depends on whether the rewards outweigh the demands for your goals and temperament.
Pros
- Direct impact: Your work can influence strategy, player development, roster decisions, health planning, revenue growth, or fan engagement.
- Intellectually engaging work: The problems are complex, data-rich, and rarely solved by one simple metric.
- Connection to sports: The role allows data professionals to work in an industry they may already follow closely.
- Growing use of analytics: More organizations are applying data to performance, business, media, and technology decisions.
- Transferable skills: SQL, statistics, programming, visualization, and business intelligence can apply beyond sports.
Cons
- Competitive entry: Many candidates want to work in sports, so portfolios, internships, and networking matter.
- Irregular hours: Game schedules can require nights, weekends, holidays, and long weeks.
- High-pressure deadlines: Analysis may be needed quickly before games, drafts, trades, broadcasts, or executive meetings.
- Messy data: Sports data can be incomplete, inconsistent, proprietary, or difficult to compare across contexts.
- Stakeholder resistance: Not everyone will immediately trust data-driven recommendations, especially when they challenge tradition.
Who is most likely to thrive?
This career is a strong fit for people who can combine technical discipline with humility and communication. You need to be comfortable saying what the data suggests, what it does not prove, and how confident stakeholders should be. If you want a lower-pressure path that still builds marketable skills, comparing what are some certificate programs that pay well may help you explore related options.
Sports analytics offers advancement through two main routes: leadership and specialization. Some professionals move into management, where they lead teams and shape strategy. Others become deep experts in a niche such as player performance, scouting, injury risk, fan analytics, or sports technology.
Clear advancement paths
- Entry-Level/Junior Sports Analytics Specialist: Supports data collection, cleaning, reporting, and basic analysis. This stage is about learning workflows, building trust, and understanding stakeholder needs.
- Sports Analytics Specialist (Mid-Level/Analyst): Designs models, owns dashboards, presents insights, and works more directly with coaches, managers, executives, or clients.
- Senior Analyst/Lead/Manager/Director of Sports Analytics: Leads projects, mentors analysts, sets analytics priorities, manages high-impact initiatives, and may advance into executive roles such as Vice President of Analytics.
Specialization opportunities
- Performance Analytics: Focuses on player development, tactical evaluation, training load, efficiency, and injury prevention using advanced metrics and wearable tech.
- Scouting & Recruitment Analytics: Supports drafting, transfers, roster construction, player valuation, and long-term team-building strategy.
- Business & Fan Engagement Analytics: Uses customer, sales, and marketing data to improve revenue, loyalty, sponsorship performance, and fan experience.
- Sports Technology & Innovation: Develops AI-driven products, predictive tools, tracking systems, and scalable analytics platforms.
- Media & Broadcasting Analytics: Turns data into stories, graphics, rankings, predictions, and audience-facing insights.
- Injury Analysis: Works with workload, biomechanics, medical, and performance data to support athlete availability and return-to-play decisions.
- Betting & Fantasy Sports Analytics: Builds projections, pricing models, simulations, and user-facing tools for sports gaming markets.
How to move up faster
- Document measurable outcomes from your work, such as improved reporting speed, better model accuracy, or decisions influenced.
- Learn to present to senior stakeholders in concise, decision-ready language.
- Build cross-functional credibility with coaches, scouts, medical staff, business teams, or product teams.
- Choose a specialization that matches both your strengths and market demand.
- Mentor others and show that you can improve team processes, not just complete individual analysis tasks.
If you like parts of sports analytics but are unsure about the sports industry’s competitiveness, hours, or limited number of team roles, several related careers may offer similar work with different trade-offs. These options use overlapping skills in data analysis, performance evaluation, visualization, and decision support.
- Data Analyst (General Industry): Uses data analysis, statistics, dashboards, and reporting to support decisions in industries such as finance, retail, healthcare, technology, or government.
- Business Intelligence Analyst: Turns raw business data into dashboards, reports, and insights for leaders. This path can be a strong fit for candidates who enjoy visualization and stakeholder communication.
- Performance Analyst (Corporate/HR): Evaluates individual, team, and organizational performance metrics to improve productivity, training, and workforce planning.
- Scout (Sports): Assesses player potential using observation, film review, statistics, and communication with coaches or front-office staff.
- Healthcare Data Analyst (Sports Medicine): Applies analytics to health, rehabilitation, injury trends, and patient or athlete outcomes.
How to compare alternatives
| Career | Best fit if you enjoy | Key difference from sports analytics |
| Data Analyst | General problem-solving with data | More industries and openings, less direct connection to sports |
| Business Intelligence Analyst | Dashboards, reporting, and executive decision support | Often more business-focused than performance-focused |
| Performance Analyst | Measuring improvement and productivity | May focus on employees or organizations instead of athletes |
| Scout | Talent evaluation and player projection | Requires stronger observational and relationship skills, with less technical modeling in some roles |
| Healthcare Data Analyst | Health outcomes, risk indicators, and applied analytics | More clinical or healthcare-focused, with possible regulatory considerations |
The best choice depends on what you want most: proximity to sports, technical depth, income stability, work-life balance, or long-term advancement. If you are unsure, build a portfolio that can serve both sports and non-sports employers so you keep more options open.
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: "One of the most meaningful parts of my role is working alongside coaches and athletes to translate raw data into clear strategies that genuinely affect how games are played. I'm often involved in building predictive models that influence player rotations or defensive setups, and knowing that my insights contribute to real-time decisions is incredibly fulfilling. It's like my work extends beyond just numbers; it actively shapes outcomes on the field. Seeing a player develop because of those insights makes me feel part of their growth. That connection keeps me passionate about what I do.
—Darius"
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: "Breaking into sports analytics was tougher than I expected because the field is packed with people who are just as passionate about sports and data as I am. I spent countless hours developing my skills in SQL and Python, building a portfolio from scratch by analyzing public game datasets and sharing my findings online. Networking was essential—I reached out to professionals and accepted every critique, even when it was tough to hear. Making public mistakes in my early work was humbling, but it forced me to refine my approach and improve. That persistence paid off when I finally secured my current role as a Sports Analytics Specialist.
—Amina"
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: "There's a unique satisfaction when a coach uses my analysis to make a call that actually turns the tide of a game. I remember logging long nights debugging complex models to track player efficiency, and those moments where the numbers lead to a winning strategy make every challenge worth it. After years of perfecting my techniques and deeply engaging with the analytics community, landing a position managing data insights for a major league team was incredibly rewarding. Being recognized for this work and contributing directly to team success feels deeply personal. It's the best feeling in sports analytics.
—Nolan"
Key Findings
- Sports Analytics Specialists use data to support decisions in performance, scouting, health, business operations, media, technology, and fan engagement.
- The role requires both technical and communication skills. SQL, Python or R, statistics, visualization, sports knowledge, and stakeholder communication are especially important.
- A bachelor’s degree in sports analytics, data science, statistics, sports management, business, or a related field is a common starting point. Internships, practicum work, capstone projects, certifications, and portfolios can strengthen employability.
- Advanced degrees can be valuable for specialized research, leadership, or technical roles, but they should be weighed against cost, time, and the value of work experience.
- The average salary for sports analytics stands at $125,326 annually, or about $60.25 per hour, with reported figures varying by role, employer, location, and experience.
- Employment in entertainment and sports occupations, including sports analytics specialist roles, is projected to grow 6% through 2033.
- Sports analytics can involve evenings, weekends, holidays, and long weeks because work often follows live sports schedules. Typical weekly hours range from 50 to 66.
- The best candidates prove their value with practical projects, clear explanations, sport-specific context, and the ability to connect analysis to real decisions.
Other Things You Should Know About Sports Analytics
In 2027, sports analysts are in demand primarily in professional sports teams, media companies, and sports tech firms. These industries focus on using data to enhance athletic performance, fan engagement, and strategic decisions, creating diverse opportunities for analytics professionals.
Emerging technologies like machine learning and artificial intelligence are significantly shaping sports analytics careers in 2027. Professionals now leverage advanced algorithms for predictive modeling and performance analysis, offering competitive advantages in decision-making for teams and organizations.
- Certified Sports Data Analyst https://www.scienceforsport.com/certifications/certified-sports-data-analyst/
- Do Sports Analysts Have a Good Work-Life Balance in 2025? https://www.tealhq.com/work-life-balance/sports-analyst
- Winning with data: professional sports analytics https://capd.mit.edu/resources/winning-with-data-professional-sports-analytics/
- The Rise of Sports Analytics | GIS https://gis.sport/news/the-rise-of-sports-analytics/
- The Role of a Sports Analyst: Career Path, Skills & Job Trends https://fundaspring.com/blogs/careers/sports-analyst-career-guide
- Sports Analyst Job Description Template https://devsdata.com/sports-analyst-job-description-template/
- Sports Analytics & AI FAQ https://www.sportsmanagementworldwide.com/courses/data-science-analytics/faq
- Example Job Description for Sports Analytics Manager https://yardstick.team/job-description/sports-analytics-manager
- Sports Analyst https://www.usecanyon.com/job-titles/sports-analyst
