Sports Analytics & AI FAQ

  Will the Sports Analytics & AI Course get me a job?

Absolutely — this course is designed to accelerate your career. SQL, Python, R, machine learning, and AI are among the most in-demand skills in sports today. When combined with mentorship from analytics pioneer Ari Kaplan, you immediately stand out to teams, leagues, and sports technology companies.

Many SMWW students get hired because of:

  • The technical skills they learn
  • The projects they build
  • The networking connections they make
  • The resume/reference support from Ari Kaplan

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While no course can promise a job, this experience gives you every possible advantage.

Sports organizations rely heavily on candidates who can combine programming skills with sports-specific analytics knowledge — which is exactly what this course provides.

 What jobs in sports use R, Python, and SQL?

Nearly every analytics role in sports requires these tools. They are foundational to how teams collect, structure, analyze, and interpret data.

Sports/Operations Roles:

  • Player Evaluation Analyst
  • Scouting Analyst
  • Performance & Sports Science Analyst
  • In-Game Strategy Analyst
  • Salary Cap & Roster Modeling Analyst

 

Business Roles:

  • Ticketing Analyst
  • Revenue Optimization Analyst
  • Marketing & Fan Data Analyst
  • Sponsorship Analytics Specialist

 

All major sports leagues hire analysts who use Python, R, and SQL because they are the core programming skills required for modern sports analytics roles.

  Will this course help me beyond sports?

Yes — these skills are directly transferable to career paths in:

  • Tech
  • Finance
  • Healthcare
  • Insurance
  • Government
  • Startups

SQL, Python, machine learning, and AI are high-demand skills in nearly every industry.

Learning these tools for sports prepares you for data roles across the global job market.

 What are the prerequisites to taking Sports Analytics & AI Course?

There are no prerequisites. You don’t need previous experience in programming, statistics, or math.

You’ll learn everything from the ground up, including:

  • SQL
  • R
  • Python
  • Data visualization
  • Machine learning
  • GenAI tools
  • Platforms like Databricks, DataRobot, and Orange.ai

 

This course is beginner-friendly, while still delivering advanced value for analysts looking to upskill.

This is one of the only analytics courses teaching industry-grade sports data skills with no prior experience required.

 What is Sports Analytics?

Sports analytics is the use of statistical analysis, predictive modeling, and AI to evaluate players, understand performance, optimize in-game decisions, and support front-office operations.

SMWW offers analytics courses in Football, Baseball, Basketball, Hockey, Soccer, and this flagship Sports Analytics & AI program.

Sports analytics blends data, statistics, and AI to help teams make smarter decisions and gain competitive advantages.

 Should I take the Sports Analytics & AI Course or one of the sport specific analytic courses?

They complement each other but focus on different needs.

  • Sport-specific courses explain how analytics applies to one sport — scouting, strategy, player evaluation, etc.
  • Sports Analytics & AI teaches the technical tools (Python, SQL, R, AI, ML) used across all sports.

If you want a long-term career in analytics, both are extremely valuable.

Sport-specific courses teach the game; this course teaches the coding and AI skills needed for a full-time sports analytics career.

 Will Sports Analytics & AI be applicable for my sport?

Yes — data science is now used in every sport, including:

  • Football
  • Baseball
  • Basketball
  • Hockey
  • Soccer
  • F1
  • Motorsports
  • Tennis
  • Cricket
  • Rugby
  • Pickleball

 

Ari Kaplan has worked with MLB organizations, McLaren F1, IndyCar, USTA, and international football clubs — so examples can be tailored to your sport.

Analytics and AI now influence performance, scouting, and strategy across every major sport.

 Will the Sports Analytics & AI Course help me as a coach?

Yes. Coaches use analytics daily to improve:

  • Scouting efficiency
  • Lineup decisions
  • Player matchups
  • Game planning
  • Opponent tendencies
  • Performance tracking

 

Coaches who use data make more informed decisions and gain a competitive advantage over teams that rely solely on intuition.

 What is the Sports Analytics & AI Course?

Sports Analytics & AI is the modern evolution of traditional analytics, combining:

  • Machine learning
  • Predictive modeling
  • Computer vision
  • Tracking data
  • GenAI
  • Automated insights

 

These tools power player evaluation, performance improvement, injury prevention, roster construction, and game strategy.

Sports Analytics & AI uses advanced machine learning and artificial intelligence to improve decision-making across sports organizations.

 What is the purpose of sports analytics?

The purpose of sports analytics is to help teams make better, faster, more accurate decisions through data.
Analytics enables teams to:

  • Identify undervalued players
  • Optimize tactics and strategies
  • Reduce inefficiencies
  • Understand opponents
  • Improve player development
  • Predict performance outcomes

 

Data-driven organizations consistently outperform teams that rely solely on instinct.

 What sports use analytics?

Every major sport now relies heavily on analytics.
 

United States:

NFL, NBA, MLB, NHL, MLS, WNBA, NWSL, NCAA

International:

Premier League, La Liga, Bundesliga, Ligue 1, Serie A, Liga MX

Some teams even employ 20–50 analysts within their analytics and data departments.

Analytics is a major competitive edge across every league worldwide.

 How do you become a sports data analyst?

Most analysts follow a path that includes:

  • Learning SQL, Python, and R
  • Building a sports analytics project portfolio
  • Understanding machine learning and AI workflows
  • Learning how to visualize and communicate insights
  • Networking within sports
  • Gaining mentorship from experts

This course is specifically designed to guide you along each of these steps.

Successful sports analysts blend coding ability with insight-driven storytelling — both taught in this program.

 Is a Sports Analyst a good career path?

Yes — sports analytics is one of the fastest-growing fields in sports. Analysts are needed in:

  • Player evaluation
  • Sports performance & science
  • Front-office scouting
  • Strategy & game planning
  • Business intelligence
  • Sports tech companies

 

Data roles are expanding across all levels of competition, and the skills also apply to high-paying industries outside sports.

Sports analytics offers strong career growth, competitive salaries, and global opportunities.

 What skills are needed to become a sports analyst?

Top skills include:

  • SQL
  • Python
  • R
  • Data visualization
  • Predictive modeling
  • Machine learning
  • Statistical analysis
  • Communication & presentation
  • Creativity in finding new insights

 

Sports analysts need strong programming, statistical, and communication skills to succeed in modern front offices.

 Still gave questions?

Call SMWW anytime:
US/Canada: 1-877-SMWW-Now
London: +44 (0) 871 288 4799
International: +1-503-445-7105