Sports Analytics & AI FAQ

Starting a Career in Sports Analytics

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. It blends data, statistics, and AI to help teams make smarter decisions.

What is the purpose of sports analytics?

The purpose of sports analytics is to help teams make better, faster, and more accurate decisions through data. Analytics helps teams identify undervalued players, optimize tactics, reduce inefficiencies, understand opponents, improve player development, and predict performance.

What sports use analytics?

Every major sport now relies on analytics, including the NFL, NBA, MLB, NHL, MLS, WNBA, NWSL, NCAA, the Premier League, La Liga, Bundesliga, Ligue 1, Serie A, and Liga MX. It is also used in motorsports, tennis, cricket, rugby, and pickleball, and some organizations employ large analytics departments. Examples in the course can be tailored to your sport.

How do you become a sports data analyst?

Most analysts learn SQL, Python, and R, build a sports analytics project portfolio, understand machine learning and AI workflows, learn to visualize and communicate insights, network within sports, and gain mentorship from experts. SMWW’s Sports Analytics & AI course is designed to guide you through each of these steps.

Is sports analyst a good career path?

Yes. Sports analytics is one of the fastest growing areas in sports, with analysts needed in player evaluation, sports performance and science, front office scouting, strategy and game planning, business intelligence, and sports technology companies. The skills also apply in industries outside sports.

What skills are needed to become a sports analyst?

Top skills include SQL, Python, R, data visualization, predictive modeling, machine learning, statistical analysis, communication and presentation, and creativity in finding new insights.

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

Nearly every analytics role in sports uses these tools. Sports and operations roles include player evaluation analyst, scouting analyst, performance and sports science analyst, in game strategy analyst, and salary cap and roster modeling analyst. Business roles include ticketing analyst, revenue optimization analyst, marketing and fan data analyst, and sponsorship analytics specialist.

Will this course help me beyond sports?

Yes. SQL, Python, machine learning, and AI are in demand in tech, finance, healthcare, insurance, government, and startups, so the skills transfer to data roles across the job market.

Will the course help me as a coach?

Yes. Coaches use analytics to improve scouting efficiency, lineup decisions, player matchups, game planning, opponent tendencies, and performance tracking, which can give them an advantage over teams relying solely on intuition.

About the SMWW Sports Analytics & AI Course

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, generative AI, and automated insights. These tools support player evaluation, performance improvement, injury prevention, roster construction, and game strategy.

Who teaches the Sports Analytics & AI course?

The eight week course is taught by sports analytics pioneer Ari Kaplan, Head of Evangelism at Databricks and a recognized Data and AI Leader of the Year, who has worked with MLB organizations and Formula 1. Students follow along as he codes live in SQL, Python, and R.

What are the prerequisites for the Sports Analytics & AI course?

There are no prerequisites. You do not need prior experience in programming, statistics, or math, because you learn everything from the ground up, including SQL, R, Python, data visualization, machine learning, generative AI tools, and platforms such as Databricks, DataRobot, and Orange.

What will I learn in the course?

The course covers the value of analytics across sports, an end to end walkthrough from data to AI, the fundamentals of AI and machine learning, sports data and technology deep dives, data management with SQL, programming with Python, and data visualization. It is beginner friendly while still delivering advanced value for analysts who want to upskill.

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

It can help, though no course can promise a job. Graduates credit the technical skills they learn, the projects they build, the networking connections they make, and the resume reference from Ari Kaplan. Sports organizations look for candidates who combine programming skills with sports specific analytics knowledge, which is what this course provides.

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

They complement each other. The sport specific courses, in football, baseball, basketball, hockey, and soccer, show how analytics is used in one sport for scouting, strategy, and player evaluation. Sports Analytics & AI teaches the technical tools used across all sports, so if you want a long term analytics career, both are valuable.

What if I need one-on-one assistance?

SMWW staff are available by phone throughout the course for one on one help with coursework, live sessions, or career questions. Call 1 877 SMWW NOW in the United States and Canada, +44 0 871 288 4799 in the United Kingdom, or +1 503 445 7105 from anywhere else. Our offices are in Portland, Oregon, and London, England.

Do you have more questions?

Call the office and ask! 1-877-SMWW-Now in the US or Canada. In London at +44(0) 871 288 4799. Everywhere else at +1-503-445-7105.