Maximum Performance and Technology in Sports

via Coursera Specialization

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Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Duration & workload

8 weeks, 10 hours a week

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Overview

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This specialization explores the technologies transforming modern football, from advanced data analysis and machine learning to AI-powered scouting and next-generation performance systems. Across four applied courses, learners will trace the evolution of football analytics, build essential statistical and technical foundations, examine real-world tactical and scouting case studies, and discover the emerging innovations that will shape how teams train, recruit, and compete in the future. The curriculum integrates event data, tracking data, live data, and machine-learning techniques with cutting-edge tools such as Large Language Models, computer vision, web scraping, contextual load monitoring, and automated tactical recognition. Through examples from elite football and pioneering research, learners will understand how AI is reshaping decision-making across performance analysis, talent ID, recruitment, coaching, strategy, and club operations. Developed for analysts, coaches, scouts, and professionals seeking to future-proof their skills, the specialization emphasizes practical implementation. Across each module, learners apply concepts to match situations, evaluate data workflows, and experiment with emerging technologies. By completing the program, they will gain a forward-looking, industry-ready understanding of how technology, automation, and intelligent systems will redefine performance and competitive advantage in football.

Syllabus

Course 1: How Did We Get Here?The Development of Analytics in Football
Course 2: Must-Know Concepts - Basic requirements for data analysis
Course 3: Future of data and technology in football
Course 4: Applied Football Analytics - Looking at real-world cases

Courses

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17 hours 19 minutes
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This course explores how football analytics is applied in real-world professional environments, moving from theory to practice across performance analysis, scouting, club strategy, and media. Learners will follow the workflows used by elite analysts, from pre-match preparation and in-match evaluation to post-match reporting, and understand how clubs build data-driven processes to support coaching and decision-making. Through detailed case studies, the course examines how scouting databases are designed, how key performance metrics and ranking systems are built, and how clubs integrate qualitative evaluations with advanced models such as xG, xT, packing, clustering, and weighted scoring systems. Learners will also analyse how clubs assess team performance using Expected Points, Monte Carlo simulations, phase-of-play analysis, set-piece modelling, squad planning, ageing curves, and contract evaluation. Finally, the course explores how analytics influences football media, from radar charts to real-time broadcast overlays, and the psychology behind numbers, helping learners recognise common misinterpretations and build stronger data-driven arguments. By the end, learners will understand how analytics supports tactical insights, recruitment, forecasting, and storytelling across the football ecosystem.
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21 hours 16 minutes
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This course explores the emerging technologies that will shape the future of football analytics, from advanced tracking systems and live data pipelines to machine learning, LLMs, and AI-driven decision tools. Learners will understand how innovations such as computer vision, skeleton tracking, body-orientation models, and contextual load monitoring are transforming the way clubs analyze performance, manage fatigue, and make in-match tactical adjustments. The course also examines how AI is revolutionizing scouting and recruitment: from automated LLM-generated scouting reports and narrative “wordalisation,” to web scraping, social-media analysis, and multimodal data pipelines. Finally, learners will explore cutting-edge developments in performance AI, including reinforcement-learning concepts inspired by AlphaZero, supervised learning for tactical tagging, and AI assistant-coach systems that recognize patterns across thousands of match situations. By completing this course, learners will gain a forward-looking understanding of how machine learning, automation, and next-generation data systems will redefine competition, recruitment, coaching, and performance across the football industry.
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18 hours 54 minutes
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This course explores how football evolved from intuition-driven decision-making to a fully data-powered sport. Learners will uncover the origins of sports analytics through the “Moneyball” revolution in baseball, the rise of shot-quality models in basketball and hockey, and the early pioneers who first applied scientific thinking to football. From Charles Reep’s handwritten data charts to Lobanovskyi’s cybernetic model, the course traces how ideas from statistics, engineering, and computer science gradually reshaped how the game is played and understood. As technology advanced, video analysis, event data, and tracking systems transformed how clubs evaluated performance, recruited players, and designed tactics. Case studies show how modern clubs exploit data to find market inefficiencies and gain competitive advantages. By the end of this course, learners will understand the milestones, people, and technologies behind football’s analytics revolution — and how they paved the way for the sophisticated models used today.
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17 hours 3 minutes
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This course introduces the essential mathematical, statistical, and data-handling concepts required to work effectively in football analytics. Learners will build a solid foundation by exploring measures of central tendency, variability, probability distributions, standard deviations, and confidence intervals, the core concepts that underpin all analytical reasoning in sport. Through football-specific examples, the course explains when to use different estimators, how to interpret uncertainty, and why choosing the right distribution is critical when modeling performance and match events. Beyond statistics, learners discover the ecosystem of football data itself, including data on counts, GPS tracking, event data, and skeletal tracking, and understand how each type is collected, structured, and used in professional analysis. The course also introduces key analytical tools such as APIs, web scraping, Python, data structures, and visualization principles using Tableau, Power BI, and Matplotlib. By the end of this course, learners will be equipped with the mathematical intuition, technical fundamentals, and data-literacy skills needed to analyze football effectively and to transition smoothly into more advanced analytical and modeling techniques.