20630 - INTRODUCTION TO SPORT ANALYTICS
Course taught in English
Go to class group/s: 31
Class-group lessons delivered on campus
Students are expected to have attended a core course in statistics and to be familiar with basic calculus and linear algebra.
This course provides the analytics requirements of a Sports Management program. It is also an opportunity for applied work for all students interested in Data Science. All applications in the course will be based on the statistical software R. The course is taught through a combination of lectures, class discussion, group presentations. Students are required to read assignments from the texts as well as additional sources provided by the instructor. Students must attend class prepared to engage in discussions; have, articulate and defend a point of view; and ask questions and provide comments based on their reading and on their own R applications.
- Introduction to R Programming for Sport Analytics: Importing data, Transformation and Decriptive Data Analysis.
- Factor Model in Sports: An application to Basketball Statistics.
- Evaluating Athletes using data-driven methods.
- Big Data Visualization in Sport. Software Packages.
- Ratings Sport Teams with Regression Analysis.
- Sport Strategies: a Probabilistic Approach.
- Statistical Methods for Evaluating Leagues Parity.
- Guest Speakers.
- Data site WYSCOUT, OPTA (UK ) STATS (US) CIES Football observatory API format, basketball-reference.com, NBA.com.
- Improve their capability of construct empirical model and implement them via specification, estimation and simulation usign the package R.
- Gain an understanding of basic statistical concepts and their applications in the sports world.
- Obtain a broad survey of the methods used in sports data acquisition, processing, analysis, visualization and implementation.
- Learn to use a statistical software package (R) to analyze, interpret, and present a solution to a problem utilizing available data and various sport management perspectives, as well as best practice prediction technologies.
- Improve their overall problem solving and critical thinking ability.
- Face-to-face lectures
- Online lectures
- Guest speaker's talks (in class or in distance)
- Exercises (exercises, database, software etc.)
- Group assignments
The course is taught through a combination of lectures, class discussion, group presentations and guest speakers. Students are required to read assignments from the texts as well as additional sources provided by the professor. Group assignments are allocated during the course.
|Continuous assessment||Partial exams||General exam|
- W.L. WINSTON, Mathletics, Princeton University Press, 2009.
- D.J., BERRI, M.B.SCHMIDT, S. BROOK, The Wages of Wins, Stanford University Press, 2006.
- A.K. SINGH, DE ALLEN, R in Finance and Economics. A Beginners Guide, World Scientific Publishing, 2017, Ch 1,2,3,4.
- F. HEISS, Using R for introductory Econometrics http://urfie.net/read/mobile/index.html#p=4, 2016.
- XIE YIHUI, Dynamic Documents with R and Knitr, Chapman and Hall.
- James, Witten, Habstie and Tibshirani (2011) An Introduction to Statistical Learning- With Applications in R
- Stock J. and M.Watson (2020) Introduction to Econometrics, 4th edition
- P. Zuccolotto and M. Manisera (2020) Basketball Data Science – With Applications in R, Chapman and Hall/CRC.