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Course 2018-2019 a.y.

20630 - INTRODUCTION TO SPORT ANALYTICS

Department of Finance

Course taught in English


Go to class group/s: 31

DES-ESS (6 credits - II sem. - OP  |  12 credits SECS-P/05)
Course Director:
CARLO AMBROGIO FAVERO

Classes: 31 (II sem.)
Instructors:
Class 31: CARLO AMBROGIO FAVERO


Prerequisites

Students are expected to have already attended a core course in Statistics.


Mission & Content Summary
MISSION

This course provides the analytics requirements of a Sports Management program and it provides an opportunity for applied work for students in Business Analytics and Data Science. 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. 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.

CONTENT SUMMARY
  • Introduction to  Programming  for Sport Analytics: Importing data, Transformation and Decriptive Data Analysis.
  • Factor Model in Sports: An application to Basketball Statistics (Winston Ch.28) and Soccer Statistics (to be developed with WYSCOUT data).
  • Evaluating  Athletes  using data-driven methods (Winston ch 29).
  • Big Data Visualization in Sport. Software Packages (Court Vision). The Science of Moving Dots and Sports. Case Study:Second Spectrum (www.secondspectrum.com,  https://www.ted.com/talks/rajiv_maheswaran_the_math_behind_basketball_s_wildest_moves?language=it).
  • Ratings Sport Teams with Regression Analysis.
  • Sport Strategies: a Probabilistic Approach (Winston Ch 30).
  • Statistical Methods for Evaluating Leagues Parity.
  • Guest Speakers (WYSCOUT, Second Spectrum, MyAgonism).
  • Data site WYSCOUT, OPTA (UK ) STATS (US) CIES Football observatory API format, basketball-reference.com, NBA.com.

Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Improve their overall problem solving and critical thinking ability.
  • 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.
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Learn to use a statistical software package (R, Python) 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.

Teaching methods
  • Face-to-face lectures
  • Guest speaker's talks (in class or in distance)
  • Exercises (exercises, database, software etc.)
DETAILS

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.


Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  •     x
    ATTENDING AND NOT ATTENDING STUDENTS

    The final grade depends exclusively on the performance at the written individual exam.


    Teaching materials
    ATTENDING AND NOT ATTENDING STUDENTS

    Required Readings:

    • D. OLIVER, Roboscout and the four factors of basketball success, 2004 http://www.rawbw.com/~deano/articles/20040601_roboscout.htm.
    • D. OLIVER, Basketball on paper: Rules and tools for performance analysis. Potomac Books, 2004.
    • W. Winston, Mathletics:  How  Gamblers,  Managers,  and  Sports  Enthusiasts  Use  Mathematics  in  Baseball,  Basketball, and Football,  Princeton University Press, 2009. 
    • Other articles throughout the course.
    Last change 12/06/2018 09:24