Course 2019-2020 a.y.

20630 - INTRODUCTION TO SPORT ANALYTICS

Department of Finance

Course taught in English
Go to class group/s: 31
CLMG (6 credits - II sem. - OP  |  SECS-P/05) - M (6 credits - II sem. - OP  |  SECS-P/05) - IM (6 credits - II sem. - OP  |  SECS-P/05) - MM (6 credits - II sem. - OP  |  SECS-P/05) - AFC (6 credits - II sem. - OP  |  SECS-P/05) - CLELI (6 credits - II sem. - OP  |  SECS-P/05) - ACME (6 credits - II sem. - OP  |  SECS-P/05) - DES-ESS (6 credits - II sem. - OP  |  SECS-P/05) - EMIT (6 credits - II sem. - OP  |  SECS-P/05) - GIO (6 credits - II sem. - OP  |  SECS-P/05) - DSBA (6 credits - II sem. - OP  |  SECS-P/05) - PPA (6 credits - II sem. - OP  |  SECS-P/05) - FIN (6 credits - II sem. - OP  |  SECS-P/05)
Course Director:
CARLO AMBROGIO FAVERO

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


Suggested background knowledge

Students are expected to have already attended a corse 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  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 (WYSCOUT).
  • 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 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.

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • 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.

Teaching methods

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

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. Group assignments are allocated during the course. 


Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
     x
  • Group assignment (report, exercise, presentation, project work etc.)
     x

ATTENDING AND NOT ATTENDING STUDENTS

The final grade is an equally weighted average of the the final written exam and the group assignments.


Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

Required Readings:

  • 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.
  • L. BRANDES, E. FRANCK, Who made who? An Empirical Analysis of competitive balance in European Soccer Leagues, Eastern Economic Journal, 2007.
  • D. HADDOCK, L.P. CAIN, Measuring Parity: tying into the idealized standard deviation, Journal of Sport and Economics, 2006.
  • R.H. KONING, Balance in competition in Durch soccer, The Statistician, 49, Part 3, pp.419-431.
  • S. SZIMANSKY, Income inequality, competitive balance and the attractiveness of team sports: some evidence and a natural experiment from English Soccer, the Economic Journal,111, F69-F84, 2001.
Last change 16/06/2019 21:01