CARLO AMBROGIO FAVERO

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.
Projects will be allocated to groups of attending students. Project reports and their presentation will be part of the evaluation for attending students.

Presentations on the use of analytics in the Sport Business: 

Using Analytics for a Euroleague Basketball Team, presentation by Mario Fioretti, Assistant Coach, Olimpia Milano
Using Analytics in the European Soccer Industry, presentation by Mark Nervegna, Head of Strategy and Analytics, Raiola Global   

Pre-Requisites: Students are expected to have attended a core course in statistics and to be familiar with basic calculus and linear algebra.
Teaching Assistant: Office Hours will be held online viaTeams, the Teaching Assistant  will follow students both on projects and on exercises
Gabriele Carta, gabriele.carta@unibocconi.it, office hours 
Past Exams: 2019_1, 2019_2
Mock Exam May 2023: exam, data, R code with solutions
Exam 23rd May 2023: exam, data, R code with solutions 

Dynamic Documents with R Markdown
build a report with all results and comments
An introduction to R Markdown
an illustrative R Markdown code

Github and Github Desktop
A tutorial online 

Project 1: Getting sport data from the web  with R  
The objective of this project is to illustrate how data on sports could be efficiently retrieved from the Web (via API and/or webscraping). Students should feel free to choose their preferred field and application.
Accessing APIs from R a tutorial , an R code for the tutorial, Accessing data from Github using an R code 

Project 2: Creating Web Applications with Rshiny  
The objective of this project is to create a Sport related web application with RShiny. An Illustration based on NBA data is provided together with projects produced in 2020. Students should feel free to choose their preferred field and application.
Slides of Andrea Maver's presentation 
Online tutorials on mastering RShiny  
Learning Shiny with NBA DATA  (by Julia Wrobel),
http://juliawrobel.com/tutorials/shiny_tutorial_nba.html, https://andreamaver.shinyapps.io/EuroleagueApp/
Programmes for NBA Shiny short version , Programmes for NBA shiny long version 
Rshiny example

Instructions for those who have opted for the Shiny Project in 2020  are available HERE. 

Project 3: An Application of Unsupervised Machine Learning to Sport Analytics 
The objective of this project is to apply unsupervised machine learning, and in particular cluster analysis, to finding groups in Sport Analytics data.
P. Zuccolotto and M. Manisera (2020) Basketball Data Science – With Applications in R, Chapman and Hall/CRC. (Chapter 4) 
link to basketball analyzeR: https://bdsports.unibs.it/basketballanalyzer/
James, Witten, Habstie and Tibshirani (2011) An Introduction to Statistical Learning- With Applications in R 

LINK to the recorded Presentation of the Cluster Analysis project 2020: https://eu-lti.bbcollab.com/recording/8570729e9532435b951e9b40de8470a5
SLIDES and  Rmd codes  

Project 4: An Application of Supervised Machine Learning to Sport Analytics 
The objective of this project is to apply supervised machine learning techniques , and in particular techniques to solve the many predictor problem to predict top athletes compensations. 
Students should use as a benchmark the model presented in the lectures and evaluate it against alternatives generated by modern machine learning techniques.
A further possibility for a group undertaking this project is the costruction of a data challenge related to the topic of the project using the data challenge website of Bocconi University.

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,  Chapter 14

Project 5: Evaluating the Home Advantage Effect from quasi-Natural Experiments 
Following the COVID shock many games in many sport were played without attendance within "bubbles" in which no team had the "home advantage effect". The objective of this project is to use sport data to construct a quasi-natural experiment for the evaluation of the Home Advantage Effect.
Stock J. and M.Watson (2020) Introduction to Econometrics, 4th edition,  Chapter 13
Presentation of N.Sita(2020) thesis on Evaluating the Home Advantage in NBA 

Project 6: Measuring Competitive Advantage and its effects 
The objective of this project is to introduce, discuss the concept of Competitive Balance in the Sport Industry. Both a discussion of the theory and applications are possible. 

PRESENTATION SLIDES

Berri D.J.,M.B.Schmidt and S. Brook(2006), The Wages of Wins, Stanford University Press, Ch 3,4
Brandes L. and E.Franck(2007) "Who made who? An Empirical Analysis of Competitive Balance in European Soccer Leagues" Eastern Economic Journal
Haddock D. and L.P.Cain(2006) "Measuring Parity:Tying into the Idealized Standard Deviation", Journal of Sport and Economics
Koning R.H.(2000) Balance in competition in Dutch soccer, The Statistician, 49, Part 3, pp.419-431
Szimansky S.(2001) "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

Project 7: Load Management and Injury Risk  
A recent report denied the existence of a significant statistical relationship between load management and injury risk in the NBA. The objective of this project is a critical analysis of  the report, which will be made available to the groups taking this choice.
https://www.espn.com/nba/story/_/id/39288379/nba-report-no-link-load-management-less-injury-risk
 

Project 8: The Relevance of Popular Shareholding Contribution to Team Perfomance  
A recent report provided evidence on the popular shareholding contribution to team perfomance in european soccer. The  objective of this project is a critical analysis of  the report, which will be made available together with the original data  to the groups taking this choice

Course Content Summary

Section 1: Sport Analytics. an Introduction
SLIDES

The Questions in Sport Analytics. 
The Answers
Modelling Data in Sports
Theory Based Models
Supervised Machine Learning
Unsupervised Machine Learning

References
Berri D.J.,M.B.Schmidt and S. Brook(2006), The Wages of Wins, Stanford University Press  
Berri D.J., M. B. Schmidt (2010) Stumbling On Wins.Two Economists Expose the Pitfalls on the Road to Victory in Professional Sports-FT Press 
Goldsberry K.(2019) Sprawlball. A visual tour of the new era of NBA, Houghton Mifflin Harcourt
James, Witten, Habstie and Tibshirani (2011) An Introduction to Statistical Learning- With Applications in R,
Shea S.(2014) Basketball analytics. Spatial Tracking
P. Zuccolotto and M. Manisera (2020) Basketball Data Science – With Applications in R, Chapman and Hall/CRC.
Winston W.L.(2009) Mathletics, Princeton University Press

Section 2: An introduction to R
SLIDES

Install R and R studio on your computer and learn how to run them
Learn what is a package and how to install it
Understand what is a view
define a default directory 
have some fun with R Shiny 
An online introduction to R 
R Code
Torfs Brauer "A Very Short Intro to R" , SOLUTIONS FOR the Torfs-Brauer TO DO LIST 

Data-Objects in R
Data Objects in R (data types) and Data Structures In R (Vectors, Matrices, Arrays, Data Frames, Lists)

Data Handling in R
Importing and Exporting, transforming and selecting data 

Getting Data from the web with R

Programming and Control Flow
if-else statements, using switch, loops, functions in R

all R codes used in Singh and Allen are downloaded at 
http://www.rforresearch.com/r-in-finance-economics
R CODES (from Singh and Allen) : Data Objects, Data Handling, Getting Data from the web, Programming, binomial model included

References
Singh AK and DE Allen(2017) R in Finance and Economics. A Beginners Guide, World Scientific Publishing, Ch 1,2,3,4
Heiss F. (2016)  Using R for introductory Econometrics http://urfie.net/read/mobile/index.html#p=4, 
Yihui Xie, Dynamic Documents with R and Knitr,  Chapman and Hall 

EXERCISE 1 Write an R code that answers to all the ToDo points  in  Torfs P. and C. Bauer(2014) “A (very short) introduction to R” ,
EXERCISE 2 An introduction to Data Handling, SOLUTION

Section 3: Graphical and Descriptive Analysis of Sport Statistics (NBA data)
SLIDES

Graphical Analysis
Correlation Analysis
QQ plots and Histogram
Subsetting data and TS plots
Introduction to model building and Simulation

The NBA database: download and import in R. teamsoverall2023.csv, datafiles, programme to build database from datafiles
https://www.basketball-reference.com/leagues/NBA_2023.html, programme to update data by webscraping  
R CODES :  code1, code2, please not that you need to create Teams_overall2023.csv to run the codes 
EXERCISE 3:  text, code


Section 4: The Linear Regression Model 
SLIDES 1 
SLIDES 2

Models for Experimental and non-Experimental Data
Models as outcomes of reduction processes
Model Estimation: the OLS and its properties 
Interpreting Regression Results: Statistical Significance and Relevance 
The Effects of Model Misspecification 

AN APPLICATION,THE FOUR FACTOR MODEL  R code
EXERCISE 4:   The Four Factor Model,  NOTES , solution


References
Winston W.L.(2009) Mathletics, Princeton University Press, Chapter 28 

Section 5: Using Models to Weight NBA Statistics  
SLIDES 1, SLIDES 2

Weighting Statistics to measure performance
Correlation analysis
The NBA Efficiency Measure
Using a Model based on Possession
Offensive Efficiency and   Defensive Efficiency
Modelling Wins
Evaluating Statistics by Simulation: Monte-Carlo and Bootstrap methods
Completing the Model
Evaluating Players' Efficiency: WINS, assists and WINS48    

R CODES: team_stat , players_stat, data on players, NOTES
EXERCISE 5:   text, SOLUTION , SOLUTION AS RMD
EXERCISE 6:    text, notes, SOLUTION 
References
Berri D.J.,M.B.Schmidt and S. Brook(2006), The Wages of Wins, Stanford University Press, Ch 6,7