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20630 Introduction to Sport Analytics


Introduction to Sport Analytics

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. 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.

Pre-Requisites: Students are expected to have attended a core course in statistics and to be familiar with basic calculus and linear algebra.


Course Content Summary

Section 1: The Modelling Approach to Sport Analytics

Topics of the lectures

Why models ? 
The checklist of model building
Graphical and Descriptive Analysis
Model Specification
Model Estimation
Model Simulation
Model Evaluation 
An Application: mapping boxscores statistics into Team Performance 
References
Winston W.L.(2009) Mathletics, Princeton University Press
Berri D.J.,M.B.Schmidt and S. Brook(2006), The Wages of Wins, Stanford University Press  
SLIDES    An Overview of the Data with R Shiny

Section 2: An introduction to R

Before the lectures:
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

Topics of the lectures
Data Objects in R (data types) and Data Structures In R (Vectors, Matrices, Arrays, Data Frames, Lists)
Data Handling in R (Importing and Exporting data)
Programming and Control Flow (if-else statements, using switch, loops, functions in R)
Dynamic Documents with R Markdown: build a report with all results and comments

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 

Torfs Brauer "A Very Short Intro to R" 
An introduction to R Markdown
Intro R Code (from Singh and Allen) 
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 ObjectsData HandlingProgramming

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” , solution 
Group: Jacob Hylin, Nicola Rogante
EXERCISE 2 An introduction to Data Handlingsolution 
Group: Daniel Hame, Daniel Coppel,Jan-Niklas Hannig,Jonas Heidenreich


Section 3: Graphical and Descriptive Analysis of Sport Statistics 
The NBA database: download and import in R. readme.doc
https://www.basketball-reference.com

Topics of the lectures
Graphical Analysis
Correlation Analysis
QQ plots and Histogram
Subsetting data and TS plots
Introduction to model building and Simulation  

R CODES :  codedata 
SLIDES
EXERCISE 3:  textSOLUTION
Group:Massimiliano Minorati, Michele Molteni,Filippo Forconi,Elyas Sadou

Section 4: The Linear Regression Model 
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 
SLIDES
 

AN APPLICATION,THE FOUR FACTOR MODEL  slidesRcode
EXERCISE 4:   The Four Factor Model, SOLUTION, NOTES 
Group:Yevhen Samofal, Andrea Bianchi
References
Winston W.L.(2009) Mathletics, Princeton University Press, Chapter 28 


Section 5: Using Models to Weight NBA Statistics  

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_statplayers_statdata on players
SLIDESSLIDES ON PLAYERS EFFICIENCY, NOTES
EXERCISE 5:   textSOLUTION
Group:Andrea Premoli, Massimo Cittadini 
EXERCISE 6:    textSOLUTION1SOLUTION2notes

References
Berri D.J.,M.B.Schmidt and S. Brook(2006), The Wages of Wins, Stanford University Press, Ch 6,7 

Section 6: Measuring Competitive Advantage and its effects   

Competitive Balance inthe Sport Industry
The Noll-Scully Measure
CR concentaration Measure 
Herfindal Index
Competitive Balance in European Football
Evidence from a Natural Experiment on English Soccer 
SLIDES

EXERCISE 7:    text, solution 

References

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 attractivenessof team sports:some evidence and a natural experiment from English Soccer" the Economic Journal,111, F69-F84 

Section 7: Evaluating Soccer players performance (in collaboration with WYSCOUT)  

PlayeRank: multidimensional and role aware evaluation of soccer players performance
Modeling players performance 
Weigting performance indicators 
Computing performance ratings 
Classifing player roles  
Ranking players 

R CODESdata players serie Acodes for downloading datacode for clusteringcode for ranking
SLIDES 
SLIDES PRESENTATION MASSUCCO  PART 1 
SLIDES PRESENTATION MASSUCCO  PART2
SLIDES PRESENTATION MASSUCCO  PART 3


References
Pappalardo, Cintia, Ferragina, Massucco, Pedreschi, Giannotti (2018) "PlayerRank: Multi-dimensional and role-aware rating of soccer player performance"

 

Last update 07/05/2019



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