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Instructor Led Sports Analytics Workshop

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Dr. Stylianos Kampakis, Instructor - Data Science for Sports Injuries Using R, Python, and Weka

Dr. Stylianos Kampakis

Stylianos (Stelios) Kampakis, PhD is an expert data scientist, member of the Royal Statistical Society, honorary research fellow at the UCL Centre for Blockchain Technologies and startup consultant living and working in London. He has more than 10 years of experience in machine learning and analytics, including 4 years of working in sports analytics with Tottenham Hotspur FC, and 3 years working on social media analytics. He has delivered university courses at University College London, the Cyprus International Institute of Management, and the Innopolis University. He also runs his own consultancy and executive education company called Tesseract Academy.

Learn data science by working on real-world problems on sports injury prediction

  • Learn how to use data and create predictive models to predict and reduce injury incidence.
  • Data Scientists will get an edge in applying for jobs in sports injury, whereas medical professionals will get an improved undrestanding and skills in how data science can help their practice.
  • Instructor: Worked with Tottenham Hotspur FC, of British Premiere League to build predictive models for football injuries.

Duration: 4h 24m

Course Description

Sports analytics is a new field in data science which promises to revolutionise the world of sports. The use of data to study and predict injuries has come into the front of research in the last few years and can completely change the game for team and individual sports alike. This course deals with the use of data and advanced techniques in injury prevention and treatment. Some of the issues discussed are: 1) How can we assess the importance of a factor towards injury? 2) How can we predict injuries before they take place? 3) How can we predict recovery after an injury has taken place? 4) How should data be recorded in order to analyse the relationship with injuries? 5) How can we deliver these models in a way that can aid decision making within a club? This course focuses on football (soccer), but the lessons taught also apply for other team (and individual sports). The course targeted towards sports scientists, data scientists and medical practitioners. The tools used are R, Python (the most popular computer languages for data science) and Weka (a GUI tool for machine learning, useful for those who do not want to delve in coding). The course explains all the most important concepts in statistics and machine learning and how these relate to injury prediction and exposes different use cases based on real-world examples, where data is analysed in order to aid injury-related decision making within a soccer team. The course outlines each single step in the solving the problem, from defining the problem, to the analysis, to presenting results. Professionals from a non-technical background (sports scientists and medical practitioners) will benefit the most from the high-level explanation of the technical concepts and the tutorial on the Weka software, which does not require any coding skills. Data scientists will benefit the most from the exposition and analysis of the use cases. The use cases replicate a real-world setting, where the data scientist has to deal with trade-offs, conflicting results and multiple stakeholders. This course should also interest any individual or sports club that wants to learn how to use data in order to reduce injury incidence. The course will also be of wider interest to anyone who is willing to learn how to use data science in a real world setting, as it exposes a complete overview of the basic concepts in data science, the most popular tools for data science (R, Python and Weka) and real-world scenarios, similar to those that are met in practice in many organisations. Skills learned: Data collection, data wrangling and manipulation, statistical analysis, survival analysis, predictive modelling, machine learning Tools used: R, Python, Weka Soft skills: Communication skills, learn about the intricacies of sports data and working with sports clubs Syllabus 1. Outline a. Audience b. Objectives c. Tools 2. Data in sports clubs a. Segregation, subjective opinion and lack of standards b. Culture and organization c. Data protocols and spurious relationships d. Data problems 3. Aiding decision making a. Basic principles b. Data limitations and concept limitations c. Aiding decision making: basic principles d. Statistics vs Machine Learning e. Presenting results 4. Model testing and metrics a. Metrics overview b. Statistical metrics c. Machine learning model validation 5. Use case 1: Survival analysis a. A quick introduction to R b. Survival analysis i. Current uses in sports ii. Types of models c. Dataset i. Exploratory analysis d. Analysis e. Results f. Summary and exercises 6. Use case 2: Injury prediction based on exposure records a. Problem introduction b. Introduction to Weka i. Using Weka ii. Feature selection c. Analysis d. Results e. Summary and exercises 7. Use case 3: Predicting the recovery time after an injury a. A quick introduction to Python for data analysis b. Problem introduction c. Analysis d. Results e. Interpreting results i. Understanding the results ii. Communicating the results to the club f. Summary and exercises 8. Summary

What am I going to get from this course?

1)  Assess the importance of a factor
towards injury?
2) Predict injuries before they take
3) Predict recovery after an injury has
taken place?
4) How should data be recorded in order to analyze
the relationship with injuries?
5) How to deliver these models in a way that
can aid decision making within a club?

Prerequisites and Target Audience

What will students need to know or do before starting this course?

  • Skills: Data wrangling, survival analysis, predictive modelling
  • Tools : R, Python, Weka
  • Soft skills: Communication skills, learn about the intricacies of sports data and working with sports clubs

Who should take this course? Who should not?

This course should interest any individual or sports club
that wants to learn how to use data in order to reduce injury incidence. It is
meant for all levels. Beginners will mostly gain from the introduction to basic
statistical and machine learning concepts and tools. Intermediate and advanced
students will gain more from the handling of the specific use cases.


Module 1: Introduction to sports injury analytics

Lecture 1 Introduction

Lecture 2 Course description
Lecture 3 Exercise

Module 2: Data in sports clubs

Lecture 4 Introduction and data segregation
Lecture 5 Subjective opinion and data standards
Lecture 6 Culture and data collection protocols
Lecture 7 Data problems

Module 3: Aiding decision making

Lecture 8 Introduction and data limitations
Lecture 9 Epistemological limitations
Lecture 10 Sports related limitations
Lecture 11 Principles of decision making
Lecture 12 Statistics vs Machine Learning
Lecture 13 Metrics and presenting results

Module 4: Model testing and metrics

Lecture 14 Introduction and statistical models
Lecture 15 Lecture
Lecture 16 Machine learning tips
Lecture 17 Metrics classification
Lecture 18 Metrics for regression

Module 5: Survival analysis

Lecture 19 Survival analysis introduction
Lecture 20 Types of survival models
Lecture 21 Survival analysis
Lecture 22 Problem statement
Lecture 23 Data exploration
Lecture 24 R primer
Lecture 25 Cox regression
Lecture 26 Parametric models
Lecture 27 Survival curves
Lecture 28 Summary

Module 6: Injury prediction based on exposure records

Lecture 29 Problem introduction
Lecture 30 Solution framework
Lecture 31 Applying the model
Lecture 32 Problem specification
Lecture 33 Weka introduction
Lecture 34 Data preprocessing
Lecture 35 Weka experimenter
Lecture 36 Classification
Lecture 37 Feature selection
Lecture 38 Summary

Module 7: Predicting the recovery time after an injury

Lecture 39 Introduction
Lecture 40 Python primer
Lecture 41 Pandas introduction
Lecture 42 Data cleaning
Lecture 43 Data and predictive modelling
Lecture 44 Predictive modelling - part A
Lecture 45 Predictive modelling - Part B
Lecture 46 Results
Lecture 47 Exercises

Module 8: Course Summary

Lecture 48 Course summary and conclusions


8 Reviews

Jodi D

December, 2016

Thomas S

May, 2017

The course has a practical value for sports managers and beneficial for them. They can apply the modern technology of data science to manage their individual sports field. Indeed, it is possible to reduce injury incidents with predictive models and data application. I find the course is of immense help and beneficial emotionally as sports has an emotional appeal. The learning experience of the course is great. The quality of the course is also excellent and all encompassing. Predicting injuries and recoveries really greatly help in managing a sport.

Fato C

May, 2017

An excellent course. Learning data science by working on real-world problems on sports injury prediction has great appeal to sports fraternity. The content is very much education focused.

Kevin L

May, 2017

The learning experience of this course is very active. It definitely carries a beneficial value to those who can learn from this course along with their demanding physical experience in the sports.

Rashana P

July, 2017

Great course! This course is not just teaching you how to use data science. You study how to link your data with your sports problem and support people to arrive at a better resolution.

Jonathan W

July, 2017

Great introduction and lessons regarding data science for sports. Well planned and effective courses! I learn a lot about data techniques and the whole process of data analysis, including preparing the data analysis process with scientific guidance, making persuasive illustrations and giving a high quality presentation!! I have learned a lot!

Prashanth K

July, 2017

Excellent Course!!! Enjoy the learning experience!!! Highly recommend this course to anyone who wants to learn data science for sports. Good Luck.

Rabia Nur D

July, 2017

Good subject for a newcomer to become familiarized with data science. Very extensive and precise content presentation. Usable and useful learning tools