Fall 2019 - STAT 490 D100

Selected Topics in Probability and Statistics (3)

Practice of Sports Data Analytics

Class Number: 9651

Delivery Method: In Person

Overview

  • Course Times + Location:

    Tu 11:30 AM – 2:20 PM
    BLU 10921, Burnaby

  • Exam Times + Location:

    Dec 16, 2019
    3:30 PM – 6:30 PM
    AQ 5037, Burnaby

  • Prerequisites:

    Dependent on the topic covered.

Description

CALENDAR DESCRIPTION:

Topics in areas of probability and statistics not covered in the regular undergraduate curriculum of the department.

COURSE DETAILS:

Intended audience: This course is open to upper-division students interested in sports analytics. Faculty of
Science students will have enrolment priority.

Duration:
13 weeks

Format:

3-hr weekly class meetings in various formats: including seminars, lectures, computer labs, and field trips.

Proposed schedule:

The table below lists tentative speakers and topics, which are subject to change. Readings and assignment
dates remain to be determined.


Week Lecturer Role & Organization Topic Readings & Assignments
1 Dr. Dave Clarke Associate Professor, Biomedical Physiology and Kinesiology, SFU Course organization & overview  
  Dr. Kerry MacDonald Director of Sport Science, Medicine and Innovation, Volleyball Canada How analytics helped UBC win the USPORTS Volleyball Championships  
  Dr. Ming- Chang Tsai Lead, Biomechanics and Performance Analysis, CSIP Intro to sports analytics in Canadian elite sport; data set descriptions  
2 Dr. Dave Clarke Associate Professor, BPK, SFU Performance analysis: sport strategies and tactics; Biomechanics of sport technique  
  Ryan Brodie Lead, Data Solutions, CSIP  
3 Dr. Dave Clarke Associate Professor, BPK, SFU Longitudinal monitoring of training and performance Reading quiz
4 Liz Johnson Lead, Physiology, CSIP Risk management: recovery and fatigue Sport observation
  Dr. Doug Richards (remote) Sports Medicine Physician and Professor, University of Toronto Risk management: injuries
5 Kurt Innes Director, Talent Development, CSIP Identifying and monitoring performance potential  
  David Hill Director, System Excellence, CSIP Communicating with coaches; data in System Excellence  
  Justin Detlor (remote) Canadian Tire Financial Services Podium Pathway, Gold Medal Profile  
6 Dr. Matt Jensen Sport technologist Role of technology development in sport science Interdisciplinary workshop
  Reynald Hoskinson Vice-President of Technology, Form Athletica Working in sport technology
7 Dr. Nick Clarke Co-Lead, Strength and Conditioning, CSIP; Sr. S&C Coach, University of Victoria Athlete physical preparation  
  Dana Agar- Newman Co-Lead, Strength and Conditioning, CSIP  
8 Dr. Dave Clarke Associate Professor, BPK, SFU Data analytics pipeline: collection/curation, storage, management, cleaning, manipulating, analysis Project proposal
  Dani Chu, Eli Mizelman, etc. Graduate student mentors  
9 & 10 Dr. Tim Swartz Professor, Statistics and Actuarial Sciences, SFU Statistical models for sport data  
11 Dr. Peter Chow-White? Professor, School of Communication, SFU Communication strategies  
12 TBD   TBD; may include career planning in sport, open computer lab  
13 Various CSIP Victoria field trip Student project presentations  
         

COURSE-LEVEL EDUCATIONAL GOALS:

By the end of the course, students will be able to

  1. Model an attitude of professionalism through exposure to top-level sport professionals, including but not limited to
  2. ambition, positive outlook, enthusiasm, attention to detail, persistence, strong work ethic, eagerness to help others, and resourcefulness.
  3. Describe the disciplines within athlete integrated support teams (e.g., physiology, biomechanics, strength & conditioning, sport technologist, etc.) including their roles and responsibilities, the “culture” of their discipline, methods of problem solving, and data collected by their discipline to aid with problem solving.
  4. Conduct a comprehensive statistical analysis of data collected by sport practitioners, including data quality checking and cleaning, visualization, calculation of descriptive statistics, application of a statistical model and statistical inference, and assumption verification.
  5. Communicate the results of the data analysis in a clear manner that enables sport practitioners to use the results in decision making.Reflect on whether a career in sport is suited for them, and if so, develop a realistic plan to pursue that career.

Grading

  • Reading Quiz 10%
  • Observation and question-generating activity 10%
  • Interdisciplinary teaching & learning activity 20%
  • Project proposal: description of the problem/question, brief literature review, description of the data set and its collection, proposed analysis 10%
  • Project final report: technical report, communication strategy 30%
  • Oral presentation of project results 20%

NOTES:

Participation in the course field trip is optional but strongly recommended.

REQUIREMENTS:

Pre- or corequisites:

Open to upper-division Science majors with appropriate research methods and data analysis backgrounds.

Those enrolling in BPK 420 must have BPK 304 Inquiry and Measurement in BPK or equivalent.

Those enrolling in STAT 490 must have STAT 330 Introduction to Mathematical Statistics and STAT 350
Linear Models.

Those with relevant backgrounds but who lack the prerequisites may seek instructor permission to enroll.

Materials

REQUIRED READING:

The instructor will provide articles on foundational topics in sports analytics and discipline-specific information. The course reading quiz will be based on these readings.

Department Undergraduate Notes:

Students with Disabilites:
Students requiring accommodations as a result of disability must contact the Centre for Accessible Learning 778-782-3112 or csdo@sfu.ca


Tutor Requests:
Students looking for a Tutor should visit http://www.stat.sfu.ca/teaching/need-a-tutor-.html. We accept no responsibility for the consequences of any actions taken related to tutors.

Registrar Notes:

SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS