SPORTS HACKATHON

Sports Analytics Hackathon

Participates:

The event will be open to all CU students.

Speakers:

James Curley (Professor of Psychology)

Zaheer Benjamin (Head of Business Planning & Analytics, Chelsea Football Club) -- will speak remotely

Judges:

James Curley (Professor of Psychology)

Greg Wawro (Professor of Political Science) 

Coaches:

Zhuxi Cai (Recent Graduate of the MA in Statistics at Columbia University)

Xianyun Wang (Recent Graduate of the MA in Statistics at Columbia University)

Guangyu Wang (Recent Graduate of the MA in Statistics at Columbia University) 

Location:

SSW Room 903 (1255 Amsterdam Ave, New York, NY 10027)

Schedules:

16^th April, Saturday, 9:00-17:00

17^th April, Sunday, 9:00-17:00

Data Description:

The projects will be using PUBLIC DATA in sports field.

Submitting Projects:

Final submission consists of: Title/Name of project; a short description of project (~300 words); name of team & team members; data source; code (linked through Github); screen-cast or screenshots describing the project. A link will be provided for project submissions on Sunday.

Presentations:

Presentations will be judged during an electronic poster session on Sunday where judges will go around and ask teams to present their projects projected on a display or screen. Teams will present using their own computers. Time allotted for each presentation is 7 minutes (strictly enforced).

Judging criteria:

- Technical Difficulty

- Impressiveness

- Creativity


- Usefulness/Practicality

Awards:

To be determined

Sponsors:

Department of Statistics | Columbia University

Center for foundations of data science, Data Science Institute

Task Description 

The theme of this hackathon is uncovering actionable insights from publicly available NBA datasets. You should use the provided list of questions to motivate your team’s project. You are not expected to answer every question or sub-question in the list - it is better to have a great answer to a narrow question than having a so-so answer to a broad question. Visualization or modeling approaches are both relevant, so long as your work illuminates some aspect of the datasets.