An opportunity with the Milwaukee Bucks for a senior thesis! While the talk has past, feel free to follow up with Mike, Seth, or one of us to get more information:
Are you interested in writing a senior honors thesis using NBA data?
Mike and Seth (the analytics team at the Milwaukee Bucks) are open to sharing data and advice to students interested in NBA data projects. If you are interested in pursuing this, please come to the talk today (info below).
As part of his talk, Mike will describe the types of data that are available. Then, you will then need to submit a proposal for your project.
What should the proposal contain?
A proposal will likely contain these four elements:
(1) A focused question and a hypothesis.
(2) A description of the data that you will use.
(3) A rough description of how you would like to process the data.
(4) Preliminary thoughts on the types of analysis that will be performed and an idenficiation of key hurdles.
What makes a proposal great?
The proposal should clearly communicate the aims and methods. The proposal should be focused and interesting. If it is not obvious, it should explain why the proposed question is answerable with the available data. The very best proposals use the publicly available data (there is a lot of it) to perform a preliminary analysis or a “feasibility study”. Finally, the final product of the research should be useful for the team.
How will the proposal be judged?
Does the proposal clearly communicate the aims and methods?
Is it focused and interesting?
If it is not obvious, does it explain why the proposed question is answerable with the available data?
Is the final “product” of the research useful for the team?
The very best proposals use the publicly available data (there is a lot of it) to perform a preliminary analysis or a “feasibility study”.
How long should it be?
No more than 2 pages. Shorter is better.
How do I submit the proposal?
About the Talk:
Abstract: In this presentation, we will discuss data science through the field of professional basketball. However, many of the topics covered will have wider applications. We will discuss our approach to basketball analysis using specific examples of data design, automation, and research. We will also discuss the importance of succinctly communicating the analysis and visualizing conclusions. Following the presentation, we will allow time for Q+A.