Post #4: Summary and Conclusion

As my previous post (linked here) promised, this post is a summary post for my entire project. It is a long post, but I have a lot to say.

[Read more…]

Post #3: Part 2, End of Research

In my previous post (linked here), I explained that Part 2 of my project had been refined to only include one of the three ideas I had initially intended to use. Since, I’ve created a spreadsheet on Microsoft Excel containing each of the 88 coaching changes I found either after the 2016-2017 season or after the 2017-2018 season. For each coaching change, I recorded the number of wins and the point differential from the season before and the season after the coaching change. I then determined the expected number of wins for the team in the year after the coaching change, and I looked at how closely these “Expected” win totals aligned to the actual win totals from the season after the coaching change.

[Read more…]

Post #2: End of Part 1

Since my previous post, I have completed┬áthe first part of my project and planned my approach for the second part. I began by taking 3-4 days to compile the data set I needed to use. This was incredibly tedious because, for each of the 1053 team-year combinations, I had to correctly save and format the file to make it usable in my program. Once that was done, I ran the code I’d written (which I described in my first post), in order to create a master dataframe.

[Read more…]

Post #1: Data Collection and Beginnings

The goal of my project is to analyze the correlations between scoring and success in college basketball, and then to narrow my focus to teams that have had coaching changes in the past two years in order to determine whether those trends hold up and can be used to predict team success for new coaches.

[Read more…]