Other modeling solutions including K nearest neighbors have emerged as possible solutions,
though data wrangling has remained a problem. A success from this week came in creating a solid timeline with broken up goals that should make staying on track easier.
The next step will come in changing the data form to be modeled correctly.
Week 3:
2/15/26
Running smoothing techniques on this cross-sectional data has proven to be a challenge, as solutions such as
summarizing average points by age have proven to be non-intuitive. I have found possible solutions in vector exponential smoothing
to alleviate this issue. Right now, the focus is primarily on the data containing end of year rankings rather than weekly, so it will be
nice to see what works well and what does not before moving on to more granular data.
Week 2:
2/8/26
After meeting with my advisors I was introduced to possible methods for continued EDA. I also found
more data sources that may help in exploring factors such as age and court surface performance affecting player rankings.
Techniques were discussed as possibilities to remove this seasonality element to allow for smoothing techniques.
Had to do a little bit of wrangling to create the table for top 100 rankings for the entire timeframe, but should now have a much easier time exploring the data.
Weeks 0-1:
2/4/26
This week started with an unfocused idea for a project and ended with a much more clear direction of where we are headed.
A big accomplishment from this week was finding the datasets that will drive this project.
Additionally, I have been doing some basic exploratory data analysis, including identifying possibilities for functional form and modeling techniques.