I just finished a personal project whose goal is to predict the world championship ranking from previous scores in the season (for male and female single skaters).

The obvious way to rank skaters is to take their average score of the season and rank them from highest to lowest. However, one potential problem with this approach is that the scores are averaged over different events, and no two events are the same (think different judges, ice conditions, or event altitudes). Therefore, I came up with different ranking models that can somehow tease out the skater effect (how good a skater intrinsically) from the event effect (how does an event affect the score of a skater). The models themselves are essentially just simple linear models, but I’d never thought about using linear regression this way. I’ve also documented how my models perform over the baseline model, which is the average season score model mentioned above.

If you have any feedback or ideas on my project, please don’t hesitate to let me know!

PS. I’m in the process of cleaning up the code that I used for the analysis, and will soon add the link to the Github repo in the write-up. I’m writing a part 2 on even more complicated models to rank skater and will post them here when I’m done writing it.