paper: Comparison of Statistical Prediction Models

[Caleb Sykes]

Thread created to discuss this paper.

This paper provides a comparison of common statistical prediction methods in order to determine which methods have the most predictive power. To see each model's predictions for each match during the period 2008-2016, as well as each team's rating before each match during this period, go to its corresponding workbook. The "Data Summary and Methodology" workbook contains details on each model, a FAQ, a summary of predictive capabilities of each model, and a side-by-side comparison of each model for the year 2016.


I am continuing on my journey of building a predictive model for the 2017 season. Here, I compared a bunch of different predictive methods to determine where my efforts will be best spent. The extremely short version is that, in order of most predictive to least predictive, we have:
Calculated Contribution to Score (OPR)
WM Elo
Average Score
Calculated Contribution to WM (CCWM)
Average WM
Calculated Contribution to Win
Adjusted Winning Record

I was surprised how predictive average score was, and generally how similar the "average" methods were with the "calculated contribution" methods. Moving forward, I am planning to continue development of WM Elo and Calculated Contribution to Score methods, and take some kind of weighted average of those two.