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.