I’ve managed to fire up my old wonky backtesting system. It doesn’t have a lot of ability yet, but it gives some interesting information. Here’s what I’ve found.
|Year||Random||uNet||Prev. Week||Beatloop Str.||Fractional||BPower/Win/Loop||Str. of Beatwins||Bucklin||UPower||UPower/Loop||UNet-Lookahead|
So, there’s some food for thought. Here’s how to read it – the records in parentheses are the actual beatpaths records, meaning, the games that have actual beatpath relationships. All the other methods in the table are tiebreaker methods; meaning ways to rank teams that don’t have beatpath relationships. All are based off of the same beatpath graph, and so all start from the records in the left column. We wouldn’t expect those methods to have a higher win percentage than the beatpath win percentage. These are various tiebreaker methods that I have tried out in the past. I haven’t tried out Weighted or Moose’s other ranking system – I’d expect Weighted to not perform very well, and the other ranking system to perform very well. This is also the vanilla method of finding beatpath relationships – not beatflukes or iterative.
I am curious what the historical win percentage of Isaacson-Tarbell is, at least over the same five-year period. This page indiciates Isaacson-Tarbell’s win percentage long-term is 62.29%, which would indicate that perhaps the best strategy to follow is 1) pick the team with a beatpath relationship to another team, 2) If there isn’t one, pick the team with the winning record, 3) if they’re tied, pick either the home team or the one selected by the beatpaths tiebreaker.
Hopefully in the future I’ll be able to look at how the beatpaths percentage (64.13%) compares to beatflukes, iterative, or whatever other kind of beatloop resolution scheme we come up with.