Every year I pick an “official” algorithm. This year’s official algorithm was a basic vanilla algorithm, mostly because I realized I didn’t like the sudden dramatic shifts in rankings in the “beatflukes” variant that I used last year. Throughout the season, a couple more variants were developed and discussed in the comment threads. The following [...]
We’re getting some increased traffic (clicking my ads will help btw!), so I thought I’d take the opportunity to explain a couple of things regarding what this site is about. I’ve written about it elsewhere, but more’s always better! A beatpath graph is a graphical representation of all of the games/matches in a season. It [...]
Beatpaths was really fun during the entire 2005 NFL Season, but there’s been one niggling issue that I’ve been trying to figure out how to address for the start of the 2006 NFL Season. The beatpaths graphs don’t really make a lot of sense until each team has a few games under their belts so [...]
A couple entries back, we discussed the projections of the 2005 NFL Season. They were based off of Week 13′s beatpath graph, using the graph to project winners for each of the last four weeks of the season, updating itself and its projections each week. The current projection system is at about 64% accuracy, which [...]
I can’t believe it took me this long to think of it, but it occurred to me that by using the beatpath graphs and the power rankings, I could project the shape of the rest of the NFL season. Obviously this won’t really be accurate (the picks are at 64%, not 100% – we’re not [...]
Under what circumstances should a beatloop be broken? When beatloops are removed, it means that data about team relationships are being taken out of the graph. So far, beatloops have only been broken if smaller beatloops with common teams were removed first. This means the system is fairly liberal about removing beatloops. But I’ve started [...]
I’ve managed to put a prediction checker into the beatpaths algorithm – checking a few of the algorithm variants for how well they’ve predicted the outcomes of games. I should first start with a caveat – beatpaths is not really intended to be used as a predictive tool. It merely attempts to accurately represent the [...]
One of the things I’m toying with for future algorithm variants is how to measure “Strength Of Beatgroups”. Here’s an example. For this last week in the NFL, only four teams have no beatlosses: Indianapolis, Denver, Tampa Bay, and Carolina. These are the only possible options for who can be ranked #1. The question becomes [...]
Here’s an easier explanation of BeatPower, which is how we go from the Beatpath graph to the Power Rankings. Take a look at the results for after Week 9 of the 2005 NFL Season: And the beatloops that were taken out of the graph: ARI=>SF=>STL=>ARI TEN=>BAL=>CLE=>TEN TEN=>HOU=>CLE=>TEN PHI=>OAK=>DAL=>PHI PHI=>KC=>WAS=>PHI BUF=>MIA=>NO=>BUF BUF=>NYJ=>TB=>BUF MIN=>NO=>CAR=>MIN NO=>CAR=>GB=>NO MIA=>DEN=>KC=>MIA MIA=>CAR=>TB=>MIA [...]
I think we’re ready to transition from a “beta” power ranking to a “release candidate” power ranking. In the beta series, the power ranking stuck to a “King Of The Hill” approach, where it was difficult for a team to sink in the rankings unless another team developed a beatpath over them. All in all, [...]