Your mileage may vary on the validity of the Sagarin ratings, but history shows a sustained accuracy in determining straight up winners across sports and levels. Even given the volatility of Sagarin's early season ratings, which carry over last year's performance until the current season adds enough data to stand on its own, not taking fully into account the current team's relative competitiveness... the ratings are to a great degree accurate, from 65-70%.
- Against the spread is another story, and to a system all systems seem to only have a consistent 47-55% accuracy against the spread, but that's another issue for down the road.
- While there are a variety of other predictive systems, I go with Sagarin given its longer history of accuracy (dating back to 1985), and because Sagarin covers all major sports compared to other systems focusing on certain sports. The methodology has a consistent accuracy across the board in all sports, college or pro.
As many others certainly do, I started out in the Ballhype contest using Sagarin's ratings as a guide. The only problem, of course, is that you typically end up picking the favorites, which comes with little reward since so many others are picking the favorites as well. You receive a small fraction of a point when you win, and lose the full one point when you lose. A single loss can undo several wins in an instant. However, when the underdog scores the upset, the Hensleys of the world pick up massive points and blow by everyone at once, even after missing on several other underdog picks where the favorite won.
Eventually, I started taking the Hensley route and picking all the underdogs in the pro games, as that's where a regular number of upsets occur. Even the worst pro teams often manage to win 30-40% of their games, and the best teams can lose 20-40% of theirs. Simple logic would indicate that scoring 5-10 points around 20-40% of the time and losing only one point 60-80% of the time will still lead to a big net gain.
At the same time, I noticed that many of the dozens of college games in football and basketball were such lopsided contests that picking the underdog still didn't make sense, such as the Florida football team against, say, Chattanooga. Even Hensley himself avoided picking an underdog in some contests. Many college games had 9-11 picks for the favorite and 0 for the underdog, and that favorite rolled to victory, a meager but assured 0.09 points for everyone.
Seeing that dual phenomenon, I felt there was a way to improve on Hensley's underdogs-only method, a middle ground where you could pick a favorite and have a good chance to win, while knowing when to pick an underdog. Every now and then Sagarin ratings would indicate an underdog was the most likely team to win but these instances weren't frequent.
However, some comparisons were closer than others. Some Sagarin comparisons showed lopsided differences between teams, while some leaned one way but were very close. Obviously, not all picks were equal, and I recalled my poker research and discussions of expected value. Knowing the relationship between probability and expected value, I realized that there had to be a direct correlation between the marginal difference in Sagarin ratings between two teams and the probability of each team winning. Putting that correlation and the idea of expected value together, I decided there had to be a way to devise a system that would maximize the return on each Ballhype Golden Picks selection.
"Why do this?", you ask. "Who cares? It's just a game." Yes it is. And so is, say, sportsbook wagering. The difference is that the latter nets you money when you win. Knowing that poker players utilize odds and expected value concepts to play poker profitably over the long run, I realize that EV concepts could cross apply to selecting teams provided systems of rating teams that showed a consistent correlation in picking winners. While point spreads provide an additional challenge over Ballhype in picking winners, I figured I could cross that bridge if/when I confirmed that such systems worked in the confines of the Ballhype contest, which operates on a similar scope with straight up picks.
The big obstacle was determining a consistent method for devising a team's probability to win. That was the next step in my research....
[Continued in Part 4]