Computer System Makes Best Sports Bets

schliz writes to tell us that a new computer system using the "Logistic Regression Markov Chain" (LRMC) has proven to be the most efficient system at predicting sporting event outcomes. The system was tested on the 2008 US NCAA basketball season and picked all four of the finalists. "Similar to other rankings systems, LRMC uses the quality of each NCAA team's results and the strength of each team's schedule to rank teams. The method has been designed to use only basic scoreboard data, including which teams played, which team had home court advantage and the margin of victory."

For the first time in a while...

[insertwackynamehere]

The final four were also all #1s in their league. Coincidence? This has never happened before I believe and if the computer calculates odds the way the teams are ranked, then this may not always be so reliable.

Re:Why not test it for the past 10 years?

[Anonymous Coward]

Why would 10 years be so much better than the 9 years they analyzed?

I'm not convinced

[drsquare]

If I had a computer that could predict sports results, I wouldn't tell anyone about it. I'd take a briefcase full of cash down to the bookmakers.

Data mining

[26199]

Doesn't say whether the test was done on in-sample or out-of-sample data. That is, did they test using the same data that was used during development?

If so, the results are worthless. You can make a "system" that says anything you want given enough tweaking. (This is often the problem with apparently successful computer trading models).

Great sample

[Idiomatick]

Great sample... They should test the algorithm on maybe 80 historical seasons and maybe we will be able to see something.

Re:Making sports bets

[Mr. Underbridge]

Is always a question of statistics with a random noise involved.
The amount of noise involved strongly depends on which sport that is involved. Basket is a sport where a lot of points is scored, which in turn means that the noise is relatively low while football (what americans call soccer for some strange reason and what americans call football is more like rugby) has a lot of noise since the ability to score a goal there is depending a lot on luck.

American football, over the course of a full game, has coarse scoring jumps (7pts for a touchdown) but luck plays a surprisingly small role. This is why good teams have very high winning percentages and poor ones have such low winning percentages. Not sure how that dynamic works in futbol, but the luck factor isn't as large as you'd think.

The reason the LRMC method is well-suited to NCAA basketball is that A) there are a lot of games, and B) the good conferences don't play the bad ones much. That means that a high-order Markov model is a good way to determine who would beat whom through a game of "I beat a team that beat a team that beat a team that beat you" sort of thing.

I came up with a version of this independently before I stumbled over these guys last year. It's pretty fun and works quite well. It's certainly much better than the polls, and in most cases last year my system was within a point or two of the Vegas spread. It's also pretty good at recognizing underdogs early - mine had Davidson and Drake before they were in the polls.