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."

Comments

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.

Making sports bets

[Z00L00K]

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.

This essentially means that counting points is a good way to score a basketball team while counting goals won't give much clue to how good a given football team is. You must look at other factors on a football team instead. And not all those factors can be as easily measured. Of course - the other factors are also important for a basket team. Other factors involved are the composition of players, individual player mood/health/inspiration, latest matches, history between the teams, referee behavior, weather, spectators, location, timezone etc. Add to this the element of randomness caused by the impact of the ball on a surface, player positions at certain points of the game etc.

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.

Best bet is not to bet...

[Bazman]

One of our research assistants started doing something like this about ten years ago, fitting a statistical model to previous soccer match results and the home/away effect. He rounded some of us up to chip in a few pounds each week and off he went to the bookies to bet on the outcome of his model.

Now, any statistical model (such as this LRMC thing, or the techniques m'colleague used) will only give estimates of the odds. It might say that the probability of team A winning is 0.6. Now, if the bookies are offering you a return of 0.7 then it's worth a bet. If the bookies rate it 50-50 then it's not worth a bet.

  The trouble is that any statistical model worth its salt is going to produce probabilities that add up to 1.0, whereas the bookies' odds can add up to 1.2 or so. That's how they play the game and make their profits.

  So after a season where we made a few pennies profit, and got some press interest (including a team from BBC Tomorrow's World filming us playing football), my friend realised the best thing to do was not to bet at all.

  And instead he went into the business of supplying odds to bookmakers. From where he now sits at the top of a rather large business empire!

  I might pop him an email to see what his current techniques are, but back in the day it was something similar to this LRMC thing.

Re:My NCAA predictor code had the same result!

[Anonymous Coward]

Really? Here's mine:

pick :: Ord a => [[a]] -> [a]
pick = map minimum

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.

RTFA

[sarahbau]

I know this is Slashdot, but why can't people RTFA before commenting? They aren't using the seeds or rankings in the program - only game stats, home quart advantage, etc. They ran it on the last 9 years of data and it picked final four teams 30% more often than analysts. (30/36 vs 23/36).

The linked article didn't mention it, but from the GA Tech web site, it said that it correctly identified several overrated teams that lost early on (like Georgetown), and underrated teams that went farther than expected (like WVU). The program picks Kansas to win this year.

Great sample

[Idiomatick]

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