Over the last few weeks I've spent a lot of time browsing the SBR forum. A number of threads referenced a company called Accuscore - website is accuscore.com. They are in the business of predicting winners of professional sports contests using simulation techniques. I myself have written a baseball simulator with limited success so I started looking around to see if I could find out how well Accuscore has done.
To make a long story short, I sent the good folks at Accuscore about $30 to try out their system and more importantly to me - get access to their archived result files.
Here's what I found out.
Accuscore calculates the probability of each team winning a game by running 10000 or more simulations of the contest. If the calculated winning probability of the visiting team is greater than the probability assigned to that team by the sports book that you use, then you bet on that team. If not you bet on the opponent. You bet as close to line closing as possible.
7799 games were bet from April 3, 2006 to May 8, 2009 for a return of $26240.78 using $100.00 wagers. That's a ROI of 3.365 percent.
I've read comments in this forum to the affect that that's a pretty mediocre result - not so!!!
How do you determine how good a result that is? The way I do it when evaluating my own simulator is to use Monte Carlo techniques. I build a file containing one entry for each game I simulated - generally I run a simulation for the regular MLB season so I will have about 2430 games in the file. Each game entry has two numbers, the money you would have won on a $100 wager if you had picked the winning team and a $100 loss.
You then randomly pick one of the two numbers for each game entry in your sample and sum the numbers. That is one simulation. You do that a large number of times, keeping track of whether or not the sum exceeds the amount earned by your simulator.
For Accuscore the results for 100000 simulations were
11636 simulations had a sum > 0.00
1033 simulations had a sum > 10000.00
32 simulations had a sum > 20000.00
1 simulation had a sum > 26240.78
In other words, the probability of doing better than Accuscore is 1 in 100000 or .00001 That is very good. Incidentally I didn't use Ganchrow's Z technique since for a large number of games, the amount lost due to the vig becomes very large and is more and more difficult to overcome. In an 8000 game sample using an 8 cent line and $100 wagers you can expect to give the book about $12000. The Z method assumes no vig.
More to come...
To make a long story short, I sent the good folks at Accuscore about $30 to try out their system and more importantly to me - get access to their archived result files.
Here's what I found out.
Accuscore calculates the probability of each team winning a game by running 10000 or more simulations of the contest. If the calculated winning probability of the visiting team is greater than the probability assigned to that team by the sports book that you use, then you bet on that team. If not you bet on the opponent. You bet as close to line closing as possible.
7799 games were bet from April 3, 2006 to May 8, 2009 for a return of $26240.78 using $100.00 wagers. That's a ROI of 3.365 percent.
I've read comments in this forum to the affect that that's a pretty mediocre result - not so!!!
How do you determine how good a result that is? The way I do it when evaluating my own simulator is to use Monte Carlo techniques. I build a file containing one entry for each game I simulated - generally I run a simulation for the regular MLB season so I will have about 2430 games in the file. Each game entry has two numbers, the money you would have won on a $100 wager if you had picked the winning team and a $100 loss.
You then randomly pick one of the two numbers for each game entry in your sample and sum the numbers. That is one simulation. You do that a large number of times, keeping track of whether or not the sum exceeds the amount earned by your simulator.
For Accuscore the results for 100000 simulations were
11636 simulations had a sum > 0.00
1033 simulations had a sum > 10000.00
32 simulations had a sum > 20000.00
1 simulation had a sum > 26240.78
In other words, the probability of doing better than Accuscore is 1 in 100000 or .00001 That is very good. Incidentally I didn't use Ganchrow's Z technique since for a large number of games, the amount lost due to the vig becomes very large and is more and more difficult to overcome. In an 8000 game sample using an 8 cent line and $100 wagers you can expect to give the book about $12000. The Z method assumes no vig.
More to come...