tanda
07-25-2002, 03:25 PM
I have posted several times with results from testing of software that I am designing.
Here is another report:
Rs Ws W% Odds ROI IV
Favorite 415 137 33.0% 184.85 0.78 2.71
230 61 26.5% 110.85 0.75 2.18
43 9 20.9% 17.55 0.62 1.72
2 0 0.0% 0.00 0.00 0.00
690 207 30.0% 313.25 0.75 2.47
Short 208 55 26.4% 157.00 1.02 2.17
Price
466 106 22.7% 313.15 0.90 1.87
196 34 17.3% 101.65 0.69 1.43
36 7 19.4% 27.60 0.96 1.60
906 202 22.3% 599.40 0.88 1.83
Mid 60 6 10.0% 31.40 0.62 0.82
Price
572 82 14.3% 430.45 0.90 1.18
556 69 12.4% 395.70 0.84 1.02
158 12 7.6% 83.40 0.60 0.62
1346 169 12.6% 940.95 0.82 1.03
Longshot 6 0 0.0% 0.00 0.00 0.00
321 20 6.2% 221.10 0.75 0.51
1054 66 6.3% 1058.30 1.07 0.51
1347 28 2.1% 537.40 0.42 0.17
2728 114 4.2% 1816.80 0.71 0.34
Total 5670 692 12.2% 3670.40 0.77 1.00
Value 2336 316 13.5% 1986.50 0.99 1.11
Non-Value3334 376 11.3% 1683.90 0.62 0.93
Quite frankly, I am very excited with these results. I am unaware of any mechanical method that identifies a class of horses composing over 40% of the field who return 0.99 on the dollar.
First, I have grouped the horses by odds range with sub-totals. Within each odds range, the horses are sub-categorized by the handicapping methodology that I have developed and coded.
Second, it is clear that there are identifiable groups of horses who are money burners and out-performers. I have designated those that significantly out-perform the take and breakage as "value" and the others as "non-value". The "non-value" horses are not necessarily non-contenders; they win 0.93 of their fair share of races. But, they return 0.62. More importantly, a flat bet on all "value" horses returned 0.99.
Third, this method is completely objective. The numbers are inputed/imported to the software which designates each horses category. There is no user subjectivity. All races with each horse having one US past performance line were played at all tracks over a period of days .
Fourth, it appears that a handicapper who restricts action to "value" horses will not have to do much to increase the 0.99 to a positive R.O.I.
Fifth, the methodology does not end with the "value"/"non-value" designation. Instead, it categorizes the horses then makes betting decisions from among the "value" horses. I have not tested those betting decisions; that test is commencing now. An oddline is created to make betting decisions. The test will be of: 1) 20% overlays, 2) all overlays, 3) the largest overlay (or smallest underlay) and 4) two horse bets on the largest overlays/smallest underlays.
Here is another report:
Rs Ws W% Odds ROI IV
Favorite 415 137 33.0% 184.85 0.78 2.71
230 61 26.5% 110.85 0.75 2.18
43 9 20.9% 17.55 0.62 1.72
2 0 0.0% 0.00 0.00 0.00
690 207 30.0% 313.25 0.75 2.47
Short 208 55 26.4% 157.00 1.02 2.17
Price
466 106 22.7% 313.15 0.90 1.87
196 34 17.3% 101.65 0.69 1.43
36 7 19.4% 27.60 0.96 1.60
906 202 22.3% 599.40 0.88 1.83
Mid 60 6 10.0% 31.40 0.62 0.82
Price
572 82 14.3% 430.45 0.90 1.18
556 69 12.4% 395.70 0.84 1.02
158 12 7.6% 83.40 0.60 0.62
1346 169 12.6% 940.95 0.82 1.03
Longshot 6 0 0.0% 0.00 0.00 0.00
321 20 6.2% 221.10 0.75 0.51
1054 66 6.3% 1058.30 1.07 0.51
1347 28 2.1% 537.40 0.42 0.17
2728 114 4.2% 1816.80 0.71 0.34
Total 5670 692 12.2% 3670.40 0.77 1.00
Value 2336 316 13.5% 1986.50 0.99 1.11
Non-Value3334 376 11.3% 1683.90 0.62 0.93
Quite frankly, I am very excited with these results. I am unaware of any mechanical method that identifies a class of horses composing over 40% of the field who return 0.99 on the dollar.
First, I have grouped the horses by odds range with sub-totals. Within each odds range, the horses are sub-categorized by the handicapping methodology that I have developed and coded.
Second, it is clear that there are identifiable groups of horses who are money burners and out-performers. I have designated those that significantly out-perform the take and breakage as "value" and the others as "non-value". The "non-value" horses are not necessarily non-contenders; they win 0.93 of their fair share of races. But, they return 0.62. More importantly, a flat bet on all "value" horses returned 0.99.
Third, this method is completely objective. The numbers are inputed/imported to the software which designates each horses category. There is no user subjectivity. All races with each horse having one US past performance line were played at all tracks over a period of days .
Fourth, it appears that a handicapper who restricts action to "value" horses will not have to do much to increase the 0.99 to a positive R.O.I.
Fifth, the methodology does not end with the "value"/"non-value" designation. Instead, it categorizes the horses then makes betting decisions from among the "value" horses. I have not tested those betting decisions; that test is commencing now. An oddline is created to make betting decisions. The test will be of: 1) 20% overlays, 2) all overlays, 3) the largest overlay (or smallest underlay) and 4) two horse bets on the largest overlays/smallest underlays.