pie publishing again

[sligg]

Results as 11/22/02

Bets: 158

Wins: 67

W%: 42.4

$2 bets: 357.4

Net: 41.4

ROI %: 13.1

There are eight days left so the sample should have more than 200 bets. The data is entered on an Excel spreadsheet and I'm sure I can increase the ROI by eliminating some subsets. One subset would probably be eliminating selections that go off at odds of less than 1/1.

Anyone want to suggest other subsets to consider.

[jackad]

Sal,
I've only been tracking this for the last 6 days, but I notice that both win frequency and ROI are better when no selections identified as "nod" are bet.
Jack

[sligg]

jackad

nod=no wins at the distance in the races listed in the pp.

There were 60 bets with the nod factor

27 won: 45%

Net: $27.4

ROI: 22.8%

Even though this factor is negative these stats are better than
the total sample thus far:

ROI of 22.8% vs 13.1%

How do you explain it other than it's contrary to good handicapping.

[sligg]

jackad

Maybe nod is the subset I'm looking for.

If you extract the nod stats from the total stats here is what the sample in my first post looks like:

Bets: 98
Won: 40
W%: 40.8
Net: 14
ROI %: 7.1

What do you think-worth pursuing?

[jackad]

I've also noticed that during the last 6 days those selections with a "D" (capitaized) showed 6 wins and 2 losses for a profit $8.60 (ROI 53%). But obviously a VERY small sample.

I suggest that even a sample sizeof 200 races may sbsequently crash. It's just happened to me with an approach that I initially had high hopes for.
Jack