[Hornswoggled]

[COLOR=#000000 !important]Hello everyone 1st post here,
I need help from the more Mathematically minded people on this forum. I have read quite a few threads here and have no doubt I'm asking this question at the right place.
I have a data base of results with their predicted Ratings from 100 - 70 and their S/R%.
For years I have been pricing horses in upcomming races in the same way and have noticed there always appears to be a bias in my method towards the shorter priced horses not being short enough and the longer priced not being long enough. Below is how I've always done it.

A B C D
Horse Rate S/R% Normalised Div
1. 100 .228 2.7
2. 95 .101 5.2
3. 92 .082 6.4
4. 90 .069 7.4
5. 85 .044 11.9

The rating is entered into the upcomming race spreadsheet col [b], it then looks up and returns the strike rate col [C], the strike rates are then summed and the individual runners' strike rate is then divided by the total and the Div is calculated in col [D]. This can be done for fields up to 24 runners.

I have since read of another [much better] method whereby a 'logistic value' is created from the strike rate% data and that 'logistic value' is normalised into dividends. Does anyone understand this or know how to do it ?

Your help will be greatly appreciated.
Thanks.[/COLOR]

[MonkeyF0cker]

I'm not entirely sure what you're referring to as a "logistic value" but multinomial logistic (LOGIT) regression is often used to handicap horse racing. Unfortunately, the complexity of such a model goes far beyond simply normalizing a column in a spreadsheet.

[statictheory]

As someone who plays them for a living. I would just
Urge you not to go that route. Only a couple per race are trying to win. Put your focus on the morning line in relation to the opening price.almost impossible to win handicapping.I can't spill the beans more