Betting Method Rules

Horse racing systems get a bad press because there is so much rubbish being peddled.

Systems that promise substantial profits but in reality they are just junk. Systems marketers and builders fall into three main camps. 1. Con men: Strong names but I think deserved. These are people who have no interest in horse racing systems but they have discovered that there are a lot of hungry buyers who would like to win money on betting. They spend all of their time crafting the sales letter that will draw in the unsuspecting customer. They know that however poor the method is that only a small percentage will ask for a refund. 2. Idiots: These are people who have found what they believe is a profitable method but in reality it turns out to be just some coincedental wins. These people dont understand the problems caused by making a system fit the data available. The fact is that given any set of data the determined person can find a set of rules that could be applied to that data that will find enough winners to make a winning system.

Hindsight is 20 20. 3. The Expert: There are of course people who know about researching trends, who realise that a trend found in one set of data needs to be re tested on another different set of data before it can be considered reliable. If you are to buy a betting method then these are the people you should be following. Here's my step by step plan for creating a workable horse racing system 1. look at lots of old data to locate an indicator of winners. Something that looks to be common to a lot of successes that could maybe become the basis of a method. 2. Hone that idea by adding known sensible filters, things like days since last run (recent runs indicate that the horse is likely to fit), course and /or distance winners (have proven they can win in that race type) etc. If you have a filter that says the horse must have run between 1 to 4 days ago or 10 to 20 days ago (IE but not 5 to 9) then you are just making your method fit the data. 3. And this is the critical step. Try your completed method on raw data. This is how you know you are on to something genuine. And if you then adjust it a bit so it looks good on both sets of data then you are back-fitting again.