Betting Method Rules

Horse racing methods get bad publicity because there is so much rubbish being peddled. Systems that promise substantial wins but in reality they are just rubbish. Method vendors and creators fall into three main types. 1. Scammers: Harsh words but I think justified. These are people who have no affinity with betting methods but they have realised that there are a lot of raving fans who would like to make money from betting. They spend all of their time writing the sales message that will draw in the unsuspecting punter. They know that however poor the

method is that only a tiny percentage will ask for their money back. 2. Idiots: These are people who have found what they believe is a profitable system but in fact it turns out to be just some coincedental wins. These people dont realise the problems caused by making a method fit the data available. The thing 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. Predicting the past is always easier than predicting the future. 3. The Professional: There are of course people who know about researching trends, who realise that a factor found in one set of data needs to be fully tested on another different set of data before it can be considered reliable. If you are to buy a betting system then these are the people you should be following. Here's my step by step plan for creating a reliable horse racing method 1. look at lots of past data to find an indicator of winners. Something that looks to be common to a lot of successes that could maybe become the basis of a system. 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 system fit the data. 3. And this is the crucial step. Test your finished method on fresh data. This is how you know you are on to something worthwhile. But if you then adjust it a bit so it looks good on both sets of data then you are back-fitting again.