Open the Racing Form. Look at the past performances for any horse. What will you find? Contradictions. This horse was second last time. That's good. But he's finished second in five of his ten starts. That's bad. He showed early speed last time. That's good. But he's liable to be faced with speedier competition in this race. That's bad. He's got a good trainer/jockey combo. That's good. But the jockey is 1 for 30 at this meet so far. That's bad. 

And what isn't contradictory is often irrelevant. For instance, handicappers used to develop systems based on the idea that less weight carried was a good thing. It makes sense, right? Put enough weight on and you could stop a locomotive. Then studies came out that showed that the more weight a horse carried, the more likely he was to win and the higher his effective payback would be. Whoops. Or regarding current handicapping practices, here's a question: what's the most widely-used handicapping factor? My guess would be that the old standard, the horse's finish in its last race, is still the most-considered factor by the public at large. Yet large studies have shown that there is less wager value to be found in last race winners than in horses who finished third or out-of-the-money but in the front half of the field. In other words, it's an irrelevant factor. 

Faced with contradictory and irrelevant information like this, what's a handicapper to do? Do all the contradictions just cancel each other out? What factors are relevant? What straws are worth groping for in this data swamp that we call handicapping?

An important rule of thumb comes from a source I've mentioned before: David Dremen, in his stock-market investing book Contrarian Investment Strategies: The Next Generation. Dremen posits this rule: "Don't rely solely on the �case rate.' Take into account the �base rate' � the prior probabilities of profit or loss."

Dremen's investigation into the cognitive biases of those making predictions clarified a common human flaw. When making decisions, we tend to become excessively involved in the details of the particular situation we're facing (the case rate) and disregard the outcome of similar situations in the past (the base rate). The base rate is our best and most logical guide to what will happen in this situation. The problem is, we tend to get involved with the story today and forget how all the similar stories ended in the past.

In racing, the base rate is best defined by the A/E Ratio (Actual win% divided by Expected win%). The question you must know the answer to is this: Have horses with this characteristic won more than they should have given their odds? If a factor or method shows a strong positive A/E, it's an important base rate factor that you can ground handicapping decisions on. Conversely, a strong negative A/E can also be used, for elimination purposes.

For instance, a software program I designed might tell me that horses with an outside post position were winning way more than they should at a certain track/surface/distance. This is valuable base rate information. If I ignored this base rate information, I might have handicapped the race, saw that there was an early speed horse on the rail, and mistakenly given that horse an edge.