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Track Tracts

I�ve still got my copy of Winning at the Races by William L. Quirin, Ph.D. Picked it up as a new book in 1979 at Smith News in Pershing Square in downtown Los Angeles. At the time, Smith News was the only place in town that catered to horse-playing degenerates, so it was a Mecca for me. I could stop there during my 2 � hour-each-way, two-transfer bus ride that took me from West L.A. to Arcadia, where sat Santa Anita, the beloved home of all horse-playing degenerates.

Winning at the Races was and is a classic in the field. Quirin looked at many aspects of handicapping through a statistical lens, and he used (gasp!) a computer to do it. Well before the time of ubiquitous personal computers and databases galore, Quirin, a professor of mathematics and computer science, was on the job. And a fine job he did.

One of the metrics that Quirin developed to analyze handicapping factors he called "Impact Value" or "IV." As he put it, "IV statistics are calculated by dividing the percentage of winners with a given characteristic by the percentage of starters with that characteristic." According to Quirin, "An IV of 1.00 means that horses with a specific characteristic have won no more and no less than their fair share of the races." An IV of 2.00 meant these horses won twice as much as expected; an IV of .50 meant they won half as much as expected.

So, Impact Value is a ratio: the win percentage of a group of horse divided by the percentage of starters that belong to that group. For instance, let�s say you�re looking at the group of horses that won their last start. Their win percentage is 17.0% and their percentage of starters is 11.2%:

Impact Value = win% / % of starter
Impact Value = 17.0 / 11.2
Impact Value = 1.52

For last-out winners, you end up with an Impact Value of 1.52. In other words, the IV says that these horses win 52% more than they should.

But do they really? Trouble is, there�s a flaw in this metric. What if you knew that horses that won their last start went off at odds lower than average? In fact, they do. That would mean that the horses in this group might be winning a lot because of their low odds. Their winning the last race might not have anything to do with it. In fact, it�s possible that they are winning less than they should given their odds.

And that�s the key: given their odds. The denominator in this ratio needs to factor in the odds of the horses in the group being looked at. That�s why the better way to measure the impact of a certain factor is to divide the actual win percentage of horses with a given characteristic by the expected win percentage of that group, given their odds. I call this the A/E Ratio � the Actual Win% divided by the Expected Win%. It�s a much better metric than the old IV measurement. (I don�t mean to dis Quirin - he was apparently aware of the limitations of IV, since he always combined its use with a couple of other metrics which he called $NET and EW [Expected Number of Winners].)

Anyway, taking our example of the group of horses that won their last start, their win percentage was 17.0%. Let�s say their average odds was 4.0 to 1. We first need to calculate their expected win percentage. The calculation is:

Expected Win% = (1 / (Odds-to-1 + 1)) * (1 - Track Take)

If we imperfectly estimate the track take as 18 cents on the dollar or .18, then the calculation goes like this:

Expected Win% = (1 / (Odds-to-1 + 1)) * (1 - Track Take)
Expected Win% = (1 / (4.0 + 1)) * (1 - .18)
Expected Win% = .2 * .82
Expected Win% = .164

We expect horses with average odds of 4.0-to-1 to win about 16.4% of the time. Plugging this into our A/E calculation, we get:

A/E Ratio = Actual Win% / Expected Win%
A/E Ratio = 17.0 / 16.4
A/E Ratio = 1.03

So, given these numbers, the true impact of a horse winning the last race is 1.03, not 1.52. Winning last out means a horse will win only 3% more than it should, not 52% more, as the IV indicates.

In the time that I�ve used the A/E ratio, the only drawback I�ve seen is that it can be misleading with small samples. Say you have 10 horses being studied: half of them went off at 2/1 and half went off at 20/1. Their average odds would be 11/1. If two of the 2/1 horses won, you�d have an A/E ratio of 20% / 6.8% or 2.94. This is obviously misleading. This problem disappears with a larger sample because you will tend to have a mix of odds levels, not just a bunch of low odds horses and another bunch of longshots.

"The A/E ratio is the single best measurement of the power of a particular handicapping factor."

This may all seem pretty esoteric, but it�s my contention that the A/E ratio is the single best measurement of the power of a particular handicapping factor. It truly tells you the impact that a factor has, and removes the confounding influence of the odds of the group of horses being studied. It avoids the drawbacks of Return On Investment calculations, which can be easily thrown off-kilter by a couple of longshot winners. A/E is not something that you can readily calculate in your head, but it is the gold standard of the art and science of thoroughbred predictions. NC

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