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The Difference Between Probability & Profitability

POSTED Jul 19, 2014
By
Derek Simon

If there
is one mistake that I see both new and veteran handicappers make time and time
again it is confusing probability with profitability — often in very
inconsistent and haphazard ways.

For
instance, most bettors know that the post-time favorite wins approximately 1/3
of the time, making it a highly predictive factor. In fact, we can measure just
*how* predictive by employing “impact
values,” which were explained by Dr. William Quirin in his masterful work
“Winning at the Races.”

Impact
values, or IVs, are calculated by “dividing the percentage of winners with a
given characteristic by the percentage of starters with that characteristic,”
Dr. Quirin explained.

“An IV of
1.00 means that horses with a specific characteristic have won no more and no
less than their fair share of races,” the good doctor concluded. Similarly, an
IV greater than 1.00 denotes that a particular factor is producing *more* than its fair share of winners,
while an IV below 1.00 means that it is producing *less *than its fair share.

With that
in mind, take a peek at the digits I obtained in an examination of 14,505 races
featuring a sole betting favorite (no entries):

Winners:
5,409

Winners:
2,017

Win Rate:
37.3%

IV: **2.76**

What this
means is that the post-time favorite can be expected to win 2.76 times more
often than random chance would dictate — which is great.

However,
before we break out the top-shelf pork rinds and don our party hats, let me
introduce another metric — one that I came up with several years ago called the
*odds-based impact value*, or OBIV.

The OBIV is based, not on field size,
but on the average odds of the horses meeting the criteria of the study. The
advantage of such an approach is that it more accurately assesses the factor
being tested (provided the factor is not odds) by using an established and
highly predictive methodology instead of random chance to determine the
expected win rate.

*Note: The reason the “normal” range is 0.80-0.85 is to account for the
various straight takeout rates and breakage points.*

So, harkening back to our study above, we
find that post-time favorites produce an OBIV of 0.81 — which helps to explain
why, despite a high IV, the ROI on such steeds is negative to the tune of about
16 cents on the dollar.

The OBIV also explains why merely
seeking high-IV, i.e. obvious, factors never makes money in the real world — although
many handicapping gurus have advocated just that.

Tim Maas, author of “Overlay
Handicapping,” took it one step further: He used a variety of IV values to produce
a fair odds line. Now, before I illustrate the folly of this, I want to credit
Maas for at least attempting to use disconnected, or independent, variables in
his method (this is another area that gets horse players into trouble — evaluating
dependent variables as though they are independent, e.g. speed and form).

Among the factors that Maas obtained IV
value for were Quirin speed points and average earnings per start. To keep this
demonstration simple, I will provide my own IVs for specific subsets of these
factors — mainly, I will look at horses with at least eight Quirin speed points
and horses with the highest average earnings per start in the field:

* At least 8 Quirin speed points

Number: 5,068

Winners: 878

Win Rate: 17.3%

IV: **1.31**

OBIV: 0.83

* Highest earnings per start in the
field

** **(If the horse
had fewer than five starts this year, the last two racing years were used)*

Number: 13,069

Winners: 3,331

Win Rate: 25.5%

IV: **1.91**

OBIV: 0.83

By combining these two factors in a makeshift
system, we would expect an IV of approximately 2.50 (using Maas’ technique of
multiplying the individual Ivs):

* At least 8 Quirin speed points

* Highest earnings per start in the
field

** **(If the horse
had fewer than five starts this year, the last two racing years were used)*

Number: 841

Winners: 248

Win Rate: 29.5%

IV: **2.12**

OBIV: 0.87

On the positive side, the numbers are
vastly improved from those for each individual factor — even the OBIV is nominally
better. However, they’re still not good enough to show a profit. In fact, the
$2 net return of $1.67 (-16.5% ROI) is *less*
than the $2 net return for post-time favorites ($1.68).

Ouch. Two highly predictive factors and
they produce more red ink than simply watching the tote board and playing the
post-time favorite.

And the situation doesn’t get any better
when one asks for minimum odds (as Maas did by insisting on a “fair” price) — in
fact it gets worse:

* At least 8 Quirin speed points

* Highest earnings per start in the
field

** **(If the horse
had fewer than five starts this year, the last two racing years were used)*

* Odds of 3-1 or greater.

Number: 372

Winners: 56

Win Rate: 15.1%

IV: **1.15**

OBIV: 0.87

Of course, what all this tells us is that,
in order to make money as opposed to just cashing tickets at the racetrack, one
must look for unique factors and/or use known factors in unique ways.

There is a difference between what is predictable and what is profitable.

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