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Old 09-27-2020, 10:22 PM   #23
Nitro
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Join Date: Feb 2009
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Quote:
Originally Posted by MPRanger View Post
Been listening to The Perfect Bet on Audibel. It was interesting that the author said Bill Benter the Hong Kong horse racing expert found that betting overlays lost him money until he adjusted to a number between his model and the tote board.

Also that only a population of about a thousand horses raced between the two Hong Kong tracks and shippers are rare. Seems like a database could easily be maintained. Maybe that's why he could use a model vs handicapping with tried and true elements of handicapping.

All thru the book the author refers to top contestants in racing and sports as predictions. I don't see handicapping as making predictions but evaluations where price is the determining factor.

Great book though.
Getting back on topic: You might also want to consider what Bill Benter has personally stated on this topic. If you don’t recognize the name here’s a link:
http://www.worlds-greatest-gamblers....illiam-benter/
I certainly respect his credibility, because of his obvious accomplishments and capabilities.
His comments (below) are without a doubt significant to anyone interested attacking this game:

Quote:
Excerpts from:
“Computer Based Horse Race Handicapping and Wagering Systems:”
A Report by William Benter

INTRODUCTION
The question of whether a fully mechanical system can ever "beat the races" has been widely discussed in both the academic and popular literature. Certain authors have convincingly demonstrated that profitable wagering systems do exist for the races. The most well documented of these have generally been of the technical variety, that is, they are concerned mainly with the public odds, and do not attempt to predict horse performance from fundamental factors. Technical systems for place and show betting, (Ziemba and Hausch, 1987) and exotic pool betting, (Ziemba and Hausch,1986) as well as the 'odds movement' system developed by Asch and Quandt (1986), fall into this category. A benefit of these systems is that they require relatively little preparatory effort, and can be effectively employed by the occasional race goer.

The complexity of predicting horse performance makes the specification of an elegant handicapping model quite difficult. Ideally, each independent variable would capture a unique aspect of the influences effecting horse performance. In the author's experience, the trial and error method of adding independent variables to increase the model's goodness-of-fit, results in the model tending to become a hodgepodge of highly correlated variables whose individual significance's are difficult to determine and often counter-intuitive.

Additionally, there will always be a significant amount of 'inside information' in horse racing that cannot be readily included in a statistical model. Trainer's and jockey's intentions, secret workouts, whether the horse ate its breakfast, and the like, will be available to certain parties who will no doubt take advantage of it. Their betting will be reflected in the odds. This presents an obstacle to the model developer with access to published information only. For a statistical model to compete in this environment, it must make full use of the advantages of computer modeling, namely, the ability to make complex calculations on large data sets.

The odds set by the public betting yield a sophisticated estimate of the horses' win probabilities.

It can be presumed that valid fundamental information exists which can not be systematically or practically incorporated into a statistical model. Therefore, any statistical model, however well developed, will always be incomplete. An extremely important step in model development, and one that the author believes has been generally overlooked in the literature, is the estimation of the relation of the model's probability estimates to the public's estimates, and the adjustment of the model's estimates to incorporate whatever information can be gleaned from the public's estimates. The public's implied probability estimates generally correspond well with the actual frequencies of winning.
BTW the last 2 sentences express exactly how Mr. Benter was able to achieve his success.
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