Dr. Z's Beat the Racetrack

Category: Puzzles & Games
Author: William T. Ziemba, Donald B. Hausch
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by PaulHoule   2022-09-24
My take is this.

Most people who work with machine learning and AI follow the herd using certain methods of evaluation that don't necessary lead to working systems. I'm more familiar with applications to business, text analysis, and a bit about finance such as algo trading.

This is one of the missing links


which is a gambling system that really makes money. The book is not very mathematical, Dr. Ziemba has written a lot about hedge funds, algo trading, etc. and probably in his body of work there is something that covers the same ground and is more mathematically rigorous.

by PaulHoule   2022-07-24
It's wrongheaded to go about horse racing from the viewpoint of "betting on the best horse". Even if your judgement is a bit better than average, the track takes a 15-18% vig and you'll still lose money unless you outperform that.

The right way to think about it is "finding a good bet". A few times a day you find a situation where a place or show bet is terribly mispriced compared to win. The tote board gives highly accurate odds to win but sometimes you see place or show paying almost the same as win except you have two or three chances to win instead of one. In a case like that if you believe the win odds are fair, place or show is a slam dunk and you can really make money that way.

See https://www.amazon.com/Dr-Beat-Racetrack-William-Ziemba/dp/0...

by PaulHoule   2022-02-11
No, Kelly is about what fraction of your bankroll you should bet if you want to maximize your rate of return for a bet with variable odds.

It's essential if you want to:

* make money by counting cards at Blackjack (the odds are a function of how many 10 cards are left in the deck)

* make money at the racetrack with a system like this https://www.amazon.com/Dr-Beat-Racetrack-William-Ziemba/dp/0...

* turn a predictive model for financial prices into a profitable trading system

In the case where the bet loses money you can interpret Kelly as either "the only way to win is not to play" or "bet it all on Red exactly once and walk away " depending on how you take the limit.

by Terretta   2019-09-07
> A breakthrough came when Benter hit on the idea of incorporating a data set hiding in plain sight: the Jockey Club’s publicly available betting odds. Building his own set of odds from scratch had been profitable, but he found that using the public odds as a starting point and refining them with his proprietary algorithm was dramatically more profitable. He considered the move his single most important innovation, and in the 1990-91 season, he said, he won about $3 million.

The article suggests he came up with arbitrage vs. other bettors in 1990, a kind hedge fund for horse racing “without precedent”.

On the contrary, my college roommate and I had Dr. Z’s Beat the Racetrack from 1987:


> Benter had achieved something without known precedent: a kind of horse-racing hedge fund, and a quantitative one at that, using probabilistic modeling to beat the market and deliver returns to investors.

In fact the 1987 edition was even preceded by Beat the Racetrack by William Ziemba from 1983, a decade earlier than the innovation described in detail here:


From the book blurb:

William Ziemba and Donald Hausch explain the fundamentals of track racing and show how patterns of public inefficiency in betting pools can lead to you reaping big payoffs. Rather than focusing on the complicated details of thoroughbred handicapping, the groundbreaking “Dr. Z” system offers mathematical models based on stock-market analysis.

William T. Ziemba is professor of management science at the University of British Columbia, Vancouver, Canada. He is an expert in operations research and portfolio management and has served as consultant to the Canadian government on lotteries and pari-mutuel betting systems. Donald B. Hausch has a doctorate in managerial economics and decision sciences from Northwestern University, and is currently teaching in the School of Business at the University of Wisconsin, Madison.