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

If you believe the odds to win are right at the track you will see sometimes that the payouts to place and show are really high in comparison, sometimes almost as high to win but you get 2x or 3x the chance to get a payout.

You are not looking for a good horse, you are looking for a good bet and you will sit out many races until a good opportunity comes up. Maybe 2 or 3 races a day have a bet with odds so favorable to overcome the track’s rake.

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

https://scikit-learn.org/stable/modules/calibration.html

if you have a prediction that says "this is in class A" that's not very useful in itself. If your predictor is calibrated and says "there is a 55% chance that this is in class A" you probably don't want to take action that prediction but if it says "there is a 97% chance this is in class A" you will take action.

IBM Watson won at Jeopardy because it was calibrated and could make a rational decision of whether or when it should hit the button.

From an algo trading point of view the other thing you need to turn predictions into actions is

https://en.wikipedia.org/wiki/Kelly_criterion

I bet there is some similar way to turn predictions into actions based on control theory. Speaking of betting there is a practical application of this in this book

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

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.

⬐ BOOSTERHIDROGEN

Really good insight is there any books other than Beat Racetrack ? Thanks

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...

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.

⬐ kqr

That is a much narrower view of the Kelly criterion than the general concept.

The general idea is about choosing an action that maximises the expected logarithm of the result.

In practise this means, among other things, not choosing an action that gets you close to "ruin", however you choose to measure the result. Another way to phrase it is that the Kelly criterion leads to actions that avoid large losses.

⬐ PaulHoule

Actually

https://en.wikipedia.org/wiki/Kelly_criterion

"The Kelly bet size is found by maximizing the expected value of the logarithm of wealth, which is equivalent to maximizing the expected geometric growth rate"

In real life people often choose to make bets smaller than the Kelley bet. Part of that is that even if you have a good model there are still "unknown unknowns" that will make your model wrong some of the time. Also most people aren't comfortable with the sharp ups and downs and probability of ruin you have with Kelley.

⬐ kqr

I've long found that Wikipedia article woefully lacking in generality.

1) The Kelly criterion is a general decision rule not limited to bet sizing. Bet sizing is just a special case where you're choosing between actions that correspond to different bet sizes. The Kelly criterion works very well also for other actions, like whether to pursue project A or B, whether to get insurance or not, and indeed whether to sleep under a tree or on a rock.

2) The Kelly criterion is not limited to what people would ordinarily think of as "wealth". It applies just as well to anything you can measure with some sort of utility where compounding makes sense.

The best overview I've found so far is The Kelly Capital Growth Investment Criterion[1], which unfortunately is a thick collection of peer-reviewed science, so it's very detailed and heavy on the maths, too.

[1]: https://www.amazon.com/KELLY-CAPITAL-GROWTH-INVESTMENT-CRITE...

⬐ PaulDavisThe1st

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