With regards to "good spots and bad spots"; if one could find the situations were the formulas are underperforming and I'm sure there are flaws - one could even find situations better than before..?

What do we actually know about what goes in to these formulas? Some insight, I guess, is to be found here: https://www.wired.com/2002/03/betting/ (both Benter and The Efficiency of Racetrack Betting Markets mentioned, thanks again for that recommendation Rocky, a copy is on it's way).


An extract:

The bedrock of a predictive betting system resides in a massive collection of data on each horse - including details about the tracks and jockeys. "You massage all of that information into a mathematical equation that can be used for predicting probabilities," he explains. "If you wanted to get started in this, you would spend a year building the probabilities system, and it could cost $1 million to put together." And that data bank needs constant updating.
Benter, for example, has employees whose sole job it is to review race tapes after every meet. They judge each horse on 130 characteristics - attributes like speed during the first third of the race, whether it got bumped coming out of a turn, the quality of its recovery from the bump, and, of course, how it finished - and assign numerical grades. This information goes into the database, where it can be cross-referenced and called up to help predict the outcome of any impending race that particular horse runs in.

The computer essentially simulates the race before it happens, based on what has transpired in the past and any anticipated conditions in the future. The software then determines each horse's likelihood of winning a race. When a horse's computer-generated odds are better than the public's odds, the team slams in its wagers. "You create a model that can analyze each type of bet, judge the conditions [in terms of money in the pool and the associated odds], and tell you when it will be most favorable to bet," explains Ziemba. "You do not necessarily want to bet a ton every time - you only do it when you can find advantages."

One top bettor explains it like this: "Our computer program churns through the history of the horses and adjusts all the probability in a very sophisticated way. Having established the probability of the horses, we feed that into our betting program, which looks at all the odds for the various outcomes. It looks at your true chances of winning with the latest payoff odds and calculates what the best potential bets are, based on the chances of winning and the odds. Then it runs through all the probabilities.

"The mathematical aspect involves [following] a basic formulation that all successful gamblers use - whether they know it or not," the bettor says. "It's having what mathematicians call a positive expectation on the bet. You multiply the probability of winning times the payoff odds of one bet. Let's say the horse is 20-1. If it has a .05 probability of winning, you multiply that by 20-1. You get 1.0 - or 1-1 - and that is a fair payoff bet.

"But if that same horse is paying 25-1, then it has a positive expectation. Now it is 1.25 [or 1.25-1]. It gives you a 25 percent edge. Given that you know the true probability of winning, the amount to bet is a closed-form problem based on how much you can lay down without hurting your odds."

Designing the software to do all this is a delicate operation with seemingly endless pitfalls that can disastrously skew the results. "You have to understand," says Ziemba, "that building this system, maintaining it every week, and updating the model once a year is a lot of work." And doing the work does not necessarily guarantee success. Benter went broke at least once before his system was efficient enough to turn a steady profit. "Every year, more and more people come here and leave with their tail between their legs," says Dufficy.
Whoever writes the team's software needs to decide early on which aspects of a horse's performance to take most seriously. For instance, if a debuting horse's odds of winning are 50-1 and it wins its first race, the software will note that - and might be inclined to view untried horses with long odds as good bets. So the system must be tweaked to give little weight to those outcomes.

Other, more ambiguous factors - turf firmness, recent time trials, second-place finishes, and the jockeys' racing styles, to name a few - must also be taken into account. "Memory is another thing," suggests Kelly Busche, an economist who has taught at Hong Kong University and consulted for one of the major teams in town. "How quickly do you discount information? And to what degree? What happened two seasons ago should carry less weight than what happened last season. You need a model and a database that are both agile and robust enough to handle a variety of ever-changing situations."

-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

...And then they "calibrate"... And calibrate. And calibrate, and calibrate. And as soon as, or actually probably before, you find a flaw - that door will be shut down with their latest adjustment.

And this was, what, 15 years ago?

Paradoxically, at least paradoxically for some of us, using Thoro-Graph and a "tunnel view reliance" on Kool-Aide theories against these guys doesn't seem like the worst idea.

All you need is a Practical Joke to come in once in a while.

They weren't exactly all over him..!