I strive to be data driven vs. static rules. What data has shown me is that pace handicapping is for real and all other factors are dubious at best. Never was able to make much sense of Trainer stats. Is a 22% trainer (read 78% loser) that much better than a 15%er? I suspect yes but negligible. And if you get into the weeds then you end up with too few observations to rely on. I like the BRIS PPs format in general and have come to believe Thorograph figures and form factors are also useful but no panacea on their own. If they show Horse A is really a slow animal that has to have a lifetime best, it's usually so in reality. Rich Strike was in the ball park that day btw.

Wednesday evening is a good option for me. If not, Monday or Thursday. I don't have a preference on what the number limit should be for beta release...deferring to Dave on that.

Hi, fellow deTERMINATORs. My name is Atakan (Kahn is the nick) and I am based in Las Vegas. I grew up in Turkey, where I first went to horse races with my dad as a kid. I've been in the U.S. since mid-90s...first to study than with work. I have been in the software industry in the capacities of Product Manager/Marketing Manager including stints with Big Tech as well as startups. Currently, I'm with a Machine Learning software startup helping enterprises make sense out of their data, e.g., fraud detection.
As a handicapper, I've been mostly betting on big days due to time constraints and had a mixed bag of results but not profitable in aggregate. During the pandemic, I developed my own statistical models on top of Brisnet data and had better success...work in progress. I'm here to improve and better understand the fascinating problem of handicapping, which strikes me as majority science and minority art/luck. If things turn profitable too, I won't be too upset about it! :)

Dave is right about Deep Learning/Neural Nets being prone to overfitting to the data at hand. In my experience, when we are dealing with tabular data (as opposed to hard to feature engineer data types like images or video input) even more primitive algorithms like Logistic Regressions can outperform overly complicated and brittle pure Deep Learning/Neural Net approaches.

Nest had her troubles but what a filly...great effort!

A lot to digest here. I have been a Thorograph sheets user for a number of years and it seems they really like Nest in this race drawing a line on her Oaks performance and emphasizing she'll have some weight advantage against the boys. Plus the Curlin factor = bred for the distance. Not to be overlooked...probably the odds will have shortened when the race is off.

Thanks, RanchWest. The Derby sure felt like a chaotic race to my bankroll! :))

Does this approach work on races with many starters like the Kentucky Derby? For instance, was the Mine That Bird derby a chaos race?