Chuck Simon

As most of you have heard, Charles Simon passed away yesterday at age 57. 

Although a lot of you knew Chuck better than I, I still felt a strong kinship with him. 

Conversations with him over the years were always what we want conversations to be - enlightening, learned, respectful, and most of all fun. 

I think back on my time with Chuck fondly, and absolutely loved chatting with him for his very good Going in Circles pod a year or two ago. 

On the blog, and on twitter I continually joked with Chuck, relaying that I wanted to to make him "President of HISA". Like most of the satire and goofiness here, there's more than a grain of truth to it. 

For Horse Racing Commissioner I'd want, in no particular order -

Someone smart, who understands the game. One that knows the difference between banamine less than 48 hours out, and a real needle; one that understands lasix; stable management; one that knows trainers who are good and no damn good. 

Someone with a wide range of experience. Mucking stalls, betting, the business of the sport. One that could talk with the same respect and brevity with the recent immigrant groom of the three horse in the sixth as he or she does with Jim Gagliano of the Jockey Club. 

Someone that understands the politics of the game. And has the skill and personality to navigate it. 

Someone that gets along with others to get things done. Someone we can all say "that's my horse racing commissioner" about. 

Like I said, there was more than a grain of truth to it. 

Chuck had this marvellous way about him. When you disagreed with him it was never a shut door and you still felt listened to. This worked to his advantage with me personally more times than not, because I usually ended up on his side of the argument after stepping back and thinking about his point of view. He was ridiculously sharp. 

Twenty or thirty years from now I sincerely expected Chuck to be arguing about the use of a rabbit in the Sword Dancer; why this new synthetic lasix adjunct was good or bad for the game; why that jockey or driver made a brilliant or bonehead move. But sometimes life doesn't work that way, and sadly we were reminded of that yesterday. 

I hope there's an afterlife where this good man - Charles Simon - could look down and read what's being said about him today. It's been all true. 

Please allow me to extend my deepest condolences to Chuck's closest friends and family. I will very much miss him. May he rest in peace. 

AI and Data Can Put a Number on Handicapping "Guesses"

 The NFL announced today a few new features in their data and stats offerings. 

One - tackle probability - I found to be pretty Jetsons. 

Traditional statistics like solo and assisted tackles have long been used to measure defensive performance, but they often fail to capture the nuances of the game. Enter Tackle Probability, a revolutionary AI-driven metric developed in collaboration with the Amazon Web Services Professional Services team and trained on AWS SageMaker. 

Tackle Probability leverages a tree-based machine learning modeling architecture to process millions of data points per game, incorporating 20 different features for each of the 11 defenders every tenth of a second to estimate the chances of a tackle. By predicting the likelihood of a successful tackle after a handoff or catch, the model converts these probabilities into detailed metrics like tackle opportunities, missed tackles, group tackles and more.

So, we're now looking at player movements as a real modelable data point. 

We know the NFL shoves mega-bucks into these things, whereas in horse racing we're wondering if the first-time gelding data is real or memorex, but it theoretically portends some amazing stuff for this sport, right?

We often watch, say, a completed turf race and wonder (and argue on twitter) what a faster pace would've meant to our losing closer. Imagine being able to punch in our own fractions and having an AI spit out the new finish. 

We all have the horse racing disease where results bias elicits the feels. When a rider makes a winning move it's a great move, a losing one is boneheaded. But what are the true probabilities of the move? Was Borel and Street Sense going up the wood a probabilistic losing move, not a winning one, based on his speed and margin at the finish? Could Borel have been dumb for risking being shut off, when he could've won without incident going wide?

Let's take something remarkably simple. Imagine if we had a personal AI model that spit out accurate fair odds after the race based on each horse's position, movements, pace, etc. A back-marker 40-1 shot could've raced like a 6-1 shot with this buried line - this happens all the time - and we'd actually have that quantified for ourselves to use for next time. 

I'm sure many of you as sharp players could think of a hundred things you'd want to see with this data. 

Right now, smart players and sophisticated tools can predict with accuracy some of these things. But we're truly guessing. We're guessing if our horse would've won by four if the jock went up the wood, just like we're guessing that safety Kyle Hamilton would've made that tackle at a 47% higher rate than Josh Metellus. 

The NFL and Amazon AWS and AI tools are taking some of the guesswork out of it for a very popular sport. At some point we'd have to figure horse racing tries to join that party. 

Have a nice Thursday everyone. 

 

A Class Dropper Trick

I handicapped a race last week and found a class plunger that was going to be well bet, and it got me thinking a little bit about the phenomenon. 

From datamining, it becomes pretty apparent that class droppers i) tend to win races at a higher than average level and ii) they're well bet because it's simply a very easy angle that anyone can spot. 

In the Skeptical Handicapper, author Barry Meadow illustrated this, where droppers (in purses between 20% and 40%) showed a fairly decent ROI of $0.79. Databases I use show similar. But it's very obvious that it's impossible to make money with this angle. 

What we've learned is that not all class drops are the same, so subsetting these horses into live drops versus not live drops is paramount for profit. 

One trick I've learned has nothing to do with datamining, and I'll share it with you for those interested. 

I like to look at a horse's previous replay at the higher class - which is often a poor in the pack finish  - and zero in on other horses getting a similar trip. If those horses are quality animals who would likely be chalk or near chalk in this class, and my horse out runs (or paces or trots) them, I consider this a very live drop. 

If my horse is struggling against them (regardless of the final time or speed figure) it tells me he is probably off form, and one I want to avoid. 

If you've never done this "race within a race" handicapping on drops, I think you'll find some pleasant surprises. It's remarkable to me when a tout blindly talks about a horse dropping in class and makes him or her a green light bet. Oftentimes the horse didn't fire at all the previous start, yet they are still hammered in the pools and run pretty meh. Class drops blindly drive money. Lots of it. 

Zigging and zagging is important in a high takeout game. Sure we can use databases to model trainer stats, degrees of class drops in an algo fashion, and a few other data tricks to subset these horses (to some degree of help). But using our eyes and races within a race is a neat way I've used to learn something that the data doesn't tell us. 

Have a super weekend everyone.