Please note: I am functioning proportionally half my normal state which has resulted in the videos coming out a bit choppy. Partly due to the medication I’m taking too. 🙂
Handicapping Standouts
I’ve made some videos today that explain how I walk through the list of Standouts and decide which ones are potential bets including Ed’s and my reasons for passing or why they are considered. Apologies for the roughness to these. It’s the best I am able to do with how I’m feeling. Also want to mention the reason for going fast is because of the video size verses what we’re able to upload for you to view and there is much to share which is why.
And in order to follow here is the pdf for today’s Standouts 1-27-23
What’s in our Betting App – What do we Have?
We’ve built a module that calculates and then assigns a rating to applicable statistics (Layoffs, Claims, Debuts, Won Last Race from our horse racing database beginning Jan 1, 2014) along with how each variable performs to these statistics separated by and specific to each track, class, distance and surface and when one of these applicable stats is in today’s race at today’s track, class, distance and surface that includes any and all shifting taking place such as with today’s jockey, owner, equipment, medication, workout patterns leading into the race and so on assigns this rating in a form of hierarchy derived through the algorithms, percentages weighed against the other runners within this same race, that then projects an outcome to the order of finish.
Then the module separates out the runners within a race whose top rating is at least 13 points higher than the next runners rating and lists these as Standouts.
The ultimate goal is for the runners that make it to the list of Standouts will Win on average 60 to 70% of the time and have an in the money hit rate on average of 80% or more so Zen Racing Stats is profitable flat betting all.
Since Version 2 we win on average between 28 and 36%, discovered through tracking the results that we actually place more than we win and have an on average overall hit rate of 63 to 72.5% in the money. Considerably up from Version 1 though we’re only about 30% in.
We’ll be releasing a copy of our program to all Zen shareholders who have signed the NDA and License agreement as soon as my equistats partner says ready (hopefully early this week) and are in the process of making videos that identifies what we have and because there is a lot to go over I thought it best to write out the constants.
These are specifics, essential to keep in mind when viewing each race because none of this is mainstream. As an example, at first glance of this screen shot you may view Todd Pletcher numbers impossible, too low as Pletcher has lots of MSW runners.
He does, but it is key to note that these numbers are specific to AQU, at 7 furlongs on the dirt in a MSW class yet the MSW purse may be 50K and tomorrow he has the exact same everything but the purse may be 75K so his numbers will be different yet the display of MSW will look the same which explains why you may see different. This also defines how powerful any positive percentage is because you will also view many zeros in a race which simply means this is the first time today’s trainer is running in all things specific to this race.
Without getting too off topic an excellent point to make is during this building process we track results and from these we learn what to adjust meaning it is possible that separating MSW by purse payout may be too tweaked and the numbers might increase if we combine all purse levels. We noticed that this happen with Distance and understood adjustments that we should make. Yet in order to ready Version 2 so we could show proof of concept so we could raise additional capital to continue progressing we had to wait. Perhaps inclusion of such would have increased our win percentages even more.
The follow defines the constants:
Track Specific:
Only identifies races that any one trainer or trainer/jockey combination ran at this specific track. Does not include overall record
Class Specific
Class is segmented by purse so as an example when you see a Trainer enter a runner in a Claiming Race with a purse of $10,000 at 6 furlongs on the dirt at Aqueduct, and same trainer has a runner in another Claiming Race with a purse of $20,000 at 6 furlongs on the dirt at Aqueduct, even though the program displays that today’s race is a Claim for both, the actual calculation on the number of tries and win place and shows will be different from the $10,000 purse even though everything else is the same.
Distance Specific
Each distance 5f, 5.5f, 6f, 6.5f, 7f, 7.5f, 8f, 8.5 and so on is calculated specific to.
Also, different by surface so 5f on the turf is not the same as 5 f on the dirt.
Surface Specific
Most race tracks have more than one surface so all information provided is specific to each.
4-Race Form Cycle Applicable Statistics for:
Layoffs:
1st After a Layoff
2nd After a Layoff
3rd After a Layoff
4th After a Layoff
Claims:
1st After a Claim
2nd After a Claim
3rd After a Claim
4th After a Claim
Debuts:
1st Race Debut
2nd Race Debut
3rd Race Debut
4th Race Debut
Won Last Race:
1st Race After a Win
2nd Race After a Win
3rd Race After a Win
4th Race After a Win
Even though the statistical count stops and is only identified within the module when in the 4-Race form cycle, the algorithms record all finishing positions.
and
The percentages are not cumulative and actual finishing positions.
This week will be filled setting up access to our program and making videos to walk through. All shareholders that would like a copy of our program are welcome to this when ready after signing the NDA and the Licensing Agreement. Let us know if you did not receive these docs and want a copy by emailing my assistant Jon at edbain@edbain.com and he’ll send these to you.
After this I will share a plan/idea on a way Zen can continue forward in my absence with a beneficial solution to all involved, even our non horse player investors that could take care of operating costs and being able to do so without going through crowd funding.