The *Roll* of the trap dice

We've gone to the dogs.
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jimibt
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MemphisFlash wrote:
Thu Oct 15, 2020 8:16 pm
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another 3 in a row
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MemphisFlash
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As Requested, :D :D :D :D
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Anbell
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MemphisFlash wrote:
Thu Oct 15, 2020 5:03 pm
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you were saying!!
whats that interface?
sniffer66
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I'm confused. The correlation question is based on the same venue, surely ?
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MemphisFlash
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first 3 this morning
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jimibt
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looks like there's devils work at play here... 1st the 666 yesterday, now the devil's little helper - 555 :D

so yeah, looks like this idea of a roll of a dice is potentially a no dice. it does make you wonder tho if there are calculations that would allow you to review random distribution in any way. my original thinking tbh was how this would stack up if used against a BIG dataset and fed into a machine learning model. you'd use the trap wins (as the features) in concatenated sequences (123,236,366,666,662) etc where each group was composed of the last 3 results. using this model, you'd input the last TWO resuts (concateneated - i.e. 12, 23, 36 etc) and it would use the model to attempt to predict the 3rd digit. (in my view, this would predict a potential trap to avoid in terms of a lay bet, rather than trying to pop the winner).

as i said, i set this thread up for a bit of fun and am getting exactly the feedback i'd hoped for in terms of proof that randomness can come in many forms, therefore some sort of pattern recognition might be a fun approach.

anywayz .... :D
towelfox
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I would be very surprised to see that produce consistent success.

I have the dataset but not the inclination to spend the time trying it out. I'm certain there is more to picking a winner.

Remember, the dogs are not randomly distributed but placed in a trap by the RM in graded races.

Good luck 😃
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jimibt
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towelfox wrote:
Fri Oct 16, 2020 9:12 am
I would be very surprised to see that produce consistent success.
lol - for sure. i'd be equally surprised but having spent some time using ML.Net, i've witnessed some interesting outcomes when using the CORRECT (caps mode to signify raised eyebrows ;)) model against apparently unconnected data events. in my other life, we use ML in an application that reviews all the tv advertising across numerous channels in an attempt to identify advertisers misrepresenting the demograph that they have pitched their campaign against (in tv world, advertising is sold at varying rates, depending on the demograph that you are targetting, therefore ABC1 adults is theoretically the most expensive target, so some advertisers position their campaign to capture that audience without actually paying the full cost).

in short, thinking that some big data modelled in some way might produce either quite interesting or garbage results... more fun and frolic tbh
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MemphisFlash
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dogs done for the day, now working on footy
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MemphisFlash
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Thank you boring Derby!!!, lol .
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darchas
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MemphisFlash wrote:
Fri Oct 16, 2020 9:40 pm
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Thank you boring Derby!!!, lol .
:D
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