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Sartin Methodology Handicapping 101 (102 ...) Interactive Teaching & Learning - Race Conditions, Contenders, Pacelines, Advanced Concepts, Betting ...

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Old 08-12-2016, 06:39 PM   #1
Bill V.
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FTS work out Patterns at Del Mar MS

Mark showed us a beauty MS First Time Starter winner from Penn National
in this thread

http://paceandcap.com/forums/showthr...t=10534&page=2

Mark then gave a excellent breakdown on the trainers pattern and how the horse reacted and became fit and ready to win .

Here are the MS winners from the current Del Mar 2016 summer meet.

First here are some stats from my DB

and then I have screen captured all the winners workout info from RDSS

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Old 08-12-2016, 06:41 PM   #2
Bill V.
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More MS 63 winners

Open and State bred

FTS winners

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Old 08-12-2016, 06:43 PM   #3
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More

Del Mar MS63 and MS s63

Name:  _9.JPG
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Name:  _11.JPG
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Name:  _10.JPG
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Old 08-13-2016, 08:53 AM   #4
Mitch44
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Great post Bill. I can see many patterns here and gems that can be mined if one uses the Doc's 55.00 FPS guidelines etc. Even better yet the other advancements in RDSS such as FPD etc. Overall a much unused tool,however in the box that old heads take advantage of to get a handle on condition. Also looks like some nice odds on some of those FTS. Good miners know their tools and dig deeper.
Good capping,
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Old 08-22-2016, 11:36 AM   #5
Tim Y
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classic apophenia
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Old 08-22-2016, 11:40 AM   #6
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another fact seen all the time with the mishandlig of data:

In statistics and machine learning, apophenia is an example of what is known as overfitting. Overfitting occurs when a statistical model fits the noise rather than the signal. The model overfits the particular data or observations rather than fitting a generalizable pattern in a general population.
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Old 08-22-2016, 02:14 PM   #7
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Overfitting occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. A model that has been overfit has poor predictive performance, as it overreacts to minor fluctuations in the training data.

Two other things complicate this small sample size (sample error) along with confirmation bias: only looking for data that support the incoming premise, NOT ALL the DATA.
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