Hi Ocho
Yes, UK greys are a different scenario all together. 6 RUNNERS and I've read all boxes even out for the win. But AUS is a squeeze in the middle.

p.s- Keep up the trading as you've found your gift!

No it's not automatically disadvantaged.
Depends on the track and distance, but most importantly the racing style of the other runners versus the runner in question.

But the stats may show different because they incorporate those not disadvantaged and those disadvantaged.

It's important to take note of how many races were not disadvantaged and the strike rate, versus those disadvantage and the strike rate.

It's the same with most scenarios, only punters are not good at splitting data in general, only the pros.

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Hi Chrome
I agree the market will factor most things into the odds. I just love laying 6 at odds below $6 and watching it get smacked frequently in the first 50 metres.
By the way, your work on IAS ratings is a gem and showing 9.4% POT. Thnx

Looking at 90 dog races today:

box 6 won 8 of them or 8.89%
box 5 won 15 of them or 16.67%
box 4 won 6 of them or 6.67%

Illustrates box 5 is not twice as good as boxes 4 and 6, no way.
Listen to the audio or watch the video and it's not hard to see that it's not the box, it's the makeup of the field.
That's why champion dogs can start in any box and win, average dogs need a good draw and luck.
Poor dogs, doesn't matter what box, they need pure luck.

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I'll add I lay 6 between $3-$6 to avoid the really good one's you're referring too. Out of the measly 8 winners today how many fell within this price range?

Only one of them was between $3.00 and $6.00 so good going, the rest were two odds on dogs and the rest were outsiders.

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Thanks Chrome. Would have been another good day if BF was up. Under-thought but has caveman logic.

The backfitters out there should ask themselves the question, how can Joe Public get it right time in time out in there probs / actual results, yet when they run their back fitted systems, they fall into a hole at some later stage and they have wild fluctuations/variations.

My response was a method in how to have very little variation thru the data spaces, plain and simple.
Case closed.

Re Box 6

I can post some stats re the #6 box by justing adding one more variable you can identify plenty of dogs to lay.
As I keep a national database for greyhound racing i can provide an example.

One wouldn't use a blanket statement and say #6 is the worst box at Cranbourne 520m
These are the avg finish times and the avg split times fro the above distance.

1 2 3 4 5 6 7 8
31.168 31.075 31.115 31.134 31.107 30.938 31.025 31.109

1 2 3 4 5 6 7 8
5.624 5.620 5.626 5.641 5.604 5.588 5.607 5.625

One would ask why? This is where the physical artifact of the track takes over.

Woof,

I think Im starting to understand. Let me paraphrase this to check I have this right.

The first thing I do is find variables which give me an understnading of the crowd odds. Once I find variables (such as barrier) which give an approximation of the crwod odds based on that variable I think start to categorise the actual results of any type of category. This could be last start winners, favs under $3 , top rated horse by tatts, top tipster selection, etc. It might even be more specific categories such as track/barrier combinations.

Once I have these two things I plot them on a scatter plot. By doing this with two different colour sets (say red and blue) I will visually be able to see clusters of where certain categories do deviate from the expected odd distribution.

Based on this clustering I should be able to find profitable situations that long term will be profitable ?

Is this right ?

If it is this still feels like backfitting but I guess your generalising the backfitted rules rather then being specific.

Of course I might have taken this the wrong way completely. Can you tell me if I am on the right path or if I have gone walking off on my own path again.

The variables you will be trying to find are ones that are "crowd behaviour" variables not handicapping variables such as Favs under $3.00 etc. don't think like a computer handicapper, this is where people go wrong they try and do analysis by what works with my handicapping, instead you need to be finding races statistically alike. You need to categorize by underlying statistically similarities You then have knowledge in how to apply effective handicapping based on these categorizes.