July Smartsigger published

June issue of SmartSigger magazine has been published. If you are a subscriber, check your members area and if you are not, then I would advise you to check it out. There is 30 day trial period which includes access to archive of past issues. This should give you an idea of content available if you were to subscribe.

My article this month is about normalising ratings in order to get a view on how race is shaped for each rating. So answering not only which is better but also by how much it is better.

New month – New SmartSigger

By lhourahane profile (Flickr) [CC BY 2.0 (http://creativecommons.org/licenses/by/2.0)], via Wikimedia Commons

It is March and Cheltenham is upon us. Coincindentally this months issue of SmartSigger is titled Cheltenham Special  in preparation for the festival. Main point being article about key trends for each race over the four day festival.

My own article for the month is about beaten favourites and if there is any money to be made with that information. Most interesting finding for me was the big difference between top and bottom success rates for trainers when their beaten favourites run again. For full list you need to subscribe the magazine but below I present top and bottom five trainers based on strikerates.




Top 5

J Ferguson257035.71%53.6676.66%11.4133.91%
D Lanigan175034.00%45.1090.20%2.8515.25%
A O'Brien5619528.72%-34.12-17.50%-5.20-4.10%
W Mullins8329428.23%21.847.43%-6.43-3.77%
N Henderson5921127.96%16.167.66%3.113.07%

Bottom 5

M Dods8829.76%-42.91-52.33%-8.45-44.05%
H Candy5539.43%-36.09-68.10%-3.76-24.06%
R Harris4666.06%-20.45-30.99%-2.71-37.34%
Mrs R Carr3545.56%-37.74-69.88%-6.29-63.85%
Richard Guest51134.42%-72.53-64.18%-7.47-55.23%


New issue of SmartSigger!

Today marks the day for February issue of SmartSigger magazine. My article this month is about a system where I utilize some Racing Dossier ratings with some of my own creation. Test for that system is ongoing at Race Advisor forums, but so far results have not been quite up to par but I am confident that results will improve.

All in all I think it is well worth a read and if you are not already a subscriber you can get your first month for free to see if the content is such that you wish to read it in the future months as well.

I am also excited about competition Michael Wilding is publishing this month were competitors need to create a system based on given historical dataset. Systems are then matched against test data to see whos system gives the best results. Unfortunately one is only able to participate if you are able to access Race Advisor forums which can only be gained buying any of Michaels products.

I already have few ideas on how to approach the competition and I will write about them here, atleast after the fact when I have seen if results were there 🙂

How accurate are overnight Betfair prices?

For one system test I am running I have been collecting early Betfair prices the night before for all of the selections before making the final decision. And I started wondering that how accurate they are. I only have data for little over 800 selections and they are part of currently profitable set of selections so that might color the results but still, I am surprised at the accuracy of the early odds. Chart over different odds ranges is shown below.

Actual number winners over the data is 179 and expected number based on early odds is 183 while expected number of winners based on BSP is only 162. Well, that shouldn’t surprise as system wouldn’t be making profit against BSP unless it was  beating the accuracy of BSP.

I need to see if I can get hold of more and more complete set of data that wouldn’t be coloured by my selection process to be able to do more throughout analysis.

This subject actually touches on very first article I wrote for SmartSigger about accuracy of Racing Post forecast and if it would be possible to make profits when betting against the public.

New Smartsigger and Artificial Neural Networks

Latest issue of Smartsigger was released a few days ago. Among other great articles there is mine where I document my first forays into Artificial Neural Networks. As I am relative beginner and wrote that article from that perspective I am constantly learning new things. One outcome of this continuous learning is that after writing that article I have already changed my toolset. Instead of Fann I am now using AI4R which I actually find to suit my workflow a lot better.

Speaking of ANN’s, I am almost done with my second version of network which determines if run a horse was a good, ok or in worst case a poor one. As I am writing this I teaching it flat races and once that is done I am able to calculate results for each last time out for runs ran in 2013 and have a first look at what effect (if any) this has on strike rate and profits. So one step closer to Suitability Score.

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