Results for July Baseball bets

Again one more month has passed and baseball season has turned towards the end, four months behind and three to go. July included all star game break of almost a week which meant a slightly lower number of selections last month.

Anyway, we are back on track and adjusted stakes seem to do the trick, althoug all bets were on profit as well in July.

 All selectionsAdjusted flat stakesAdjusted stakes
Selections1918888
Winners1035151
Staked1918892
Strikerate53.93%57.95%57.95%
P/L8.5411.1612.86
ROT4.47%12.68%13.98%

I think following chart is quite informative, it shows cumulative P/L on daily level for all bets as well as adjusted stakes.

And we can also see that overall, adjusted stakes have creeped ahead of all selections in early July. This was mainly due to dry spell starting around 22nd of June where all selections P/L dropped to just little more than 6 points. Adjusted stakes suffered as well, but not nearly as much as all selections. Overall adjusted stakes are currently standing at around 5% return on turnover while all selections are at little over 2% return.

All in all, it would seem that Zcode is offering some pretty decent selections and I have started thinking that maybe I should do similar test run with NHL hockey bets during the wintertime.

Neural Network Diary #3: Thoughts about inputs and ratings

Recently I have been thinking about inputs that would use in the neural network and as mentioned earlier, most will come from Racing Dossier-service. I don’t wan’t to include too many but then again not too few either. Currently I am planning to include following list of ratings.

  • Shorpro – Projected speed rating in todays race
  • SpdfigLR – Speed rating in last race
  • SHorAvD – Average speed rating at todays race distance
  • PFP – Current form class level of horse, this rating starts at 1500
  • MClSLr – Money Class Shift From Last Race. Prize money of todays race divided by prize money of last race. Anything greater than 1.07 is a shift up in class, anything less than .93 is a drop in class.
  • Raiform – Rating assessing last three races
  • Course, Distance or Course/Distance winner

I am still thinking that I might add something measuring how succesfull horse has been when it comes to pricemoney.

Originally I was planning on normalising ratings but that was before I came up with that list and now that I think of it, I might just as well use them as they are and dividing with suitably big number to bring them to less than one. Money Class shift and Course/Distance winner I am putting in as boolean values.

Only problem with that is the fact that speed figures above can be less than zero, I need to find a way to handle that.

Neural Network Diary #1: The Beginning

For a while now I have been planning on combining some ideas that I have used in the past and things that I have wanted to learn more about. And I have decided to write a diary of sorts which would serve a dual purpose of documenting this for my own benefit and potentially acting as a tutorial of sorts for others interested in pursuing similar ends.

My plan is pretty simple. I plan to create a neural network and output of that network would be further adjusted with Monte Carlo simulation. End results of this combination should be most likely winner and likelihood for that so that in addition to selection a value price would be calculated for it as well.

I am going to concentrate on 5-7 furlong All Weather races ran in UK and Ireland. Idea is to structure network in a way that pair of runners is modeled as one row of data (This is lifted from old Smartsig article, reference to which I need to dig up). Winner of future race is predicted then by comparing all pairs in the race and finding out which one wins most of these virtual duels.

This is also where I plan to utilize Monte Carlo simulation, so instead of one run through the network I am going to do it ten thousand times, or whatever figures seems like reasonable for the use when I get to that point.

As I am basically learning by doing here I welcome all comments and suggestions any reader might have.

Results for May baseball bets

Time to review how month of May was when it came to my baseball betting. April ended on a high note and I had high expectations for May. Unfortunately thas was not to be and May was losing month for my adjusted stakes. This time around plain picks worked better and were actually pretty consistent when compared to April results, return on turnover was almost the same in both months.

 All selectionsAdjusted flat stakesAdjusted variable stakes
Selections220108108
Winners1205555
Staked220123123
Strikerate54.55%50.93%50.93%
P/L10.23-12.8-3.21
ROT4.62%-10.41%-2.97%

Below is a chart showing P/L movement since the beginning to the end of May.

 

Results for April baseball bets

Guess this won’t be weekly review after all and after giving this a bit more thought I came to conclusion that monthly is better way to go.

Anyway, from the table below you can see results for bets for the month of April.

 All selectionsAdjusted flat stakesAdjusted variable stakes
Selections1829292
Winners975151
Staked18292126
Strikerate53.30%55.43%55.43%
P/L8.336.8117.82
ROT4.57%7.40%14.14%

Overall decent results and atleast for this sample it seems that my idea is holding true as return on turnover is best with my adjusted stakes and even to level stakes return is better when compared to just flat betting all selections.

But, one thing that I only now checked was what if I just adjusted stakes and didn’t filter the bottom end out (currently I drop out bottom of the adjusted stake so that four point bet becomes one point bet and anything else is discarded). Bets I do filter out are just about break even so it does make sense to drop them out, but what would have happened if I had just adjusted the stakes? Well, profits would have doubled but total stakes would have grown four fold at the same time so return on turnover was just little over 6%. Based on this I will move on to May without changing anything.

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