Results after one week of baseball

Below you can see results after first week of baseball bets. Table has three columns, first one shows the results for all selections available from Zcode, second and third column share selection criteria (confidence level over 40%) but second one is flat stakes and third one to adjusted stakes.

I included second column to act as a reference to see if my adjusted staking is of any value. And based on this small sample it does seem to have value, results are better by all metrics. Strikerate is really good I think, I would expect it to hover at slightly under 60%

 All selectionsAdjusted flat stakesAdjusted variable stakes
Selections412424
Winners231515
Staked231538
Strikerate56.10%62.50%62.50%
P/L8.94
6.9116.02
ROT21.80%28.81%42.16%

I might make this kind of status check a regular feature. Let’s see about that next week.

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

TrainerWinnersRunnersSR%P/LROT%VSPVSP%
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

TrainerWinnersRunnersSR%P/LROT%VSPVSP%
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%

 

Trainer consistency per lay off range

So far I have been using days since last run on per horse basis. But for a while now I have wanted to explore that further. Article in old Smartsig sparked an idea (again!). In issue 9.08 from August 2002 (page 30) there was an article about quick return trainers, ie. trainers who have been succesful with horses that return to action within 7 days.

But I didn’t want to look only quick returners but all different day ranges. Ofcourse there are hundreds different number of days that horse can return after so I needed to group them somehow. To get some kind of idea how the runs are grouped I took data from 2012-2014  and divided them evenly to 6 groups. This lead to some interlap between certain dates and grouping was also a bit counterintuitive. For that reason I ended up with almost but not quite even grouping as follows.

Days sinceGroup
No data-1
00
1 - 71
8 - 142
15 - 213
22 - 284
28 - 505
51 - 2006
200+7

After determining group for each run in my database I calculated two values for each run by comparing trainers success with that Days since-group. First, Suitability Score by comparing ratio of good and bad runs and second strikerate for that trainer in that Days since-group.

Hypothesis was that higher the suitability score or strikerate in a group then higher the strikerate these horses would have. And this was true to certain extent. Two charts below from 2013 and 2014 show strike rates on a range -100 to 100 for suitability score and 0 to 100 for strike rate.

Interestingly strikerate drops as as we get to top end of the range as was the case with trainer form as well. This might partially be due to smaller number of samples.

 

Black cat in a Ballpark

Random enough headline? I picked up a book called “How to Find a Black Cat in a Coal Cellar” after encouraging review by James Butler of Betfair Pro Trader. And the review didn’t lie, it is a good book and I can wholeheartedly recommend it. And as mentioned by James in his review, while it is written from a viewpoint of analysing tipping services nothing prevents one from using the same ideas when analysing homegrown systems and strategies. I plan to use these tests for my own systems later on but as a practice run I will begin with analysing a betting record I have from last year.

At the beginning of my betting life I ran a tipster proofing blog in Finnish. One of the tests and the longest running one was for a service called Sports Picks Buffet. Service collected tips from several sources and then chose best bets based on ratio between opinions of different tipsters. Service alternated between seasons with MLB and NBA and during my test all tips were for baseball. Buffet was ran under a pseudonym and fake Phd credentials which person behind the system came clean about a short while after my test concluded. Currently the service is run under a presumably correct name and shortened name of The Picks Buffet.

First. lets look at basic info about the betting record. There were total of 336 bets over roughly a three month period and all were baseball moneyline bets. During the test period meagre profit of 6.38 points or return on turnover of 1.90% was achieved. Strikerate during the test was 60.12% and average odds were 1.71. In the chart below you can see how bets unfolded.

As you can see, there was an healthy upwards trend in the results, which was followed by a decline and a new climb. Profits never rose to any extreme heights.

How about statistical significance? Testing this was biggest takeaway from the book I mentioned at the beginning of the post. Anyway, for these bets we can calculate a p value of 34.04% meaning that there is over one third of a chance that results above are purely due to chance alone. Black cat book recommends a conservative limit of 0.01% and when value should be below of that for results to be considered statistically significant I think it is safe to say that these results are not.

If we were to draw a normal distribution with average results above we would get a figure as below. But ofcourse there were many bets above and below of 1.71 that was the average. Betting with odds of 1.71 and fair odds of 1.66 there would still be a 26% chance of negative return.


(Sport) Picks Buffet is priced at pretty expensive 125$ per month and I don’t think these results quite justify that kind of a pricetag. Especially when there is service providing basically a same kind of service for cheaper. I haven’t personally tried this, but based on info on their site it could be used in similar way. So to me Scamdicappers seems to offers the same thing, only for multiple US sports and for 50$ per month.

 

Beginning of an era!

Welcome to this new blog. Where I am going to write about horse racing and betting. Name of the blog comes from two words Racing and Analyst, simple as that. At least initially blog is going to concentrate on flat and jump racing from UK and Ireland. My analysis style is systems and statistics so the content of this blog will lean on heavily in that direction.

But this short introduction will have to do for now, I need to get back to final tweaks and adjustments to get this train moving. Hope to see you back when I have actual content to show.