As I wrote in the beginning of May this season in F1 is really good for punting. It was impossible some of the best drivers last year to finish without a single win and Hamilton did it. He gave good profit too. 15 was the coefficient for him. I was a bit surprised to see him winning in Hungary, because on this track Brawn was excepted to be better than everybody else.
Unfortunately, I picked the wrong driver. I preferred Kimi, but there is hope for him to win a race. It would be better for me if something happened with Lewis’ car, but we will see.
However, I am glad that I’ve found a good niche for profitable betting.
I cannot say I am satisfied with the results from this research. I definitely expected more from Gauss less square approximate method. I see that there is profit, but I expected more. Even so, 17% pure profit from 31 matches with betting on under over in basketball is not bad.
Here are the stats.
| % L + |
%i L + |
G L + |
Gm L + |
Avg. L + |
Avg.m L + |
| 61.3 |
54.8 |
41.9 |
58 |
45.1 |
38.7 |
If we start talking in profit percentage, best results shows my old method of betting, where you betting under or over depends from all previous matches for both teams. The profit there is 17%. However, as I learned from previous NBA regular season this method is going to be less profitable at the end of the season, because bookies start to level under – over percentage to 50-50 for almost all teams. Of course this can be used too, but you have to be good enough to use it.
The other method which is giving profit is Gauss less square approximate with using last 10 mutual games as data. The profit there is 10%. I hope this method to be more stable and to keep those 10% to the end.
I have profit in other section too. This is a method very similar to the first one I wrote above, but with dividing of data.
The other three methods are below the line, but I will wait until the end of regular basketball season in WNBA for final conclusions.
Last week my betting study was occupied only with the research in basketball under or over ending. I wrote a few times about it and I will do it now, again. It is time for evaluation after the first 18 matches already played.
I won’t be explaing again which methods I use. You can read more about them here.
There is 16.1% profit in two of the profits. The other four are below the line after those 18 games. However, as we all know we have to wait for the final matches for making analysis.
Here is the table with the result after 18 games in percentages:
|
% L +
|
%i L +
|
G L +
|
Gm L +
|
Avg. L +
|
Avg.m L +
|
|
61.1
|
50
|
33.3
|
61.1
|
38.8
|
44.4
|
One mark here. All odds here are 1.91
I was a bit tired with previous colors of this site and I decided to change them. And I made it. It is a bit strange looking site, with too much white on it, but I like it. I hope you like it too.
As you know I am Bulgarian and I will publish a Bulgarian version of this site which will be online soon.
It is a bit strange for me but my basketball under over research shows similar results. I use 6 methods for picking matches and all of them give results around 50%. This of course is 100% unsatisfying, but my research will continue.
Here are the results after 9 matches on WNBA:
When I use my old method with all previous matches – 4 from 9;
When I use my new method with all previous matches from the sector where bookie line is – 5 from 9;
When I use Gauss theory for less square approximate with the last 10 matches for each team – 4 from 9;
The same method used with the last 10 mutual games – 5 from 9;
When I use average scores from data above both are 4 from 9.
As I posted earlier I was going to bet on Under for Phoenix with Martinagale. Finally it gave profit and I am satisfied with full pockets. 
Crossing fingers this to continue.
I am starting a quick research on a few different strategies about betting on under or over at basketball games. I will use WNBA because it is only one major championship where matches are on right now. Few words about those different strategies: the first one I have used before in NBA last regular season. I follow the under – over stats for every team and when the match is going to be played I just take a look to numbers. For example Washington and Atlanta will play tonight – Washington has 50-50 until now and Atlanta has 30-70 under – over percentage. So, I will bet over here.
The other way of betting is similar, but a bit more difficult. The principle is the same, but the way of collecting the data is different. For every team I make different sectors divided by the border given by the bookie for under or over. Then I check the previous matches for this particular team for that particular border and compare the percentage between both teams on that border and bet for under or over with greater percentage.
The third way I will check to the rest of the season is far more difficult. I read in the forum about using Gauss’ least squares approximate principle for betting. It was about football, but I think it’s best to put it into practice with under – over betting. So I chose to use it here. For data I will use the last ten matches for every team – so 20 games.
The same method will be used for my fourth hypothesis, but the difference again will be of the way I collected data. Here I will use the last 10 mutual games between the teams.
I will check other two ways of making tips, but they are far less effective I think. I will check them only because it is easy to collect data. I will check the last 10 matches for every team and I will calculate the average scores so it will be up to me to guess if the match will be under or over.
Finally, the same method will be made, but with data collected from the last 10 mutual games between the teams.
Crossing fingers.