Betting resultPosted by Tobias Bramhed Fri, February 12, 2016 10:17:49
2016 started best possible way:
I managed to get matched in 820 unique markets, turned 322576 SEK (approx. 33875 EUROS) which ended up in a profit of 9516 SEK (approx. 999 EUROS). This equals a ROI of 2.9 %, which is far above my long term target (1.5%). A good start makes it easier to stay with my models even on a rainy day.
When splitting these results down to back type (1,X,2,O/U):
The only problem sign right now is when backing the away team, 0.1 % is to low although the normalised ROI implies that I had some bad luck. The exposure is also to low to act on.
Last year february was a real poor month for me, since then there have been many improvements (and disimprovements...) on the model so if I at least can make a break even in february then I am ahead of 2015 results.
GeneralPosted by Tobias Bramhed Wed, January 13, 2016 11:26:50
As you might know from previous posts, Malmö FF is the football team in my heart. Being without a coach for a while (since Åge Hareide left to coach the Danish national team) it is now confirmed that the new coach is Allan Kuhn.
Totally unknown for me, but comes with an interesting CV as coach and assistent coach for some of the bigger Danish teams as Ålborg, Randers and FC Midtjylland.
It will be a very interesting season, and I welcome Allan to Malmö!
DataPosted by Tobias Bramhed Wed, January 13, 2016 11:08:11
Lately I have been working on improving my data handling and storage process. The previous set up I used was to export all data into .csv files, and then import it to SAS and from there creating SAS tables from where I could do my analysis.
This was an easy set up, but not very efficient.
So, now I have set up a proper SQL database. I chose the PostgreSQL as its free and supports unlimited size tables. Now my bot speak directly to the SQL database, inserting and retrieving data directly.
So far so good, it seems to be working without any issues - and it seems to keep the tables smaller (in size) than the SAS tables.
The improvement continues on all parts! Good data is essential for good analysis.
Betting resultPosted by Tobias Bramhed Wed, January 13, 2016 10:26:25
2015 wrap up!
Another year passed by and there have been quite much development on the bot, and improvement on the model.
To sum it up: The model executed 6754 bets that were fully or partial matched. Of the 10 months that the bot ran, there were only one loosing month. A total ROI of 0.7%, 2.4 MSEK turned and 16 000 SEK won.
So what do I hope for 2016? Its not of any value to have financial goals, I will just try to improve the model, bot and risk management as much as possible and hope for the best...
But walking into 2016 with much better starting point than in 2015, a reasonable guess would be to turn at least 4 MSEK, and reach a ROI of 1 %. If that's the case it would mean a profit around 40000 SEK.
So happy 2016 and lets kick some ass at Betfair :)
Betting resultPosted by Tobias Bramhed Sat, August 29, 2015 12:52:40
Here comes the results with July data:
As you can see July was pretty slow with only 384 bets. 0.3% in ROI gave me a whopping 500 SEK :) Thats about 50 Euros.
The underlying ROI is on 1.9%, this indicates that I have had some bad luck. Comparing to May the underlying ROI was also 1.9% but I managed to squeeze out 1.6% in real ROI.
GeneralPosted by Tobias Bramhed Thu, August 06, 2015 15:49:13
Congrats to my all time favorite soccer team, Malmö FF, for qualifying for the play-offs in Champions League. A fantastic home win, 3-0, beating Red Bull Salzburg for the second year in a row.
Betting resultPosted by Tobias Bramhed Mon, July 13, 2015 21:55:50
Results update with June:
These summer months :( Very few soccer matches compared to "normal" months. With some luck I managed to get a positive ROI in my wallet, but the underlying ROI (normalised) is -0.9%. In 2014 I also had some problems in June, but after that I had a great performance from July-October. Looking forward to the autumn!
AnalysisPosted by Tobias Bramhed Thu, July 09, 2015 14:14:40
I keep on mining my data and sometimes it is just interesting to get some graphs on the most basic data, looking at the simple things. This is a graph showing how the distribution of pregame favourite is. Data is from all Betfair soccer matches that I have recorded during the last year.
1 = Home team is pregame favourite
X = Draw is pregame favourite
2 = Away team is pregame favourite
E = No clear pregame favourite
I will be back looking further on this by applying some filters.