Books_20170913

Top10 supported soccer teams – Looking for edges – I/III

I decided to look into the European soccer teams with the biggest supporter base. Doing a quick google on the subject I found the top10 teams to be:

1. Manchester United
2. Real Madrid
3. FC Barcelona
4. Chelsea
5. Arsenal
6. Liverpool
7. FC Bayern Munich
8. AC Milan
9. Juventus
10. Paris Saint-Germain

I found the list at totalsportek.com, it is their ranking (based on followers on social media, TV-viewership, shirt sales and sponsorship deals), and I decided to go with that. I have three questions I want to explore:

1. If many “play-for-fun” supporters bet on their favourite team, can laying them pre-game be a winning strategy?
2. What if one of these teams take a one goal lead in-play. Should I back or lay?
3. What if their opponent takes the lead by one goal. Should I back or lay?

I will split this subject into three different blog post, starting with the pre-game question. My hypotheses is that when big amounts of supporter money hits the line the odds on the big team will get eaten, therefore there might be an edge laying the big team.

I use my own recorded data on Betfair with the following notes:

1. The pre-game odds that I use is recorded a few minutes before the kick-off.
2. If it’s a match between two top10 teams, then it’s excluded.
3. Data from 2013 until today.
4. Not complete data, some matches have not been recorded and I know nothing about them.

The result I get is seen in the the table below:

post_20161128_1

I am looking for a strategy that have been consistent over time, and where I have some exposure. Both Premier League and Primera Division have a history of positive yield when laying any of the top10 teams. Maybe this could be an interesting pre-game strategy for 2017? It’s big markets and you can probably get large sums matched pre-game.

Books_20170913

Tools and infrastructure for analysing big data

coder

 

Its been some time since last update, I have been spending my (very limited) spare time recoding my whole analysis infrastructure. I use the PostgreSQL database with my bot, but it just dumps raw data into a database. I need to adjust, clean and process my data – something I have been using SAS to do earlier. SAS is a great tool for analysing data, but not that great when it comes to structuring millions and millions of lines. So I have rewritten my SAS code into SQL code instead. I will now create all my data tables (used in analysis) by SQL code into my database, this have increased the speed of creating my tables.

For analysis I will use the free edition of SAS (University edition), it is basically made for training so it has some limitations. One of the things I stumbled into was the issue of connecting my database to SAS and reading directly from it. It is NOT featured in the free edition, but it can be overruled by a few simple tricks. Download the PC Files Server for SAS, run it in the background and libname it with something like:
LIBNAME z PCFILES SERVER=computername port=9621 DSN=raw USER=username PASSWORD=xx;

This unlocks the free edition to actually being useful for big volume data analysis. As I get more and more data I also have to sharpen the infrastructure so that it’s manageable in size and time.

As I a last improvement I am going to write a simulation module in VBA costume made for my needs, sometimes it’s just not possible to fit all my needs into a fabricated software.

I have now spent many hours to create some analysis tables which are exactly the same as earlier, not as funny as improving models and looking for areas with edge but with the improved structure I will be in a better position when I start to analyse. Do the hard work and hopefully get the benefits later 🙂