Though I had planned not to share any shot maps before I had discussed the concept of Expected Goals and my model properly first, after watching last night’s game between Norrköping and Djurgården I just couldn’t help myself. Now some of you probably have seen this kind of plot before so I will leave out the explanation for later posts.
Here’s last night’s high-scoring game. Being a lifelong Djurgården supporter this was not a pleasant game to watch, and to add to the pain I actually had a bet on the under here. Sigh.
While I don’t believe much in year to year trends such as Team A vs. Team B always produces a lot of goals in today’s modern football where players and managers change teams frequently, watching the game I got a vague feeling of déjà vu and just had to look up this fixture from the last few years.
Looking at the games from seasons 2014 and 2013, it seemed there is some truth to the myth. But while it may look like this particular fixture usually end up a high scoring affair, in the other seasons in my database (2011 and 2012), the games ended 2-1 and 1-1 respectively. Furthermore, Djurgården actually only had two players starting in all three games: Kenneth Høie and Emil Bergström. The same goes for Norrköping with only David Mitov Nilsson and Andreas Johansson starting all three games.
With so few players playing all three games and the games therefore being played under completely different preconditions, I think we can safely put this high-scoring trend down to pure coincidence. I still feel like a fool for betting the under though.
Welcome to my blog, where I will be sharing my thoughts on football analytics and other things related to that field. Since my approach and initial interest in the subject come from betting, I will also be writing about odds, bookmakers, gambling, probability, statistics and more. I will also talk about how I use quantitative methods through my newly found programming skills to deal with these things.
Even though football analytics is quite a new field, much good work has already been done, especially on the top European leagues where high precision data from the likes of Opta is available covering pretty much every ball touch of every game. Therefore I’ve chosen to focus on my native Sweden’s (not so) famous top league, Allsvenskan – currently in a modest 20th place on the UEFA club ranking. Because of the low interest and limited access to advanced stats compared to the top leagues, not much work have been done here and I know of only two other blogs covering Swedish football with a quantitative approach, styktipset i sista stund and fotbollssiffror.
My own analytics work will focus a lot on the concept of Expected Goals and I will discuss how I use it for assessing both team and individual player skill, pre-game simulations, post-game analysis and even league predictions. All this I do with the help of the programming language Python, which really have opened up a new world with unlimited resources since I decided to sit down and learn it. For years I’ve dabbled in a low form of football analytics through Microsoft Excel, using only basic stats trying to come up with ways of predicting football games. Though it has not (yet?) made me filthy rich, Python has been really good to me and helped me develop my analytical mind.
Even though I consider myself more of a reader than a writer, starting this blog have been on my mind for some time now, and even if nobody would ever read it I still think it’s a worthwhile project, if only to process my thoughts by translating them into words. Cynic as I am though, I will not share all my methods, programming code or any betting tips – but instead try to discuss things through general examples in order to hopefully get some feedback and develop new ideas. This is really the main purpose with this blog as I strongly believe in making as much ideas, data and technology as possible public in order to ignite new thoughts and discussion.
But now I should wrap this first post up as it is already too long. In the next post I will talk about the advanced data available for Sweden’s top leagues, its advantages over basic stats but also its limitations and problems compared to Opta’s data.