Is football really a simple game?! The hidden networks behind Bayern's success!

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With the power of network visualization, dynamics of football games can be understood better than ever. Maven7’s analyst team is a huge fan of sports (check out our last analysis about the chances of the Hungarian water-polo team at London Olympics), especially football. 

As everybody knows it, "football is a simple game; 22 men chase a ball for 90 minutes and at the end, the Germans always win". So then why do so many people admire this simple form of entertainment? Why do dozens of analysts try to predict who will win a certain game or championship? Why is betting a huge business? The answer is as simple as football, because this game is not simple at all! Behind every pass, attack and goal, human dynamics have a strong impact. Network Analysis can give a new approach to understanding team dynamics during football games. 

Our recent infographic shows the hidden networks of two finalists of Champions League’s 2013. Let’s face the big question; can network science provide the answer why Bayern won and not Dortmund? 

If you look at the pictures, similarities and differences are easily noticeable. Network structures and patterns resemble each other because of the same line-up structure. Two defenders (greens) had strong mutual pass connections at both teams, but Dortmund focused on the right and Bayern on the left back. Teams have preferred defensive midfielders - Schweinsteiger and Gündogan, they were the top choice to pass to in midfield. OK, so both teams are German and both have same line-ups, but what isthe difference then?

Why did Bayern win?

Dortmund’s midfielder, Reus was the preferred player to pass to from the attacking midfielders. The penalty that Dortmund received also came from a situation after a pass to Reus. 

At the attacking midfield, Bayern is more active on the wings, and their whole network is not that centralized as Dortmund’s. Bayern’s midfield played in a better cooperation; their network shows more mutual connections, and Ribery’s supportive role on the left wing makes the whole attacking part very successful. Unfortunately, Dortmund’s attacking midfield has no mutual connection, and the whole midfield has only one as well. In comparison; Bayern’s attacking midfield has mutual connection between Robben and Ribery, and the midfield also has 3 mutual connections (Schweinsteiger - Ribery, Müller – Robben, Ribery – Martinez), which may show stronger cohesion in the midfield. 

Also, the midfield players’ performance of the two teams indicates their teams’ performance. Schweinsteiger played and passed more actively and punctual (87 tries, 73 times successful – 84%) than Gündonan (56 tries, 31 times successful – 62%), and while Bayern had altogether 640 passes and their efficiency was 72%, Dortmund had only 448 passes with 60% efficiency. 

An interesting fact is, that those attacks, which started from the goalkeeper, are more likely happening by the players of Dortmund. In general, Dortmund’s defense played a more attacking role; while Dante passed mostly to the back, Boateng passed to the front. 

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Social Media and the Power of Networks 2. – Key Opinion Leaders on Twitter

The increasing impact of social media gives modern marketing a lot to think about; Facebook, Twitter, Tumblr, Flickr, Pinterest, Google+ and hundreds of blogs are only the tip of the iceberg, and it seems impossible to be up-to-date on all the channels. To look at them one by one seems illogical, since the key aspect of the generated content lays in the network effect, that enables the vast exchange of information. What remains to be done? This three-part series introduces Maven7’s newest research focusing on the network effect, and therefore making life easier for online marketing, PR, and product management experts.

In contrast to the Facebook-boom that began 2-3 years ago, and reached it’s 3 million user population in Hungary last year, the Twitter community seems to be growing at a slower pace. The Twitter company was launched in 2006 in San Franscisco, and has around 30 thousand Hungarian visitors a day, similar to the blog hosting site Tumblr.

Why bother with them at all – you may ask? The majority of Twitter and Tumblr users come from an urban environment, most of them are high-status people living in Budapest. Microblogs spread information – especially negative ones – very fast. Here is a comparison: a „tradiotional” online medium might be busy with a story for a whole week, whereas on Twitter – given that the right person spreads it – the same information is distributed within 2.5 hours! Therefore it is of great importance, to keep these outlets under control as much as possible. It is not a coincidence, that Hollywood celebrities like Charlie Sheen (with his 7.5  million followers) get paid around 50thousand dollars per tweet. Our survey conducted during Spanish election season showed that even an average person can have substantial effect on voters. This leaves no second thoughts about monitoring the information that gets to these loyal, high presitge consumers.

National key opinion leaders (famous journalists, bloggers, athletes) are active on multiple scial media platforms, but the small number of follower bases point to the fact, that the person with the most followers is not neccesary the most influental one, when it comes to information distribution. We need to find out, which tweeter is the most relevant one, and has the power to form opinions when it comes to our products. We can achive this through Twitter data using the methods of data mining. The user’s position in the network is another key factor (i.e. how many followers does the user have in common with our competing brand). Compared to Twitter, Facebook has open activity data, which means that we can easily access information regarding the users network of contacts.

Social Media and the Power of Networks 2. – Key Opinion Leaders

There are multiple ways we can build networks from the connections of Twitter users. First of all we can regard the distributors (people related to the brand,  or the brand’s official page) as the source of information, and link individual users to them, based on who retweeted the source’s message. Furthermore, the users themselves have followers and friends online, the latter one representing a stronger status, that can be interpreted as a network itself (for more, check our previous article on a follower- andfriend-based network). The picture shows a network of retweeted messages related to an FMCG product distributor and its competitors.

Social Media and the Power of Networks 2. – Key Opinion Leaders on Twitter pic 2.

The second picture represents the choice between data sources, that have the most influence on our consumer basis. The yellow boxes are the key opinion leaders(KOLs), who can reach out to the major part of the community in only three steps. They hold a central position in the network, because they have the biggest follower- and friendbasis.

Through analysis of Twitter data we can not only locate the key opinion leaders and characters of a brand, but with the help of location information we can also interpret product placement related research. A good example of using location data is our previous article on the optimallocalization af ATMs. 

To be continued.

Social Media and the power of networks 1. – The Hungarian blog scene

Because of the spreading of Social Media usage the modern marketing agencies age are just gazing. Facebook, Twitter, Tumblr, Flickr, Pinterest, Google+, hundreds of blogs and so on without end. It is impossible to follow so many sites up-to-date. Also, it is not enough to only eximes these sites seperately, because the essence of the content generated by the users is the network effect, which causes the fast information spread. What can we do then? In our series, where we collected some of the latest network research results by Maven 7, we show how these findings can facilitate the on-line marketing specialists’, the PR specialists’ and product managerers’ lives.

We may trace back the beginning of Social Media to the blogs.  These contents are not the ordinary Social Media model, as we understand it today, but here the contents are already generated by users, as a major attribute of web2.0. The largest benefit of blogging is that anybody can write anything in a topic, and so the number of topics is practically endless. In 2006, the early times of Hungarian blogging, it was yet easy to keep count of bloggers. Then, thanks to its easy use, it rapidly started to increase.

But why are blogs so important for us after all? Considering that already more than ten thousand blogs exist and their reading blooms, a company needs to know who the Key Opinion Leaders are within the blog space, who have impact on their consumers. Network research provides help exactly in this. With the help of our unique methodology we can show who are the Key OpinionLeader is of a given topic, and we also can forecast who are the Rising Stars of the next generation.

Our research identified the Key Opinion Leaders (KOLs) of IT related blogs within a Hungarian blog service provider.  According to the network approach we presume that readers of same blogs are interested in the same topics. So by examining the group of users and visitors, the most important characters of the topic can be identified. The importance of a blog not only depends on the number of visitors and clicks, but also that what kind of other blogs the same user reads at the same time. It is more expensive to advertise on a Key Opinion Leader blog (green node), than on an other blog, which is connected to it (red node). With our method anybody can identify new, relevant and not „used up” opinion leaders, and may also validate previous campaigns.

It is also an important question, which other topics are related to our professional field. For example, how much overlap is there between the community of IT blogs and the community of game blogs? The following picture shows how the IT community is connected to other blogs. The bigger the nodes are the more connection a given topic has to other topics.

                                                                                                              

Beyond the identification of blogs’ KOLs and Rising Stars we can identify KOLs of a specific topic within the whole Hungarian online space by using other sources from the Social Media, such as Facebook, Twitter, LinkedIn...

To be continued....