Professional sport these days is rife with in-depth analyses and statistics on player and team performance. Players are now often equipped with wearable devices to monitor their health and fitness by the minute. Increased betting on sport has added a whole new dimension to the desire for predictive analytics and anything that might assist the punters in predicting the result of a game.

What makes sport such an attraction to a large percentage of the world’s population is that despite the science that is being brought to sport, there is still significant uncertainty in the results. We all applaud the times when the ‘team of champions’ is upset by the underdog ‘champion team’. Who can forget the US amateur ice hockey team overcoming the all-conquering Russians at the 1980 Winter Olympic games? Equally memorable is the failure of the all-conquering US Basketball ‘Dream Team’ at the Athens 2004 Olympics. The search for that ‘X-Factor’ that drives the champion team to overcome the odds is the modern coach’s dream. In this post we will explore an area of sports analytics that is largely under-exploited.

For the novice sports punter the first port of call for team intelligence is the player profiles. The unwritten inference is that if you are well informed about the players and their individual strengths and weaknesses, then you will be able to predict team performances well. For example, if we go to the FIFA statistical support site for the 2014 World cup, this is what we find:

Again, the majority of the statistics profile individual player performance; how many minutes they played, goals scored, passes made, free kicks taken, tackles made, even which parts of the field the player occupied.

Incongruous however is that since football is a team game, why is there so little recorded about how they collaborated with each other on the field? We regularly see the NBA coach using small whiteboards to identify the passing structure wanted.  I had to dig into the FIFA data to find some evidence of passing records of how the players interacted with each other i.e. connection data. I found it hidden away in the ‘Passing Distribution’ statistics. So what might this largely overlooked data provide us with? Can the network data provide us with the missing intelligence needed to predict that ‘x-factor’ that successful teams are blessed with?

Our analysis technique of choice is social network analysis (SNA). Traditionally, SNA is used to identify relationship networks in communities or large enterprises. Its application to sport is novel but not unprecedented as this academic study shows. The study used FIFA 2010 world cup statistical data and traditional SNA centrality scores to assess team performance. We decided to build on this by using similar data from the FIFA 2014 World Cup site for the game between eventual champions Germany and Portugal. We chose this game as Germany were convincing winners and therefore there would be a greater chance of our analyses identifying an ‘x-factor’ difference. Rather than use traditional SNA centrality scores, we decided to use the behavioural SWOOP personas that we designed to characterize collaboration behaviours of staff participating in enterprise social networking (ESN) platforms. The five personas are Engager (Linking), Catalysts (Energizing), Responder (Supporting), Broadcaster (Telling) and Observer (Watching) and we felt that they could be mapped to the following behavioural archetypes, that we might see on the football field:

Our SWOOP Personas are classified according to the posting patterns of the ESN participants. The order that they are shown in the table above is also what we believe is the order of most positive impact on collaboration performance. For example, an engager is able to balance the number of posts, replies and likes that they make with those that they receive. We see the Engager as the strongest persona for collaboration. A Catalyst might be the target for many passes. They may take more risks in pushing the ball forward and therefore more passes might go astray, leaving them with an excess of passes received over successful passes completed. A responder will make more passes than they receive, perhaps because in their ‘cleaning up’ work; they may intercept more passes from the opposition, leaving them with an excess of passes made over passes received from a teammate.  A Broadcaster also has an excess of passes made over passes received, but perhaps their passes come more from fixed ball situations like free kicks or corner kicks, rather than intercepts. Finally, the observer characterises someone who really isn’t in the game that much.

With these characterisations in mind, we took the passing distribution data from the Germany Portugal match into our SWOOP SNA analysis:

The passing distribution shows the number of times a pass has gone from one player to another. The network is therefore directional as shown in the above matrices. The number of passes between two players can indicate strength of the connection between those players. We can represent these passing patterns in a social network diagram (sociogram):

The thicker lines relate to number of passes. The layout algorithm clusters more frequent connectors closer together physically. Qualitatively, the sociogram does appear to show Germany as a tighter outfit, in terms of their passing patterns, than Portugal. However, we need to look at the quantitative data to be sure of any marked differences:

We can see that the tighter passing patterns of the German team is confirmed by the higher number of Engager personas (6 vs 1) and even then the Portuguese Engager was a substitute playing the least minutes. The Catalyst persona is the next most valued in our view and on this dimension Portugal has 7 vs Germany’s 4; suggesting that Portugal played a more expansive, yet more risky, pattern of play. The actual result was a 4-nil win to Germany.

We also wanted to do a similar analysis for the World Cup final game between Germany and Argentina:

In contrast to the Germany-Portugal game, the ‘Engager’ score was much closer (5-4), though two of Argentina’s Engagers were substitutes playing less minutes. The score was a very narrow 1-nil win to Germany in overtime. Compared to the previous game, there were also more Broadcasters on both sides. We surmised that broadcasters may start play from fixed ball positions i.e. they make more passes than they receive. Perhaps this reflects the stop-start nature of the final. Overall though, there is some evidence that team success might be predictable using relationship derived personas.

While we find the results interesting and intriguing, for us this analysis is a fun diversion; and therefore we are careful not to claim too much in terms of groundbreaking research. That said, we are looking to have our on-line personas identified with contexts beyond the online social networking field, so we think this analysis qualifies.

We close this article with some food for thought:

• How much are sports teams really like work teams? There are defined roles and expectations in both. Sports teams however have clearer success criteria.

• How much is the persona related to the role in the team versus the individual playing style?

• How much might the personas change based on the context of the game and game specific tactics i.e. both in sport and work teams, how adaptable can the members be from their ‘preferred’ behaviour persona?

• And the big question. Can relationship analytics predict the x-factor in team success, independent of player specific profile information?

Of course much more research work would need to be done. But we are happy to have been able to provide another example of how collaborative behaviours can span many contexts and not just be online specific.

Learn more about SWOOP: www.swoopanalytics.com