Can Collaboration Personas work with Sports Teams?

KEEPIA View from the Top – David Thodey Interview - Part 1- Why Enterprise Social Networking- (3)

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:

Fifa table

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 howeveSwoop teamsr is that since football is a team game, why is there so little rec
orded 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:

Behavioural Persona Classifier Football Player Characteristic
Engager Roughly equal number of passes received as completed passes made Someone who is a central connector linking plays
Catalyst Receives more passes than completed passes made Someone who wants the ball and pushes the team forward
Responder Completes more passes than they receive (assumes they make more intercepts) A good support player; cleans up the plays
Broadcaster Completes more passes than they receive (assumes they take free kicks and corners) Takes the big kicks but does not back up or intercept that much
Observer They have a low level of participation Usually a bench player, but perhaps on the field, does not get involved that much.

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:

Passing data

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):

Germany Portugal

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:

Germany     Portugal    
Player Minutes Persona Player Minutes Persona
NEUER 94 Responder PATRICIO 94 Responder
HOEWEDES 94 Responder ALVES 94 Responder
HUMMELS 74 Catalyst PEPE 36 Responder
KHEDIRA 94 Engager VELOSO 47 Catalyst
OEZIL 64 Engager COENTRAO 66 Responder
MUELLER 83 Catalyst RONALDO 94 Catalyst
LAHM 94 Engager MOUTINHO 94 Catalyst
MERTESACKER 94 Engager ALMEIDA 27 Catalyst
KROOS 94 Catalyst MEIRELES 94 Responder
GOETZE 94 Engager NANI 94 Broadcaster
BOATENG 94 Broadcaster PEREIRA 94 Catalyst
SCHUERRLE 29 Catalyst EDER 66 Catalyst
PODOLSKI 10 Engager COSTA 47 Catalyst
MUSTAFI 19 Responder ALMEIDA 27 Engager

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:

Germany     Argentina    
Player Minutes Persona Player Minutes Persona
NEUER 129 Responder ROMERO 129.00 Broadcaster
HOEWEDES 129 Broadcaster GARAY 129.00 Responder
HUMMELS 129 Broadcaster ZABALETA 129.00 Broadcaster
SCHWEINSTEIGER 129 Broadcaster BIGLIA 129.00 Engager
OEZIL 124 Engager PEREZ 87.00 Catalyst
KLOSE 89 Catalyst HIGUAIN 79.00 Engager
MUELLER 129 Engager MESSI 129.00 Broadcaster
LAHM 129 Engager MASCHERANO 129.00 Broadcaster
KROOS 129 Engager DEMICHELIS 129.00 Catalyst
BOATENG 129 Engager ROJO 129.00 Broadcaster
KRAMER 30 Responder LAVEZZI 47.00 Engager
SCHUERRLE 98 Broadcaster GAGO 41.00 Catalyst
MERTESACKER 4 Observer PALACIO 49.00 Engager
GOETZE 39 Catalyst AGUERO 82.00 Catalyst

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:

Is Being a ‘Lurker’ a Good or Bad Thing?

Swoop: Observer Persona

KEEPIA View from the Top – David Thodey Interview - Part 1- Why Enterprise Social Networking- (1)

Lurkers are often painted in a negative context, as those that take but don’t give back. Sometimes however, communities are designed for lurkers/observers e.g. Technical Support Forums. But even in this context one could argue that a lurker benefiting from some expert advice might still add value by acknowledging an expert contribution. So how should lurkers/observers be viewed?

This post continues the series the deeper dives into the specific measures included in the SWOOP Collaboration Framework #swoopframework. The ‘Observer’ behavioural persona; is the fifth and final collaboration persona with the ‘Engager’, ‘Catalyst’ , ‘Responder’ and ‘Broadcaster’ personas.

How Measured?

The ‘Observer’ persona is simply calculated against a minimum activity level. In the current implementation with the Yammer platform, it is not possible to measure ‘views’, so only those that have at some time interacted with the platform e.g. a ‘like’, ‘reply’ etc., can be assessed. Somewhat arbitrarily we have classified anyone who has interacted on the platform less than once every 2 weeks, over a 3 month period, as an ‘Observer’.


The ‘Observer’ persona is the most populous of the personas, accounting on average, close to 80% of all active participants, in the benchmarking studies done to date. We believe this reflects the maturity (or lack thereof) of many of the corporate social networking platforms; as Charlene Li has written about here. An alternative argument is that Enterprise Social Networks can still add value even with lower participation rates i.e. the ‘lurker’ value proposition. Research from IBM indicates that there are a variety of community types that can form within Enterprises (Community of Practice, Team, Technical Support, Idea Lab, Recreation), which demonstrate different patterns of connectivity. One could reasonably argue that a Technical Support community adds value by making experts available to less expert ‘Observers’; and therefore a larger number of observers is expected. The same argument however could not be made for a Team, Idea Lab or Community of Practice, where the fundamental design is for inclusive membership.

As our analytics framework targets collaboration, and as observation is a one-way channel, the ‘Observer’ is seen as negative persona. This is not to say that the platform is not providing value to ‘Observers’; it most probably is. However we believe that the most productive value that can be gained from a social networking platform is when people collaborate. Consistent and frequent collaboration demonstrates continuous knowledge sharing, co-operation, co-ordination and therefore performance. In our view the ‘Observer’, with perhaps the exception of Technical Support users, should always be looking to upgrade their status to one of the more positive personas.

What should this mean to you?

If you are one of the on average 80% of enterprise staff who are classified as ‘Observers’, you may want to reflect on what the impact on your career might be by staying on the sidelines. While currently you may feel comfortable being part of the majority, there is clear evidence that the leaders of the future are those that can pro-actively build their relationship networks. You may think that you can do most of your networking and relationship building off-line, but the digital divide is rapidly disappearing.

Of course there are situations where being an ‘Observer’ is appropriate. If you are new to social media or indeed new to the organisation, it would be prudent to spend some time observing the network interactions, understanding who the network leaders are and what the unwritten protocols might be. However, like the ‘Broadcaster’ persona, it should only ever be a temporary status for you. Once you are confident on the value you could add as one of the positive personas, you should jump in and start interacting.

In summary, the ‘Observer’ persona is passive and from a collaboration perspective, seen as a negative persona. The current status quo however is that the vast majority of participants on social networking platforms are ‘Observers’. The reasons for this are complex and have been explored previously. Some of the issues are related to multi- technology platform channels i.e. collaboration is evidenced say more in email or other messaging platforms. Overall however, we believe that the collaboration persona classifications can stand independent of the technical platforms being used. Ultimately it will/may be necessary to draw data from multiple digital channels to accurately represent an individual’s true collaboration persona.

Is There a Place for ‘Broadcasters’ on Conversational Platforms? SWOOP: Broadcaster Persona

KEEPIA View from the Top – David Thodey Interview - Part 1- Why Enterprise Social Networking-

This post continues the series the deeper dives into the specific measures included in the SWOOP Collaboration Framework #swoopframework. The ‘Broadcaster’ behavioural persona; is the fourth collaboration persona with the ‘Engager’, ‘Catalyst’  and ‘Responder’ personas.

How Measured?

Like the ‘Responder’ persona, the ‘Broadcaster’ is calculated as having a significant surplus of contributions made, over responses received. Broadcasters are differentiated from Responders due to the original posts outnumbering the number of replies they provide.


‘Broadcasters’ as the name infers, suggest that these people have a bias toward publishing over the more conversational aspects that would include more liberal use of the ‘reply’ and ‘like’ contributions. Because a collaboration platform is founded on active and engaged conversations, the ‘Broadcaster’ persona is generally seen as negative. That said, there would be instances where an individual might correctly adopt a ‘Broadcaster’ persona. For example, if you are responsible for launching a brand new initiative, say like a new marketing campaign, or organizing an event. For a limited time you would be required to undertake substantial original postings to begin the initiative.

What should this mean to you?

While the ‘Broadcaster’ persona may be considered a negative one, it could be that circumstances may require you to adopt this profile from time to time. The important principle however is that it should not be considered a persona to aspire to permanently. If you feel that your role requires you to continuously adopt a ‘Broadcaster’ persona, you may want to consider whether the collaboration platform is the appropriate vehicle for you. It is likely that your organisation will have other platforms for publishing content e.g. Intranet, Document Management Systems etc..

In summary, the ‘Broadcaster’ persona tends to prioritize publishing over conversation. It is generally seen as a negative persona, unless used sparingly for situation specific instances. We do not see productive communities or teams having or needing regular ‘Broadcaster’ members.




Marcus Dawe Joins SWOOP Board

Marcus and Cai Image

Marcus Dawe with SWOOP Co-Founder Cai Kjaer

We are thrilled to announce that Marcus Dawe has joined our SWOOP board. Here we have an exclusive interview with Marcus exploring areas including what attracted him to SWOOP and how he balances it all as a serial tech entrepreneur.

  • Marcus, great to connect. Firstly congratulations on joining the SWOOP board. As a serial tech entrepreneur who’s been successful with many ventures, what attracted you to SWOOP?

Thanks. Well I’m a data scientist at heart and have always got some data projects going on. I’ve developed and have worked on some of the largest data systems in Australia. We are now firmly in the age of data, where new products, service insights, market advantages, productivity advancements can all be derived from the way we manage information. SWOOP has an awesome team with the two co-founders whom I worked with at CSC, so it was a really easy decision to join them.  I had been mentoring them through the startup phase and their passion and enthusiasm is the essential ingredient to this great venture.

  • Why is SWOOP different to other tech start-ups?

SWOOP is the best example of how a startup can be formed within a year, a product developed and major customers such as banks using it within 12 months to improve their collaboration and productivity.  It was able to achieve this through strong focus, a great technical capability and most importantly 20 years of deep domain knowledge that has been coded into the product.  I’m convinced that SWOOP now is the leader in the field of social collaboration analytics – deep insights into organisations.

  • You and SWOOP Co-founder Cai Kjaer recently met Scott Farquhar, the co-founder of Atlassian recently, has was that?

Scott was very generous with his time.  We met him at his office and he opened with “What can I do for you?” What followed was a strategy session to help us with SWOOP and our go-to-market model, pricing and lessons from his experience in cloud software sales. We also identified that we could help Atlassian as well with SWOOP. Scott is a great asset to the global software scene.

  • On the subject of inspiring leaders, SWOOP recently published a three part blog series interviewing David Thodey about his time at Telstra and use of entreprise social network Yammer. What can we learn from David’s approach?

David is a real leader. Recognised globally as a top leader and able to drive results by building teams and empowering them. David identified that he needed to improve communications in his organisation and implemented Yammer to achieve this. For us it is the logical next step that the analysis and feedback of that data is what drives insights and helps rapid change by informing from the employees up and the CEO down.

If I were a CEO of a multi-national I would be managing multiple businesses/projects. I however choose to run multiple startups as a kind of Group CEO. I’ve always managed multiple projects by having great teams and co-founders and my best talent is strategy.

  • Anything else you’d like to share?

Our roadmap is looking good for SWOOP. We’ve established our credentials in Yammer and are moving to all the other social channels within enterprises. Watch out for us.

Thanks for your time Marcus and all the best with your ventures.

More about Marcus Dawe:

Marcus is a serial tech entrepreneur and has had success with his many of his ventures. Most notably eDIME Internet Agency which he founded and sold to Computer Sciences Corp (CSC) in 2000. At which time was the largest provider of Internet strategy and sites to Federal Government with 25 departments and agencies outsourced including Prime Minister, AEC, Defence, ASIO, ATSIC, DVA and many others.

He is the CEO of a carbon storage and utilisation company Mineral Carbonation International which is proving the viability of large scale, safe and permanent storage of CO2 in carbonates. Those carbonates can be used in building products such as cements, bricks and plasterboards. This is funded by NSW Govt, Commonwealth and Orica Ltd for 4 years.

Marcus is the co-founder and CEO of Health Horizon, a global showcase for health innovation launched in 2015 and is now the largest database of heath innovation globally.

His board experience is balanced – sitting on government committees, business boards of startups, scientific & non-for profit public and private organisation boards & committees. He is a graduate of the Australian Institute of Company Directors (AICD).

Contact him at: