Half-Life of Teams

3166609346_0f56c16078_bI have been thinking about the half-life of sporting teams and organisations.

Wikipedia notes that the term ‘half-life’ may  be used generically “to refer to any period of time in which a quantity falls by half, even if the decay is not exponential”.

I have been interested in the half-life of teams for a long time. My interest was stimulated almost forty years ago as I started my coaching career. I recall an eminent American baseball coach talking about three teams within a team: a team that is going, a team that is here now, and a team that will be. From that time on I have been thinking about transition and stability in team structures. I think half-life is a vital part of this stability.

Back in 1981, I attended a Galton Foundation conference on Sport and Mental Health. I was fascinated by a conference presentation Maurice Yaffe made on interaction in football teams. Maurice was the first psychologist to become a member of the British Olympic Association’s medical advisory committee. He lectured on clinical sports psychology.

Maurice’s work resonated with some of my readings in the figurational sociology of sport, particularly with Clive Ashworth‘s notions of figurations. Ian Franks, David Goodman and Gary Miller explored human factors in team games in their 1983 paper and extended my interest in interaction.

I thought about Maurice’s work recently when I was discussing team selection with a range of coaches. I thought about him too when I read Dean Lusher, Garry Robins and Peter Kremer’s paper on The Application of Social Network Analysis to Team Sports (2010).

Satyam Mukherjee (2012a) has used a network analysis to look at batsmen and bowlers in cricket. Satyam discusses performance data in terms of the possibilities for “balanced team selection”. In a second paper (2012b), Satyam generates batting partnership networks (BPN) for different teams and “determines the exact values of clustering coefficient, average degree, average shortest path length of the networks and compare them with the Erdos-Renyi model”.

I think Jonathan Sargent and Anthony Bedford’s paper Evaluating Australian Football League Player Contributions Using Interactive Network Simulation (2013) extends this discussion in a very interesting direction.

There is a growing literature on the performance of football managers and their longevity including a recent paper by Adrian Bell, Chris Brooks and Tom Markham, The performance of football club managers: skill or luck?  (2013). Maria de Paola and Vincenzo Scoppa (2012) provide data from Italy’s Serie A. Francisco González-Gómez, Andrés Picazo-Tadeo, and Miguel García-Rubio, (2011) share information from Spain in their paper The impact of a mid-season change of manager on sporting performance. Matthew Hughes and his colleagues (2009) share a twelve-year perspective on manager change.

3071059901_2aa91c455b_bThese references have encouraged me to think even more about who drives a half-life. Saturday evening here in Australia gives me an opportunity for some empirical research. A much-changed British Lions’ rugby union team faces an Australian team with just one change. The changes to the Lions’ team have brought together ten players from the same nation, Wales, as the core of the team. Australia have added a seasoned player to a team that won the second test match in the series.

Photo Credits

Fabio Capello (Dave, CC BY-NC 2.0)

Wales v Australia 2008 (Alan, CC BY NC-ND 2.0)

3 thoughts on “Half-Life of Teams”

    1. Hello, Satyam

      Thank you for sharing the link. By coincidence I have been looking at some of your other papers:

      (complex network analysis)
      (batsmen and bowlers)

      Best wishes


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