#AsianCup2019: end of group games

The group stages of the 2019 Asian Cup concluded with Lebanon’s defeat of North Korea on game 36 of the tournament (link).

In the group games:

  • 96 goals were scored
  • the team that scored first did not lose in 28 games
  • the higher Elo rated team did not lose in 31 of the 36 games
  • the five higher Elo rated teams that lost were: Thailand (v India); Australia (v Jordan); Bahrain (v Thailand); Syria (v Jordan); Saudi Arabia (v Qatar).

The 96 goals were scored in these 15 minute time intervals:

A matrix and bar chart of when goals were scored in the group games by 15 minute time period.

The posterior outcomes of these games compared to naive priors proposed prior to the tournament:

A matrix and bar charts that compare prior probabilities with posterior probabilities after 36 games
A line diagram to compare prior and posterior probabilities.

The knockout stages begin on Sunday, 20 January with the game between Jordan and Vietnam (link).

Innocent Climbs and …

Simon Kuper has written about José Mourinho in the Financial Times today (link). The article is titled How José Mourinho failed to reinvent himself as football evolved. It contains this chart:

Elijah Meeks observed:

Simon’s article and the visualisation sent me off to look for my copy of Pat Riley’s The Winner Within (link). Chapter 1 of the book discusses an innocent climb. Pat suggests:

When a gifted team dedicates itself to unselfish trust and combines instinct with boldness and effort – it is ready to climb. (1993:31)

He concludes:

A team in an Innocent Climb can feel the power surging, so internal rivalries, turf wars, and selfish behaviour patterns are set aside. (1993:37)

The and … part of the title is Chapter 2 of Paul’s book: The Disease of Me. The chapter starts with a quote from James Basford “it requires a strong constitution to withstand repeated attacks of prosperity”. It ends with this summary:

The Innocent Climb – success through unselfishness – has been shattered by the Disease of Me. The team is awash with petty rivalries. Greed and resentment eat away at the team’s togetherness and undermine its ability to collaborate. Factions divide the loyalties within the team. Personal performance slides. No matter how fast an Innocent Climb uplifted a team, the Disease of Me cuts it down even quicker. (1993:54)

The chart in Simon’s article is a powerful indicator of rise and fall. I have not seen that use of ELO ratings before. My own record of this season is much more basic.

I have been tracking each EPL team in the context of their position in last season’s league table. Grey indicates results as expected by ranking. Gold is an unexpected win or draw. Red is an unexpected loss or draw. There are three Xs in my graphic that indicate managerial change. (Note Manchester City are in red. As champions last season they cannot have any gold games. Promoted teams have an opportunity to have gold success because of their ranking. Wolves have been the standout team in this matrix. Their one red event was the week 14 defeat at Cardiff.) The matrix indicated early on that Manchester United were having some difficulties.

Complexity and Models

Mark Upton shared two links recently that set me off looking at complexity and models.

John Launer (2018) observes “the study of complex adaptive systems (CAS), also known as complexity science, is burgeoning”. In his article, he proposes “the idea that the fundamentals of complexity are in fact extremely simple” and suggests that “complicated descriptions of complexity may fail to capture its most important qualities, and that simple ones, especially those that use metaphor and appeal to intuition, may be better ways of doing so”.

John asks “in a world where prediction can never be certain, are there nevertheless some general rules that can reduce uncertainty, so that our actions stand a better chance of achieving their intended results?”.

John adapts Jeffrey Braithwaite and his colleagues’ (2017) consideration of complexity science in healthcare to suggest ways to promote complexity thinking. These include:

  • Resisting the temptation to focus on an isolated problem and looking for interconnections within the system.
  • Things happen when you least expect them.
  • Looking for patterns in the behaviour of a system, not just at events.
  • Keeping in mind the system is dynamic, and it does not necessarily respond to intended change as predicted
  • Drawing up a model of the system surrounding a problem.

(Note: in their paper, Jeffrey Braithwaite et al (2017:3) observe “Complexity refers to the density of interactions between different components (agents, parts, elements, artefacts) in a system or a model representing a system, and which produce roles and behaviours that emerge from those interactions. Complex Systems are rich in collective behaviour”. They make a distinction between  complicated (“a lot going on with all the components”) and complex (interrelatedness, emergent behaviour, self-organisation and dynamics).

Jeffrey and his colleagues make use of and adapt Thomas Kannampallil and colleagues’ (2011) visualisation of complex and complicated:

I liked John’s view of interrelatedness: “Human groups engaged in an endless dance of mutually responsive interactions, in which everyone including yourself plays a part”.

The second link from mark introduced me to Joshua Epstein’s (2008) lecture Why Model? The lecture distinguishes between “explanation and prediction as modeling goals, and offers sixteen reasons other than prediction to build a model”.

Joshua notes “the choice, then, is not whether to build models; it’s whether to build explicit ones. In explicit models, assumptions are laid out in detail, so we can study exactly what they entail. On these assumptions, this sort of thing happens. When you alter the assumptions that is what happens. By writing explicit models, you let others replicate your results” (2008: 1.5).

16 Reasons

  • Explain (very distinct from predict)
  • Guide data collection
  • Illuminate core dynamics
  • Suggest dynamical analogies
  • Discover new questions
  • Promote a scientific habit of mind
  • Bound (bracket) outcomes to plausible ranges
  • Illuminate core uncertainties.
  • Offer crisis options in near-real time
  • Demonstrate tradeoffs / suggest efficiencies
  • Challenge the robustness of prevailing theory through perturbations
  • Expose prevailing wisdom as incompatible with available data
  • Train practitioners
  • Discipline the policy dialogue
  • Educate the general public
  • Reveal the apparently simple (complex) to be complex (simple)

These encapsulate the freedom to doubt (Feynman, 1955). Joshua concludes:

the most important contribution of the modeling enterprise—as distinct from any particular model, or modeling technique—is that it enforces a scientific habit of mind, which I would characterize as one of militant ignorance—an iron commitment to “I don’t know.” That is, all scientific knowledge is uncertain, contingent, subject to revision, and falsifiable in principle. (This, of course, does not mean readily falsified. It means that one can in principle specify observations that, if made, would falsify it). One does not base beliefs on authority, but ultimately on evidence. This, of course, is a very dangerous idea. (2008: 1.16)

Both of the references shared by Mark are excellent prompts to reflect on how we address the interrelatedness of sport behaviours. I thought Joshua’s help in sharing openly what we do juxtaposed two fascinating second order conversations: ‘freedom to doubt’ and ‘militant ignorance’.

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