Michael, Picking and Predicting

Somewhere on my book shelves I have a worn copy of Michael Oakeshott’s edited edition of Thomas Hobbes’ Leviathan (1946).

It may had been even more worn had I realised that he was the co-author (with Guy Griffith) of A Guide to the Classics: or How to Pick the Derby Winner (1936). Michael and Guy were Cambridge fellows at the time. The publisher of a new edition of the book noted:

The book takes the abstraction out of the Derby by attacking the systems which had been developed by generations of ‘form’ experts. It exposes theoretical solutions as fraudulent – instead it applies hard-headed empirical and historical analysis.

Michael Oakeshott was an influential political philosopher in the twentieth century and I found it fascinating that he applied his approach to picking a Derby winner subsequently “to his analysis of rationalism in politics”.

I like Paul Franco’s (1990:1) view of Michael. He was, Paul suggests:

a traditionalist with few traditional beliefs, an “idealist” who is more sceptical than many positivists, a lover of liberty who repudiates liberalism, an individiual who prefers Hehel to locke, a philosopher who disapproves of philosophisme, a romantic and a marvelous stylist.

I think this makes him perfectly suited to a role as analyst, particularly with his interest in second order questions.

Ed Smith (2017) says of the Derby book (despite some of its assumptions dating):

Although the specifics have dated, the ­intellectual disposition is more relevant than ever, especially as sport is experiencing a revolution driven by data analytics. All decision-making in sport (not just gambling, but also recruitment and selection by coaches) hinges on probability. Oakeshott’s second chapter – to what extent does past form determine future performance? – now preoccupies sport’s cleverest thinkers and mathematicians.

Michael’s approach was to use historical judgement. Ed notes:

The more we know about data in sport, the more the Oakeshott position – confidence in good judgement rather than scientific “proof” – gains strength.

He concludes:

Oakeshott’s ideas on racing provide a case for the value and usefulness of the humanities – inexact but wise, sceptical but informed by deep knowledge.

I think this is excellent advice and comes in a week when Alan McCall, Maurizio Fanchini and Aaron Coutts (2017) urge caution about prediction in sport.

Their invited commentary in the International Journal of Sports Physiology and Performance:


  • Highlights the common misinterpretation of studies investigating association to those actually analysing prediction
  • Provides practitioners with simple recommendations to quickly distinguish between methods pertaining to association and those of prediction.

I do believe that the quest for prediction can be undertaken with humility and the humanities.

Ed’s alert to Michael Oakeshott’s work is very timely. It speaks to the possibilities of disciplined historical insights in conjunction with the remarkable innovations in data capture and analysis. It should encourage us to think about we construct analyses of performance that entangle past present and future.

Photo Credit

Epsom Derby 2010 (Monkeywise, CC BY 2.0)

Going for Home (Monkeywise, CC BY 2.0)

Winning and Losing in the Regular Super Netball Season 2017

The regular season in the inaugural Suncorp Super Netball Competition concluded last weekend. The Vixens, Lightning, Giants and Magpies have progressed to the playoffs.

I have been following Champion Data’s coverage of the games played. There were two drawn games in the regular season (Firebirds v Lightning, week 1; Vixens v Swifts, week 3).

My median profiles for winning and losing teams in the remaining 54 games over 14 rounds were:

I used BoxPlotR to visualise some of the data too.

A comparison of Winners and Losers

The data for this visualisation:

These data are available in this GitHub Repository.

Photo Credit

Twitter (RSN927am)


About BoxPloR

“This application was developed with Nature Methods as described in this editorial and this blog entry. Nature methods also dedicated a Points of View and a Points of Significance column to box plots.

This application allows users to generate customized box plots in a number of variants based on their data. A data matrix can be uploaded as a file or pasted into the application. Basic box plots are generated based on the data and can be modified to include additional information. Additional features become available when checking that option. Information about sample sizes can be represented by the width of each box where the widths are proportional to the square roots of the number of observations n. Notches can be added to the boxes. These are defined as +/-1.58*IQR/sqrt(n) which gives roughly 95% confidence that two medians are different. It is also possible to define the whiskers based on the ideas of Spear and Tukey. Additional options of data visualization (violin and bean plots) reveal more information about the underlying data distribution. Plots can be labeled, customized (colors, dimensions, orientation) and exported as eps, pdf and svg files.

Complex Systems in Sports

Thumbnail picture of the Camp Nou Stadium from end on.An international congress of complex systems in sports is being held in Barcelona in October 2017. The venue is the Camp Nou Stadium.

There is a call for abstracts.

The two-day program includes presentations from:

Scott Kelso (Principles of Coordination)

Wolfgang Schöllhorn (Differential Training)

Rafel Pol (Cons-Training in Team Sports)

Robert Hristovski (Unpredicatability in competetive environments)

Jaime Sampaio (Dimensions of Performance)

Paco Seirul-lo (Closing remarks)

A thumbnail picture of the 1899 Auditorium that can seat up to 400 attendees.There are seven workshops:

Game and performance analysis

Training and learning methodologies


Performance assessment in sport

Developing resilience

Athletes as complex adaptive systems

Interpersonal coordination

News of the conference appeared as the Sante Fe Institute is running its open, online course Introduction to Complexity. When I enrolled, there were 2367 other students following the course.

The syllabus for the course is:

  • What is Complexity?
  • Dynamics and Chaos
  • Fractals
  • Information, Order, and Randomness
  • Genetic Algorithms
  • Cellular Automata
  • Models of Biological Self-Organization
  • Models of Cooperation in Social Systems
  • Networks
  • Scaling in Biology and Society

To complete a week of connections, I received an alert to Mark Upton’s post, Seeking the Edge of Chaos. Mark notes:

I’ve been mashing up these ideas around order, chaos and complexity in a team sport context for a while now…

I have been thinking about these ideas too and this week’s alerts have been a timely reminder about their relevance and evidence of the growing community of practice around them.

This is a different epistemic environment now compared to my first foray in 1996. The challenge remains the same, I think: how do we share the story of complexity in sport settings without it being an abstract concept.

Hosting a conference at the Camp Nou is a great place to accept this opportunity.

A photograph of the entrance to the Camp Nou Experience at FC Barcelona taken by Andrew Booth.

Photo Credit

Camp Nou Tour (Andrew Booth, CC BY-ND 2.0)