#ACUGCPA: Introductions to #performanceanalysis

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On Monday 8 February, I have an opportunity to meet students enrolled on the Australian Catholic University’s Graduate Certificate in Performance Analysis course.

It is the first day of an intensive study week at the University’s Strathfield campus.

The program for the week is here.

I have two presentations to share on Day 1. Both are hosted on Google Slides.

The first is a partial account of the Evolution of Performance Analysis.

Slide1

The second is a discussion of Sharing Data and Competitive Advantage.

Slide 2

I am hopeful that presentation one on the evolution of performance analysis can contribute to a crowdsourced, global history of performance analysis. This crowdsourced history would extend beyond an anglo-centric account and tap in to the rich diversity of performance analysis practice.

Presentation two gives me an opportunity to explore some of the issues about open educational resources that fascinate me. I do see immense opportunities to share analysis. The increasing availability of event data is creating secondary data possibilities that could connect performance analysis communities of practice.

Both of the presentations build upon ideas explore in Clyde Street.

Photo Credit

Bondi Icebergs (Winam, CC BY-NC-ND 2.0)

#relearn motets: sharing voices

Spem

I saw Alex’s tweet about #relearn 2

… and thought about how we connect very many voices in a cooperative adventure. Perhaps we can learn from music for these narratives.

I think motets might help us.

Which took me to my CD of the Tallis Scholars and their rendition of Spem im Alium.

There is a YouTube version (9 minutes 40 seconds) for 40 voices:

This what it sounds like with 700 voices (11 minutes 33 seconds).

A flash mob rendition (9 minutes)

The Tallis motet was composed in the sixteenth century.

I liked this part of the Wikipedia account:

The work is a study in contrasts: the individual voices sing and are silent in turns, sometimes alone, sometimes in choirs, sometimes calling and answering, sometimes all together, so that, far from being a monotonous mess, the work is continually presenting new ideas.

This to me seems to be the essence of the #relearn conversations and the narratives (oral and written) that are emerging. I like the inclusive potential of these narratives: a choir of 40, a choir of 700; and a flash mob all seem great ways to share.

Visualising Performance Data: #ENGvAUS Netball Test Series 2016

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I have been following the three-match netball test series in England.

Australia won the series 3v0.

I found Champion Data’s record of the games very helpful.

In advance of more interactive visualisations, I am sharing a box plot of goals scored in the 12 quarters played.

3 Test Box Plot

I used BoxPlotR to generate this visualisation.

(In this plot, centre lines show the medians; box limits indicate the 25th and 75th percentiles as determined by R software; whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles; data points are plotted as open circles. n = 12 sample points.)

I have been thinking about random walk behaviour in court games.

My plot of shots at goal and goals scored for the first test match is:

RW1

This was the closest game in terms of goals scored. England missed just five attempts at goal in the whole game (Australia missed eleven).

In all three tests, Australia had more goal attempts than England.

I was thinking about how to visualise momentum in these games.

For the second test, I thought I would identify any examples of one team dominating play. These data do indicate a shift in momentum in the game and highlight Australia’s dominance of the third quarter.

My blocks of colour are intended to indicate dominance of play by Australia (dark green) or England (red) per minute played.

M2T

I see some fascinating opportunities to create interactive visualisations of these data.

Photo Credit

Third Test (Australian Diamonds, Twitter)