2018 Netball #QuadSeries in South Africa

The Netball Quad Series concluded at the weekend in South Africa. The teams in the tournament were: Australia, England, New Zealand and South Africa.

Champion Data provided data for the tournament. I have used their data for my secondary analysis.

My record of the six games as a box plot (using BoxPlotR) for winning teams per quarter (light green) and losing teams per quarter (light blue):

The 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, outliers are represented by dots; data points are plotted as open circles. n = 6 sample points.

Photo Credits

Molokwane hails 2018 Netball Quad Series (SABC, Twitter)

One more quarter (Samsung Diamonds, Twitter)

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.

#NWC2015: secondary data analysis, scenarios and consequences



The volume and quality of data made available online from international sporting events is making secondary data analysis more possible. I tend to look at data that do not have too complicated operational definitions but that permit some granularity.

Champion Data are provide the data services to the 2015 Netball World Cup in Australia. Champion Data have worked with netball since 2009.

We are into the second week of competition and into the Qualification Phase of matches.

Scoring Patterns

Champion Data offer the following choices in looking at game data (a grab from the Australia v England game):


I used the ‘All’ and ‘Scoring’ tabs to access their visualisation of a scoring worm.

I wonder if we can use these worms as scenarios for coach and player conversations.

The International Netball Federation’s ranking of teams was published on 1 July 2015 and provides a context for each of the fixtures.

From the Preliminary Phase of the tournament:

Fiji v Wales

Opening game of the World Cup. Fiji ranked 7 and Wales 8.


Malawi v South Africa

Malawi ranked 6, South Africa 5. First game in the tournament for both teams.


Jamaica v England

Jamaica ranked 4, England 3. Second game for both teams.


New Zealand v Australia

New Zealand ranked 2, Australia 1. Third game for both teams.


Zambia v Fiji

Zambia ranked 16, Fiji 7. Third game for both teams.


Uganda v Wales

Uganda ranked 14, Wales 8. Third game for both teams.


From the Qualification Phase of the tournament:

South Africa v Wales

South Africa ranked 5, Wales 8.


New Zealand v Jamaica

New Zealand ranked 2, Jamaica 4.


England v Australia

England ranked 3, Australia 1.


Game Outcomes


In an article in today’s Conversation (Australia), Rob Moss, James McCaw and Jodie McVernon (2015) observe:

While stronger teams will tend to win more often than weaker teams over the course of a season, the outcome of each game is much less predictable. In fact, it’s stochastic … Being stochastic doesn’t mean that there are no patterns or rules; it means that any individual outcome is subject to unpredictable effects.

In the nine scoring worms grabbed from the Champion Data screens:

3 higher ranked teams won the 3 Qualification games.

3 of the 6 Preliminary games were won by lower ranked teams.

In the Preliminary games won by lower ranked teams, Champion Data’s track of scoring indicates some tipping points in these games. Are these the unpredictable events mentioned by Rob, James and Jodie?:

Wales’ two intense periods of scoring in the second quarter of the game v Fiji:

Wales 2F



Malawi’s two scoring bursts v South Africa, one in the second quarter:


and one in the fourth quarter:


New Zealand’s second quarter burst of scoring v Australia:


What If?

I see enormous potential in sharing macro- and micro-secondary data as scenarios for game-theoretic coaching conversations. In the three examples of a lower ranked team winning, it would be interesting to discover if they had prepared and trained for this eventuality.

I am becoming more and more interested in the linking of scenarios and consequences in training environments. The growing amount of secondary data available is making the construction of such scenarios more possible.

These are days of thick data and exciting coaching opportunities. We might start to develop our own Vulnerability and Consequence Adaptation Planning Scenarios.


Photo Credit

Ever heard of netball? (Jake Almer, CC BY-NC 2.0)

Sonia Mkoloma (Naparazzi, CC BY-SA 2.0)

Australia and England (Naparazzi, CC BY-SA 2.0)