Introduction
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:
and
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)
