Ozzie Sport

This packet contains raw information.    It is provided as a courtesy to the wider community to give benchmarks for information pertaining to the online communities for the Australian and Kiwi women’s sport.   The hope is that others can use this information to do a greater student of this important community.  The packet is licensed using a creative commons license.  Please feel free to share this.

If you are involved with women’s sport in Australia or New Zealand, please get in touch with me.  I would love to talk to you about this data to better understand the backend of what is driving some of these numbers.  If you’re in Canberra or Sydney, I’d love to do a coffee.

Any feedback people have regarding this data and the small accompanying analysis would be appreciated.  A lot of what I think is going on is based on my own history in media fandom, and based on what I learn about Australian sport through my ongoing research. I sometimes get bogged down in numbers and don’t always understand why numbers are they are, just that they are that way.

If anyone wants a similar packet about Australian sport (like the AFL or NRL or the A-League), please get in touch and I can see what I can do.  I’m generally pretty happy to share my data.

Related Posts:

• St Kilda Saints Nude Photo Controversy (part 1) : Nick Dal Santo
• Distribution of Australian sports fans by league and location
• Online Activity in the Wake of the Melbourne Storm Controversy
• WNBL on LiveJournal and bebo

If you’ve been following along with my blog, you know that the end goal is for me to complete my dissertation and get my PhD.  At various times, I work on various chapters.  I’ve done some editing on the methodology chapter and the literature review section.  I’ve done additional editing on my introduction.  I still need to figure out what I want to do with my footnotes.  (For the Storm chapter, I just totally removed them.  I didn’t bother with them in the Monaghan piece.  I forgot that I had them in Akermanis paper.)   Do I want them in or out?  Bah.

I wanted to give people an idea of where I really am in this process.  Thus, I’ve put everything together into one pdf file.

This is my dissertation so far.

If you have any feedback on it, please feel free to share.  Feedback is love.

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As the focus of my topic has shifted a bit, I’ve gone back and modified my methodology section a little bit.  I’ve only posted the last bit that I’ve rewritten, rather than the whole thing.

Methodological Approach
The methodology used in this study will be a population study. To provide context for the findings, other methods will be utilized.  The exact method for conducting the population study will differ depending on the site being examined.  Therefore, most of the methodology used in this study will briefly be explored inside specific chapters.  A detailed explanation for all methodologies for all sites can be found in Appendix 1: Site Specific Methodologies.

Despite the specific methodologies for different sites, there are several broad methodologies that are used as a component of population study.  The type often is dependent on the type of fan being looked at.  The first involves measuring the population of viewers.  The second involves determining the size and characteristics of the population that identify with a club, athlete or league. The third involves measuring population shifts over time. To a degree, a fourth method of predictive analysis will be utilized to determine if the answers discovered using the three previous methods can predict the size and shape of Australian sport going into the future.

Determining the population of viewers is generally very simple.  A site is visited and a number is recorded.  In the case of Alexa, website viewers would be determined by recording the rank of the site around the world and in Australia.  If a site has enough visitors, Alexa may provide some demographic data about site viewers.  Twitter viewership involves recording the total number of Tweets using specific keywords.  For Wikipedia, viewers are determined utilizing Wikipedia article statistics provided at http://stats.grok.se/ .  For YouTube, total viewers are determined by recording information about a video and the number of views for that video.  This particular method is used to determine the total interest in an athlete, club or league.  No assumptions regarding classification of fan type can be made as looking at a webpage, tweeting about a topic or watching a video are largely passive activities that do not imply a person wants to be publicly identified as supporting the club.  The point of using this methodology is to determine the reach for a league, club or athlete.

Population size and characteristics involve determining what people want to be publicly connected to an athlete, club or league.  On Facebook, this is determined using Facebook’s advertisement page at https://www.facebook.com/ads/create/ .  People who like fan pages or list a topic as an interest on their profile appear in those results.  Different keywords and demographic characteristics are chosen and the results are then recorded.  On LiveJournal, profile interests and community membership are used to determine who identifies with an athlete, club or league.  Once that is determined, demographic information on their profile is recorded.   On Twitter, profile information is recorded for people who follow selected accounts on the site. On Yahoo!Groups, membership to a group dedicated to a club or athlete determines if a person identifies with them.  Profile information is then pulled from the Yahoo!Group members list.  On YouTube, this information comes from the profiles of those who upload video featuring an athlete or club.  This methodology differs from viewers as the group being examined has taken active steps to express allegiance to the athlete, club or league by sharing that interest on public profiles.

Throughout the sections that required population size and characteristics information, there was a dependence on user listed locations to determine the geographic location of members of the Australian sport fan community.  To provide consistency across all sites looked at, a list was developed that included user generated location, city, state and country.  The city, state and country were determined based on intelligent guesswork.  For example, as the focus of the research is Australia, if Melbourne was standing alone, the assumption was that the user meant Melbourne, Victoria, Australia and not Melbourne, Florida, United States.  Spelling variations and nicknames were also used to determine location.  For example, Brisvegas is a nickname for Brisbane, Queensland, Australia.   Often patterns of cities were looked for assuming the standard pattern of city, state, country or country, state, city or city, country or city, state.  To aid in processing location lists more quickly, when using an automated tool such as the one for Twitter, the user-generated list was supplemented with one created by the author.  This list included all Australian cities listed using the patterns of postal code and city, state and city, country, and city, state, country.  The completed list contains over 65,000 variants that were listed by the author or user created.  This information can be found in Appendix 2: User locations to city, state, country.

The third methodology is a repeat of the two previous types.  There is an additional step of gathering this data on multiple dates to check for historical patterns and determine if there are shifts in the composition of a fan community or for the audience interested in an athlete, club or league.

In order to provide framework for the data, the analysis will be done as a series of cases involving events that take place in Australian sport fandom.  Most of these cases will involve the AFL or the NRL.  As the cases evolve, the results will become predictive analysis: Do previous cases suggest patterns of predictable behavior in how fan populations respond to certain situations?

Related Posts:

• Methodology: Draft/Free writing (part 5)
• Who is more sport mad (re: professional sport)? Americans or Aussies? Methodology question
• Methodology: Draft/Free writing (part 4)
• Location, location, location: Why can’t Twitter users standardize their locations?

Australia lost its World Cup bid to Qatar. As an American going to university in Australia, I am sad on a number of fronts. That said, on my Twitter stream, there appear to be a number of people who are a bit sad but they want to get back to the business of football, the football that Australia that Australia does best.

Still, I want to investigate that Australia claim a bit. The only large scale information that I have that could be useful (I don’t exactly want to use LiveJournal or bebo to be representative of Australia. The communities there are just too small.) involves Twitter. The methodology I used is described a bit in Australia Sport Census: Most popular leagues in Melbourne on Twitter.

There are a couple of possible issues with the A-League and my Twitter data. The first is that I’m not as actively looking for Australian A-League accounts as I could be. My organic finding tends to be hung up a bit on AFL accounts as that’s where most of the people I follow come from. When I go hunting for new accounts, I tend to go looking for women’s accounts. I’d like to believe that the A-League is underrepresented. If that’s the case, the numbers by cities could be a bit off.

In Melbourne, the A-League ranks as 8th most popular and the World Cup Australian related accounts rank 11th. In Sydney, the World Cup for soccer ranks 12th and the A-League ranks 13th. In Brisbane, the A-League ranks 11th and the World Cup ranks 14th. In Adelaide, the A-League ranks 10th and the World Cup ranks 15th. In Canberra, the World Cup ranks 12th and the A-League ranks 17th. In Newcastle, the A-League ranks 5th. In Townsville, the A-League ranks 4th. In Gosford, the A-League ranks second. In Essendon, the A-League ranks 7th. This isn’t a bad position to be in. Often, three of the places above those involve cricket, where Shane Warne counts for Cricket Victoria, Michael Smith counts for Cricket New South Wales and ESPN’s Cricket site counts for Cricket Australia. That distribution pushes those numbers down a lot. The rankings also include things like the Rugby World Cup and Swimming Australia. It also counts Andrew Bogut for Basketball Australia. (He’s Australian and he plays for the Boomers.)

All things considered, the space that the A-League occupies isn’t a bad place to be. The league and the national teams have clearly established itself on Twitter, having found a significant soccer fanbase inside the country. It certainly is better placed than the NBL and ANZ Championship. In many cities, the A-League does better than the Super League. The A-League is also much better placed than second tier sports like the ABL and AIHL. It often does better against other popular sports like surfing, cycling and hockey.

That out of the way, what are the most popular cities for soccer? I don’t have comparative rankings for cities. (I suppose I should be able to get that at some point.) I can give the total number of followers by city and that’s what can be found in the table below.

Related Posts:

• One week Twitter change for Australia/New Zealand sport related accounts
• Most popular Australian (and New Zealand) sport related Twitter accounts (January 2)
• Most popular Australian (and New Zealand) sport related Twitter accounts (January 1)
• Most popular Australian (and New Zealand) sport related Twitter accounts (December 11)