Ozzie Sport

ecademy is a small social network for business professionals that is an alternative to LinkedIn. It has a small number of people connected to Australian sport. In using and evaluating ecademy, it should be looked at from the perspective of using it liked in: Developing a network of employees, volunteers and athletes who can help network with other professionals to bring in an audience of potential sponsors and higher income bracket fans.

Given the nature of ecademy, the total number of people who have clubs listed in their profiles should not be gauged as a representation of fandom on the network. Rather, it should be looked at as industry professionals who want to be affiliated with the club or league.

One of the challenges involved with measuring membership on ecademy is that team sport names may not be the name of organization that runs the club or league. This could possibly exclude some people actually connected to them. For example, in the W-League, most teams are run by the state football associations, not by a specific organization with the club’s name in the title.

The table below gives an idea as to the historical size of the Australian and Kiwi women’s sport community on the network.

Related Posts:

• Blogger and Australian/Kiwi women’s sport
• 43 Things and Australian women’s sport
• Care2 and Australian/Kiwi women’s sport
• BlackPlanet and Australian/Kiwi women’s sport

Compete is an analytics company similar to Alexa. It also collects information from user installed toolbars to calculate how many estimated unique visitors sites get each months. Compete’s calculations are based on US visitors only. Given that, it has limited usage for female Australian sport teams. It’s best usage potential is likely for individual female athletes who have sponsorship deals. In this context, the athlete’s reach in the American market can begin to be understand, especially in the context of other athletes from around the world that compete in the same sport and with all female Australian athletes.

This thinking is born out when traffic stats are looked at. The Australian tennis player, Sam Stosur, has the most American traffic of everyone looked at. Her traffic peaked during the northern Summer and when most of the tennis competitions were going on. There appears to be an exception counter to this rule with the Adelaide Adrenaline that cannot be easily explained as the June 2010 does not appear to coincide with the AWIHL season. It does coincide with the men’s season, where the domain is shared by the men’s team. The men’s team has an American player.

Related Posts:

• Site rank and Australian/Kiwi women’s sport
• Alexa rank for Australian leagues and clubs
• eBay.com.au and Australian/Kiwi women’s sport
• Blogger and Australian/Kiwi women’s sport

care2 is a small social network dedicated to people who are interested in making a difference. There is a small population of Australians and Australian sport fans on the network. The network is interesting because it allows its users to blog, create petitions, join groups and upload photos. Despite being a niche network for activist, it has all the features that most social networks have an its users take advantage of them to post about their own interests. The type of user here is different than some of the other types of fans online and given their activist interest, they might be worth courting as they may provide positive brand associations if their activist work matches with the philosophy of the team.

While the Socceroos may have four mentions in blogs and the Fremantle Dockers have twenty-four, Australian women’s sport do not fair nearly as well.

Related Posts:

• Blogger and Australian/Kiwi women’s sport
• 43 Things and Australian women’s sport
• Data dump: Australian sport on bebo
• Perth sport fandom size

BlackPlanet is a small social networked targeted at African Americans. There is a small community of Australian sport fans on the site. They tend to prefer rugby. The development of an Australian and Kiwi women’s sport community on the site is probably hampered by the tiny community that exists for men. Some of the women’s sport, like netball and cricket, are also a bit niche and not popular in America. Even if there was a desire to create interest on this network, there is not likely the onsite population to make it happen.

Related Posts:

• Blogger and Australian/Kiwi women’s sport
• Bebo and Australian/Kiwi women’s sport
• Most popular female Australian (and New Zealand) sport related Twitter accounts (November 24)
• Most popular Australian (and New Zealand) sport related Twitter accounts (November 24)

43 things is a goal setting community found at www.43things.com/.  Compared to professional sports for men, the women related goals are less likely to exist and less likely to involve a component that expresses fandom such as a desire to buy a shirt or attend a championship match.  This presents two issues.  First, the number of goals related to women’s sport is less than men’s: There is clearly a gap in interest levels between men and men’s sport that needs to be addressed.  Beyond that, it highlights the idea that women’s sport does not have the built in spectatorship that men’s sport has and people do not identify as clearly as fans of these teams.

The table below begins to give an idea as to the amount of interest in randomly selected women’s clubs, leagues and athletes on 43things over time.  As you can see, there isn’t. For comparisons sake, the Fremantle Dockers have 5 goals, the Collingwood Magpies have 6, Hawthorn Hawks have 4 and the Melbourne Storm have 2.

It appears, based on the audience using the site, that growing the women’s sport related goals in an organic manner, done with out deliberation on the part of various stakeholders, appears unlikely.  There is barely an audience for men and the Australian population using the site is much smaller than on other networks.

Related Posts:

• Blogger and Australian/Kiwi women’s sport
• Most popular Australian (and New Zealand) sport related Twitter accounts (November 24)
• Most popular Australian (and New Zealand) sport related Twitter accounts (November 18)
• Most popular Australian (and New Zealand) sport related Twitter accounts (November 16)

And just to mix it up today and support the idea that I actually do write things, this would be a draft of some methodologies that will be headed into my appendix. A lot of it feels really simple, already gets stated inside existing chapters as I use the data and would eat up word count in my methodology in a really unhealthy way. (I’ve got about 20 of these methodologies to do. I should really do a summary about each particular site I’m using too. We’ll see. This section first.)

Appendix A
Data Gathering Site Specific Methodologies

43 Things
User information
To get user information from 43 Things, the first step was to identify goals that related to an Australian or New Zealand based sport club.  Searching using various keywords, reading the goals that related to the keywords and determining if they related to the search accomplished this.  Once a goal was identified and people were identified as having completed the goal or intending to complete the goal, the goal was recorded on a row in a 43 Things specific spreadsheet.  In a separate column, the team and league to which the goal related to were also recorded.  After this was completed, the user pages for people with that goal were visited.  Their username, city, state, country, birthday, website, and date joined were all recorded.  The last notation to the row was to include the date that this information was gathered.
During the time that this data was gathered, 43 Things changed the information that was available on the profiles.  This was done sometime between early June 2010 and  early November 2010.  Subsequently information such as city, state, country, birthday, website were not available on user profiles.  Only data gathered prior to this time exists except in cases where the data was checked at a later date and the user’s information had previously been recorded.

Total user information
Attempts were made to benchmark the level of interest in a team by recording the number of relevant search results on 43 things.  This was done by documenting the league and team that were being searched for in a row.  After that, the keyword used for the search was recorded in the same row.  The search was then completed and the total results were recorded.  A textual analysis of the search results was conducted and the total number of relevant results was recorded.  Of those relevant results, the total number of people working to accomplish them was then recorded.  Finally, the date the search was conducted was recorded.

Alexa
Site rankings
A list of websites related to Australian and New Zealand sport was created.  This was recorded on a spreadsheet, with columns that listed the league and team that the domain featured.  For every domain on the list, the page about the domain on Alexa was checked.  The Alexa page URL for the domain was also recorded on the relevant row.  When visiting the page, the world rank was recorded.  If an Australian rank was also available, it was recorded in a separate Australian specific column.  Next, the date that this information was gathered was recorded.  After that, any notes the author had regarding the site were recorded.  This was mostly to identify the type of domain or if it ranked in a country outside Australia.  Lastly, in some cases, the paragraph of information provided by Alexa regarding the site’s traffic and demographics was recorded.

Bebo
User  information
Profile information from bebo users was gathered by running a search related to a specific team or league.  The league and team that the search was related to was documented.  Once this was done, the people search results were copy and pasted to Notepad.  The search results were then formatted for pasting to the bebo user spreadsheet. Once copy and pasted, the author attempted to convert user-inputted locations into real locations of city, state, country.  The location field results were then found in columns for city, state, country instead of a location column.  The columns that existed then were league, team, name, gender, age, city, state, and country.  A final column was added that recorded the date this data was gathered.

Videos, Groups, Band information
There are three different search tabs on bebo beyond people that have information about the community size and audience for Australian and New Zealand sport.  They are Video, Groups and Bands.  Searched related to a specific team, player or league were run.  The relationship between the searches and the league and club were recorded.  The search results were then copy and pasted to Notepad where the results were formatted so they could be pasted on to a separate bebo related spreadsheet.  Once this was done, the following headers where information could be found included type, total (fans/viewers/members), loves, profile views, group created, genre, city, state, country, uploaded, uploader, and description.   The city, state and country information was documented using the same methodology as the bebo profile information.  Finally, a column was added that included the data that this data was gathered on.

Total search results
Total search results data came by recording the search term used, and recording the team and league that connect to that search term.  Once that was done, the total results were recorded for People, Video, Music, Groups, Apps and Skins. The date the search was conducted was then recorded.  Finally, any notes regarding the search or its results were recorded.
When recording the results, in almost all cases, the total results were included.  In a few select cases, generally when the results were 20 or less, the total number of results deemed relevant were recorded.  This was deemed important for smaller sport fan communities where one or two videos or groups may represent the whole community.  An example search where this was done involved a search for “Giants Football Club” because the results picked up rugby league teams and the New York Giants.

BlackPlanet
User information
User information was gathered once a search had been conducted and resulted in a user appearing.  If a user appeared for that search, the league and team related to that search were recorded.  The user name was then recorded.  The user page was then opened and the following data was collected: Country, gender and age. Lastly, the date this information was gathered was documented.

Total user information
To gather total user information, a search phrase was thought of and recorded.  In separate columns, the league and team the search related to were recorded.  The profile search was then conducted and the total number of results were recorded.  Finally, the date the search was conducted on was recorded.

Blogger
User information
User information was gathered once a search had been conducted and resulted in a user appearing.  If a user appeared for that search, the league and team related to that search were recorded.  The user name was then recorded.  The user page was then opened and the following data was collected: Age, Gender, Astrological sign, City, State, Country. Lastly, the date this information was gathered was documented.

Total user information
To gather total user information, a search phrase was thought of and recorded.  In separate columns, the league and team the search related to were recorded.  The profile search was then conducted and the total number of results were recorded.  Finally, the date the search was conducted on was recorded.

Related Posts:

• Socceroos: A creative research data dump (part 1)
• Distribution of Australian sports fans by league and location
• The Impact of Jason Akermanis’s Comments on the Western Bulldogs’s Online Fanbase
• Data absent context can change the meaning: Did Julia Gillard hurt the Bulldogs?

I thought this might be a bit interesting so I decided to post it.  Basically, for the period between November 7 and November 13, I got the daily total for all the keywords that led to OzzieSport.  This data is available from Google Analytics.  For every keyword, I tried to assign a league and a team.  This wasn’t always possible as keywords related to writing a PhD proposal aren’t sport related.  Once that was done, I totaled everything up by team to see which teams and leagues gave me the most search related traffic over the past week.  (This is all entirely subjective in that the SEO work I do/don’t do has a large impact on the findability of my blog.  Ditto for the topics covered. Nonetheless, this is interesting to me.)

Yeah, poor Joel Monaghan. His plight gave me a fairly above average search traffic boost. :/

Related Posts:

• Most popular Australian (and New Zealand) sport related Twitter accounts (November 24)
• Most popular Australian (and New Zealand) sport related Twitter accounts (November 18)
• Most popular Australian (and New Zealand) sport related Twitter accounts (November 16)
• Most popular Australian athletes, clubs, leagues and sport organizations on Twitter (November 7)

This isn’t actually fully revisited. I started writing this about a week ago and then have had extremely limited internet access. Given that, I thought I would post what I have so far and finish the rest later.

Online Activity in the Wake of the  Melbourne Storm Controversy Revisited (incomplete)

On April 22, 2010, the news of salary cap violations on the part of the Melbourne Storm broke online in such publications as the Fox Sports, on television including ABC news and on multiple social networks including Facebook and Twitter. By April 23, the news was available in various print publications including The Australian and the Sydney Morning Herald. During the news coverage, NRL fans learned that the team had been fined $1.8 million, stripped of two premiereships and were not eligible to earn points towards 2010’s premiership. (“Melbourne storm stripped,” 2010) The team was being punished for salary cap violations over the past five years, where the total cap violation in that period was $1.7 million with $400,000 of that total cap violation occurring in 2009. (“Melbourne storm stripped,” 2010)

The consensus at the time in the media was that this would hurt the team in terms of maintaining a fan base. In the three-week period after the news broke, this did not appear to be the case: The team maintained or grew its online fan community. In addition, there was more fan interactions in the Melbourne Storm fan community than there had been prior to the controversy. This defied conventional wisdom. The numbers deserve a followup to determine if the Melbourne Storm managed to capture short-term interest and translate it into long-term, season long, interest in the club.

This article will revisit numbers from May to determine how successful the club was. Specifically, interest patterns as expressed on networks like 43things, bebo, Facebook, LiveJournal and its clones, Twitter, Wikia, Wikiedia, Yahoo!Groups and YouTube. The article will prove that as the season progressed, interest in the Melbourne Storm declined relative to other teams in the National Rugby League.

43things

In the May analysis, a goal setting site called 43things was looked at. The site has a small group of Australians on it who have set professional sport related goals.

On April 1, 2010, the site was searched for any goals that connected to the Melbourne Storm. Only one goal related to the Melbourne Storm was found. It is “Go to a Melbourne Storm Game.” (1) Two people, erynne and mmcpharlane, had listed this as a goal they were working towards completing. When checked again on May 10 and October 24, no one had added any additional goals related to the Melbourne Storm. The two individuals who had listed “Go to a Melbourne Storm Game” remained the same.

Mailing lists

During much of the 1990s, mailing lists were one of the most popular tools for fans to use in order to communicate with each other. The creation of mailing lists became much easier when sites like egroups, coollists, topica, Yahoo!Groups and Google groups were created. In some corners of Australian sport fandom, mailing lists have played an important role in helping fans support their interest in clubs.

At one point, there was a semi-active community for the Melbourne Storm community on Yahoo!Groups. (2) When Melbourne Storm Yahoo!Groups were looked at in May, the controversy had no effect on the groups: No new content was posted on these lists. Only one (3) had new content posted between May and October; this new content was a generic newsletter that is sent out to several other NRL related lists and was not published specifically for this list. (4) There is no long tail effect of the club’s fan community on Yahoo!Groups as the community has long since moved on and the controversy didn’t activate a community that has largely been inactive since 2001.

YouTube

YouTube is the largest video site online. It is also the second biggest search engine online. (Hill, 2008) It is a popular site for sport fans; several teams around the world for different sports capitalize on this by having their own official accounts including the Chicago Red Stars (5), Real Madrid (6), and Perth Glory (7). Beyond the presence of official team accounts, fans upload many videos. Fan videos can be music videos, news clips, and video blogs. The frequency of uploads is one way to determine interest in a club.

When the original analysis was completed in May, it did not include YouTube data. Data was only gathered in June and October, several months after the controversy went down. In addition, the total upload data gathered only included a few teams: Brisbane Broncos, Canberra Raiders, Gold Coast Titans, Melbourne Storm, Parramatta Eels, and Wests Tigers. Despite the lack of pre-controversy data, interesting post controversy numbers were discovered.

When compared to all other teams, the Melbourne Storm were the only team where the number of videos mentioning them decreased. The two players looked at both faced losses in the total number of videos that mentioned them. Like the Storm, both of these players were involved in a major controversy during the season.

There are likely three reasons that could be attributed to the decline in videos. The first is that YouTube removed the videos because of copyright issues. This is plausible and if it is the case, it may not be the fault of the Storm as many of the copyright disputes on YouTube involve the background music. Given the lack of discussion in the NRL community about YouTube crackdowns in terms of NRL content or music, this reason just seems unlikely. The second reason is the creators may have deleted their content and their videos. This feels more plausible. Many people delete their online content when they are job hunting, because they are embarrassed by it or because of privacy concerns. The third reason is that the uploader no longer likes the team: They do not want to be associated with them or embarrassed by their previous support of them. This reason seems the most plausible. In the context of the Melbourne Storm, it fits given the patterns with the individual rugby league players who endured major controversies. In actuality, the reduction in videos is probably a combination of the second and third reasons. If true, it suggests that controversies lead to a reduction in user-uploaded content and the deletion of existing material: The YouTube audience for the team contracts.

References

Hill, J. (2008, October 16). YouTube surpasses Yahoo as world’s #2 search engine. TG Daily. Retrieved October 24, 2010, from http://www.tgdaily.com/trendwatch-features/39777-youtube-surpasses-yahoo-as-world%E2%80%99s-2-search-engine

Melbourne storm stripped of two premierships for salary cap breach. (2010, April 22). Fox Sports, Retrieved from http://www.foxsports.com.au/story/0,8659,27022196-5018866,00.html

Footnotes

1. The page for the goal can be found at http://www.43things.com/things/view/2535563/go-to-a-melbourne-storm-game .
2.Yahoo!Groups is the most popular mailing list host.  Archives are also available for these lists.
The list was http://au.groups.yahoo.com/group/MelbourneStorm .
3.While these e-mails were generic to the National Rugby League and might be considered spam if they were posted on a list with a more active administrator, a few did mention the Melbourne Storm.  A few even referenced the controversy.  Because of the nature of the posts, these few posts were not counted.
4.The Chicago Red Stars official YouTube can be found at https://www.youtube.com/user/ChicagoRedStars .
5.Real Madrid’s official YouTube account can be found at https://www.youtube.com/user/realmadridcf .
6.Perth Glory’s official YouTube account can be found at https://www.youtube.com/user/PerthGloryTV .

Related Posts:

• Distribution of Australian sports fans by league and location
• Data dump: Australian sport on YouTube
• Most popular Australian (and New Zealand) sport related Twitter accounts (November 24)
• More popular Australian (and Kiwi) sport related Facebook fanpages and groups (November 23)

This is the last of my data dumps from this weekend.  I’ve heard that the penetration of the Alexa toolbar in Australia is really low.  Given that, these numbers are probably less than reliable.  Still, given the absence of other public data, you work with what you have.  This is one of those cases.  Because of some issues, this only includes NRL and AFL urls.  (My actual list is a bit longer and includes more teams.)

Related Posts:

• More popular Australian (and Kiwi) sport related Facebook fanpages and groups (November 23)
• More popular Australian (and Kiwi) sport related Facebook fanpages and groups
• Most popular Australian athletes, clubs, leagues and sport organizations on Twitter (October 11)
• Most popular Australian athletes, clubs, leagues and sport organizations on Twitter (October 4)

My dissertation topic involves doing a demographic and geographic study of Australian sport fandom online. There are several sites and social networks where you can get publicly available demographic data to begin to formulate a picture of the user population, and then segment that population out by interest in a league, sport and athlete. I’ve spent a lot of time looking at Twitter, Facebook and LiveJournal. Recently, partly because of a trip to the Wikimedia Foundation and discussions with a few people at UCNISS, my interest in who was contributing to Australian sport wiki articles on Wikipedia increased.

Finding out who edited Wikipedia articles using publicly available information is a bit of a challenge. The most reliable information for who edited comes from IP address information. IP addresses can provide an idea as to the geographic location of the contributor. It is easy enough, with the help of a friend, to create a tool that pull the history of a Wikipedia article, get a list of IP addresses that edited the article, feed the IP address into another tool that will pull up the general location of the contributor. (One of my favorite visualizations of this type of information is WikipediaVision.) The data isn’t always accurate and if I was looking primarily at New Zealand, a country without its own dedicated IP address range, this would be even less reliable. Still, for my purposes, this data works pretty well.

This data is still pretty limited. There are a lot of articles that are edited by non-anonymous users. Sometimes, it is possible to get demographic and geographic information about Wikipedia contributors by viewing their profile pages. This can just be time consuming to do manually if an article has a large number of contributors as you need to view a lot of user pages. It becomes a deterrence for trying to collect geographic information about article contributors.

I was looking for a more time effective and accurate method of collecting geographic and demographic information about contributors that is publicly available on their user pages. The easiest and quickest way to get this information on a mass scale is to utilize user box information. Many user boxes, when included on a user page, put the user into a category. These categories are often then linked through the Wikipedian category structure. Beyond that, user boxes involve templates. It is easy to get a list of articles (user pages) that the template is included on.

The methodology that I selected from this point is rather straightforward. It involved:

1. Select a category.
2. Copy and paste the list of articles (user pages) in the selected category to an Excel spreadsheet. Sort the list alphabetically. Copy and paste only the user pages to Notepad. Replace * User with blank. Copy and paste this list back to Excel.
3. Create a filter where the cell contains / . Select those cells. Copy them to notepad, replace / with [tab] in order to remove user subpages from the list. Copy this back to Excel. Select only the column with usernames.
4. Run an advance filter in order to remove all duplicate rows.
5. Copy this list back to the dedicated spreadsheet. Label all those users with the category from which they were pulled in a unique column.
6. Repeat steps 1 to 5 until all the categories that you want to have included are included.
7. Merge/Group all the rows by username.

This method may not be the most efficient way of going about doing this. It can probably be improved by automating some of these steps. In my case, step 7 was not able to be completed using Excel. I had to e-mail the file to @woganmay, who I believe converted the file to a mySQL database, used the group feature, converted the results back to csv and e-mailed the file to me.

In my case, I did not complete this for every category. Some categories did not seem worth it time wise as they had too few user pages to be included. In other cases, the categories were just too big to do. This included all the members of User de, User en, User es, User fr, User it, User jp. Only a selected number of categories were included because of time constraints. Data gathering was focused on categories that I perceived would have the greatest number of Australians and other possible contributors to Australian related articles. When these categories were more exhausted, categories with between 1,00 and 5,000 articles were selected.

There are all sort of limitations to this data. First, not everyone includes userboxes on their profile pages. This means that there could be a lot more Australians on Wikipedia than indicated by userbox inclusion on a user page. The assumption for the resulting data is that proportional representation exists for various categories. So while there are X amount of Christians and Y amount of Atheists, the assumption that the relationship between X and Y will always be proportional to the actual population on Wikipedia. Whatever data is available thus has to be viewed as good enough or supplemented by going to individual user ages to see if other information is available when a user appears where no information for someone when running against the history of the article.

Second, even when they do exist, there are often useful pieces of information that are missing. For example, in an Australian context, there is a userbox for Rugby League fans. There is not however a userbox for Australian rules footy fans. There are also not user boxes and categories for fans of NRL or AFL teams. (This type of user box and category exists for National Hockey League teams.)

About halfway through this process, I realized that this data could be useful for analysis beyond who is editing Wikipedia. At the moment, I’ve only totaled data I have for Australians. It is pretty fascinating and would be neat to go further with: How does the proportional size of the Australian Wikipedian population compare against the actual population? Does the size of the Australian Atheist versus Christiah community actively reflect the proportions in Australian society? Or is the Australian Wikipedian community demographically distinct from the greater population?

The following tables include the data based on people who were included in Wikipedians in Australia and its subcategories and Australian Wikipedians. A copy of the raw data can be found at October 9 – Wikipedia English Data – Australians.xls. The data is provided without comment though any attempts at explaining the patterns found are very much appreciated.

Related Posts:

• Online Activity in the Wake of the Melbourne Storm Controversy
• Melbourne Storm and Wikipedia
• Most popular Australian (and New Zealand) sport related Twitter accounts (November 24)
• More popular Australian (and Kiwi) sport related Facebook fanpages and groups (November 23)