Ozzie Sport

I wasn’t intending to run this again until tomorrow.  Things came up.  I’m going away for the weekend and I want to get everyone’s follower information to see if I can’t begin to map that again.  Also, it’s been a month since I last that information.  (It took about five days.  That was back when Shane Warne had only 198,000 followers.)  I like benchmarking that data to see if there are geographic shifts in where people follow are from.  (Has the location of where Team GWS followers come from?)  So yeah, I figured I would run the script this morning instead of a week from now.

This particular list only includes AFL related accounts. (And everyone on it appears on the list of most popular for all of Australia.)   I’m hoping all the labels are right.  If anything is incorrect, players switched team, I’m missing people… let me know and I’ll fix it.  And yeah, I’ve ranked this one so you can more easily see who is popular and who is not.

Related Posts:

• Most popular Australian (and New Zealand) sport related Twitter accounts (November 24)
• Twitter follower information
• Most popular Australian (and New Zealand) sport related Twitter accounts (November 18)
• Most popular Australian (and New Zealand) sport related Twitter accounts (November 16)

I like random data. You never know when it will turn out to be handy. Last evening and this morning, I bench marked some totals regarding how many digg submissions existed for various keywords. After that, I copy and pasted some of those search results, and labeled them by league/team. In addition to that, I recorded profile information for people who had submitted things to digg. If I looked at their profile and they had other digg listings that they submitted or had dugg, I added those teams to the list. Given the issues with finding out the true total of results for these teams, the data I have is just a sample. These samples are not large. (This data is not easy to manually mine.)

For all teams where I have 5+ people who submitted a link related to a team or dugg a link related to a team, I decided to calculate the mean, median and mode.

There appear to be some differences between people who submit links to /digg AFL related links by team.  If this holds true for a larger sample (currently between 9 and 16 diggers) would be an interesting question.  I don’t know how feasible that will be given the time issue.

My digg data is available here.

Related Posts:

• 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)
• More popular Australian (and Kiwi) sport related Facebook fanpages and groups
• Most popular Australian (and New Zealand) sport related Twitter accounts (November 18)

Last week, I asked my Twitter followers if there was any team that anyone might data about. I said I’d pretty much be happy to dump as much of the data I have regarding those teams (or athletes) into a document that might provide some interesting insights into the teams. Even if not, it would offer benchmarks for further study. A lot of what I have is random and really depends on what sites I’m looking at, how often I update, how motivated I am get additional data, etc. I tend to go hunting when I have a purpose and those periods see an increase in specific teams. What I need rarely turns out to be what I have but that’s a challenge with creative research.

That all said and done, what my followers requested were Collingwood, Hawthorn and Port Adelaide. The links below are to the pdf files containing the data I have about them. (There might be a few small things left out.)

• Collingwood Magpies (.pdf)
• Hawthorn Hawks (.pdf)
• Port Adelaide Power (.pdf)

Related Posts:

• Distribution of Australian sports fans by league and location
• 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)
• More popular Australian (and Kiwi) sport related Facebook fanpages and groups

A number of times, I’ve posted most popular lists. This table takes the data found on those charts and looks at the historical growth for Port Adelaide Power related Twitter accounts. (If any of these players are no longer with Port Adelaide, please let me know. Conversely, if you know of any Port Adelaide accounts I’m missing, let me know.) If a cell is empty, it just means that I did not collect data for that account on that date.

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)
• A social media/web snapshot of Collingwood, Hawthorn and Port Adelaide

While on vacation, a friend of mine recommended I read Want to be Retweeted? Add Hashtags to Your Tweets! The article is rather interesting and looks at the #hashtags that led to the most ReTweets.

I have a collection of AFL related tweets that I update semi-regularly. More often than not, I use these for geographic related analysis. My AFL tweet collection has a series of issues though that make me hesitant to always draw definitive conclusions from it. These issues include:

• Incomplete tweet set for keywords: I only get what is available from Searchtastic.
• Incomplete tweet set based on keyword limitations: Even supposing I could get all the tweets related to a keyword, I can’t find every reference to the AFL as there are too many possible words that could reference the AFL and its clubs.
• Incomplete tweet set because of time: Some keywords were searched for earlier and some later. Not all keywords were looked at over the same period.
• Irrelevant tweets: Keywords like #gosaints may refer to the St. Kilda Saints or the New Orleans Saints. AFL may refer to the AFL-CIO, a union in the United States. Unless every tweet is examined, irrelevant tweets will remain in the data set.

These are major obstacles to doing any sort of content analysis for Twitter when specific subgroups are looked at. Many of these issues are ones that other researchers are likely to have too: You can’t get the complete data set, will include garbage data and may not be entirely timely.

That said, despite these limitations, I still wanted to know what are the most popular #hashtags for AFL related tweets and what type of hashtags are likely to be ReTweeted.

To get my dataset of Tweets, first I went to Searchtastic, ran a series of searches over the course of 6 weeks, and exported those results to Excel. I noted the keyword I searched for and the team that keyword is related to. The keyword searches that were run on Searchtastic during this period included: #afl, #aflbluestigers, #aflcatslions, #afldemonsswans, #afldogscats, #afldogsfreo, #afldogspower, #afleaglesblues, #aflfooty, #aflhawksdemons, #aflkangasbombers, #aflkangasfreo, #AFLPAQUIZ, #aflpiestigers, #aflpowercrows, #aflpowereagles, #aflsaintshawks, #aflswanshawks , #brisbanelions, #dreamteam, #footy, #fremantle, #freo, #freo AFL, #goblues, #gobombers, #Gocats, #gocrows, #gopies, #gosaints, #goswans, #NMFC, #northmelbourne, #sackermanis, #supercoach, #sydneyswans, #teamgws, #thekennel, #wafl, #westcoast afl, ackermanis sacked, ACT4GWS, Adelaide Crows, Adelaide_FC, afl Bombers, AFL GWS, AFL Magpies, afl pies, AFL Tony Lockett, Aker Bulldogs, Akermanis, akermanis dogs, akermanis gillard, Australian rules, blues afl, Brad Johnson, Brad Johnson AFL, Brady Rawlings, brisbane afl, Brisbane Lions, Brownlow Medal, Cameron Mooney cats, Carlton Blues, Chris Judd Carlton, Collingwood, collingwood AFL, Collingwood Magpies, crows afl, Damien Hardwick, DemonsHQ, Dockers AFL, eagles afl, eagles perth, Eric Mackenzie , Essendon afl, Essendon Bombers, etihad kilda, folau afl, folau israel, Fremantle Dockers, fremantle ruckman, GCFC, geelong cats, Gold Coast Suns, harry_o, Hawthorn Hawks, HawthornFC, Jason Akermanis, Jeff Kennett, Judd Blues, kilda, kilda AFL, kilda riewoldt, lions afl, Mark Harvey, mcg kilda, Melbourne Demons, milburn cats, Nathan van Berlo Crows, North Melbourne Kangaroos, PAFC, Peter Szental, Port Adelaide Power, portadelaidefc, Richmond Football, Richmond Tigers, Rodney Eade dogs, roos afl, sackermanis, spoon eagles, spoon lions, spoon tigers, spoon wce, swans afl, swans footy, swans ruckman, Sydney Swans, tigers afl, Travis Johnstone, Trent Cotchin, WCE AFL, West Coast Eagles, and Western Bulldogs. These keywords represent all teams and often include multiple keywords for them.

The next step was to remove all duplicate tweets. As some tweets contain multiple keywords and some searches were run more than once over that 6-week time period, that possibility existed. To do this, three columns were copy and pasted to a new worksheet: Username, Tweet and In Reply To. On Excel, I did this using Filter -> Advanced Filter -> Unique records only. This process took the total tweets from 8,523 to 5,427.

The third step was to identify all the hash tags used in this data set. To do this, I copy and pasted all the tweets to Notepad. I ran a find and replace for [space]# and replaced with [tab]#. I copy and pasted these back, removed all cells that did not start with a #. After this was done, the data set was copy and pasted back to Notepad. Another find and replace was done, this time, [space] was replaced with [tab]. This was pasted back to excel and all cells that did not start with # were deleted. The completed list was 2,833 total hashtags used. Of these, 556 were unique. The following table counts their total usage:

The next step was to count the total @ replies and hashtag uses. To do this, all the unique tweets were copied to a new worksheet. A filter was created to list all tweets that did not include an @ sign. These tweets were deleted. This brought the total tweets from 5,427 to 1,844. The next step was to create a filter to show all tweets that did not include a #. These tweets were then deleted. This brought the total Tweets down to 832. The next step was to look remove tweets that did not contain “RT @”, “RT:@”, “retweeting @”, “retweet @”, “via @”, “thx @”, “HT @”, or “r @”. This leaves 458 tweets that are retweets that contain hash tags.

The same process was conducted to count the hash tags that was used for all hash tags and RT @. The following list was created of people who were the most ReTweeted (or mentioned in the ReTweeted tweet) where the tweets contained #hashtags and the #hashtag totals for ReTweets. These were then totaled and counted, resulting in the following table:

Based on this sample, (because the whole of the population of AFL tweets is not available and cannot the total cannot be counted to determine the representative value of the sample) the most RTed #hashtag in a Tweet related to the AFL is #gosaints. #dreamteam comes in second. #gocats comes in third. #afl comes in fourth.

The #gosaints tag is popular and the team’s official account has encouraged its use. More recently, New Orleans Saints fans have used it as the NFL pre-season has started. #dreamteam is one of the two most popular AFL fantasy leagues and those fantasy leagues are big into fostering community. Beyond these top four, the most popular tags tend to involve specific match ups. If all tags related to match ups had been included around the time that the games took place, it is highly possible more would be on that list.

In terms of who gets RTed and mentioned in ReTweets, official accounts dominate: The AFL, Carlton, St. Kilda, Hawthorn, the Bombers, Geelong and the Swans. The first non-team account to appear is P. Diddy and he is followed by a number of influential Australian social media people. The next person / non-official AFL account to appear is Julia Gillard. AFL players do not appear to be ReTweeted en masse when compared to their club sides. This isn’t to say that people don’t actively read and follow them. If looking at ReTweets without #hashtags, @harry_o would appear on top with 50 mentions. It is probable that clubs are using #hashtags and players are not; there could be a digital cultural awareness divide between the two.

A copy of the data and some of my step by step process making it workable can be found at RTAFL.xls. I find the whole thing really interesting. If anyone has any tools to make dataming easier or has a suggestion for additional research in this area, let me know as I’d love to hear it.

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 weekend, the Melbourne Demons are playing the Port Adelaide Power in Darwin.  This game is one of two AFL games being held in Darwin this season.  I’m rather keen on geographic patterns in fan communities.  Where are they located?  How many people are there?  What is the size and interest level in a particular place?  Given that there isn’t an AFL team based in Darwin and the nearest team is team is over 3,000 kms (1,800+miles), it would be hard to figure out what team allegiances would be based on.  (The Canberra game with the Swans had a large number of people who barracked for the Sydney based team.  Canberra’s distance from Sydney and the Swans support of AFL Canberra are probably the major reasons for that.)  I wanted to explore what those loyalties would be in the Northern Territory to the exclusion of other states.

There really is no good way about getting numbers for the Northern Territory with out picking up everyone else across the country.  And even when that isn’t the case, people frequently will list themselves as residing or belonging to the next biggest city even if they don’t reside there.  This is highly problematic when you’re looking to see if there are pockets of team support in the suburbs and rural areas where city affiliation is more important when dealing with a wider, more international audience that may not have heard of Freemantle but may have heard of Perth, or who may not have heard of Geelong but do know where Sydney is.  There are ways to tease those patterns out by removing the major cities, like Melbourne, where the core is very tiny.  And I’m digressing because even when you can do that, it is rather hard to still just get data off major networks about a person’s interest by city, while excluding other states.  I can’t do that on Facebook, LiveJournal and its clones, bebo, blogger, orkut, 43things, LinkedIn, Twitter, care2… the list goes on and on.  There is no easy solution other than getting everyone and then, after the data is collected, filtering it down by state.

While I have a lot of data of that sort already, not many people live in the Northern Territory.  (For the Adelaide Crows, across six networks and with 75 fans, only one is from the Northern Territory.)  It is really hard to get regional patterns inside the Northern Territories.  My solution to try to figure this out was go to Google.com.au, put in the team’s name and the city.  (I got the list of cities I used from a list of postal codes for the Northern Territory on Wikipedia. I was logged out of my Google account.  I did not use the API.) My list of cities was 114 long after I removed cities with multiple postal codes.  City names, when they included more than one word, were put in quotes.  Team names were put in quotes.  An example search with that would be “Melbourne Demons” “Alice Springs”.

This is all fine and dandy.  You can easily repeat the results.  You should be able to get regional patterns on a large scale that you can’t get with maps.google.com.au or video.google.com.au or bebo. Everything theoretically should work to get a some one accurate picture of the interest level by city in the Northern Territory for both teams.  Except, well, no.  Midstream, methodology begins to change.  Things I had not necessarily thought of come in to play.  First, there are duplicate city names.  This is an issue for Palmerston, which is a city in New Zealand, a city in the Northern Territory and a suburb in the Australian Capital Territory.  Second, some cities have common names or share names with people.  This is the case for Gray, Northern Territory.  It is the case for another city that shares a name of a player for a different AFL team.  This issue might be correctable by adding a “Northern Territory” or an NT to the search phrase.  I did this for Palmerston.  I just didn’t do it consistently because Google did not always realize NT meant “Northern Territory” and there were three wildly different search results in some cases.  It becomes just easier to ignore and accept that search results are going to be faulty.  The third major issue was Google spelling.  This issue can be less obvious unless you actually look at the results.  Moil is a city in the Northern Territory.  Google helpfully wanted correct my spelling by pulling up results featuring the word Mobile.  Moil and Mobile are not the same thing.  Karama and Karma are also not the same thing.  Google, if you don’t specifically tell it that these are not the same thing, treats them as if they are.  When I found this, I did correct the results number by putting a + in front of it to force Google to only pull up results with that exactly spelling.  Outside those two examples, I did this for Katherine, Elliott, Farrar, Gray, Gunn, Malak, Millner, Mitchell, The Gardens, and The Narrows. This helped insure slightly more relevance and didn’t create the problems of what is the preferential way to indicate that a city is in the Northern Territory.

The methodology problems out of the way, it is time for the results.  I couldn’t get a good visualization tool.  (The ones I tend to use aren’t really good with the Northern Territory.  I’ll find a fix for that in the future.)  Therefor, the easiest way to see the results is to download the xls file or the csv file.  The results, to me with out the aid of a map, are pretty boring when compared to methodology but still interesting.  On the whole, it looks like there is more interest in the Melbourne Demons than there is in the Port Adelaide Power.  If I give each team a point if they are more popular in a particular city, the Demons easily win the day with 93 to the Power’s 15 and with six cities being tied.  If I add up all the search results (each city gets added.  This number has little relationship to the total pages in the Northern Territory because many pages reference both teams or multiple cities in the Northern Territory), the Demons also win with 114,368 total pages compared to the Power’s 64,191.  The ratio to cities and total pages is not particularly close.  The Power are more popular in 13% of cities and represent 35% of total pages.

The top city for Port Adelaide Power is represented by the following search: “Port Adelaide Power” Driver NT.  Driver is a popular common word so it is highly probable that this is not accurate, even with the attempt to correct for the Northern Territory by adding NT to the search.  The next city that “prefers” the Power based on total search results is Parap, with 839 results.  For the Melbourne Demons, “Melbourne Demons” +Mitchell is the top city.  That’s another problematic place as this is a common surname.  The next most popular city based on total search results for the Melbourne Demons is Yuendumu with 12,200 page results.   What is interesting here is that Darwin and Alice Springs do not appear at the top of the list, even when we exclude Driver and Mitchell.  When the Demons and Power lists are combined and sorted descending by pages per city, Darwin doesn’t appear until the 12th spot for the Demons and 18th sport for the Power.  Alice Spring doesn’t appear until 32 for the Demons and 39th position for the Power.  The biggest population bases in the territory are not generating the most references for either teams.

I’m not entirely certain why “big” cities don’t rank higher.  Are all the cities ahead of them problematic with their names where steps were not taken to correct for that?  Or is it possible that more rural fans are reliant on the Internet to express their fannishness for a team?  Are there players from these rural communities playing in the AFL so local news sources give additional attention to players that they would not get in more urban areas?  It is possible.  The real reason is probably rather complex.

So if you’re going to the game in Darwin this weekend, you probably see more people barracking for the Demons.

Notes:

1. I could theoretically get data from Facebook’s advertiser page for the number of people who list an interest and live with in a certain distance of a city.  There are just a few limitations.  First, not every location in the Northern Territory is listed.  Second, since Facebook forced users to like their interests, things have been in a state of flux and I’ve found zeros where there should not be zeros based on the number of people who like a fan page that Facebook uses and its default for a search of that interest.

2.  There are other ways I might have gone about doing this besides Google, including searching local newspapers for references to a team.  There are just limitations there in that not every location has its own newspaper and it excludes a lot of fan created references on sites likes bebo and blogger where the audience may be different than the ones that newspapers market to.  I might also have tried a geolocation based search.  I just haven’t found a good one yet that is based in Australia.  And even the ones I have seen tend to focus on Twitter and Foursquare.  AFL fandom is located more than just there.

3.  The methodology problems are a recurring problem when doing any sort of social media or web based research with the intent to create data sets.  It is why I’m generally deeply skeptical of any numbers I see unless some one clearly states their methodology, explains the problems and provides their data to give benchmarks.  This methodology issue also probably explains why much of the research done in regards to social media involves case studies and qualitative style research: The data is just so problematic to attain.

Edited to add: Visualization of this data. It isn’t perfect. There are a number of erroneous data points. (Anything outside of the Northern Territory is incorrectly placed on the map.) That said, it begins to give an idea of these patterns going on… though looking at the map, I don’t really see what I would consider overwhelming patterns. One of the islands is all Melbourne Demons. I had some data for about 15 cities for the North Melbourne Kangaroos that I overlaid to give this a bit more perspective. At some point, I should do every city in the Northern Territory, corrected as much as possible for the problems discussed above, with every team on the map.

Related Posts:

• 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)
• More popular Australian (and Kiwi) sport related Facebook fanpages and groups
• Most popular Australian (and New Zealand) sport related Twitter accounts (November 18)