Save my name, email, and website in this browser for the next time I comment. Almost no effort is made to ensure that these figures are accurate. Incidentally, West Ham also used to hold that unique distinction; but then they moved from Boleyn Park/Upton Road to the Olympic Stadium.
Still, I think this shows how increasing the processing time can produce better results, albeit with diminishing returns. Step 3: Wait a few seconds. Coincidentally, this is very close to a place called Lindley Hall Farm, suggested as the actual geographic centre of England; so the centre of English football is pretty much the centre of England itself. Well a geographic technique called Voronoi Polygons helps us solve that, and FME is more than capable of calculating those (click to enlarge): Each polygon represents an area of land closer to one particular stadium than any other. The answer is further below.
Enter your email address to receive our newsletter on all things sport, data and analysis. So these figures are good for 2018 only, but the geographic extremes are fairly predictable and (I imagine) unlikely to change: Plymouth there, with the distinction of being the most southerly and most westerly club; no wonder they have the longest away journeys. It’s just a list of English football teams and their home grounds stored in a comma-separated text file: Notice that I have the latitude and longitude (as degrees, minutes, seconds) for each ground. The team with the shortest overall journey is Derby Country, even though they play four more games than Premier League teams. This workspace also uses the ShortestPathFinder; but instead of being fed a series of journeys between two stadiums, it is fed a single journey between all of them: The line as built just passes through all stadiums in random order, but the ShortestPathFinder has a parameter that lets the order be changed to the optimum, so I use that. i.e. Very shortly you will get a link to download the results: Click the Download Results button and there you will have it: a list of your teams in an Excel spreadsheet, in order of longest journeys, and a KML dataset for use in Google Earth that shows all of the separate fixtures and travel distances. So if you want to try out any of these techniques, first fire up FME (if you don’t already have it running 24/7) and then try out these templates: Notice that with the Away Game Journeys workspace, you can only run one CSV file at a time, otherwise all the teams get mixed up together.
It’s free to try and there are also free licenses for users such as students, non-profit organisations, and home users.
So the source data I used for this is very simple. Of course, you’re also very likely to absolutely loathe the second-closest club, and there are many other reasons why you choose your club, so it’s not a perfect relationship!
Amazing post! It shows how far each team travels to away matches, and how far a supporter who went to every away game would have to travel! Poor Cardiff. I also have a dataset of Ordnance Survey open data from which I extracted roads (specifically A Roads, B Roads, and motorways). if you live in that area, geographically your local team is Manchester United.
Neat, eh? English football league pyramid; Old football league grounds; Welsh Premier League; Non-League. So the source data I used for this is very simple. We use Mailchimp as our marketing platform. They are Cambridge, Charlton, Colchester, Gillingham, Ipswich, Norwich, and Southend.
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