Networking Lions: Conclusions
Thanks for checking out the last two days worth of blog posts, and I do honestly hope you enjoyed them on some level. Even if your overall conclusions might only be summarized by words like “neat” or “kinda cool.”
I’ve always seen mapping as a way to generate research questions. A chance to look at things you feel like you understand from a different perspective. The past two days worth of maps (Heat Map) (Club Map) and the birthplace map have been interesting experiments for me in doing exactly that. Here’s a bit about what I liked, some of the limitations, and some lessons learned.
First, let us get the limitations out of the way. I still can’t embed these maps into WordPress. I’m looking at other mapping software, FusionTables is free and really easy to use for me. Most other programs I’ve checked out lack one or both of those functions. I’m also still having problems with stacking places. If you looked closely at the map you may have wondered where a certain player or club went, it might be hiding under another one. Best example of this would be Real Madrid. The pin for their youth team, and David Mateos is hiding Kaka and the first team. A travesty of exclusion if there ever would be one in mapping soccer right? Luckily this doesn’t happen much and all the clubs and players are represented on the cards. I’ll keep playing with the data and see if there is a way to unstack them. On the birthplace map I used actual latitude and longitude, and just moved points over a bit.
The curious case of Martin Patterson. Some Orlando City fans may not even know who this person is. He played four games for the club before he had his contract terminated for staying in New York City and missing the flight back to Orlando so he could party with Sean St. Ledger. While four games isn’t insignificant, and there are players who made this list with less games, as a journeyman player he played for a large selection of clubs. I felt that adding all the clubs he trained with to the map would have given a massive over importance to England. So I skipped him, just like he skipped the flight home. I’m alright with it.
I think these maps can tell us more than the birthplace maps for the obvious reasons. Where people have trained and learned is far more important than simply their birthplace. I still think the birthplace map can tell us something, and they are infinitely easier to construct.
I think in these two maps, as more data points are added, the heat map can add a level of understanding that a heat map of birthplaces wouldn’t add. I’d love to figure out ways to add weight to clubs that have trained more than one player, allowing for more intense areas in the heat map.
I think in the future I’d like to add the high schools of American players, when available. I’m also considering placing the points on the home stadiums of clubs, instead of just the city. This might help with point stacking as well.
Adding the club badges might be a nice touch, so I’ll look into that for a future version.
Finally, there is a way to merge maps, so I’m looking into merging the birthplace and training maps. I think those two collections of data sets could be enlightening.
As Orlando City and MLS continue to grow the game in the United States, understanding where players are coming from and how players find their way into the league will always be important. Will Orlando City keep their bias towards UK trained players, or will the long predicted Brazilian shift occur. I read that Orlando City had signed two players to OCB from Cameroon, just today. Perhaps negating the comment I made about Orlando having not attracted talent from Africa to their MLS side yet. As the game grows will players trained in Asia and the Middle East start to find their way onto this map? Certainly they have for other MLS clubs.
Looking at a more established club could illuminate some of these questions about MLS generally. I’ve started a birthplace map for one of the first clubs, and just mapping every player’s birthplace has taken awhile. Starting with a new club like Orlando City means I can add data year by year without a massive task to start. Just doing three seasons worth was actually relatively time consuming. Still, I’ll be looking for chances to map other clubs as time allows.
Oh and shout out to this person for the map pictures. Thanks internet stranger for your free use photos.