I have been trying to come up with an idea that I feel is simply feasible and continue to run into the same problem: too big. The amount of data I am able to access with nothing more than a Google search for any of the ideas I have considered is nothing short of terrifying. However, in the spirit of scholarly adventure, I’ll throw out a few ideas and see what you guys think.
I’m a sports junkie. My father was a sports junkie and his father before him; so it looks as though this project is going to be geared towards sports. What I had considered doing was some kind of NHL player breakdown assessing the relationship between proximity to a community center with an ice surface and chance at making it to the NHL. Given that I’m looking for recent trends and trying to limit my data base, I thought I might take every player to play a game over the last 20 years and plot them geographically.
It’s at this point in time that I run into a bit of a crisis. I can’t decide exactly how to go about best plotting the data I have considered. Obviously I could do a simple tag of each player at their municipality, look at where there were the most players and the least then make assessments about how many minor league rinks they had access to during their youth. Just a couple issues with this however. I have to be careful not to include all rinks for all players as rinks have been built since certain players have joined the league, rendering them nonexistent during their your. Also, I have to have some kind of distance limit for what I consider to be an accessible rink, which seems to me somewhat arbitrary.
I also have a few more issues with pursuing this project in its current state. Issues such as am I going to look at statistics for these players and consider the impact of the distance not just on their ability to reach the NHL, but also their level of performance once there? Will I consider the availability of public transit for youth from the city in creating distance limits for what is considered an accessible rink? These are questions which I hope to address in a manner which is responsible in that it does not skew the data.
The ultimate goal of this assignment would be to create geographic hot spots for youth hockey scouting and identify areas prone to NHL player development. I have also considered taking a more geographic approach, where I look at the relationship between rink availability and temperature, and the impact that has on ones chance to make the NHL. I think it would be interesting to illustrate the relationship between average temperature and the odds of a person from a given place making the NHL/ the odds any given NHL player was facing when they began pursuing their NHL aspirations.
Steve Boyles Digital Humanities Blog 
A compelling problem, for sure, and like you say, the data might be a bit overwhelming. Think about simplifying – maybe just Guelph, or just Toronto – just players born in 1980s. Let’s talk about this. (http://www.cabdirect.org/abstracts/20033169492.html)
Hey Steve
I have a very similar idea for my project, plotting the geographic locations of NHL players. I however, would more be gauging the success of players from certain parts of the world, and how these trends have changed over the past decades. I believe you have a very good idea here, but I believe it requires a very large amount of data, including where rinks are, and addresses of these player, if I have the right idea of what you are trying to do here. I do have one question though. Are you just planning on at Canada, North America or all of North America and Europe? If so have you considered the different hockey cultures that exist overseas? Just some things to consider.
Although my ambition would have me pick an entire country, I think even that may be too much data. It’s incredible the amount of available information on these players. I have been looking at kmz files and the amount of plotting that you could interconnect is substantial. However, you are right in that tackling this is likely far too great a task for the time period alloted. I then have considered making this assignment provincial, if not even municiple. Considering the number of players which have played who were born in Toronto alone, it seems as though this may be the best option for creating a digital humanities project that can be accomplished but is not too small in score.
If I were going to look at hockey cultures however (Seems like a great idea) I think I might do a comparative analysis of goals and assists, but above all else penalties in minutes. This is because traditionally European hockey grassroots development has been charactirized as skating intensive since European players began playing in the NHL. North American hockey is typically described as being more physical. I think it would be interesting to consider these perspectives and see if they can be statistically substantiated or are just inaccurate presuppositions.