Using analytics to plan a (sports) trip

My latest effort into analytics of sports-related data has been a little bit different than usual.
The goal was to plan a trip around Europe to watch hockey games.

The EuroHockey website has schedules for possibly every hockey league around the world (yeah, not only Euro), thus I grabbed data about matches in the top continental leagues scheduled between September and December.

Then I opened my friend R.

As a first pass, I calculated distances between each pair of games scheduled in consecutive days and filtered down to those within reasonable travel time--thus, sorry Admiral, I'm not gonna come to Vladivostok.

Then I recursively combined the pairs to obtain longer trips and added some subjective scores to rank the calculated trips to my likings.

The main parameters I used to rank my trips were:

• Short travel distances between cities;
• Trips with games from multiple leagues to be preferred;
• A ranking of the leagues (e.g. priority to KHL games);
• Starting and ending point of the trip possibly close to home (so that I can go just by train/bus).

After letting R do its job, some interesting solutions came up, but in the end I stuck with the one mapped below.
Clicking on the icons will show games (and make sure to zoom on Prague, as I'll be there both for KHL and Extraliga).


View Max EHT 2013 games only in a larger map
Thus, starting from December 1, I'll be around 5 European countries to catch 8 games in 8 days, featuring 14 teams from 5 leagues, in 7 different cities.

Maybe I'll blog about it. Or maybe not.