In the first part of my Soccer Analytics series, I talked about general statistics of my dataset of the season 2013. As a reminder, i scraped all results from 177 domestic leagues and intra continental cups world wide. This entry will deal with the question “Who was the best team in 2013?” according to network analysis and why the result is (most likely) wrong.
Visualizing the Soccer Network
A Global Ranking via PageRank
So Al Kuwait SC, the winner of the Kuwaiti first division, was the best team in 2013 and AS Saint Michel, the champions of Madagascar, the second best. You think that is unreasonable? Well it probably is (No offence!). The thing about the approach taken is that a lot of important factors were neglected.
“Finally, our results indicate that the Random walk approach with the use of right metrics can indeed produce relevant rankings comparable to the FIFA official all-time ranking board.”
Ok so apparently it is actually possible to rank soccer teams with PageRank (implying that the FIFA ranking is relevant…). We just have to use the right metrics (the weight of the links) to get a relevant ranking. The authors describe ten different metrics which could be used. In the end it was just a simple fraction of games lost and games played that produced a ranking close to the FIFA one.
However, this will not hold for soccer clubs, since there are more factors that have to be taken into account. International matches should be weighted more, different leagues have different overall strengths, the goal differences should be taken into account, home/away wins and so on. Incorporating these factors would maybe give us a reasonable ranking. However, this would be too scientific for this blog. Or maybe I do that another time…
Yet, I found a way to produce a ranking with my simple prestige model that seems more reasonable.
I just changed the damping factor  from $0.85$ to $0.95$ to reduce the factor of randomness a bit.
Seems more reasonable. Especially since Borussia Dortmund made it to the Top 25!