Name
Abstract | ||
|---|---|---|
We provide an overview and comparison of predictive capabilities of several methods for ranking association football teams. The main benchmark used is the official FIFA ranking for national teams. The ranking points of teams are turned into predictions that are next evaluated based on their accuracy. This enables us to determine which ranking method is more accurate.The best performing algorithm is a version of the famous Elo rating system that originates from chess player ratings, but several other methods (and method versions) provide better predictive performance than the official ranking method. Being able to predict match outcomes better than the official method might have implications for, e.g., a team's strategy to schedule friendly games. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1504/IJAPR.2013.052339 | INTERNATIONAL JOURNAL OF APPLIED PATTERN RECOGNITION |
Keywords | Field | DocType |
FIFA ranking, predictive capabilities, predictive power, rankings, ratings, team strength, football predictions | Data mining,Football,Predictive power,Ranking,Rating system,Artificial intelligence,Engineering,Machine learning | Journal |
Volume | Issue | ISSN |
1 | 1 | 2049-887X |
Citations | PageRank | References |
8 | 1.15 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jan Lasek | 1 | 8 | 1.15 |
| Zoltán Szlávik | 2 | 116 | 21.40 |
| Sandjai Bhulai | 3 | 137 | 16.49 |