Gergely Csurilla, László Csató
arXiv 18 Sep 2026 · Econometrics
arXiv:2609.21674 · PDF · Extracted main text
The pre-season strengths of European football clubs are usually measured by two proxies in the literature. Football Club Elo Ratings provide strictly performance-based Elo ratings from the early days of the European Cups, while Transfermarkt valuations are crowd-based estimates of squad market values. This paper compares them by evaluating their ability to forecast the results of matches played in the UEFA Champions League and the UEFA Europa League between the seasons 2020/21 and 2024/25. The two indicators yield almost identical out-of-sample accuracy when used separately. Combining the two measures leads to a modest improvement, but the best aggregation procedure is sensitive to the forecast target. Our results suggest that seeding based on Elo ratings would be (closely) optimal.
appendix boundary found by appendix_titled_section at “Appendix” · 84% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Peeters, T (2018) Testing the Wisdom of Crowds in the field: Transfermarkt valuations and international soccer results | 1.000 | 6 | 4 | 100% |
| 2 | Coates, D. and Parshakov, P (2022) The wisdom of crowds and transfer market values | 0.928 | 4 | 3 | 100% |
| 3 | Csató, L (2024) Club coefficients in the UEFA Champions League: Time for shift to an Elo-based formula | 0.843 | 3 | 3 | 100% |
| 4 | Herm, S., Callsen-Bracker, H.-M., and Kreis, H (2014) When the crowd evaluates soccer players' market values: Accuracy and evaluation attributes of an online community | 0.843 | 3 | 3 | 100% |
| 5 | Ley, C., van de Wiele, T., and van Eetvelde, H (2019) Ranking soccer teams on the basis of their current strength: A comparison of maximum likelihood approaches | 0.843 | 3 | 3 | 100% |
| 6 | Bates, J. M. and Granger, C. W. J (1969) The combination of forecasts | 0.644 | 2 | 2 | 100% |
| 7 | Bryson, A., Dolton, P., Reade, J. J., Schreyer, D., and Singleton, C (2021) Causal effects of an absent crowd on performances and refereeing decisions during Covid-19 | 0.644 | 2 | 2 | 100% |
| 8 | Constantinou, A. C. and Fenton, N. E (2012) Solving the problem of inadequate scoring rules for assessing probabilistic football forecast models | 0.644 | 2 | 2 | 100% |
| 9 | Csató, L. and Petróczy, D. G (2026) Are penalty shootouts better than a coin toss? Evidence from international club football in Europe | 0.644 | 2 | 2 | 100% |
| 10 | Devriesere, K., Goossens, D., and Spieksma, F (2026) From groups to a single league: evaluating competitiveness in the UEFA Champions League | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 61 scored citations.