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A strategy to predict association football players’ passing skills

Author

Listed:
  • Jorge Tovar
  • Andrés Clavijo
  • Julián Cárdenas

Abstract

Transfers are big business in association football. This paper develops a generalized additive mixed model that aids managers in predicting how a football player is expected to perform in a new team. It does so by using event-level data from the Spanish and the Colombian football leagues. Using passes as a performance proxy, the model exploits the richness of the data to account for the difficulty of each pass attempt performed by each player over an entire season. The model estimates are then used to determine how a player transferred from the Colombian league should perform in the Spanish league, taking into account that teammates and rivals’ abilities are different in the latter.

Suggested Citation

  • Jorge Tovar & Andrés Clavijo & Julián Cárdenas, 2017. "A strategy to predict association football players’ passing skills," Documentos CEDE 15821, Universidad de los Andes, Facultad de Economía, CEDE.
    • Handle: RePEc:col:000089:015821
    as

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    File URL: https://repositorio.uniandes.edu.co/bitstream/handle/1992/8856/dcede2017-63.pdf
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    References listed on IDEAS

    as
    1. Tovar Jorge, 2014. "Gasping for air: soccer players’ passing behavior at high-altitude," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(4), pages 1-10, December.
    2. Krumer, Alex & Lechner, Michael, 2016. "Midweek Effect on Performance: Evidence from the German Soccer Bundesliga," Economics Working Paper Series 1609, University of St. Gallen, School of Economics and Political Science.
    3. Flores, Ramón & Forrest, David & Tena, J.D., 2012. "Decision taking under pressure: Evidence on football manager dismissals in Argentina and their consequences," European Journal of Operational Research, Elsevier, vol. 222(3), pages 653-662.
    4. Łukasz Szczepański & Ian McHale, 2016. "Beyond completion rate: evaluating the passing ability of footballers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 513-533, February.
    5. X. Lin & D. Zhang, 1999. "Inference in generalized additive mixed modelsby using smoothing splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 381-400, April.
    6. Fiona Carmichael & Dennis Thomas, 2005. "Home-Field Effect and Team Performance," Journal of Sports Economics, , vol. 6(3), pages 264-281, August.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Generalized additive mixed models; football; sports forecasting; passing.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Z21 - Other Special Topics - - Sports Economics - - - Industry Studies
    • Z22 - Other Special Topics - - Sports Economics - - - Labor Issues

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