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Success factors in football: an analysis of the German Bundesliga

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  • Hannes Lepschy
  • Hagen Wäsche
  • Alexander Woll

Abstract

Knowledge of success factors in professional football is critical. However, the identification of the success factors primarily focused on English and Spanish leagues. In this paper, three seasons of the German Bundesliga (2014/2015 until 2016/2017) with a total of 918 matches were analysed. To facilitate a more precise analysis of success factors, only close matches were included and the home and away team perspective was analysed separately. Therefore, 29 variables were included in a generalised ordered logit approach. The results showed that defensive errors, market value, goal efficiency, shots from counter attacks, shots on target and total shots have the greatest impact. Furthermore, crosses showed a negative relationship with success. Besides, the opponent and home advantage are important contextual effects. Overall, 11 and 12 variables are significant, respectively. Duel success is only significant for away teams and a higher market value seems to have a more positive impact for them. This study provides novel data and contributes to prior results from other European leagues. Future research should further investigate the impact of ball possession and distance covered. Coaches should focus on accuracy rather than on quantity as well as train fitness (physically and mentally) to lower the risk of errors.

Suggested Citation

  • Hannes Lepschy & Hagen Wäsche & Alexander Woll, 2020. "Success factors in football: an analysis of the German Bundesliga," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 20(2), pages 150-164, March.
    • Handle: RePEc:taf:rpanxx:v:20:y:2020:i:2:p:150-164
    DOI: 10.1080/24748668.2020.1726157
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    Citations

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    Cited by:

    1. Sergio Destefanis & Francesco Addesa & Giambattista Rossi, 2022. "The impact of COVID-19 on home advantage: a conditional order-m analysis of football clubs’ efficiency in the top-5 European leagues," Applied Economics, Taylor & Francis Journals, vol. 54(58), pages 6639-6655, December.
    2. Galli, L. & Galvan, G. & Levato, T. & Liti, C. & Piccialli, V. & Sciandrone, M., 2021. "Football: Discovering elapsing-time bias in the science of success," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    3. Riccardo Ievoli & Aldo Gardini & Lucio Palazzo, 2023. "The role of passing network indicators in modeling football outcomes: an application using Bayesian hierarchical models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 153-175, March.
    4. Li, Yuesen & Ma, Runqing & Gonçalves, Bruno & Gong, Bingnan & Cui, Yixiong & Shen, Yanfei, 2020. "Data-driven team ranking and match performance analysis in Chinese Football Super League," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).

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