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A contextual analysis of crossing the ball in soccer

Author

Listed:
  • Wu Lucas Y.

    (Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, V5A1S6, British Columbia, Canada)

  • Danielson Aaron J.

    (Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, V5A1S6, British Columbia, Canada)

  • Hu X. Joan

    (Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, V5A1S6, British Columbia, Canada)

  • Swartz Tim B.

    (Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, V5A1S6, British Columbia, Canada)

Abstract

The action of crossing the ball in soccer has a long history as an effective tactic for producing goals. Lately, the benefit of crossing the ball has come under question, and alternative strategies have been suggested. This paper utilizes player tracking data to explore crossing at a deeper level. First, we investigate the spatio-temporal conditions that lead to crossing. Then we introduce an intended target model that investigates crossing success. Finally, a contextual analysis is provided that assesses the benefits of crossing in various situations. The analysis is based on causal inference techniques and suggests that crossing remains an effective tactic in particular contexts.

Suggested Citation

  • Wu Lucas Y. & Danielson Aaron J. & Hu X. Joan & Swartz Tim B., 2021. "A contextual analysis of crossing the ball in soccer," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(1), pages 57-66, March.
    • Handle: RePEc:bpj:jqsprt:v:17:y:2021:i:1:p:57-66:n:6
    DOI: 10.1515/jqas-2020-0060
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    References listed on IDEAS

    as
    1. Sarkar Sumit, 2018. "Paradox of crosses in association football (soccer) – a game-theoretic explanation," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(1), pages 25-36, March.
    2. Bransen Lotte & Van Haaren Jan & van de Velden Michel, 2019. "Measuring soccer players’ contributions to chance creation by valuing their passes," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(2), pages 97-116, June.
    3. 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.
    4. Sekhon, Jasjeet S., 2011. "Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i07).
    5. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    6. King, Gary & Nielsen, Richard, 2019. "Why Propensity Scores Should Not Be Used for Matching," Political Analysis, Cambridge University Press, vol. 27(4), pages 435-454, October.
    7. Bransen Lotte & Van Haaren Jan & van de Velden Michel, 2019. "Measuring soccer players’ contributions to chance creation by valuing their passes," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(2), pages 97-116, June.
    8. Oberstone Joel, 2009. "Differentiating the Top English Premier League Football Clubs from the Rest of the Pack: Identifying the Keys to Success," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(3), pages 1-29, July.
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