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Performance indicators that predict success in an English professional League One soccer team

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  • Kerys Harrop
  • Alan Nevill

Abstract

The purpose of the present study was to identify performance indicators that may discriminate between games a soccer team won, drew and lost. A second aim was to identify those variables that best predict success for the team. The sample comprised of 46 matches played by a League One soccer team during the 2012-2013 domestic season. Offensive and defensive game-related statistics were gathered Match location was also considered. A Kruskal Wallis test and binary logistic regression were used to identify those indicators associated with success (wins). The Kruskal Wallis test identified significant differences in the number of passes, percentage of successful passes and passes made in the opposition half Significantly more passes and passes in the opposition half were made when the team lost compared to when they won and drew games (P<0.05). A significantly lower percentage of successful passes were completed when the team drew (P<0.05). The results of the binary logistic regression concluded that the team should perform fewer passes and dribbles but complete more successful passes and shots to be successful. The results indicate that for the team to be successful they should implement a direct style of play.

Suggested Citation

  • Kerys Harrop & Alan Nevill, 2014. "Performance indicators that predict success in an English professional League One soccer team," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 14(3), pages 907-920, December.
    • Handle: RePEc:taf:rpanxx:v:14:y:2014:i:3:p:907-920
    DOI: 10.1080/24748668.2014.11868767
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    Citations

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    Cited by:

    1. Daniel Link & Martin Hoernig, 2017. "Individual ball possession in soccer," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-15, July.
    2. Marcelino, Rui & Sampaio, Jaime & Amichay, Guy & Gonçalves, Bruno & Couzin, Iain D. & Nagy, Máté, 2020. "Collective movement analysis reveals coordination tactics of team players in football matches," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    3. Athalie J Redwood-Brown & Peter G O’Donoghue & Alan M Nevill & Chris Saward & Caroline Sunderland, 2019. "Effects of playing position, pitch location, opposition ability and team ability on the technical performance of elite soccer players in different score line states," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-21, February.
    4. Claudio A. Casal & José L. Losada & Daniel Barreira & Rubén Maneiro, 2021. "Multivariate Exploratory Comparative Analysis of LaLiga Teams: Principal Component Analysis," IJERPH, MDPI, vol. 18(6), pages 1-18, March.
    5. Serafeim Moustakidis & Spyridon Plakias & Christos Kokkotis & Themistoklis Tsatalas & Dimitrios Tsaopoulos, 2023. "Predicting Football Team Performance with Explainable AI: Leveraging SHAP to Identify Key Team-Level Performance Metrics," Future Internet, MDPI, vol. 15(5), pages 1-18, May.
    6. Nimai Parmar & Nic James & Mike Hughes & Huw Jones & Gary Hearne, 2017. "Team performance indicators that predict match outcome and points difference in professional rugby league," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 17(6), pages 1044-1056, November.

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