IDEAS home Printed from https://ideas.repec.org/a/taf/rpanxx/v18y2018i6p986-997.html
   My bibliography  Save this article

Analysis of playing styles according to team quality and match location in Greek professional soccer

Author

Listed:
  • Miguel-Ángel Gómez
  • Michalis Mitrotasios
  • Vasilis Armatas
  • Carlos Lago-Peñas

Abstract

The aim of the current study was to identify the styles of play of Greek Superleague soccer teams according to match location and team’s ranking using performance indicators related to attack, defence, transition and set-pieces. Data were obtained from the 2013–2014 season (n = 301 matches). The factor analysis identified eight factors (explained 64.29% of the total variance): F1 (ball possession); F2 (ending actions); F3 (individual challenges); F4 (counter-attack); F5 (set-piece); F6 (transitional-play); F7 (fouling actions); and F8 (free-kick). The results of the ANCOVA showed that home and away performances were significant different for F1, F2, F5, F6, and F7 with better values for home teams. In addition, the results of the covariate effect (team’s ranking) showed that the F1, F2 and F3 were significant reflecting an effect of ranking on these Factors when differentiating home and away teams (teams ranked 1st to 6th positions obtained greater values for F1 and F2, and less values for F3 than teams ranked 7th to 12th and 13th to 18th. The measures of tactics and strategies showed specific trends by ranking and match location that should be taken into account by coaches and performance analysts when identifying specific styles of play in soccer.

Suggested Citation

  • Miguel-Ángel Gómez & Michalis Mitrotasios & Vasilis Armatas & Carlos Lago-Peñas, 2018. "Analysis of playing styles according to team quality and match location in Greek professional soccer," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 18(6), pages 986-997, November.
    • Handle: RePEc:taf:rpanxx:v:18:y:2018:i:6:p:986-997
    DOI: 10.1080/24748668.2018.1539382
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/24748668.2018.1539382
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/24748668.2018.1539382?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Julen Castellano & Miguel Pic, 2019. "Identification and Preference of Game Styles in LaLiga Associated with Match Outcomes," IJERPH, MDPI, vol. 16(24), pages 1-13, December.
    2. Alexander John Bond & Clive B. Beggs, 2023. "Bisecting for Selecting: Using a Laplacian Eigenmaps Clustering Approach to Create the New European Football Super League," Mathematics, MDPI, vol. 11(3), pages 1-17, January.
    3. 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).
    4. Alejandro Sabarit & Rafael E. Reigal & Juan P. Morillo-Baro & Rocío Juárez-Ruiz de Mier & Auxiliadora Franquelo & Antonio Hernández-Mendo & Coral Falcó & Verónica Morales-Sánchez, 2020. "Cognitive Functioning, Physical Fitness, and Game Performance in a Sample of Adolescent Soccer Players," Sustainability, MDPI, vol. 12(13), pages 1-12, June.
    5. Gong, Bingnan & Zhou, Changjing & Gómez, Miguel-Ángel & Buldú, J.M., 2023. "Identifiability of Chinese football teams: A complex networks approach," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    6. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:rpanxx:v:18:y:2018:i:6:p:986-997. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RPAN20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.