Have a personal or library account? Click to login
Exploring Premier League Clubs Performance and Home-Away Differences Based on Passing Network Analysis Cover

Exploring Premier League Clubs Performance and Home-Away Differences Based on Passing Network Analysis

By:
Open Access
|Oct 2024

References

  1. Antequera, D. R., Garrido, D., Echegoyen, I., López del Campo, R., Resta Serra, R., & Buldú, J. M. (2020). Asymmetries in Football: The Pass Goal Paradox. Symmetry, 12(6), 1052.
  2. Araya, J. A., & Larkin, P. (2013). Key performance variables between the top 10 and bottom 10 teams in the English Premier League 2012/13 season. Human Movement, Health and Coach Education (HMHCE), 2, 17-29.
  3. Bradley, P. S., Lago-Peñas, C., Rey, E., & Sampaio, J. (2014). The influence of situational variables on ball possession in the English Premier League. Journal of Sports Sciences, 32(20), 1867-1873.
  4. Buraimo, B., Paramio, J. L., & Campos, C. (2010). The impact of televised football on stadium attendances in English and Spanish league football. Soccer & Society, 11(4), 461-474.
  5. Buldú, J. M., Busquets, J., Martínez, J. H., Herrera-Diestra, J. L., Echegoyen, I., Galeano, J., & Luque, J. (2018). Using network science to analyse football passing networks: Dynamics, space, time, and the multilayer nature of the game. Frontiers in psychology, 9, 1900.
  6. Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing social networks. Sage.
  7. Carmichael, F., Thomas, D., & Ward, R. (2001). Production and efficiency in association football. Journal of sports Economics, 2(3), 228-243.
  8. Carmichael, F., Thomas, D., & Ward, R. (2000). Team performance: the case of English premiership football. Managerial and decision Economics, 21(1), 31-45.
  9. Csárdi G, Nepusz T, Traag V, Horvát Sz, Zanini F, Noom D, Müller K (2024). _igraph: Network Analysis and Visualization in R_. doi:10.5281/zenodo.7682609
  10. Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Academic press.532.
  11. Courneya, K. S., & Carron, A. V. (1992). The home advantage in sport competitions: a literature review. Journal of Sport & Exercise Psychology, 14(1).
  12. Castellano, J., & Echeazarra, I. (2019). Network-based centrality measures and physical demands in football regarding player position: Is there a connection? A preliminary study. Journal of Sports Sciences, 37(23), 2631-2638.
  13. Clemente, F. M., Martins, F. M., & Mendes, R. (2014). Applying Networks and graph theory to match analysis: identifying the general properties of a graph. In VIII Congreso Internacional de la Asociación Española de Ciencias del Deporte (Vol. 2, pp. 587-590).
  14. Clemente, F. M., Martins, F. M. L., Couceiro, M. S., Mendes, R. S., & Figueiredo, A. J. (2014). A network approach to characterize the teammates interactions on football: A single match analysis. Cuadernos de Psicología del Deporte, 14(3), 141-148.
  15. Clemente, F. M., Martins, F. M. L., Kalamaras, D., Wong, P. D., & Mendes, R. S. (2015). General network analysis of national soccer teams in FIFA World Cup 2014. International Journal of Performance Analysis in Sport, 15(1), 80-96.
  16. Destefanis, S., Addesa, F., & Rossi, G. (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, 54(58), 6639-6655.
  17. Gama, J., Dias, G., Couceiro, M., Passos, P., Davids, K., & Ribeiro, J. (2016). An ecological dynamics rationale to explain home advantage in professional football. International Journal of Modern Physics C, 27(09), 1650102.
  18. Grund, T. U. (2012). Network structure and team performance: The case of English Premier League soccer teams. Social Networks, 34(4), 682-690.
  19. Grund, T. U. (2016). The relational value of network experience in teams: Evidence from the English Premier League. American Behavioral Scientist, 60(10), 1260-1280.
  20. Santana, H. A., Bettega, O. B., & Dellagrana, R. (2021). An analysis of Bundesliga matches before and after social distancing by COVID-19. Science and Medicine in Football, 5(sup1), 17-21.
  21. Jamieson, J. P. (2010). The home field advantage in athletics: A meta-analysis. Journal of Applied Social Psychology, 40(7), 1819-1848.
  22. Jones, P. D., James, N., & Mellalieu, S. D. (2004). Possession as a performance indicator in soccer. International Journal of Performance Analysis in Sport, 4(1), 98-102.
  23. Kubayi, A., & Larkin, P. (2020). Technical performance of soccer teams according to match outcome at the 2019 FIFA Women’s World Cup. International Journal of Performance Analysis in Sport, 20(5), 908-916.
  24. Korte, F., Link, D., Groll, J., & Lames, M. (2019). Play-by-play network analysis in football. Frontiers in psychology, 10, 1738.
  25. Lago-Ballesteros, J., & Lago-Peñas, C. (2010). Performance in team sports: Identifying the keys to success in soccer. Journal of Human kinetics, 25(2010), 85-91.
  26. Lago-Peñas, C., & Lago-Ballesteros, J. (2011). Game location and team quality effects on performance profiles in professional soccer. Journal of sports science & medicine, 10(3), 465.
  27. Lepschy, H., Wäsche, H., & Woll, A. (2020). Success factors in football: an analysis of the German Bundesliga. International Journal of Performance Analysis in Sport, 20(2), 150-164.
  28. Legaz-Arrese, A., Moliner-Urdiales, D., & Munguía-Izquierdo, D. (2013). Home advantage and sports performance: evidence, causes and psychological implications. Universitas Psychologica, 12(3), 933-943.
  29. Maimone, V. M., & Yasseri, T. (2021). Football is becoming more predictable; network analysis of 88 thousand matches in 11 major leagues. Royal Society Open Science, 8(12), 210617.
  30. Mangiafico, S.S. 2016. Summary and Analysis of Extension Program Evaluation in R, version 1.20.05,revised2023.rcompanion.org/handbook/.(Pdfversion:rcompanion.org/docume nts/RHandbookProgramEvaluation.pdf.)
  31. Moore, J. C., & Brylinsky, J. (1995). Facility familiarity and the home advantage. Journal of Sport Behavior, 18(4), 302.
  32. Neave, N., & Wolfson, S. (2003). Testosterone, territoriality, and the ‘home advantage’. Physiology & behavior, 78(2), 269-275.
  33. Oberstone, J. (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, 5(3).
  34. Passos, P., Davids, K., Araújo, D., Paz, N., Minguéns, J., & Mendes, J. (2011). Networks as a novel tool for studying team ball sports as complex social systems. Journal of Science and Medicine in Sport, 14(2), 170-176.
  35. Passos, P., Araújo, D., & Volossovitch, A. (2017). Performance analysis in team sports. London: Routledge, Taylor & Francis Group.
  36. Pollard, R., & Pollard, G. (2005). Venteja de ser el equipo local en fútbol: una reseña de su existencia y causas. Rev Int Fútbol Ciencia, 3(1), 31-44.
  37. Ponzo, M., & Scoppa, V. (2018). Does the home advantage depend on crowd support? Evidence from same-stadium derbies. Journal of Sports Economics, 19(4), 562-582.
  38. Pina, T. J., Paulo, A., & Araújo, D. (2017). Network characteristics of successful performance in association football. A study on the UEFA champions league. Frontiers in Psychology, 8, 266057.
  39. Ribeiro, J., Silva, P., Duarte, R., Davids, K., & Garganta, J. (2017). Team sports performance analysed through the lens of social network theory: implications for research and practice. Sports medicine, 47, 1689-1696.
  40. Sarmento, H., Clemente, F. M., Araújo, D., Davids, K., McRobert, A., & Figueiredo, A. (2018). What performance analysts need to know about research trends in association football (2012 2016): A systematic review. Sports medicine, 48, 799-836.
  41. Lago, C., & Martín, R. (2007). Determinants of possession of the ball in soccer. Journal of sports sciences, 25(9), 969-974.
  42. Link, D., & Anzer, G. (2021). How the COVID-19 pandemic has changed the game of soccer. International Journal of Sports Medicine, 83-93.
  43. Van Damme, N., & Baert, S. (2019). Home advantage in European international soccer: which dimension of distance matters? Economics, 13(1).
  44. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications.
  45. Zeng, Y., & Zhang, H. (2022). Analysis of influencing factors of passes in the chinese super league. BMC Sports Science, Medicine and Rehabilitation, 14(1), 1-10.
Language: English
Page range: 51 - 61
Published on: Oct 18, 2024
Published by: International Association of Computer Science in Sport
In partnership with: Paradigm Publishing Services
Publication frequency: 2 times per year

© 2024 CYY. Yang, O. Kolbinger, published by International Association of Computer Science in Sport
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.