journal article·2022-12-09Open access

Match Movement Profiles Differences in Spanish Soccer Competitive Leagues According to Opposition’s Team Ranking: A Comparison Study

José C. Ponce-Bordón, Hadi Nobari, David Lobo-Triviño, Tomás García-Calvo, Jesús Vicente-Giménez, Roberto López del Campo, Ricardo Resta, Javier Fernández-Navarro

Applied Sciences· Volume 12· Issue 24

Abstract

(1) Background: This study analyzed the differences in match movement profiles according to opponent quality (i.e., match balance) in the professional Spanish soccer leagues over four consecutive seasons (from 2015/2016 to 2018/2019); (2) Methods: The ChyronHego® system was used to record competition movement data from all matches played in the First (Liga Santander; n = 1520) and Second Spanish Division (Liga Smartbank; n = 1848). The total distance (TD) and high-intensity running distance (TD > 21 km∙h−1) covered with and without ball possession (TDWP and TDWOP, respectively) were analyzed using a Linear Mixed Model, considering the opponent quality contextual variable; (3) Results: Results showed that teams covered a significantly greater TD when played against the lowest quality teams in L1 (p < 0.05), while in L2 teams covered a significantly greater TD when played against the highest quality teams (p < 0.05). Teams covered a significantly greater TDWP and TDWP > 21 km∙h−1 when playing against the highest quality teams in both L1 and L2 (p < 0.05). On the contrary, playing against the lowest quality teams in L1 versus the highest quality teams in L2 implied more TDWOP and TDWOP > 21 km⋅h−1 (p < 0.05); (4) Conclusions: The present study indicates that match movement profiles depend on contextually related variables.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Sports Performance and Training · Sports Analytics and Performance · Sports injuries and prevention

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Faculty of Sport Sciences, University of Extremadura, 10003 Cáceres, Spain

Department of Motor Performance, Faculty of Physical Education and Mountain Sports, Transilvania University of Braşov, 500036 Brașov, Romania

Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, 18071 Granada, Spain

LaLiga Sport Research Section, 28043 Madrid, Spain

School of Science and Technology, Nottingham Trent University, Nottingham NG1 4FQ, UK

Funding

European Regional Development Fund · Government of Extremadura (Department of Economy and Infrastructure) and LaLiga Research and Analysis Sections

References (46)

Show source-provided references
1. O’Donoghue, P., and Holmes, L. (2014). Data Analysis in Sport, Routledge. [1st ed.].10.4324/9781315816357 ↗
2. Influence of contextual variables on styles of play in soccer10.1080/24748668.2018.1479925 ↗
3. The influence of match location, quality of opposition, and match status on possession strategies in professional association football10.1080/02640410903131681 ↗
4. Contextual variables and time-motion analysis in soccer10.1055/s-0031-1271771 ↗
5. García-Unanue, J., Pérez-Gómez, J., Giménez, J.V., Felipe, J.L., Gómez-Pomares, S., Gallardo, L., and Sánchez-Sánchez, J. (2018). Influence of contextual variables and the pressure to keep category on physical match performance in soccer players. PLoS ONE, 13.10.1371/journal.pone.0204256 ↗
6. Influence of contextual factors on match running performance in elite soccer team
7. Physical activities according to playing positions, match outcome, and halves during the 2018 Soccer World Cup
8. Tracking systems in team sports: A narrative review of applications of the data and sport specific analysis10.1186/s40798-022-00408-z ↗
9. The what and how of video analysis research in rugby union: A critical review10.1186/s40798-018-0142-3 ↗
10. Inter-operator reliability of live football match statistics from OPTA Sportsdata10.1080/24748668.2013.11868690 ↗
11. Match analysis in football: A systematic review10.1080/02640414.2014.898852 ↗
12. Critical process for the implementation of technology in sport organizations10.1519/SSC.0000000000000339 ↗
13. Talent identification and development in male football: A systematic review10.1007/s40279-017-0851-7 ↗
14. Chinese soccer association Super League, 2012–2017: Key performance indicators in balance games10.1080/24748668.2018.1509254 ↗
15. Normative profiles of sports performance10.1080/24748668.2005.11868319 ↗
16. Performance indicators that predict success in an English Professional League One soccer team10.1080/24748668.2014.11868767 ↗
17. Physical outcome in a successful Italian Serie A soccer team over three consecutive seasons10.1519/JSC.0b013e3182679382 ↗
18. The relationship between running distance and coaches’ perception of team performance in professional soccer player during multiple seasons10.1038/s41598-022-05519-x ↗
19. Impact of match-related contextual variables on weekly training load in a professional soccer team: A full season study10.5114/biolsport.2021.102927 ↗
20. The effects of situational variables on distance covered at various speeds in elite soccer10.1080/17461390903273994 ↗
21. Variation in top level soccer match performance10.1055/s-2007-965158 ↗
22. Influence of match location, quality of opponents, and match status on movement patterns in Brazilian professional football players10.1519/JSC.0000000000001674 ↗
23. Folgado, H., Duarte, R., Fernandes, O., and Sampaio, J. (2014). Competing with lower level opponents decreases intra-team movement synchronization and time-motion demands during pre-season soccer matches. PLoS ONE, 9.10.1371/journal.pone.0097145 ↗
24. The influence of opponent level on professional soccer players’ training and match performance assessed by using wearable sensor technology10.5114/hm.2023.117164 ↗
25. Aquino, R., Gonçalves, L.G., Galgaro, M., Maria, T.S., Rostaiser, E., Pastor, A., Nobari, H., Garcia, G.R., Moraes-Neto, M.V., and Nakamura, F.Y. (2021). Match running performance in Brazilian professional soccer players: Comparisons between successful and unsuccessful teams. BMC Sports Sci. Med. Rehabil., 13.10.1186/s13102-021-00324-x ↗
26. Effect of opposition quality and match location on the positional demands of the 4-2-3-1 formation in elite soccer10.1016/j.jesf.2019.11.001 ↗
27. Match performance of high-standard soccer players with special reference to development of fatigue10.1080/0264041031000071182 ↗
28. Match performance and physical capacity of players in the top three competitive standards of English professional soccer10.1016/j.humov.2013.06.002 ↗
29. Asian-Clemente, J.A., Requena, B., Jukic, I., Nayler, J., Santalla-Hernández, A., and Carling, C. (2019). Is physical performance a differentiating element between more or less successful football teams?. Sports, 7.10.3390/sports7100216 ↗
30. What are the differences between First and Second Divisions of Spanish football teams?10.1080/24748668.2015.11868782 ↗
31. Impact of COVID-19 lockdown on professional soccer players’ match physical activities10.1080/24733938.2021.1995033 ↗
32. Integrating video tracking and GPS to quantify accelerations and decelerations in elite soccer10.1038/s41598-021-97903-2 ↗
33. Pons, E., García-Calvo, T., Resta, R., Blanco, H., López del Campo, R., Díaz García, J., and Pulido, J.J. (2019). A comparison of a GPS device and a multi-camera video technology during official soccer matches: Agreement between Systems. PLoS ONE, 14.10.1371/journal.pone.0220729 ↗
34. Association of match running performance with and without ball possession to football performance10.1080/24748668.2020.1762279 ↗
35. Key team physical and technical performance indicators indicative of team quality in the soccer Chinese Super League10.1080/15438627.2018.1431539 ↗
36. R-Studio Team (2020). RStudio: Integrated Development for R, R-Studio Team.
37. Fitting Linear Mixed-Effects Models using Lme410.18637/jss.v067.i01 ↗
38. Heck, R.H., and Thomas, S.L. (2015). An Introduction to Multilevel Modeling Techniques: MLM and SEM Approaches Using Mplus, Routledge.
39. A new look at the statistical model identification10.1109/TAC.1974.1100705 ↗
40. Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics, SAGE Editorial. [4th ed.].
41. A general and simple method for obtaining R2 from Generalized Linear Mixed-Effects Models10.1111/j.2041-210x.2012.00261.x ↗
42. Ponce-Bordón, J.C., Díaz-García, J., López-Gajardo, M.A., Lobo-Triviño, D., López del Campo, R., Resta, R., and García-Calvo, T. (2021). The influence of time winning and time losing on position-specific match physical demands in the top one Spanish Soccer League. Sensors, 21.10.3390/s21206843 ↗
43. Relation between the physical demands and success in professional soccer players
44. Match running performance on three different competitive standards in Norwegian soccer10.1055/a-0943-3682 ↗
45. Pons, E., Ponce-Bordón, J.C., Díaz-García, J., del Campo, R.L., Resta, R., Peirau, X., and García-Calvo, T. (2021). A longitudinal exploration of match running performance during a football match in the Spanish La Liga: A four-season study. Int. J. Environ Res. Public Health, 18.10.3390/ijerph18031133 ↗
46. Tier-specific evolution of match performance characteristics in the English Premier League: It’s getting tougher at the Top10.1080/02640414.2015.1082614 ↗