Game-Related Statistics that Discriminated Winning, Drawing and Losing Teams from the Spanish Soccer League
- PMID: 24149698
- PMCID: PMC3761743
Game-Related Statistics that Discriminated Winning, Drawing and Losing Teams from the Spanish Soccer League
Abstract
The aim of the present study was to analyze men's football competitions, trying to identify which game-related statistics allow to discriminate winning, drawing and losing teams. The sample used corresponded to 380 games from the 2008-2009 season of the Spanish Men's Professional League. The game-related statistics gathered were: total shots, shots on goal, effectiveness, assists, crosses, offsides commited and received, corners, ball possession, crosses against, fouls committed and received, corners against, yellow and red cards, and venue. An univariate (t-test) and multivariate (discriminant) analysis of data was done. The results showed that winning teams had averages that were significantly higher for the following game statistics: total shots (p < 0.001), shots on goal (p < 0.01), effectiveness (p < 0.01), assists (p < 0.01), offsides committed (p < 0.01) and crosses against (p < 0.01). Losing teams had significantly higher averages in the variable crosses (p < 0.01), offsides received (p < 0. 01) and red cards (p < 0.01). Discriminant analysis allowed to conclude the following: the variables that discriminate between winning, drawing and losing teams were the total shots, shots on goal, crosses, crosses against, ball possession and venue. Coaches and players should be aware for these different profiles in order to increase knowledge about game cognitive and motor solicitation and, therefore, to evaluate specificity at the time of practice and game planning. Key pointsThis paper increases the knowledge about soccer match analysis.Give normative values to establish practice and match objectives.Give applications ideas to connect research with coaches' practice.
Keywords: Association football; discriminant analysis; game-related statistics; match analysis.
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References
-
- Armatas V., Yannakos A., Zaggelidis G., Skoufas D., Papadopoulou S., Fragkos N. (2009) Journal of Physical Education and Sport 23(2), 1-5
-
- Carling C., Reilly T., Williams A. (2009) Performance assessment for field sports. London: Routledge
-
- Carling C., Williams A., Reilly T. (2005) The Handbook of Soccer Match Analysis. London: Routledge
-
- Ensum J., Taylor S., Williams M. (2002) A quantitative analysis of attacking set plays. Insight, 4(5), 68-72
-
- Grant A.G., Williams A.M., Reilly T. (1999) Analysis of the goals scored in the 1998 World Cup. Journal of Sports Sciences 17, 826-827
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