Defnsive process of football sucessfull teams: T-pattern detection analysis - Martins, N.; Sarmento, H.; Campaniço, J.; Leitão, J. (7th World Congress on Science and Football (WCSF), 2011, 2011)
It is acknowledged that the defensive process has an important role in a football team success; however, there is a discrepancy in the scientific literature on defensive process when compared with the offensive process. The observation of sports performances has focused on the frequency of occurrence as its performance index, still is debatable whether the frequency data alone allow to successfully distinguish effective performances and less effective performances, therefore observational studies should expand their indices of performance further than the frequency (Borrie et al, 2002). The detection of play patterns is one tendency to be followed, being emphasized the potential of THEME software in detecting temporal patterns of behaviour (James, 2006). To analyse and characterise the defensive process of successful teams were the main purposes of this study. Methods The study observational design was multidimensional, nomothetic and sequential. The behaviour was coded through an "ad hoc" observational instrument combining field formats and categorical systems. Values above 0.9 for all criteria were achieved in this instrument reliability analysis, calculated through inter and intraobserver agreement. Three games of the Italian national team winner of the World Cup 2006 and 3 of the Spanish national team winner of the EURO 2008 were registered using Thème Coder software. For t- patterns detection we used THEME 5.0, (p<.005). Results & Discussion The results show that top-level teams have made a total of 459 defensive processes during the six observed games, making an average of 76.5 in each game. 11080 t-patterns were detected, corresponding to 3419 different t-patterns, ranging from a minimum of one level to a maximum of nine levels and two to 16 events. Conclusion We conclude that the scoreline and the game time influenced the characteristics of the detected t-patterns, while the numerical relationship between teams did not influence the characteristics of the detected t-patterns.