Abstract:
The goal of this study is to gain a better understanding of the factors that play a role in dominant collisions in rugby as well as the relative significance of dominant collisions as an indicator of success. By means of video footage of matches played during the 2003-2005 Super 12 competitions, notational analysis was performed and information was gathered in order to gain the relative data. The hypothesis stands that if a team is aware of the factors that lead to a dominant collision, are able to execute them in a match situation, that team should be more successful. The following key performance measurements were evaluated in order to indicate how each factor affected the level of success of a team. They are as follows: average total number of collisions for a try to be scored, average total number of forced missed tackles for a try to be scored, ratio of dominant collisions versus passes executed when a try is scored and average positive velocity change of dominant collisions resulting in a try being scored. In order to prove the hypotheses a k-sample case will be used. The samples are related, thus the data used is interval and ratio. Therefore, the test used will be the repeated measures ANOVA test, a special form of n-way analysis of variance. The statistical evaluation is the critical test value where the d.f values are as following: Key Measurement (3,8), Year Rating (2,8), Year Rating by Key Measurement (3,8). When comparing these with a statistical table for critical values of the F distribution for Ą = 0.05, the critical values are as following: (3,8): 4.07, (2,8): 4.46, and (3,8): 4.07. Thus, the statistical results are grounds for accepting all three null hypotheses and concluding that there is a statistical significance of at least 95% with an alpha of 0.05 between the means in all three instances. This shows that the data captured for the twelve teams for all tries scored by these teams over a period of three years and for the four key measurements, have a statistical significance of 95% for the readings respectively. After evaluation of the data and making use of regression analysis and multiple regressions in order to establish the correlation between log position and the four key measurements there can be no doubt that the teams that finished higher on the log did indeed perform better according to the identified key performance measurements.
