Relating external load variables with individual tactical actions with reference to playing position: an integrated analysis for elite futsal

The kappa statistic is frequently used to test interrater reliability. The importance of rater reliability lies in the fact that it represents the extent to which the data collected in the study are correct representations of the variables measured. Measurement of the extent to which data collectors (raters) assign the same score to the same variable is called interrater reliability. While there have been a variety of methods to measure interrater reliability, traditionally it was measured as percent agreement, calculated as the number of agreement scores divided by the total number of scores. In 1960, Jacob Cohen critiqued use of percent agreement due to its inability to account for chance agreement. He introduced the Cohen’s kappa, developed to account for the possibility that raters actually guess on at least some variables due to uncertainty. Like most correlation statistics, the kappa can range from −1 to +1. While the kappa is one of the most commonly used statistics to test interrater reliability, it has limitations. Judgments about what level of kappa should be acceptable for health research are questioned. Cohen’s suggested interpretation may be too lenient for health related studies because it implies that a score as low as 0.41 might be acceptable. Kappa and percent agreement are compared, and levels for both kappa and percent agreement that should be demanded in healthcare studies are suggested.

<i>Statistical Power Analysis</i> is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: <br> * a chapter covering power analysis in set correlation and multivariate methods;<br> * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and;<br> * expanded power and sample size tables for multiple regression/correlation.<br>

The use of time-motion analysis has advanced our understanding of position-specific work rate profiles and the physical requirements of soccer players. Still, many of the typical soccer activities can be neglected, as these systems only examine activities measured by distance and speed variables. This study used triaxial accelerometer and time-motion analysis to obtain new knowledge about elite soccer players' match load. Furthermore, we determined acceleration/deceleration profiles of elite soccer players and their contribution to the players' match load. The data set includes every domestic home game (n = 45) covering 3 full seasons (2009, 2010, and 2011) for the participating team (Rosenborg FC), and includes 8 central defenders (n = 68), 9 fullbacks (n = 83), 9 central midfielders (n = 70), 7 wide midfielders (n = 39), and 5 attackers (A, n = 50). A novel finding was that accelerations contributed to 7-10% of the total player load for all player positions, whereas decelerations contributed to 5-7%. Furthermore, the results indicate that other activities besides the high-intensity movements contribute significantly to the players' total match workload. Therefore, motion analysis alone may underestimate player load because many high-intensity actions are without a change in location at the pitch or they are classified as low-speed activity according to current standards. This new knowledge may help coaches to better understand the different ways players achieve match load and could be used in developing individualized programs that better meet the "positional physical demands" in elite soccer.