AUTHOR=Philipp Nicolas M. , McKay Ben , Martin Ethan , Cabarkapa Dimitrije , Fry Andrew C. , Troester Jordan TITLE=Between-rater reliability for using radar technology to quantify maximal horizontal deceleration performance in NCAA division 1 American football and female lacrosse athletes JOURNAL=Frontiers in Sports and Active Living VOLUME=6 YEAR=2024 URL=https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2024.1384476 DOI=10.3389/fspor.2024.1384476 ISSN=2624-9367 ABSTRACT=Introduction

With recent increases in the popularity of studying the physical construct of horizontal deceleration performance in team-sport athletes, the aim of the present study was to assess the inter-rater and intra-rater reliability of processing and quantifying horizontal deceleration ability using radar technology.

Methods

Data from 92 NCAA Division 1 athletes from two different athletic teams (American football and Lacrosse) were used for the present investigation. All athletes performed two trials of the modified acceleration to deceleration assessment (ADA), which consisted of a maximal 10 m sprint acceleration, followed by a rapid deceleration. Four individual raters manually processed raw, radar-derived instantaneous velocity data for the ADA, and an automated script was used to calculate metrics of interest.

Results

Primary study findings suggest moderate to excellent levels of agreement (ICC = 0.56–0.91) for maximal horizontal deceleration metrics between the four individual raters. The intra-rater analyses revealed poor to excellent consistency (ICC = 0.31–0.94) between ADA trials, with CV%'s ranging from 3.1% to 13.2%, depending on the respective metric and rater.

Discussion

Our data suggests that if a foundational understanding and agreement of manual data processing procedures for radar-derived data is given between raters, metrics may be interpreted with moderate to excellent levels of confidence. However, when possible, and when using the Stalker ATS radar technology, authors recommend that practitioners use one trained individual to manually process raw data. Ideally, this process should become fully automated, based on selected filters or algorithms, rather than the subjectivity of the rater.