Abstract
Sports such as diving, gymnastics, and ice skating rely on expert judges to score performance accurately. Human error and bias can affect the scores, sometimes leading to controversy, especially at high levels. Instant replay or recorded video can be used to assess judges' scores, or sometimes update judges' scores, during a competition. For diving in particular, judges are trained to look for certain characteristics of a dive, such as angle of entry, height of splash, and distance of the dive from the end of the board, to score each dive on a scale of 0 to 10, where a 0 is a failed dive and a 10 is a perfect dive. In an effort to obtain objective comparisons for judges' scores, a diving meet was filmed and the video footage used to measure certain characteristics of each dive for each participant. The variables measured from the video were height of the dive at its apex, angle of entry into the water, and distance of the dive from the end of the board. The measured items were then used as explanatory variables in a regression model where the judge's scores were the response. The measurements from the video are gathered to provide a gold standard that is specific to the athletic performances at the meet being judged, and supplement judges' scores with synergistic quantitative and visual information. In this article we show, via a series of regression analyses, that certain aspects of an athlete's performance measured from video after a meet provide similar information to the judges' scores. The model was shown to fit the data well enough to warrant use of characteristics from video footage to supplement judges' scores in future meets. In addition, we calibrated the results from the model against those of meets where the same divers competed to show that the measurement data ranks divers in approximately the same order as they were ranked in other meets, showing meet to meet consistency in measured data and judges' scores. Eventually, our findings could lead to use of video footage to supplement judges' scores in real time.