Although the body of literature in sport science is growing rapidly, certain sports have yet to benefit from this increased interest by the scientific community. One such sport is Ultimate Frisbee, officially known as Ultimate. Thus, the goal of this study was to describe the nature of the sport by identifying differences between winning and losing teams in elite-level competition. To do so, a customized observational system and a state transition model were developed and applied to 14 games from the 2017 American Ultimate Disc League season. The results reveal that, on average, 262.2 passes were completed by a team per game and 5.5 passes per possession. More than two-thirds of these passes were played from the mid zone (39.4 ± 6.57%) and the rear zone (35.2 ± 5.09%), nearest the team’s own end zone. Winning and losing teams do not differ in these general patterns, but winning teams played significantly fewer backward passes from the front zone to the mid zone, nearest the opponent’s end zone than losing teams (mean difference of −4.73%, t(13) = −4.980, p < 0.001, d = −1.16). Furthermore, losing teams scored fewer points when they started on defense, called breakpoints (mean difference of −5.57, t(13) = −6.365, p < 0.001, d = 2.30), and committed significantly more turnovers per game (mean difference of 5.64, t(13) = 5.85, p < 0.001, d = −1.18). Overall, this study provides the first empirical description of Ultimate and identifies relevant performance indicators to discriminate between winning and losing teams. We hope this article sheds light on the unique, but so far overlooked sport of Ultimate, and offers performance analysts the basis for future studies using state transition modeling in Ultimate as well as other invasion sports.
Handball is an Olympic team sport characterized by changes of ball possession, where teams either play on offense or defense. In this paper, we model momentary strength in handball as scoring probabilities based on a double moving average. In this cross-sectional observational study, the aim was to describe the dynamics of momentary strength in handball from both theoretical and practical perspectives. Momentary strength can be used to further characterize the nuances of the sport by identifying different phases in a match. The momentary strength model was applied to the 2019 International Handball Federation (IHF) Men's World Championship. Ninety-six games were analyzed. The results showed that momentary strength could be used to better understand the dynamic interaction process between two teams. The Spearman correlation between the teams' median momentary strength and their final ranks at the Championship was -0.830. Virtually each team faced phases in its matches with a momentary scoring probability lower than 0.10 and with 4 exceptions also higher than 0.90. Twelve out of the 24 teams showed phases with a momentary scoring probability of exactly 0 as well as exactly 1
Radio and video-based electronic performance and tracking systems (EPTS) for position detection are widely used in a variety of sports. In this paper, the authors introduce an innovative approach to video-based tracking that uses a single camera attached to a drone to capture an area of interest from a bird’s eye view. This pilot validation study showcases several applications of this novel approach for the analysis of game and racket sports. To this end, the authors compared positional data retrieved from video footage recorded using a drone with positional data obtained from established radio-based systems in three different setups: a tennis match during training with the drone hovering at a height of 27 m, a small-sided soccer game with the drone at a height of 50 m, and an Ultimate Frisbee match with the drone at a height of 85 m. For each type of playing surface, clay (tennis) and grass (soccer and Ultimate), the drone-based system demonstrated acceptable static accuracy with root mean square errors of 0.02 m (clay) and 0.15 m (grass). The total distance measured using the drone-based system showed an absolute difference of 2.78% in Ultimate and 2.36% in soccer, when compared to an established GPS system and an absolute difference of 2.68% in tennis, when compared to a state-of-the-art LPS. The overall ICC value for consistency was 0.998. Further applications of a drone-based EPTS and the collected positional data in the context of performance analysis are discussed. Based on the findings of this pilot validation study, we conclude that drone-based position detection could serve as a promising alternative to existing EPTS but would benefit from further comparisons in dynamic settings and across different sports.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.