Notational analysis (NA) systems are regularly used to quantify sports performance but have not been adopted in competitive skateboarding. In this study, we aimed to develop an NA application to quantify outcome-related aspects of skateboarding judging criteria (trick selection, obstacle selection, and execution), leveraging known skatepark obstacles and trick classification from public broadcast footage (60 Hz) of the Tokyo 2020 Olympic Men’s and Women’s semi-finals. We also assessed the intra- and inter-rater reliability of frame selection for key trick attempt events—take-off, event (obstacle interaction) start and end, and landing. Frame selection from 593 trick attempts (male = 324, female = 269) were found reliable (mean absolute difference (MAD) < 3 frames) by a single, experienced rater. Take-off (MAD: intra-rater = 1.43, inter-rater = 3.82 frames) and landing frame (MAD: intra-rater = 1.33, inter-rater = 1.55 frames) were more reliably selected than obstacle interaction (MAD: Inter = 2.04–2.26, Inter = 3.62–4.35 frames), attributed to obstacle and trick type. Generally, trick attempts over longer durations appeared less reliable, but still within useable limits; as such, the NA approach used in this study could be used for describing and understanding performance in elite street skateboarding.

The aim of this study was to describe aspects of performance in the Tokyo 2020 Olympic street skateboarding competition and compare male and female skaters. Trick attempts (TA=1118) were extracted from broadcast footage of the semi-finals and finals, using notational analysis. Descriptive analyses were adopted to characterise performance, with robust linear mixed-effects models comparing attempt scores (run [RUN] and best-trick [BT]) between the sexes. Male RUN (B=1.71 [1.43, 1.99]) and BT (B=1.85 [1.55, 2.15]) scores were higher and more variable than females. There was more trick variety during BTs, but less obstacle variability compared to RUNs. Skaters bailed a greater proportion of BT TAs (54.6%) vs. 14.3% RUN TAs. Males demonstrated greater variety than females by diversifying take-off (28.2% vs. 3.3% non-regular, respectively) and landing (25.7% vs. 6.2% non-regular, respectively) stances, attempting more unique tricks (122 vs. 74, respectively), and using larger, feature obstacles (40.7% vs. 33.9% respectively). Alternatively, females demonstrated wider course use during BTs (32.1% vs. 15%, respectively), corresponding to less feature obstacle use; perhaps indicating a barrier to engagement due to developing physical qualities. Future research should explore men’s and women’s divisions respectively to understand key factors for success.

In platform diving the take-off phase is of outstanding importance in order to achieve both a high level of performing quality and a high degree of difficulty. The diver has to produce the right forces and direction of the center of mass (COM) in order to attain the required angular momentum and dive height. To support the development of an optimum take-off technique, the Institute for Applied Training Science designed a dryland measuring and feedback system. Using the example of the dive back 1¼ somersault tucked in preparation for the dive back 3½ somersault tucked (207 C) from the 10-m-platform, kinematic and kinetic reference values for key positions were determined. Therefore, we developed a mathematical model using a multi-step examination plan with the following parts: (1) variables defined using nonparametric correlation analyses r_s of the motion parameters, (2) statistical modelling to predict values of the parameters, (3) stochastic modelling. The model is based on a selection of 18 dives from 10 different elite divers of the German Swimming Federation (DSV). The approach presented provides helpful insights into the mechanisms of an optimal take-off, enables a target-performance comparison with objective motion parameters and therefore, enables individualized feedback to guide the training process more efficiently.

The aim of this study was to describe aspects of performance in the Tokyo 2020 Olympic street skateboarding competition and compare the men's (M) and women's (W) divisions. Trick attempts (TA = 1118) were extracted from broadcast footage of the semi-finals and finals using notational analysis. Descriptive analyses were adopted to characterise performance, with robust linear mixed-effects models comparing attempt scores (run [RUN] and best-trick [BT]) between divisions. Men's RUN (β = 1.71 [1.43, 1.99]) and BT (β = 1.85 [1.55, 2.15]) scores were higher and more variable than the women. Overall, there was more trick variety during BTs, but less obstacle variability compared to RUNs. Skaters bailed a greater proportion of BT TAs (54.6%) than RUN TAs (14.3%). Men demonstrated greater variety than women by diversifying take-off (M = 28.2% vs. W = 3.3% non-regular) and landing (M = 25.7% vs. W = 6.2% non-regular) stances, attempting more unique tricks (M = 122 vs. W = 74), and using larger feature obstacles (M = 40.7% vs. W = 33.9% of TAs). Alternatively, women demonstrated wider course use during BTs (M = 15% vs. W = 32.1% of all obstacles), corresponding to less feature obstacle use; perhaps indicating a barrier to engagement due to developing physical qualities. Future research should explore men's and women's divisions respectively to understand key factors for success.

Inertial measurement units (IMUs) present promising avenues for performance diagnostics in skateboarding, yet systematic validation of their accuracy and applicability remains limited. This study validates the commercially available Spinnax Freak IMU system in the context of skateboarding, with a focus on selected trick detection and classification, distance measurement, maximal horizontal speed, maximal vertical height of the skateboard and airtime during a jump trick. A total of 23 skateboarders (4 females, 19 males; 27.4 ± 10.9 years) participated in this study. Validation methods included comparisons with established reference systems such as laser ranging for maximal horizontal speed (LAVEG), 2D video analysis for maximal vertical height of the skateboard (Kinovea), light barrier measurements for airtime detection (OptoJump Next), and a fixed metric reference (10 m) for rolling distance measurements. The evaluation was supported by statistical analyses including mean absolute error (MAE), root mean-square error (RMSE), mean absolute percentage error (MAPE), t-tests, Bland–Altman plots, linear regression, and ICC(3,1). The Spinnax Freak system demonstrated high validity in detecting trick events and in providing distance measurements that were statistically equivalent to the reference. Trick classification, maximal horizontal speed, maximal vertical height of the skateboard and airtime showed substantial errors, indicating that these outputs are not reliable for biomechanical interpretation at this point. These findings highlight both the potential and the current constraints of single-sensor setups for field-based motion capture in skateboarding. Future developments should prioritize algorithmic refinement, improved temporal resolution, and optimized event classification to enhance measurement accuracy and expand applicability in biomechanical analysis and automated training documentation in skateboarding.