ECSS Paris 2023: CP-AP03
INTRODUCTION: Running direction (clockwise vs counterclockwise) on a track may influence pacing and running variability because curved running can alter step mechanics and induce inside–outside leg asymmetries and segment-dependent mechanical demands (1–3). In 24-h ultramarathons, speed typically declines while pacing variability increases as the race progresses (4,5). When direction alternates on a fixed schedule, direction is partially confounded with race time (fatigue accumulation, circadian phase, and environmental changes). This study examined direction-associated changes in mean speed, normalized speed, and pacing variability across performance tiers using a within-athlete paired-difference approach. METHODS: Lap-derived timing data were obtained from the Soochow 24-h ultramarathon in 2024 and 2025 (N=62). Thirteen athletes were excluded due to non-completion or prolonged stoppage (rest >30 min), leaving 49 athletes for analysis. Performance tiers were harmonized into Gold (n=14), Silver (n=16), and General (n=19). The race was segmented into six consecutive 4-h intervals with alternating direction (CCW: 1/3/5; CW: 2/4/6) and into hourly bins (1–24 h). Outcomes were mean speed (km/h), normalized speed (interval speed / athlete mean race speed), and CV of lap speed within interval (sample SD/mean when ≥2 laps). Direction effects were quantified using within-athlete adjacent paired contrasts: Δ = CW − CCW for pairs (2−1), (4−3), and (6−5). RESULTS: Mean speed decreased from interval 1 to 6 by ~21% (Gold), ~23% (Silver), and ~29% (General), while CV increased concurrently, indicating reduced pacing stability in later race phases. Direction-related paired differences in speed were generally negative in early and mid-race pairs (CW slower than the preceding CCW interval) and became smaller later; for example, ΔSpeed in 2−1 ranged approximately from −0.9 to −1.2 km/h across tiers. In the late-race pair (6−5), ΔSpeed approached zero in Gold and shifted positive in Silver and General, suggesting tier-specific direction responses under greater fatigue. Direction-related changes in variability (ΔCV) were positive in earlier pairs and tended to attenuate or reverse in the late pair, indicating that direction switches may interact with race phase to influence pacing stability. CONCLUSION: A within-athlete paired-difference framework provides a practical approach to quantify direction-change effects on pacing and variability in a 24-h ultramarathon while reducing confounding from individual performance differences and race-time trends. Direction-related responses appear to be phase- and tier-dependent, consistent with evidence that curved/track running can involve asymmetric mechanics and segment-dependent loading (1–3), and may inform pacing strategy and stability monitoring around scheduled direction changes. [1] Ishimura 2016 / [2] Antúnez 2022 / [3] Shiotani 2021 / [4] Takayama 2016 / [5] Bossi 2017
Read CV CHIA-MIN WANGECSS Paris 2023: CP-AP03
INTRODUCTION: Pacing in 24-h ultramarathons typically shows a progressive decline in speed and increasing variability as fatigue accumulates, but athletes may adopt distinct pacing “phenotypes” that are not fully captured by group averages (1,2). Large-scale endurance datasets have highlighted meaningful pacing profiles (e.g., fast starters vs slow finishers) and their performance implications, supporting data-driven pacing classification approaches (3). This study aimed to identify hourly pacing phenotypes using feature-based clustering across two race years and to examine how pacing types distribute across performance tiers. METHODS: Lap-derived timing data from the Soochow 24-h ultramarathon (2024–2025) were aggregated into hourly mean speed and hourly normalized speed (hourly speed divided by each athlete’s mean speed across the 24 h). From 62 athletes, 13 were excluded due to non-completion or prolonged stoppage (>30 min), leaving 49 athletes for analysis. Athletes were stratified into three performance tiers: Gold (n=14), Silver (n=16), and General (n=19), harmonized across years. Pacing features were engineered from the 24-point hourly normalized-speed series, capturing pacing level (e.g., early/mid/late means), decline shape (overall and segment slopes), and stability (e.g., SD across 24 h, range, maximum adjacent-hour change, proportion of hours above baseline). Features were z-standardized and reduced using principal component analysis (PCA). K-means clustering was performed on PCA scores for k=2–6. Model selection used internal validity indices (silhouette, Calinski–Harabasz, Davies–Bouldin) and cluster interpretability. RESULTS: Across k=2–6, a three-cluster solution showed the best overall balance of internal validity and interpretability (silhouette≈0.41; Davies–Bouldin≈0.97). The derived pacing phenotypes were: (i) Stable pacing (dominant cluster), (ii) Fast start / steep decline, and (iii) Highly variable / stop-go (small cluster). Tier-stratified distributions showed that Gold athletes were predominantly classified as Stable pacing (13/14), whereas the General tier showed a higher proportion of Fast start / steep decline and Highly variable / stop-go patterns (13/19 and 2/19, respectively). Silver athletes were mainly Stable pacing (13/16) with smaller representation of other phenotypes. CONCLUSION: Feature-based clustering of hourly normalized speed identified three interpretable 24-h pacing phenotypes and revealed clear differences in phenotype distribution across performance tiers. The predominance of Stable pacing among higher-tier athletes supports the applied use of pacing phenotypes for athlete profiling and monitoring. Future work should link pacing phenotypes to performance outcomes (e.g., distance, late-race speed retention) and to contextual factors such as direction changes and rest patterns. [1] Takayama 2016 / [2] Bossi 2017 / [3] Smyth 2018
Read CV Ying-Hou ChenECSS Paris 2023: CP-AP03
INTRODUCTION: In the 800 m running race, athletes positioned toward the front at intermediate points tend to achieve higher final positions (Renfree et al., 2014). Additionally, spatial positioning within the pack (e.g., distance from the leader and the curb) may influence final places. However, the optimal positioning strategy across race phases and age categories remains unclear. Therefore, the purpose of this study was to clarify, separately in senior and junior categories, the relationships between intermediate position and final position, and between spatial position and final position, in the 800 m race. METHODS: Athletes competing in the 800 m at major domestic Japanese championships were analysed. The sample comprised 96 athletes in the senior category and 200 in the junior category. Race videos were analysed using QuickTime Player and Kinovea software. Intermediate position and spatial position were determined at 100 m intervals from the 200 m to the 700 m points. Spatial position within the pack was quantified as (1) distance behind the leading runner and (2) distance from the curb. Distance behind the leader was calculated by multiplying time differences between the leader and each athlete by the mean running speed over the corresponding 100 m segment. Distance from the curb was estimated based on half- lane (0.61 m) increments from the inside curb. Relationships between intermediate position, spatial position, and final position were assessed using Spearman’s rank correlation coefficients. RESULTS: Significant positive correlations were observed in both categories between intermediate position and final position, and between spatial position and final position at all measured points (p < 0.05). In the senior category, correlation coefficients between intermediate position and final position were r = 0.33, 0.35, 0.34, 0.41, 0.69, and 0.73 at the points from the 200 m to the 700 m respectively. At same points, correlations between spatial position and final position were r = 0.32, 0.30, 0.30, 0.36, 0.55, and 0.70, respectively. In contrast, in the junior category, correlation coefficients between intermediate position and final position were r = 0.32, 0.28, 0.33, 0.40, 0.62, and 0.76 across the same measurement points. Correlations between spatial position and final position were r = 0.23, 0.27, 0.35, 0.41, 0.54, and 0.69, respectively. CONCLUSION: These findings suggest the need for category-specific positioning strategies. In senior athletes, early front-pack positioning appears less critical, whereas securing a forward position by the 600 m point is strongly associated with the final outcome. In junior athletes, progressive advancement toward the front throughout the race may be more important. These results provide practical implications for coaching strategies in 800 m racing. Reference: Renfree A et al. (2014) Int J Sports Physiol Perform., 9: 362-364.
Read CV Koichiro SatoECSS Paris 2023: CP-AP03