ECSS Paris 2023: OP-AP08
INTRODUCTION: Indoor hockey is a small format version of field hockey, played with five outfield players and one goalkeeper per team. Matches consist of four 10-minute quarters, in a recommended 44-m (long) by 22-m (wide) area. Little is known of the match demands of this hockey format, despite obvious differences in team composition, match time and pitch size compared to outdoor field hockey. Local positioning system (LPS) technology, mapped to court parameters, allows the measurement of position in covered spaces, and hence allows the quantification of typical match demand metrics when performers are involved in indoor sport. METHODS: Following university ethical approval, 55 players, recruited across two tournaments, participated in the study (mean age: 24.2 (5.61) years; height: 1.7 (0.06) m; weight: 77.8 (9.72) kg). Match demands were assessed using a LPS (Catapult Sports S7 units 10 Hz, ClearSky system with 20 anchors, Catapult Sports, Melbourne, Australia). Heart rate was collected throughout matches (Polar H10, polar Electro, Kempele, Finland), and rating of perceived exertion was established immediately following the end of matches (modified Borg CR10 rate of perceived exertion (RPE) scale). Means and standard deviations were used to present the demands across 20 indoor matches (mean temperature: 18.9 (1.3) degrees Celsius; humidity: 43.7 (4.0) %) for the following metrics: total playing duration (min), total distance (m), high-speed running distance (m, greater than or equal to 15.5 km.h-1), accelerations (n, greater than or equal to 2 m.s-2), decelerations (n, less than or equal to - 2 m.s-2), average HR (bpm) and RPE (au). These metrics have been used to assess match demands in previous outdoor field hockey research (1). RESULTS: The demands of indoor hockey matches were as follows; total playing duration: 24.2 (5.6) min; total distance: 1984.1 (426.6) m; high-speed running distance: 136.0 (77.8) m; accelerations: 38.4 (15.8); decelerations: 38.5 (14.7); average HR: 170.7 (10.8) bpm; RPE: 5.5 (1.7). CONCLUSION: The data in this study builds the foundation for indoor hockey research using LPS technology, which has become commonplace in comparative indoor team sports. Access to this example data could give coaches and practitioners a better understanding of how to prepare their athletes for the demands of indoor hockey matches. [1] Lam 2021
Read CV Matt StonerECSS Paris 2023: OP-AP08
INTRODUCTION: Understanding multidimensional load characteristics during congested schedules is crucial for optimizing performance and reducing injury risk in basketball. We hypothesized that high-intensity movement patterns, rather than cumulative load alone, play a dominant role in determining technical performance. METHODS: We analyzed 21 games across three congested phases using wearable sensors (Catapult Vector S7) and the session-RPE method from 12 professional players. Thirteen internal and external load indicators were collected, including PlayerLoad, high-intensity accelerations/decelerations, jumps, and directional changes. Principal Component Analysis (PCA) extracted load factors, followed by multiple regression to examine associations with five technical metrics: player efficiency rating (PIR), efficiency (EFF), game score (GS), player impact estimate (PIE), and total contribution (PTC). Linear mixed models assessed effects of game sequence, player position, playing time, and point differential on load indicators (α = 0.05). All analyses were performed using R (version 4.4.3). RESULTS: PCA revealed two principal components explaining 67.8% of the total variance: "cumulative load" (PC1, 55.3%) and "high-intensity movement pattern" (PC2, 12.5%). PC2 showed high positive loadings for high-intensity accelerations (0.379) and jumps (0.372), and negative loadings for decelerations (-0.381), directional changes (-0.280 to -0.304), and PlayerLoad/min (-0.455), reflecting distinct movement typologies. Mixed models showed that playing time was the most consistent predictor, significantly influencing 12 out of 13 load indicators (p < 0.01). Player position had significant main effects on 9 indicators (p < 0.05); guards exhibited significantly higher deceleration and change-of-direction loads than forwards and centers (Cohen's d = 0.99–1.26). Game sequence only significantly affected PlayerLoad/min (p < 0.001), indicating declining intensity across games. Point differential showed no significant effect on any load indicator. Random effects analysis revealed that individual differences explained 61.6% of the variance in PlayerLoad/min. Regression models showed that both load factors jointly explained 19.7%–27.0% of the variance in technical performance indicators (all p < 0.001), with the highest explanatory power for EFF (R² = 0.276). Importantly, PC2 demonstrated consistently larger regression coefficients than PC1 across all models (e.g., PIR: β = 1.79 vs. 0.91; EFF: β = 1.86 vs. 1.12), indicating that movement pattern characteristics are stronger predictors of technical performance than cumulative load volume. CONCLUSION: High-intensity movement patterns—especially the balance between accelerations, decelerations, and directional changes—are more powerful predictors of technical performance than cumulative load. Findings support position-specific monitoring and personalized recovery strategies during congested schedules.
Read CV Linda GuECSS Paris 2023: OP-AP08
INTRODUCTION: Basketball is inherently dualistic often shaped by 1 vs 1 interactions of players. While basketball is on the forefront of sport analytics, match performance indicators don’t account for how and if the player is guarded by a defender. Automatically identifying defensive matchups is therefore a fundamental yet challenging task. Existing approaches are either simplistic, relying on the nearest-defender, or highly complex machine learning models, which achieves high accuracy but at considerable computational cost [1,2]. In this study, we aim to propose a feature-based optimization framework for defensive assignment and assess its performance against ground truth data. METHODS: SportVU tracking data from the 2015–2016 NBA regular season was used, providing ball and player positions at 25 Hz. A total of 1,228 possessions from 20 games are randomly selected, ending with shot attempts, standardized to a right-to-left attack, and downsampled to 1 Hz. We use motion-based features commonly applied in man-marking and ball pressures studies. After normalization, a cost function C_ij with initial weights from domain knowledge is built, where i denotes attacker and j defenders. Each frame’s cost matrix is converted to an assignment probability matrix P_ij. Then, feature weights are optimized by minimizing row and column entropy L using differential evolution. To evaluate model performance, an experienced sports analyst annotated defensive assignments for 20 random possessions by watching game footage. (1) C_ij = w1*I(i,ball-handler) + w2*d(i,basket) + w3*d(i,j) + w4*||v_i - v_j|| + w5*Delta_theta(i,j) (2) P_ij = exp(-C_ij) / sum_k exp(-C_ik) (3) L = sum_i H(P_i) + lambda sum_j H(P_j) H(p) = - sum_k p_k log(p_k) RESULTS: Our framework achieves an overall accuracy of 63.67% against expert-annotated ground truth. Among all features, distance between opponents has the largest absolute weight, making it the most influential factor in defensive assignment. Ball handler and velocity vector difference also play substantial roles. Attacker-basket distance contributes moderately, while angular difference has the smallest impact. CONCLUSION: Our study proposes an entropy-based soft optimization framework for defensive assignment, achieving 63.67% accuracy against expert labels. The study also shows that the location of the ball, players and the basket are 3 key elements for identifying defensive assignments, which places certain demands on the game dataset. By now accuracy remains below complex machine learning models, while having minimal computing costs. Future work should incorporate richer contextual and temporal constraints to improve robustness. [1] Keshri S, Oh M, Zhang S and Iyengar G 2019 Automatic event detection in basketball using HMM with energy based defensive assignment J. Quant. Anal. Sports 15 141–53 [2] McQueen A, Wiens J and Guttag J 2014 Automatically recognizing on-ball screens 2014 MIT sloan sports analytics conference
Read CV Chuqi ChenECSS Paris 2023: OP-AP08