Author(s): SAAL, C., VENZKE, S., BAUMGART, C., HOPPE, M.W., Institution: OTTO-VON-GUERICKE-UNIVERSITÄT MAGDEBURG, Country: GERMANY, Abstract-ID: 1433

INTRODUCTION:
Team handball is a technically-tactically and physically demanding team sport, whereby each playing position require highly specific skills and attributes (Sanchéz et al., 2023). An understanding of the unique demands of each position is vital for developing training programs to optimize adaption processes. Most research aimed at determining the key predictors for different playing positions in handball has traditionally employed confirmatory approaches (Venzke et al., 2024; Sanchéz et al., 2023). The comprehensive data collected from elite team handball opens up opportunities for new exploratory methods, including both unsupervised and supervised machine learning techniques. Thus, our study focuses on the identification of the most important predictors for each playing position in elite team handball by leveraging all available tracking data.
METHODS:
Data from all teams of the German Bundesliga during the season 2023/24 were obtained from the official data provider (Kinexon, Munich) with the permission of the Dalkin Bundesliga. To increase the validity, a subset was selected based on the following criteria: playing time ≥ 15 minutes, completed pass > 1, matches per player > 9. The final dataset, comprising 301 matches, 249 players, and 142 metrics, was utilized to investigate the demands associated with the six different player positions in team handball. The data set were visually inspected using the t-SNE algorithm and subsequently analyzed with a bootstrap lasso classification (k = 500, n = 249) to reduce bias of repeated measurements. Model evaluation was performed using nested resampling (10-CV). Balanced accuracy was calculated based on the aggregated confusion matrix. The estimated log-odds were used to identify the most important variables.
RESULTS:
The visual inspection revealed a 4-level categorization for the following playing positions: goalkeepers, wings, backs, and pivots with intersections between wings and backs. Balanced accuracy of the classification was very good (wings: 0.97, pivots: 0.89, backs: 0.93, goalkeepers: 1.00). Highest and lowest log-odds for wings were jumps/min high (beta = 7.7) and passes/min fatal (beta = -4.55), backs sprints/min very high (beta = 28.41), and sprints/min low (beta = -2.36), for pivots passes/min fatal (beta = 3.31) and sprints/min very high (beta = -8.72), and for goalkeepers ball possession/min recovered (beta = 3.01) and accelerations/min medium (beta = -1.00). Additional log-odds help to further clarify the position-specific demands.
CONCLUSION:
We derived position-specific profiles in elite male team handball from high-dimensional tracking data, revealing patterns that support a four-level classification (goalkeepers, wings, pivots, backs). Extending prior work (García-Sánchez et al., 2023; Venzke et al., 2024), we also incorporated ball-related metrics. Future studies should examine when and how much position-specific training should be integrated into overall training.