Bio-inspired algorithms for the characterization of excellent performance in handball players: A data-driven methodology
Bibliographic record
Abstract
Bio-inspired algorithms have been successfully applied to solve complex optimization problems. They are also widely used to train and optimize machine learning and data-driven models, providing competitive results. This study presents a novel data-driven approach to identify and quantify the factors that characterize the excellent performance of handball players, depending on the specific position in which they play. This will give us the most important characteristics that differentiate the most excellent players in their positions. Based on bio-inspired algorithms, this research delves into the complex optimization problems inherent in sports analytics. The study utilizes data from the Women’s European Handball Championship, employing seven distinct algorithms, of which six are different bio-inspired algorithms - including a hybrid bio-inspired algorithm - and one Consensus-Based Aggregation algorithm to analyze and assign weights to each player’s actions during a match. This approach is further validated by comparing the findings against the top five players in each position as recognized by the European Handball Federation (EHF). Subsequently, the established model’s robustness and applicability are tested using data from the Women’s World Handball Championships. • A data-driven methodology to analyze the sporting performance of handball players. • Bio-inspired algorithms are employed to characterize excellent performance. • Real data from international women championships are used to test the methodology. • Explainable Artificial Intelligence characterizes the best players on the court. • Identify the most distinguishable characteristics of the best high-performance handball players.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".