Is it time for a more analytical approach? Suboptimal shot selection strategies in handball
Bibliographic record
Abstract
Increased use of analytics in sports has shed some light on various suboptimal strategies that have long been considered the norm. While sports such as basketball and baseball have adapted to a changing landscape, handball seems slow to adapt. The aim of this study was to examine i) the prevalence of high- and low-probability shots in semi-professional handball, ii) whether taking high-probability shots was associated with winning and iii) whether there were any differences between the men's and women's leagues. Six seasons in the Icelandic elite division were analyzed using a mixed-effects logistic regression model (1410 games, 879 games in the men's league and 531 games in the women's league). The study found that low-probability shots outnumbered high-probability shots (54.1% to 45.9%) and that high-probability shots were associated with winning (odds ratio of 1.46, compared to odds ratio of 0.73 in the low-probability shots). This was especially pronounced in the women's league (odds ratio of 1.77, compared to an odds ratio of 1.20 in the means league). These findings suggest that some handball teams could enhance their performance by focusing on creating high-probability shots. However, the success of such efforts depends not only on the attacking team's tactics but also on the defensive strategies employed by their opponents, which significantly influence the quality of scoring opportunities. Ultimately, the interplay between offensive and defensive approaches dictates the frequency and feasibility of high-probability shots.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".