Rotational Dynamics and Trajectory Prediction of Curling Stones Using Machine Learning Techniques
Bibliographic record
Abstract
Predicting the final position of a curling stone using physical parameters is a crucial step towards enhancing the data-driven strategies and for better understanding of the game play dynamics. However, challenges such as high trajectory variability, mixed feature types, limited data volume, ice-factors and friction of stone with ice hinder the accurate forecasting. This research proposes a machine learning-based approach for predicting the final stopping position of the curling stone by leveraging features like rotational direction, spin velocity, throw velocity, and lap timings. The data set collected by the Canadian Rock Thrower and Stone Tracking system is preprocessed using normalization and categorical encoding, missing values imputation and feature scaling. the proposed approach achieves an R2score of 0.77 with an RMSE of approximately 0.54 m, meaning the predicted stopping position deviates on average by about half a meter from the actual resting point. Visualization of predicted vs Actual coordinates demonstrates models robustness and adaptability in learning physics-based patterns. This work provides a data-driven approach to classical analytical modeling in curling. The predictive framework is extensible to other sports trajectory analysis problems to offer strategic values in competitive game planning and sports analytics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".