Conformal Score Prediction in Figure Skating Competitions

Varvara Kisel, Ilia Nouretdinov, Alex Gammerman
Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 329:1076-1077, 2026.

Abstract

Predicting figure-skating scores is a challenging modelling problem because the International Skating Union (ISU) scoring system combines objective, rule-based evaluation of technical elements with subjective human judgement of artistic and performance quality. This study examines whether the final score of a skater can be estimated using only technical-element data, combining reliability (validity) and information (precision) of the prediction intervals produced by the Conformal Prediction framework for regression. In general, the results indicate that machine learning models can effectively learn scoring patterns in figure skating.

Cite this Paper

BibTeX
@InProceedings{pmlr-v329-kisel26a, title = {Conformal Score Prediction in Figure Skating Competitions}, author = {Kisel, Varvara and Nouretdinov, Ilia and Gammerman, Alex}, booktitle = {Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {1076--1077}, year = {2026}, editor = {Ahlberg, Ernst and Johansson, Ulf and Boström, Henrik and Carlevaro, Alberto and Hallberg Szabadváry, Johan and Carlsson, Lars}, volume = {329}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v329/main/assets/kisel26a/kisel26a.pdf}, url = {https://proceedings.mlr.press/v329/kisel26a.html}, abstract = {Predicting figure-skating scores is a challenging modelling problem because the International Skating Union (ISU) scoring system combines objective, rule-based evaluation of technical elements with subjective human judgement of artistic and performance quality. This study examines whether the final score of a skater can be estimated using only technical-element data, combining reliability (validity) and information (precision) of the prediction intervals produced by the Conformal Prediction framework for regression. In general, the results indicate that machine learning models can effectively learn scoring patterns in figure skating.} }
Endnote
%0 Conference Paper %T Conformal Score Prediction in Figure Skating Competitions %A Varvara Kisel %A Ilia Nouretdinov %A Alex Gammerman %B Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2026 %E Ernst Ahlberg %E Ulf Johansson %E Henrik Boström %E Alberto Carlevaro %E Johan Hallberg Szabadváry %E Lars Carlsson %F pmlr-v329-kisel26a %I PMLR %P 1076--1077 %U https://proceedings.mlr.press/v329/kisel26a.html %V 329 %X Predicting figure-skating scores is a challenging modelling problem because the International Skating Union (ISU) scoring system combines objective, rule-based evaluation of technical elements with subjective human judgement of artistic and performance quality. This study examines whether the final score of a skater can be estimated using only technical-element data, combining reliability (validity) and information (precision) of the prediction intervals produced by the Conformal Prediction framework for regression. In general, the results indicate that machine learning models can effectively learn scoring patterns in figure skating.
APA
Kisel, V., Nouretdinov, I. & Gammerman, A.. (2026). Conformal Score Prediction in Figure Skating Competitions. Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 329:1076-1077 Available from https://proceedings.mlr.press/v329/kisel26a.html.

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