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Peer-reviewedCC BY 4.0International journal of computer science in sport

Short-term performance prediction in team sports using tracking data

  • Arnaud Odet
  • Thomas Bechard
  • Sébastien Dejean
  • Cristian Pasquaretta
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Abstract

This research addresses short-term performance prediction in Rugby Union using player tracking data. Rugby’s unique characteristics as a conquest sport with strict offside rules make the ball position a crucial performance indicator. We used event data to identify ball carriers in offensive phase and considered their locations as proxy for the ball. We then aim to predict this performance indicator. We formulated this challenge as a time series prediction problem where player positions (multivariate) predict performance (univariate). Our results suggest using player tracking data improves the prediction ability

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Cite

BibTeXRIS
BibTeX
@article{odet2026shortterm,
  title = {{Short-term performance prediction in team sports using tracking data}},
  author = {Arnaud Odet and Thomas Bechard and Sébastien Dejean and Cristian Pasquaretta},
  journal = {International journal of computer science in sport},
  year = {2026},
  month = aug,
  doi = {10.2478/ijcss-2026-0004},
  url = {https://hal.science/hal-05800354v1},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Short-term performance prediction in team sports using tracking data
AU  - Arnaud Odet
AU  - Thomas Bechard
AU  - Sébastien Dejean
AU  - Cristian Pasquaretta
PY  - 2026
DA  - 2026/08/07
JO  - International journal of computer science in sport
DO  - 10.2478/ijcss-2026-0004
UR  - https://hal.science/hal-05800354v1
AB  - This research addresses short-term performance prediction in Rugby Union using player tracking data. Rugby’s unique characteristics as a conquest sport with strict offside rules make the ball position a crucial performance indicator. We used event data to identify ball carriers in offensive phase and considered their locations as proxy for the ball. We then aim to predict this performance indicator. We formulated this challenge as a time series prediction problem where player positions (multivariate) predict performance (univariate). Our results suggest using player tracking data improves the prediction ability
SN  - 1684-4769
ER  -