Final published version
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - A state-space perspective on modelling and inference for online skill rating
AU - Duffield, S.
AU - Power, S.
AU - Rimella, L.
N1 - Export Date: 28 November 2024 Correspondence Address: Power, S.; School of Mathematics, Fry Building, Woodland Road, United Kingdom; email: sam.power@bristol.ac.uk Funding details: Engineering and Physical Sciences Research Council, EPSRC, EP/R018561/1 Funding text 1: S. Power and L. Rimella were supported by EPSRC grant EP/R018561/1 (Bayes4Health).
PY - 2024/11/30
Y1 - 2024/11/30
N2 - We summarize popular methods used for skill rating in competitive sports, along with their inferential paradigms and introduce new approaches based on sequential Monte Carlo and discrete hidden Markov models. We advocate for a state-space model perspective, wherein players’ skills are represented as time-varying, and match results serve as observed quantities. We explore the steps to construct the model and the three stages of inference: filtering, smoothing, and parameter estimation. We examine the challenges of scaling up to numerous players and matches, highlighting the main approximations and reductions which facilitate statistical and computational efficiency. We additionally compare approaches in a realistic experimental pipeline that can be easily reproduced and extended with our open-source Python package, abile. © The Royal Statistical Society 2024.
AB - We summarize popular methods used for skill rating in competitive sports, along with their inferential paradigms and introduce new approaches based on sequential Monte Carlo and discrete hidden Markov models. We advocate for a state-space model perspective, wherein players’ skills are represented as time-varying, and match results serve as observed quantities. We explore the steps to construct the model and the three stages of inference: filtering, smoothing, and parameter estimation. We examine the challenges of scaling up to numerous players and matches, highlighting the main approximations and reductions which facilitate statistical and computational efficiency. We additionally compare approaches in a realistic experimental pipeline that can be easily reproduced and extended with our open-source Python package, abile. © The Royal Statistical Society 2024.
KW - approximate inference
KW - Bayesian inference
KW - competitive sports
KW - state-space models
U2 - 10.1093/jrsssc/qlae035
DO - 10.1093/jrsssc/qlae035
M3 - Journal article
VL - 73
SP - 1262
EP - 1282
JO - Journal of the Royal Statistical Society: Series C (Applied Statistics)
JF - Journal of the Royal Statistical Society: Series C (Applied Statistics)
SN - 0035-9254
IS - 5
ER -