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SUMMARY:A State-Space Perspective on Modelling and Inference for Online Sk
 ill Rating - Dr Sam Power\, University of Bristol
DTSTART:20241107T140000Z
DTEND:20241107T150000Z
UID:TALK223879@talks.cam.ac.uk
CONTACT:Prof. Ramji Venkataramanan
DESCRIPTION: In the quantitative analysis of competitive sports\, a fundam
 ental task is to estimate the skills of the different agents (‘players
 ’) involved in a given competition based on the outcome of pairwise comp
 arisons (‘matches’) between said players\, often in an online setting.
  In this talk\, I will discuss recent work in which we advocate for adopti
 on of the state-space modelling paradigm in solving this problem. This per
 spective facilitates the decoupling of modeling from inference\, enabling 
 a more focused approach to development and critique of model assumptions\,
  while also fostering the development of general-purpose inference tools. 
 \n\nI will first describe some illustrative model classes which arise in t
 his framework\, before turning to a careful discussion of inference and co
 mputation strategies for these models. A key challenge throughout is to de
 velop methodology which scales gracefully to problems with a large number 
 of players and a high frequency of matches. I then conclude by describing 
 some real-data applications of our approach\, demonstrating how this frame
 work facilitates a practical workflow across different sports.\n\nThis is 
 joint work with Samuel Duffield (Normal Computing) and Lorenzo Rimella (Un
 iversità degli Studi di Torino)\, published at "JRSS-C":https://academic.
 oup.com/jrsssc/advance-article/doi/10.1093/jrsssc/qlae035/7734616.
LOCATION:LR11\, Baker Building\, CUED
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