English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this work, we deal with the problem of rating in sports, where the skills of the players/teams are inferred from the observed outcomes of the games. Our focus is on the on-line rating algorithms that estimate skills after each new game by exploiting the probabilistic models that (i) relate the skills to the outcome of the game and (ii) describe how the skills evolve in time. We propose a Bayesian approach which may be seen as an approximate Kalman filter and which is generic in the sense that it can be used with any skills-outcome model and can be applied in the individual as well as in the group sports. We show how the well-known Elo, Glicko, and TrueSkill algorithms may be seen as instances of the one-fits-all approach we propose. To clarify the conditions under which the gains of the Bayesian approach over simpler solutions can actually materialize, we critically compare the known and new algorithms by means of numerical examples using synthetic and empirical data.

Simplified Kalman filter for on-line rating: one-fits-all approach