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Constructing Points Systems in Accumulative and Independent Sports via Bayesian Regression.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Constructing Points Systems in Accumulative and Independent Sports via Bayesian Regression./
作者:	Huang, XueGe (Coco).
面頁冊數:	1 online resource (42 pages)
附註:	Source: Masters Abstracts International, Volume: 84-05.
Contained By:	Masters Abstracts International84-05.
標題:	Statistical physics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29396056click for full text (PQDT)
ISBN:	9798357550286

Constructing Points Systems in Accumulative and Independent Sports via Bayesian Regression.
Huang, XueGe (Coco).

Constructing Points Systems in Accumulative and Independent Sports via Bayesian Regression. - 1 online resource (42 pages)

Source: Masters Abstracts International, Volume: 84-05.

Thesis (M.A.S.)--University of Toronto (Canada), 2022.

Includes bibliographical references

The goal of ranking systems is to objectively evaluate multiple performances over a certain period of time. Concerns with current points ranking systems stem from reward imbalance across tiers and selective participation. This has implications on athlete funding through national team selections and Olympic qualifications. We propose a Bayesian framework for designing and evaluating an accumulative points system that can be applied to any multi-competitor sport. In a case study for slopestyle snowboarding, we used data from world, continental, and national events between 2016 and 2019. We demonstrate through Monte Carlo simulation experiments that our Bayesian points system is both more accurate at calculating athlete ranks and more consistent when athletes attempt to game the system.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798357550286Subjects--Topical Terms:

536281
Statistical physics.
Subjects--Index Terms:

Bayesian regressionIndex Terms--Genre/Form:

542853
Electronic books.

Constructing Points Systems in Accumulative and Independent Sports via Bayesian Regression.
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520 $a The goal of ranking systems is to objectively evaluate multiple performances over a certain period of time. Concerns with current points ranking systems stem from reward imbalance across tiers and selective participation. This has implications on athlete funding through national team selections and Olympic qualifications. We propose a Bayesian framework for designing and evaluating an accumulative points system that can be applied to any multi-competitor sport. In a case study for slopestyle snowboarding, we used data from world, continental, and national events between 2016 and 2019. We demonstrate through Monte Carlo simulation experiments that our Bayesian points system is both more accurate at calculating athlete ranks and more consistent when athletes attempt to game the system.
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