Extension of the Elo rating system to margin of victory

2020 ◽  
Vol 36 (4) ◽  
pp. 1329-1341 ◽  
Author(s):  
Stephanie Kovalchik
Keyword(s):  
Rating System ◽  
Elo Rating System ◽  
Margin Of Victory

scholarly journals Who is the ‘Journal Grand Master’? A new ranking based on the Elo rating system

2017 ◽  
Vol 11 (3) ◽  
pp. 800-809 ◽  
Author(s):  
Robert Lehmann ◽  
Klaus Wohlrabe
Keyword(s):  
Rating System ◽  
Elo Rating System

Applications of the Elo rating system in adaptive educational systems

2016 ◽  
Vol 98 ◽  
pp. 169-179 ◽  
Author(s):  
Radek Pelánek
Keyword(s):  
Rating System ◽  
Educational Systems ◽  
Elo Rating System ◽  
Adaptive Educational Systems

Statistical Analysis of the Elo Rating System in Chess

CHANCE ◽  
2020 ◽  
Vol 33 (3) ◽  
pp. 31-38
Author(s):  
Arthur Berg
Keyword(s):  
Statistical Analysis ◽  
Rating System ◽  
Elo Rating System

Forecasting sales of new virtual goods with the Elo rating system

2014 ◽  
Vol 13 (6) ◽  
pp. 457-469 ◽  
Author(s):  
Lin Yang ◽  
Stanko Dimitrov ◽  
Benny Mantin
Keyword(s):  
Rating System ◽  
Elo Rating System

Player Ranking in Taekwondo: A Bayesian Elo Rating System

2020 ◽  
Author(s):  
T. S. N Peiris ◽  
R. M. Silva
Keyword(s):  
Rating System ◽  
Elo Rating System ◽  
Player Ranking

Leveraging Skill Hierarchy for Multi-Level Modeling with Elo Rating System

2019 ◽  
Author(s):  
Michael Yudelson ◽  
Yigal Rosen ◽  
Steve Polyak ◽  
Jimmy de la Torre
Keyword(s):  
Rating System ◽  
Elo Rating System ◽  
Multi Level

scholarly journals Boltzmann and Fokker–Planck Equations Modelling the Elo Rating System with Learning Effects

2018 ◽  
Vol 29 (3) ◽  
pp. 1095-1128 ◽  
Author(s):  
Bertram Düring ◽  
Marco Torregrossa ◽  
Marie-Therese Wolfram
Keyword(s):  
Rating System ◽  
Learning Effects ◽  
Elo Rating System ◽  
Fokker Planck Equations ◽  
Fokker Planck

scholarly journals Designing and Implementation of Rating Prediction

2019 ◽  
Vol 8 (11) ◽  
pp. 2847-2852
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Rating System ◽  
The Real ◽  
Fine Grained ◽  
Elo Rating System ◽  
Rating Prediction ◽  
Farthest Point

Recommender system is one of indivisible parts in web business areas. Recommender system construes the system which underwrites things for the client who wish to purchase things .One of the real inconveniences that, figuratively speaking, stays in recommender system is the virus begin problem(inactive things) which can be seen as an obstruction that spurns the cool begin things from the present things. In this paper, we want to move beyond this farthest point for cold-begin clients/things by the help of existing things. It is developed by utilizing Elo Rating system. The Elo system is widely gotten in chess competitions; we propose a novel rating association technique to get settled with the profiles of cold-begin things. The purpose of assembly of our Strategy is to give a fine-grained View on the shrouded profiles of cold-begin clients/things by inspecting the separations between nippy begin things and existing Products. To uncover the limit of methodology, we instantiate our technique on two typical strategies in recommender systems, i.e., the structure factorization based, and neighborhood based pleasing sifting. Starter assessments on five genuine instructive documents embrace the amazing quality of our methodology over the present procedures in virus begin situation

scholarly journals Who Is the 'Journal Grand Master'? A New Ranking Based on the Elo Rating System

2016 ◽  
Author(s):  
Robert Lehmann ◽  
Klaus Wohlrabe
Keyword(s):  
Rating System ◽  
Elo Rating System

scholarly journals Developing an Elo-rating System for Criminal Justice Practitioners: A Superior Method for Resource Allocation?

2019 ◽  
Author(s):  
Shahin Tasharrofi ◽  
J.C. Barnes
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Predictive Power ◽  
Rating System ◽  
Community Supervision ◽  
Decision Making Process ◽  
Rating Systems ◽  
Elo Rating System ◽  
Pathways To Desistance ◽  
Better Than

Record numbers of inmates are being released to the community for supervision each year. This poses a challenge to the agencies and officers in charge of providing supervision—there are far more offenders in need of supervision than officers can reasonably attend to. This has forced agencies to be innovative in how they allocate their resources. Empirical evidence suggests resources should be allocated to high-risk offenders, but how can officers determine which offender is at a higher risk of misconduct at any given time? Traditionally, this has been achieved by relying on standardized risk assessment. But risk assessment has been criticized for making only marginally better-than-chance predictions of future misconduct. We offer a novel solution by integrating the Elo-rating system into the community supervision decision-making process. We show, by drawing on test data from the Pathways to Desistance Study, that combining the Elo-rating system with traditional risk assessment may lead to increases in predictive power. We then discuss how Elo-rating systems could be used by community supervision agencies to more effectively prioritize their caseloads.

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