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Who's #1? : the science of rating and ranking /

"Who's #1? offers an engaging and accessible account of how scientific rating and ranking methods are created and applied to a variety of uses. Amy Langville and Carl Meyer provide the first comprehensive overview of the mathematical algorithms and methods used to rate and rank sports team...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Langville, Amy N. (Autor), Meyer, C. D. (Carl Dean) (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Princeton [N.J.] : Princeton University Press, ©2012.
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Langville, Amy N.,  |e author. 
245 1 0 |a Who's #1? :  |b the science of rating and ranking /  |c Amy N. Langville and Carl D. Meyer. 
246 3 |a Who's number one 
260 |a Princeton [N.J.] :  |b Princeton University Press,  |c ©2012. 
300 |a 1 online resource (xvi, 247 pages) :  |b illustrations 
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504 |a Includes bibliographical references and index. 
505 0 0 |t Introduction to ranking --  |t Massey's method --  |t Colley's method --  |t Keener's method --  |t Elo's system --  |t The Markov method --  |t The offense-defense rating method --  |t Ranking by reordering methods --  |t Point spreads --  |t User preference ratings --  |t Handling ties --  |t Incorporating weights --  |t "What if -- " scenarios and sensitivity --  |t Rank aggregation : part 1 --  |t Rank aggregation : part 2 --  |t Methods of comparison --  |t Data --  |g Epilogue. 
520 |a "Who's #1? offers an engaging and accessible account of how scientific rating and ranking methods are created and applied to a variety of uses. Amy Langville and Carl Meyer provide the first comprehensive overview of the mathematical algorithms and methods used to rate and rank sports teams, political candidates, products, Web pages, and more. In a series of interesting asides, Langville and Meyer provide fascinating insights into the ingenious contributions of many of the field's pioneers. They survey and compare the different methods employed today, showing why their strengths and weaknesses depend on the underlying goal, and explaining why and when a given method should be considered. Also describe what can and can't be expected from the most widely used systems"--  |c Provided by publisher. 
588 0 |a Print version record. 
546 |a English. 
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590 |a JSTOR  |b Books at JSTOR Evidence Based Acquisitions 
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650 0 |a Ranking and selection (Statistics) 
650 4 |a Ranking and selection (Statistics) 
650 6 |a Rang et sélection (Statistique) 
650 7 |a MATHEMATICS  |x Probability & Statistics  |x General.  |2 bisacsh 
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650 7 |a Ranking and selection (Statistics)  |2 fast 
700 1 |a Meyer, C. D.  |q (Carl Dean),  |e author. 
776 0 8 |i Print version:  |a Langville, Amy N.  |t Who's #1?  |d Princeton [N.J.] : Princeton University Press, ©2012  |z 9780691154220  |w (DLC) 2011036016  |w (OCoLC)823013952 
856 4 0 |u https://jstor.uam.elogim.com/stable/10.2307/j.ctt7rwdt  |z Texto completo 
880 0 |6 505-00/(S  |a Cover -- Contents -- Preface -- Purpose -- Audience -- Prerequisites -- Teaching from This Book -- Acknowledgments -- Chapter 1. Introduction to Ranking -- Social Choice and Arrow's Impossibility Theorem -- Arrow's Impossibility Theorem -- Small Running Example -- Chapter 2. Massey's Method -- Initial Massey Rating Method -- Massey's Main Idea -- The Running Example Using the Massey Rating Method -- Advanced Features of the Massey Rating Method -- The Running Example: Advanced Massey Rating Method -- Summary of the Massey Rating Method -- Chapter 3. Colley's Method -- The Running Example -- Summary of the Colley Rating Method -- Connection between Massey and Colley Methods -- Chapter 4. Keener's Method -- Strength and Rating Stipulations -- Selecting Strength Attributes -- Laplace's Rule of Succession -- To Skew or Not to Skew-- Normalization -- Chicken or Egg-- Ratings -- Strength -- The Keystone Equation -- Constraints -- Perron-Frobenius -- Important Properties -- Computing the Ratings Vector -- Forcing Irreducibility and Primitivity -- Summary -- The 2009-2010 NFL Season -- Jim Keener vs. Bill James -- Back to the Future -- Can Keener Make You Rich-- Conclusion -- Chapter 5. Elo's System -- Elegant Wisdom -- The K-Factor -- The Logistic Parameter ξ -- Constant Sums -- Elo in the NFL -- Hindsight Accuracy -- Foresight Accuracy -- Incorporating Game Scores -- Hindsight and Foresight with Ŧ = 1000, K = 32, H = 15 -- Using Variable K-Factors with NFL Scores -- Hindsight and Foresight Using Scores and Variable K-Factors -- Game-by-Game Analysis -- Conclusion -- Chapter 6. The Markov Method -- The Markov Method -- Voting with Losses -- Losers Vote with Point Differentials -- Winners and Losers Vote with Points -- Beyond Game Scores -- Handling Undefeated Teams -- Summary of the Markov Rating Method. 
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