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From Runs to Wins: A Logistics Model to Support and Improve Pythagorean Expectation in MLB (2000-2025) - Brossura
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This book analyzes and evaluates an alternative logistic model to the traditional Pythagorean expectation method for converting runs into wins in Major League Baseball (MLB). Based on official MLB data for the 2000-2019 period as a test set and a deferred validation period from 2021 to 2025, the author examines the hypothesis that a pure logistic model-inspired by the law of minority attrition and psephological tools-offers a more solid theoretical and empirical approximation.Although the formulation provides a logical foundation and an endogenous variable exponent per team and season, the results demonstrate that its predictive accuracy is practically identical to that of the adjusted Pythagorean model with an exponent of 1.83 (reaching very high coefficients of determination), evidencing a high correlation between both procedures.
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From Runs to Wins (Paperback)
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Paperback. Condizione: new. Paperback. This book analyzes and evaluates an alternative logistic model to the traditional Pythagorean expectation method for converting runs into wins in Major League Baseball (MLB). Based on official MLB data for the 2000-2019 period as a test set and a deferred validation period from 2021 to 2025, the author examines the hypothesis that a pure logistic model-inspired by the law of minority attrition and psephological tools-offers a more solid theoretical and empirical approximation.Although the formulation provides a logical foundation and an endogenous variable exponent per team and season, the results demonstrate that its predictive accuracy is practically identical to that of the adjusted Pythagorean model with an exponent of 1.83 (reaching very high coefficients of determination), evidencing a high correlation between both procedures. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9786630466959
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From Runs to Wins
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PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice articolo L0-9786630466959
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From Runs to Wins
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From Runs to Wins : A Logistics Model to Support and Improve Pythagorean Expectation in MLB (2000-2025)
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From Runs to Wins
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ISBN 13: 9786630466959
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware 64 pp. Englisch. Codice articolo 9786630466959
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From Runs to Wins (Paperback)
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Paperback. Condizione: new. Paperback. This book analyzes and evaluates an alternative logistic model to the traditional Pythagorean expectation method for converting runs into wins in Major League Baseball (MLB). Based on official MLB data for the 2000-2019 period as a test set and a deferred validation period from 2021 to 2025, the author examines the hypothesis that a pure logistic model-inspired by the law of minority attrition and psephological tools-offers a more solid theoretical and empirical approximation.Although the formulation provides a logical foundation and an endogenous variable exponent per team and season, the results demonstrate that its predictive accuracy is practically identical to that of the adjusted Pythagorean model with an exponent of 1.83 (reaching very high coefficients of determination), evidencing a high correlation between both procedures. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9786630466959
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From Runs to Wins | A Logistics Model to Support and Improve Pythagorean Expectation in MLB (2000-2025)
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Taschenbuch. Condizione: Neu. From Runs to Wins | A Logistics Model to Support and Improve Pythagorean Expectation in MLB (2000-2025) | Ricardo de la Peņa | Taschenbuch | Englisch | 2026 | Scholars' Press | EAN 9786630466959 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Codice articolo 138047167
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