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Enregistrement W2779769827 · doi:10.1353/nin.2015.0051

Baseball Maverick: How Sandy Alderson Revolutionized Baseball and Revived the Mets by Steve Kettmann

2015· article· en· W2779769827 sur OpenAlexvenueno aff
Steven P. Gietschier

Notice bibliographique

RevueNine · 2015
Typearticle
Langueen
DomaineArts and Humanities
ThématiqueAmerican Sports and Literature
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLeagueChampionshipFaithArt historyHistoryLawMedia studiesArtSociologyPolitical scienceTheologyPhilosophy

Résumé

récupéré en direct d'OpenAlex

Reviewed by: Baseball Maverick: How Sandy Alderson Revolutionized Baseball and Revived the Mets by Steve Kettmann Steven P. Gietschier Steve Kettmann. Baseball Maverick: How Sandy Alderson Revolutionized Baseball and Revived the Mets. New York: Atlantic Monthly Press, 2015. 331 pp. Cloth, $26. It takes a bit of East Coast chutzpah to suggest in the very title of a book that the man who has presided as general manager over the New York Mets for the past four seasons, during which they have amassed 77, 74, 74, and 79 wins, has revolutionized the game and revived a mediocre franchise. Dyed-in-the-wool Mets fans may believe, as spring training opens in 2015 and this review is being written, that the upcoming season will be their year, but how many others [End Page 210] think this will be the case? Oh, ye of little faith! Spend a few hours reading veteran journalist Steve Kettmann's excellent baseball biography of Sandy Alder-son, the general manager in question, and perhaps you will not be surprised if the Mets reach the post-season for the first time since Adam Wainwright dropped that enervating curveball over the plate against a helpless Carlos Beltran in the 2006 National League Championship Series. A lawyer by training, Alderson was a baseball novice when he became general counsel for the Oakland A's. Roy Eisenhardt, working in the same law firm, coaxed Alderson to Oakland just after Eisenhardt's father-in-law, Walter Haas, bought the A's in 1980. Two years later, Alderson, an ex-Marine with an inquisitive mind, was Oakland's general manager. By the end of the decade, the A's had won four division titles, three pennants, and the 1989 World Series. After the Haas family sold the team, Alderson transitioned out. He did two stints in the Commissioner's Office, sandwiched around four years and two division titles with the San Diego Padres. In his first stint in New York, he worked to improve the game's umpiring. In his second, he ironed out some difficulties in the Dominican Republic. Then, at the urging of Bud Selig, the Mets called in the fall of 2010. Kettmann's title may appear to be a brash, in-your-face marketing ploy, but his book is thoroughly researched, with Alderson's support, and carefully written. It is well worth the attention of serious fans and scholars. As the subtitle suggests, the book has two themes. The first is a necessary and overdue corrective to the notion that Billy Beane was the first major league executive to introduce sabermetrics—or what is now called advanced analytics—to the front office. Committed to learning the game and working smart, it was Alderson who ruminated on the merits of Earl Weaver's preferred offensive strategy, get two men on base and hit a three-run homer. It was he who read Bill James. It was he who listened to Eric Walker's commentaries on the local NPR station and brought him in to do some number-crunching. And it was he who rebuilt the A's farm system that produced three consecutive American League rookies-of-the-year, Jose Canseco, Mark McGwire, and Walt Weiss, and a steady record of success. In the spring following the 1989 Series, Beane's marginal career as a player was over, and he became a scout for Alderson and later his assistant and his student. The rest is history, at least according to Moneyball. What Alderson has done with the Mets is Kettmann's second and larger theme. Following the heartache of 2006, the New Yorkers had endured two September collapses and two indifferent seasons. General Manager Omar Minaya had proven not to be the savior Mets fans craved, and the Wilpon ownership team turned to Alderson. He accepted the challenge, unaware of [End Page 211] the extent to which the Bernard Madoff financial scandal, implicating the Wilpons, would tie his hands. Still, re-assembling a cadre of experts who had worked with him before, Alderson put in place a four-year plan designed to put his team in the post-season in 2014. But more than that, the GM dedicated his...

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,140
Score d'incertitude au seuil0,470

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,001
Communication savante0,0040,003
Science ouverte0,0010,002
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,1400,066

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,026
Tête enseignante GPT0,205
Écart entre enseignants0,179 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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