The Secret History of the Fastball and the Improbable Search for the Fastest Pitcher of All Time
Notice bibliographique
Résumé
Tim Wendel, High Heat: The Secret History of the Fastball and the Improbable Search for the Fastest Pitcher of All Time. Cambridge, MA: De Capo Press, 2010. 288 pp. Cloth, $25.00. Serious sports have wiled away many hours trying to answer such age-old questions as who was the all-time greatest athlete or the best at performing one particular facet of the game that they love. Boxing enthusiasts, for example, seem to never tire of arguing over who was the best heavyweight of all time, Muhammad Ali or Joe Louis, while college basketball aficionados regularly dispute the identity of the best team ever. Baseball fans are no different, endlessly contemplating such questions as who was the greatest hitter or pitcher, which slugger hit the ball the farthest, and, as discussed in this fast-paced, well-written volume, which pitcher threw the hardest. Author Tim Wendel traveled across the country in his attempt to uncover the fastest pitcher of all time, conducting archival research and interviewing numerous baseball writers, former and current players and managers, and some of the pitchers who can legitimately claim to be the fastest ever, including Bob Feller and Nolan Ryan. Not content to solely focus on modern-era players, Wendel begins his journey with a brief examination of nineteenth-century fireballers, including Pud Galvin, Amos Rusie, and James Creighton--perhaps the game's first flamethrower, who has been relegated to obscurity because he died at the age of twenty-one, just as he was coming into his own. Wendel's discussion of the fastest in the twentieth and twenty-first centuries includes most of the usual suspects--Walter Johnson, Smokey Joe Wood, Lefty Grove, Satchel Paige, Feller, Sandy Kou fax, Ryan, Billy Wagner, and Steve Dalkowski. Wendel invigorates his narrative by lacing it with colorful anecdotes about each pitcher's background, pitching prowess, and personality. We learn, for example, that Walter Johnson feared killing a batter with a pitch and regretted the one time that he went against his instincts and deliberately threw at an opposing player. We read about Lefty Grove's infamous temper tantrums after losses, Sandy Koufax's early wildness, and Nolan Ryan's early struggles--which nearly caused the future Hall of Famer to walk away from the game. But if there is a central character in Wendel's tale it is Steve Dalkowski, who, even though he might have been the fastest of them all, never made the majors due to his extraordinary wildness and his inability to bring his personal demons, including his love for alcohol, under control. The inspiration for the character Nuke LaLoosh in the movie Bull Durham, Dalkowski possessed speed and wildness that supposedly intimidated even the legendary Ted Williams, who, after watching the left-hander throw to a few batters before an Orioles-Red Sox spring training contest, asked to take some swings against Dalkowski. …
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,009 | 0,009 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,032 | 0,008 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».