The Secret History of the Fastball and the Improbable Search for the Fastest Pitcher of All Time
Bibliographic record
Abstract
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. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".