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Record W4390714897 · doi:10.5040/9781718225671

The Anatomy of Speed

2022· book· en· W4390714897 on OpenAlexaboutno aff
Bill Parisi

Bibliographic record

VenueHuman Kinetics eBooks · 2022
Typebook
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsChampionCoachingEliteManagementEngineeringPsychologyArtPolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

<JATS1:p>“In The Anatomy of Speed, Parisi has integrated science and his coaching skills to create an approach that will work for anyone wanting more speed.”</JATS1:p> <JATS1:p>Stuart McGill</JATS1:p> <JATS1:p>Professor Emeritus</JATS1:p> <JATS1:p>Author of Back Mechanic and Low Back Disorders</JATS1:p> <JATS1:p>“The Anatomy of Speed is essential reading for anyone who wants to improve speed, agility, and quickness.”</JATS1:p> <JATS1:p>Mike Woicik</JATS1:p> <JATS1:p>Six-Time Super Bowl Champion</JATS1:p> <JATS1:p>Former Head Strength and Conditioning Coach for the Dallas Cowboys and New England Patriots</JATS1:p> <JATS1:p>“Bill Parisi combines his extensive experience with invaluable input from other industry leaders to present the who, what, when, where, and how of elite speed development.”</JATS1:p> <JATS1:p>Eric Cressey</JATS1:p> <JATS1:p>Director of Player Health and Performance for the New York Yankees</JATS1:p> <JATS1:p>Owner of Cressey Sports Performance</JATS1:p> <JATS1:p>Speed is the most mythical of human capabilities. From elementary school playground races to 40-yard dashes at the NFL Combine, speed has long been the gold standard for athletic performance. But for as long as it’s been admired and obsessively pursued, a true understanding of speed has remained elusive … until now.</JATS1:p> <JATS1:p>The Anatomy of Speed is a book like no other. Equal parts science, application, and art, it takes you inside speed: how it is generated, how it is exhibited, and, most importantly, how you can better develop it. Detailed photos, enhanced by hand-drawn anatomical artwork, allow you to experience the multiple anatomical systems that need to work together, in highly coordinated unison, to create these abilities: AccelerationMaximum velocityDecelerationChange of directionAgilityManeuverabilitySpeed-specific strength</JATS1:p> <JATS1:p>You’ll then delve deeper as one of the world’s experts on speed training, Bill Parisi, translates the “why” into the “how” through in-depth interviews with top experts and researchers in the field. You will learn which drills and exercises are most effective for strengthening key muscles and how sequencing can dramatically improve training outcomes. You’ll even find programming menus to create individualized training for your athlete’s goals.</JATS1:p> <JATS1:p>The Anatomy of Speed will forever change the way you see, assess, and train for speed. If you are serious about performance, this is one book you cannot be without.</JATS1:p>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.025
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0040.017
Scholarly communication0.0070.008
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0250.008

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.

Opus teacher head0.033
GPT teacher head0.304
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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