Analysis of Knuckleball Trajectories

Slides:



Advertisements
Similar presentations
UW Colloquium 10/31/05 1 Thanks to J. J. Crisco & R. M. Greenwald Medicine & Science in Sports & Exercise 34(10): ; Oct 2002 Alan M. Nathan,University.
Advertisements

MAE 3241: AERODYNAMICS AND FLIGHT MECHANICS
1 Combining HITf/x with Landing Point Alan Nathan, Univ. of Illinois Introduction What can be learned directly from the data? Fancier analysis methods.
Baseball Statistics By Krishna Hajari Faraz Hyder William Walker.
Modern Techniques for Evaluating Hitting Alan M. Nathan University of Illinois Batted ball analysis –Initial speed and angles –Landing point and hang time.
Marlin Connelly. Out of all sports, baseball is probably the one that is most affected by physics. On a single play, there is so much going on that relates.
Pitch vs. Velocity By: Brendan Aumiller. Does the type of baseball pitch affect the velocity of a baseball?
Baseball Trajectories: A Game of Inches Jim Hildensperger Kyle Spaulding Dale Garrett.
Free Fall Acceleration
Capturing Hit F/X Data By: Greg Moore. Overview  Why Capture Hit F/X data?  How can we capture Hit F/X data?  What is Hit F/X data?  Accuracy of Hit.
1 What Have We Learned from the PITCHf/x System? A report from the summit What is PITCHf/x and how does it work? What are we learning from it? Outlook.
FALLING OBJECTS pp Freely falling bodies undergo constant acceleration. Such motion is referred to as free fall. The free-fall acceleration.
Distribution of Sample Means, the Central Limit Theorem If we take a new sample, the sample mean varies. Thus the sample mean has a distribution, called.
Image processing. Image operations Operations on an image –Linear filtering –Non-linear filtering –Transformations –Noise removal –Segmentation.
Motion based Correspondence for Distributed 3D tracking of multiple dim objects Ashok Veeraraghavan.
1 Baseball & Physics: An Intersection of Passions Alan M. Nathan Department of Physics University of Illinois
Nathan, Summit20101 Studies of Batted Ball Trajectories I.Analyzing the FFX trajectories II.Determining landing point/hang time from HFX III.Combining.
1 Baseball & Physics: An Intersection of Passions Alan M. Nathan Department of Physics University of Illinois
Free Fall Acceleration
1 Modern Technologies for Tracking the Baseball Alan Nathan University of Illinois and Complete Game Consulting.
1 Corked Bats and Rising Fastballs: Using Physics to Debunk Some Myths of Baseball September 23, 2006 Thanks to J. J. Crisco & R. M. Greenwald Medicine.
Physics and Baseball: A Report to Red Sox Nation
Stat 112: Notes 2 Today’s class: Section 3.3. –Full description of simple linear regression model. –Checking the assumptions of the simple linear regression.
Experimental Baseball Physics
Ethan and Brennen Pitching. Why and how the curveball breaks  The curveball is a type of pitch in baseball thrown with a characteristic grip and hand.
Test Credits Instruction For Teachers 9-12 Graders An appreciation for understanding undiscovered knowledge and sports. Middle Class- Suburban Students.
Page 1 AIAA, StL, October 19, 2006 Baseball Aerodynamics: What do we know and how do we know it? Alan M. Nathan University of Illinois at Urbana-Champaign.
Page 1 IMAC XXIV, January 30, 2006 Effect of Spin on Flight of Baseball Joe Hopkins a, Lance Chong b, Hank Kaczmarski b, Alan M. Nathan a a Physics Department,
Page 1 SABR36, June 29, 2006 Baseball Aerodynamics: What do we know and how do we know it? Alan M. Nathan University of Illinois at Urbana-Champaign
APS/DFD, Nov Baseball Aerodynamics Alan M. Nathan, University of Illinois webusers.npl.uiuc.edu/~a-nathan/pob Introduction.
1 Baseball and Mathematics: It’s More Than Batting Averages ---Alan Nathan.
1 Baseball and Physics: Where Albert Pujols meets Albert Einstein ---Alan Nathan.
APS/DFD, Nov The Flight of a Baseball Alan M. Nathan, University of Illinois Introduction.
1 Physics and Baseball: Having Your Cake and Eating it Too Alan M. Nathan webusers.npl.uiuc.edu/~a-nathan/pob Department of Physics University.
Analyzing Fastpitch Softball from the 2011 WCWS Alan M. Nathan University of Illinois.
PITCHfx, HITfx, FIELDfx – BASEBALLfx July
91 Lecture 9. Projectile motion - 2D motion only considering gravity (for now) v 0x = 100 ft/s Once it’s in the air, the acceleration vector points straight.
1 One dimensional Motion Motion Along a Straight Line.
Summer 2013 PHYS 218: General Physics Lecture 8: More 2D Crap… Just Kidding Read: Ch. 5.1 – 5.3.
1 How a Physicist Analyzes the Game of Baseball Alan M. Nathan webusers.npl.uiuc.edu/~a-nathan/pob Department of Physics University of.
Empirical Ionospheric Model Based on Saint Santin Incoherent Scatter Radar Data Angela Zalucha MIT Haystack Observatory/ University of Illinois at Urbana.
1 Baseball & Physics: An Intersection of Passions Alan M. Nathan Department of Physics University of Illinois
Section 2 Standard Units and Areas under the Standard Normal Distribution.
1 Baseball and Physics: Where Albert Pujols meets Albert Einstein ---Alan Nathan, University of Illinois.
Revisiting Mantle’s Griffith Stadium Home Run, April 17, 1953 A Case Study in Forensic Physics Alan M. Nathan.
Unit 3: Projectile Motion
Projectile Motion.
Deconstructing the Home Run Surge: A Physicist’s Approach
Operational Wave Model Validation SWAN
(Constant acceleration)
Sponge - A golf ball rebounds from the floor and travels straight upward with an initial speed of 5.0 m/s. To what maximum height does the ball rise?
Hitting Home Runs: How a Physicist Thinks About Baseball Alan M
Unit 3: Projectile Motion
The Mathematics of Sports: Myths and Misses
American League Central
1. Can a curveball be hit farther than a fastball? Some Aerodynamics
Last Time: Vectors Introduction to Two-Dimensional Motion Today:
35. Resolving Vectors (applications)
Projectile Motion.
Free Fall Acceleration
3-2: Kinematics in 2 D.
A Pitcher’s Best Friend
The Knuckleball Problem
Describing Motion in 3-D (and 2-D) §3.1–3.2.
Why Hitting Home Runs: How a Physicist Thinks About Baseball Alan M. Nathan University of Illinois at Urbana-Champaign
Projectiles The only force acting on a projectile is the force due to gravity (weight).
Projectile Motion Honors Physics.
Notes: Sample Means
35. Resolving Vectors (applications)
Modeling the Ball-Bat Collision
Presentation transcript:

Analysis of Knuckleball Trajectories Alan M. Nathan University of Illinois at Urbana-Champaign Trampoline effect “universal”: golf, tennis, baseball/softball, etc.. Physics is the same in each. Recently retired knuckleball pitcher Tim Wakefield 1

Issues to be Addressed The “movement” of knuckleball pitches The “smoothness” of knuckleball trajectories

Knuckleball thrown with very little spin  no Magnus force But still lots of erratic “movement” Origin of movement revealed in wind tunnel experiments

Wind Tunnel Data, 4S Orientation Mike Morrissey (MS Thesis) and John Borg (Marquette) Agrees with Watts & Sawyer, AJP (1975)

Studying Knuckleball Trajectories Using the PITCHf/x Tracking System Two video cameras @60 fps approximately orthogonal axes full 3D reconstruction tracks every pitch in every MLB ballpark all data publicly available Image, courtesy of Sportvision

Studies of Knuckleball Movement Movement = deviation of trajectory from straight line, with gravity removed Easily measured with PITCHf/x View from above 5” movement

Direction of movement vs. release speed (Jon Lester) Catcher’s View “Normal” pitches have predictable movement

Direction of movement vs. release speed (Tim Wakefield) Knuckleballs do not have predictable movement

But is the trajectory “smooth”? Fit to smooth function Examine RMS deviation of data from fit 9 Free Parameters: x0, y0, z0, vx0, vy0, vz0, CD, CL, 

278 pitches from August 29, 2011 Knuckleball (77) Normal (201) Normal and knuckleball pitches follow similar distributions Knuckleballs only slightly (few tenths of inch) less smooth

Two Examples: Which one is the knuckleball? 76 mph knuckleball rms=0.374” 75 mph curveball rms=0.373”

2011Aug29-161108

Summary of Conclusions Movement of knuckleball trajectories varies considerably from pitch to pitch Magnitude and direction quasi-random Any given trajectory is as smooth as those of ordinary pitches within limits of precision of tracking data (~0.3”-0.5”) Open questions/work in progress Are erratic movement and smoothness conclusions consistent with wind tunnel data? How can perception and reality be reconciled?