Doljin, Batdelger

Fuss, Franz Konstantin

Kim, Young-Kwan

Kwangyul, Jeong

English

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ISEA 2022 – The Engineering of Sport 14, Purdue University, 6-10 June 2022 Corresponding author email: franz.fuss@uni-bayreuth.de                                                      1  TORQUE-TO-SPIN EFFICIENCY OF PITCHES ANALYSED WITH A SMART BASEBALL   Franz Konstantin Fuss1, Batdelger Doljin2, Kwangyul Jeong1 and Young-Kwan Kim3  1 Chair of Biomechanics, Faculty of Engineering Science, University of Bayreuth, Bayreuth, D-95440, Germany 2 Smart Products Engineering Program, Swinburne University, Melbourne, VIC 3000, Australia 3 Department of Physical Education, Chonnam National University, Gwangju, 500-757, South Korea  Introduction: Sports balls are spun by applying a friction force (in addition to the normal force) to their surface, which produces a torque TR. The TR imparted accelerates the ball and thereby generates and increases its spin rate ω (angular velocity). Yet, not only TR determines the magnitude of ω, but also how efficiently TR is converted to ω. The efficiency decreases the greater the angle θ (normalised precession pn, [1]) is between TR- and ω-vectors The greater θ, the more TR is wasted for moving ω into TR (precession p). The aim of this paper is to investigate the torque-to-spin efficiency while pitching before releasing the ball. Method: For determining ω, we used a smart baseball [2], a spin-off of a smart cricket ball [3], that measures ω at a sampling frequency of 815 Hz. From ω we calculated the following performance parameters (explained in detail in [4]) with the smart ball’s software: angular acceleration α; resultant torque TR and its two components, the spin torque Ts (= α I, where I is the moment of inertia) and the precession torque Tp; power P; rotational energy; precession p (= Tp/[ωI]; speed of moving ω-vector, ideally 0); normalised precession pn (= θ = sin–1(pωI/TR; angle between TR- and ω-vectors, ideally 0°, worst case 90°); efficiency η (ratio of actual energy to ideal energy, where the latter results from the ideal case of θt = 0); ‘frequency’ f (= αmax/ωmax). The more efficient a pitching type, the smaller are p, pn, Tp, f, and the greater is η. In addition to the smart ball, we determined ω and the translational speed v of the ball with Mevo (FlightScope, Orlando, FL, USA), and benchmarked ωsmartball vs ωMevo. Four players pitched fastballs, curveballs, and sliders five times each. This research was granted Ethics approval by the Swinburne University Human Ethics Committee (no. 20191582-3216), and adhered to the Declaration of Helsinki, and informed consent was obtained. Results and Discussion: ¼ of the ωMevo data were outliers and therefore inaccurate (Fig. 1a). The average data of the performance parameters are shown in Table 1. From the Kruskal-Wallis test, there was a statistical difference (p<0.05) between ISEA 2022 – The Engineering of Sport 14, Purdue University, 6-10 June 2022 Corresponding author email: franz.fuss@uni-bayreuth.de                                                      2  fastballs, sliders and curveballs in all parameters except for ω and P. The efficiencies η of slider and curveball were marginally indifferent in the post-hoc tests (p = 0.056). All five parameters (p, pn, Tp, η, f) informing of the torque-to-speed efficiency showed the same pattern: best performance in the curve ball and worst in the fastball (Fig. 1b). This result matches the pattern seen in cricket ball deliveries: backspin deliveries (such as the fastball in baseball) are least efficient, topspin ones (such as the curveball) are most efficient, and sidespin ones (such as the slider) are characterised by intermediate efficiency [1, 4]. The outliers may be due to the Doppler radar not recording the spin rate consistently.     (a)      (b) Fig. 1a: Correlation of angular velocities  Fig. 1b: Normalised precession of three      ωMEVO vs ωSB (outliers in red)       types of pitches (box plot and average)  Table 1: averages of performance parameters; FB = fast ball, SL = slider, CV = curveball pitch v (m s–1) ω (rps) TR (Nm) P (W) p (rad s–1) pn = θ (°) Tp (Nm) η (%) α/ω (s–1) FB 34.58 24.83 0.459 31.01 108.5 103.4 0.390 18.85 29.32 SL 31.10 24.86 0.365 27.69   51.3   54.8 0.253 28.30 24.56 CB 26.21 23.55 0.277 23.38   20.7   25.0 0.123 33.56 23.05  1. Fuss FK, B. Doljin, Ferdinands RED, Beach A (2015) Dynamics of Spin Bowling: The Normalized Precession of the Spin axis Analysed with a Smart Cricket Ball. Proc Eng 112:196-201. 2. Doljin B, Jeong K, Kim Y-K, Fuss FK (2020) Profiling of a pitcher's performance with a smart baseball: a case report. Proceedings 2020, 49(1), 103. 3. Doljin B, Fuss FK (2015) Development of a smart cricket ball for advanced performance analysis of bowling. Procedia Technology, 20: 133–137. 4. Fuss FK, B. Doljin, Ferdinands RED (2021) The Smart Cricket Ball: discovery of novel performance parameters and their practical application to performance analysis, advanced profiling, talent identification and training interventions of spin bowlers. Sensors, 21(20): (article 6942): 1-28. 

Torque-to-spin Efficiency of Pitches Analysed with a Smart Baseball

https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1020&context=resec-isea

Torque-to-spin Efficiency of Pitches Analysed with a Smart Baseball

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