Mobile Computing with a 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
Abstract
1. Introduction
2. Materials and Methods
2.1. Development and Specifications of the Smart Cricket Ball
2.2. Performance Parameters
2.3. Applications of the Performance Parameters
2.3.1. Study 1
2.3.2. Study 2
2.3.3. Study 3
2.3.4. Study 4
3. Results
3.1. Study 1
3.2. Study 2
3.3. Study 3
3.4. Study 4
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Physical Performance Parameters | Training Target | Skill Performance Parameters | Training Target |
---|---|---|---|
Maximum spin rate ωR | Improve | Maximum precession p (before the torque spike) | Reduce |
Maximum angular acceleration α | Improve | Maximum normalised precession pn = θ (before the torque spike) | Reduce |
Maximum resultant torque TR | Improve | Maximum precession torque Tp | Reduce |
Maximum spin torque Ts | Improve | Efficiency η | Improve |
Maximum power P | Improve | ‘Frequency’ αmax/ωmax | Reduce |
Skill Parameter | Log10pmax | pn_max | Tp_max | η | αmax/ωmax |
---|---|---|---|---|---|
F + W + X | 4.25% | 18.63% | 32.29% | 23.19% | 6.66% |
F + W | 15.18% | 43.69% | 48.51% | 34.69% | 27.47% |
X | 5.41% | 15.97% | 25.89% | 38.53% | 6.77% |
Spin Rate ω (rps) | Maximum Precession p (rad/s) | Maximum Normalised Precession pn (deg) | Maximum Resultant Torque TR (Nm) | Maximum Spin Torque Ts (Nm) | Maximum Precession Torque Tp (Nm) | Maximum Power P (W) | Effici-ency η (%) | Ratio αmax/ωmax (s–1) | |
---|---|---|---|---|---|---|---|---|---|
10-over spell, participant 1 (finger-spin topsidespin) | |||||||||
Mean | 25.95 | 33.40 | 89.29 | 0.2913 | 0.2815 | 0.1142 | 26.83 | 54.76 | 22.20 |
RMSE | 0.73 | 2.02 | 4.93 | 0.0113 | 0.0101 | 0.0062 | 1.60 | 2.10 | 0.36 |
RMSE% (= CVRMSD) | 2.81 | 6.04 | 5.56 | 3.88 | 3.90 | 5.46 | 5.97 | 3.83 | 1.63 |
R2 | 0.0402 | 0.0146 | 0.1522 | 0.0466 | 0.0429 | 0.0293 | 0.0402 | 0.0559 | 0.0233 |
p-value (alpha = 0.1) | 0.1171 | 0.3510 | 0.0017 | 0.0924 | 0.1062 | 0.1821 | 0.1194 | 0.0650 | 0.2349 |
Trend of regression | Increase | nil | Increase | Increase | Increase | nil | Increase | Decrease | nil |
Effect on performance | Gain | nil | Loss | Gain | Gain | nil | Gain | Loss | nil |
Δ 10-over | 0.50 | 6.95 | 0.0083 | 0.0077 | 1.09 | −1.70 | |||
%change | 1.91 | 7.79 | 2.85 | 2.74 | 4.06 | −3.11 | |||
10-over spell, participant 2 (wrist-spin sidespin) | |||||||||
Mean | 28.43 | 21.12 | 80.18 | 0.2821 | 0.2238 | 0.1960 | 24.60 | 64.86 | 16.13 |
RMSE | 1.34 | 3.08 | 14.16 | 0.0213 | 0.0173 | 0.0291 | 2.85 | 4.17 | 1.12 |
RMSE% (= CVRMSD) | 4.72 | 14.73 | 17.72 | 7.56 | 7.70 | 15.04 | 11.61 | 6.40 | 6.91 |
R2 | 0.0661 | 0.0495 | 0.0178 | 0.0702 | 0.0828 | 0.0531 | 0.043 | 0.0415 | 0.2058 |
p-value (alpha = 0.1) | 0.1008 | 0.1576 | 0.4004 | 0.0895 | 0.0647 | 0.1415 | 0.1878 | 0.1943 | 0.0025 |
Trend of regression | Increase | nil | nil | Increase | Decrease | nil | nil | nil | Decrease |
Effect on performance | Gain | nil | nil | Gain | Loss | nil | nil | nil | Gain |
Δ 10-over | 1.75 | 0.0286 | −0.0254 | −2.80 | |||||
%change | 6.14 | 10.14 | −11.36 | −17.34 | |||||
10-over spell, participant 3 (finger-spin topsidespin) | |||||||||
Mean | 21.23 | 42.35 | 64.55 | 0.2410 | 0.2205 | 0.1200 | 17.75 | 50.92 | 21.22 |
RMSE | 1.54 | 5.03 | 7.04 | 0.0204 | 0.0221 | 0.0115 | 2.89 | 6.04 | 0.83 |
RMSE% (= CVRMSD) | 7.22 | 11.89 | 10.91 | 8.47 | 10.01 | 9.54 | 16.18 | 11.91 | 3.91 |
R2 | 0.0534 | 0.013 | 0.0006 | 0.0459 | 0.0732 | 0.0031 | 0.0508 | 0.4316 | 0.0764 |
p-value (alpha = 0.1) | 0.0755 | 0.3879 | 0.8500 | 0.1003 | 0.0366 | 0.6760 | 0.0837 | 0.000 | 0.0326 |
Trend of regression | Decrease | nil | nil | Decrease | Decrease | nil | Decrease | Increase | Decrease |
Effect on performance | Loss | nil | nil | Loss | Loss | nil | Loss | Gain | Gain |
Δ 10-over | −1.26 | −0.0154 | −0.0213 | −2.30 | 18.08 | −0.82 | |||
%change | −5.91 | −6.39 | −9.68 | −12.96 | 35.52 | −3.85 |
Spin Rate ω (rps) | Maximum Precession p (rad/s) | Maximum Normalised Precession pn (deg) | Maximum Resultant Torque TR (Nm) | Maximum Spin Torque Ts (Nm) | Maximum Precession Torque Tp (Nm) | Maximum Angular Acceleration α (rad/s2) | Maxi-mum Power P (W) | Effici-ency η (%) | Ratio αmax / ωmax (s–1) | Minimum Ts (Nm) Before Peak Datum | Average Tp (Nm) of a 0.123 s Window (cf. Figure 10) | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Profiling, before the training intervention | ||||||||||||
Avg | 28.31 | 40.14 | 94.69 | 0.3309 | 0.3172 | 0.1405 | 4079 | 32.38 | 39.09 | 22.93 | −0.0055 | 0.0428 |
Std | 0.8 | 2.66 | 8.82 | 0.0112 | 0.0091 | 0.0183 | 117.2 | 1.81 | 4.24 | 0.23 | 0.0104 | 0.0065 |
Min | 27.18 | 35.01 | 86.91 | 0.3156 | 0.3052 | 0.1143 | 3925 | 30.23 | 34.13 | 22.64 | −0.0223 | 0.0346 |
Max | 29.23 | 42.51 | 108.51 | 0.3463 | 0.3307 | 0.1601 | 4253 | 34.94 | 45.75 | 23.2 | 0.0033 | 0.0492 |
After the training intervention | ||||||||||||
Avg | 24.97 | 38.3 | 78.26 | 0.2781 | 0.2674 | 0.1234 | 3439 | 24.66 | 58.83 | 21.91 | 0.0092 | 0.027 |
Std | 0.78 | 2.19 | 6.07 | 0.0107 | 0.0111 | 0.0081 | 143.3 | 1.71 | 1.75 | 0.33 | 0.0042 | 0.0032 |
Min | 23.8 | 34.54 | 71.53 | 0.2603 | 0.2488 | 0.1106 | 3200 | 21.96 | 57.02 | 21.4 | 0.0006 | 0.022 |
Max | 25.84 | 40.95 | 89.45 | 0.2911 | 0.2816 | 0.1333 | 3622 | 26.41 | 61.12 | 22.47 | 0.0134 | 0.0307 |
After several days of self-training | ||||||||||||
Avg | 27.59 | 38.33 | 90.39 | 0.3197 | 0.3058 | 0.1339 | 3932 | 30.76 | 55.2 | 22.68 | −0.0003 | 0.0296 |
Std | 0.73 | 1.08 | 4.47 | 0.0139 | 0.014 | 0.0027 | 179.9 | 1.82 | 2.07 | 0.45 | 0.0042 | 0.0015 |
Min | 26.2 | 36.58 | 85.12 | 0.2953 | 0.2806 | 0.1298 | 3608 | 27.4 | 52.12 | 21.92 | −0.0055 | 0.0269 |
Max | 28.38 | 39.49 | 96.3 | 0.3386 | 0.324 | 0.1379 | 4167 | 33.04 | 58.79 | 23.36 | 0.005 | 0.0309 |
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Fuss, F.K.; Doljin, B.; Ferdinands, R.E.D. Mobile Computing with a 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 2021, 21, 6942. https://doi.org/10.3390/s21206942
Fuss FK, Doljin B, Ferdinands RED. Mobile Computing with a 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. 2021; 21(20):6942. https://doi.org/10.3390/s21206942
Chicago/Turabian StyleFuss, Franz Konstantin, Batdelger Doljin, and René E. D. Ferdinands. 2021. "Mobile Computing with a 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, no. 20: 6942. https://doi.org/10.3390/s21206942
APA StyleFuss, F. K., Doljin, B., & Ferdinands, R. E. D. (2021). Mobile Computing with a 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), 6942. https://doi.org/10.3390/s21206942