It's not all in your feet: Improving penalty kick performance with human-avatar interaction and machine learning
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GRAPHICAL ABSTRACT
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PUBLIC SUMMARY
- ■ In major football competitions, over 20% of knockout games are decided by penalty shootouts.
- ■ Thirty percent of the kicks are missed, notably because players lack specific and adapted training methods.
- ■ We developed an augmented reality simulator with a holographic goalkeeper to train sensorimotor kicking skills.
- ■ Ten sessions of machine learning-optimized training improved the sensorimotor skills of players by 28%.
- ■ This translates into a 35% increase of the success rate, thereby constituting a powerful training tool.
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ABSTRACT
Penalty kicks are increasingly decisive in major international football competitions. Yet, over 30% of shootout kicks are missed. The outcome of the kick often relies on the ability of the penalty taker to exploit anticipatory movements of the goalkeeper to redirect the kick toward the open side of the goal. Unfortunately, this ability is difficult to train using classical methods. We used an augmented reality simulator displaying an holographic goalkeeper to test and train penalty kick performance with 13 young elite players. Machine learning algorithms were used to optimize the learning rate by maintaining an optimal level of training difficulty. Ten training sessions of 20 kicks reduced the redirection threshold by 120 ms, which constituted a 28% reduction with respect to the baseline threshold. Importantly, redirection threshold reduction was observed for all trained players, and all things being equal, it corresponded to an estimated 35% improvement of the success rate. -
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REFERENCES
[1] Dalton, K., Guillon, M., and Naroo, S.A. (2015). An analysis of penalty kicks in elite football post 1997. Int. J. Sports Sci. Coach. 10: 815–827. [2] Franks, I., and Harvey, T. (1997). Cues for goalkeepers: Hightech methods used to measure penalty shot response. Soccer Journal 42: 30–38. [3] Kuhn, W. (1988). In in Science and Football, T. Reilly, A. Lees, and K. Davids, et al., eds., pp. 489–492. [4] Schmidt, R.A., Lee, T.D., Winstein, C., et al. (2018). Motor Control and Learning: A Behavioral Emphasis (Human Kinetics. [5] Kerwin, D.G., and Bray, K. (2010). The Engineering of Sport 6. In Volume 1: Developments for Sports (Springer), pp. 321–326. [6] Miller, C. (1998). He Always Puts it to the Right: A History of the Penalty Kick (Orion). [7] Palacios-Huerta, I. (2003). Professionals play minimax. Rev. Econ. Stud. 70: 395–415. [8] Bar-Eli, M., Azar, O.H., Ritov, I., et al. (2007). Action bias among elite soccer goalkeepers: The case of penalty kicks. Journal of Economic Psychology 28: 606–621. [9] Cook, R., Bird, G., Lünser, G., et al. (2012). Automatic imitation in a strategic context: players of rock-paper-scissors imitate opponents’ gestures. Proc. Biol. Sci. 279: 780–786. [10] Belot, M., Crawford, V.P., and Heyes, C. (2013). Players of Matching Pennies automatically imitate opponents’ gestures against strong incentives. Proc. Natl. Acad. Sci. USA 110: 2763–2768. [11] Naber, M., Vaziri Pashkam, M., and Nakayama, K. (2013). Unintended imitation affects success in a competitive game. Proc. Natl. Acad. Sci. USA 110: 20046–20050. [12] Era, V., Aglioti, S.M., Mancusi, C., et al. (2020). Visuo-motor interference with a virtual partner is equally present in cooperative and competitive interactions. Psychol. Res. 84: 810–822. [13] Boukarras, S., Özkan, D.G., Era, V., et al. (2022). Midfrontal theta transcranial alternating current stimulation facilitates motor coordination in dyadic human-avatar interactions. J. Cogn. Neurosci. 34: 897–915. [14] Sacheli, L.M., Musco, M.A., Zazzera, E., et al. (2022). How shared goals shape action monitoring. Cereb. Cortex 32: 4934–4951. [15] Moreau, Q., Tieri, G., Era, V., et al. (2022). The performance monitoring system is attuned to others’ actions during dyadic motor interactions. Cereb. Cortex 33: 222–234. [16] Goodale, M.A., Pelisson, D., and Prablanc, C. (1986). Large adjustments in visually guided reaching do not depend on vision of the hand or perception of target displacement. Nature 320: 748–750. ISSN: 0028-0836 (Print) 0028-0836 (Linking). [17] Prablanc, C., and Martin, O. (1992). Automatic control during hand reaching at undetected two-dimensional target displacements. J. Neurophysiol. 67: 455–469. 0022-3077 (Print) 0022-3077 (Linking). [18] Day, B.L., and Lyon, I.N. (2000). Voluntary modification of automatic arm movements evoked by motion of a visual target. Exp. Brain Res. 130: 159–168. ISSN: 0014-4819 (Print) 0014-4819 (Linking). [19] Day, B.L., and Brown, P. (2001). Evidence for subcortical involvement in the visual control of human reaching. Brain. 124: 1832–1840. ISSN: 0006-8950 (Print) 0006-8950 (Linking). [20] Gritsenko, V., Yakovenko, S., and Kalaska, J.F. (2009). Integration of predictive feedforward and sensory feedback signals for online control of visually guided movement. J. Neurophysiol. 102: 914–930. 0022-3077 (Print) 0022-3077 (Linking). [21] Gritsenko, V., and Kalaska, J.F. (2010). Rapid online correction is selectively suppressed during movement with a visuomotor transformation. J. Neurophysiol. 104: 3084–3104. ISSN: 1522-1598 (Electronic) 0022-3077 (Linking). [22] Wijdenes, L.O., Brenner, E., and Smeets, J.B.J. (2013). Comparing online adjustments to distance and direction in fast pointing movements. J. Mot. Behav. 45: 395–404. ISSN: 1940-1027 (Electronic) 0022-2895 (Linking). [23] Reichenbach, A., Thielscher, A., Peer, A., et al. (2009). Seeing the hand while reaching speeds up on-line responses to a sudden change in target position. J. Physiol. 587: 4605–4616. ISSN: 1469-7793 (Electronic) 0022-3751 (Linking). [24] Sarlegna, F.R., and Mutha, P.K. (2015). The influence of visual target information on the online control of movements. Vision Res. 110: 144–154. ISSN: 1878-5646 (Electronic) 0042-6989 (Linking). [25] Johnson, H., Van Beers, R.J., and Haggard, P. (2002). Action and awareness in pointing tasks. Exp. Brain Res. 146: 451–459. ISSN: 0014-4819 (Print) 0014-4819 (Linking). [26] Sarlegna, F., Blouin, J., Bresciani, J.-P., et al. (2003). Target and hand position information in the online control of goal-directed arm movements. Exp. Brain Res. 151: 524–535. ISSN: 0014-4819 (Print) 0014-4819 (Linking). [27] Pisella, L., Gréa, H., Tilikete, C., et al. (2000). An ‘automatic pilot’ for the hand in human posterior parietal cortex: toward reinterpreting optic ataxia. Nat. Neurosci. 3: 729–736. [28] Nagasawa, Y., Demura, S., Matsuda, S., et al. (2011). Effect of Differences in Kicking Legs, Kick Directions, and Kick Skill on Kicking Accuracy in Soccer Players. Journal of Quantitative Analysis in Sports 7: 9. [29] Morya, E., Ranvaud, R., and Pinheiro, W.M. (2003). Dynamics of visual feedback in a laboratory simulation of a penalty kick. J. Sports Sci. 21: 87–95. [30] Van der Kamp, J. (2006). A field simulation study of the effectiveness of penalty kick strategies in soccer: late alterations of kick direction increase errors and reduce accuracy. J. Sports Sci. 24: 467–477. [31] Le Naour, T., Papinutto, M., Lobier, M., et al. (2023). Controlling the trajectory of a moving object substantially shortens the latency of motor responses to visual stimuli. iScience 26: 106838. [32] Navarro, M., van der Kamp, J., Ranvaud, R., et al. (2013). The mere presence of a goalkeeper affects the accuracy of penalty kicks. J. Sports Sci. 31: 921–929. ISSN: 1466-447X (Electronic) 0264-0414 (Linking). [33] Posner, M.I. (1980). Orienting of attention. Q. J. Exp. Psychol. 32: 3–25. [34] Gray, R. (2019). Anticipation and Decision Making in Sport 342-358 (Routledge). [35] Harris, D.J., Bird, J.M., Smart, P.A., et al. (2020). A framework for the testing and validation of simulated environments in experimentation and training. Front. Psychol. 11: 605. [36] Wood, G., Wright, D.J., Harris, D., et al. (2021). Testing the construct validity of a soccer-specific virtual reality simulator using novice, academy, and professional soccer players. Virtual Reality 25: 43–51. [37] Witmer, B.G., and Singer, M.J. (1998). Measuring presence in virtual environments: A presence questionnaire. Presence 7: 225–240. [38] Slater, M., and &Wilbur, S. (1997). A framework for immersive virtual environments (FIVE): Speculations on the role of presence in virtual environments. Presence. (Camb). 6: 603–616. [39] Vignais, N., Kulpa, R., Brault, S., et al. (2015). Which technology to investigate visual perception in sport: Video vs. virtual reality. Hum. Mov. Sci. 39: 12–26. [40] Hochmitz, I., and Yuviler-Gavish, N. (2011). Physical fidelity versus cognitive fidelity training in procedural skills acquisition. Hum. Factors 53: 489–501. [41] Lathan, C.E., Tracey, M.R., Sebrechts, M.M., et al. (2002). in Handbook of Virtual Environments 443-454 (CRC Press). [42] Alexander, A.L., Brunyé, T., Sidman, J., et al. (2005). From gaming to training: A review of studies on fidelity, immersion, presence, and buy-in and their effects on transfer in pc-based simulations and games. DARWARS Training Impact Group 5: 1–14. [43] Wichmann, F.A., and Hill, N.J. (2001). The psychometric function: I. Fitting, sampling, and goodness of fit. Percept. Psychophys. 63: 1293–1313. [44] Audiffren, J. (2021). Dichotomous optimistic search to quantify human perception. International Conference on Machine Learning: 414–424. [45] Audiffren, J., and Bresciani, J.P. (2022). Model Based or Model Free? Comparing Adaptive Methods for Estimating Thresholds in Neuroscience. Neural Comput. 34: 338–359. ISSN: 1530-888X (Electronic) 0899-7667 (Linking). [46] Pearce, S.P. (2000). The Autobiography (Headline Book Publishing). [47] Owen, M. (2005). Off the Record: My Autobiography (Collins Willow). [48] Eubank, M., and Collins, D. (2000). Coping with pre- and in-event fluctuations in competitive state anxiety: A longitudinal approach. J. Sports Sci. 18: 121–131. [49] Jordet, G., and Hartmen, E. (2008). Avoidance Motivation and Choking Under Pressure in Soccer Penalty Shootouts. J. Sport Exerc. Psychol. 30: 450–457. [50] Navarro, M., Miyamoto, N., van der Kamp, J., et al. (2012). The effects of high pressure on the point of no return in simulated penalty kicks. J. Sport Exerc. Psychol. 34: 83–101. [51] Jordet, G., Hartman, E., Visscher, C., et al. (2007). Kicks from the penalty mark in soccer: The roles of stress, skill, and fatigue for kick outcomes. J. Sports Sci. 25: 121–129. [52] Wood, G., and Wilson, M.R. (2012). Quiet-eye training, perceived control and performing under pressure. Psychology of Sport and Exercise 13: 721–728. [53] Wood, G., Jordet, G., and Wilson, M.R. (2015). On winning the “lottery”: psychological preparation for football penalty shoot-outs. J. Sports Sci. 33: 1758–1765. [54] Jordet, G., Gemser, M.E., and Lemmink, K. (2006). Perceived control and anxiety. International. Journal of Sport Psychology 37: 281–298. [55] Guadagnoli, M.A., and Lee, T.D. (2004). Challenge point: a framework for conceptualizing the effects of various practice conditions in motor learning. J. Mot. Behav. 36: 212–224. [56] Gray, R. (2017). Transfer of training from virtual to real baseball batting. Front. Psychol. 8: 2183. [57] Microsoft. HoloLens 2 Documentation. https://learn.microsoft.com/en-us/hololens/. -
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Jean-Luc Bloechle, Julien Audiffren, Thibaut Le Naour, Andrea Alli, Dylan Simoni, Gabriel Wüthrich, Jean-Pierre Bresciani. It's not all in your feet: Improving penalty kick performance with human-avatar interaction and machine learning[J]. The Innovation, 2024, 5(2). https://doi.org/10.1016/j.xinn.2024.100584
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Jean-Luc Bloechle, Julien Audiffren, Thibaut Le Naour, Andrea Alli, Dylan Simoni, Gabriel Wüthrich, Jean-Pierre Bresciani. It's not all in your feet: Improving penalty kick performance with human-avatar interaction and machine learning[J]. The Innovation, 2024, 5(2). https://doi.org/10.1016/j.xinn.2024.100584
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Figure 1.
Visual scene viewed through a Microsoft HoLolens 2 headset
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Figure 2.
The different stages of a trial for the penalty taker
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Figure 3.
Average redirection threshold before (baseline) and after training
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Figure 4.
Probability of improvement of the redirection threshold for crossed (green) and reverse-crossed (red) redirection
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Figure 5.
Probability of successful redirection for crossed (green) and reverse-crossed (red) redirection
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