Abstract
By taking advantage of the available technology, researchers have focused their attention on the development of new methodologies to study human movement (Dias et al. Motor Control 18:221241, 2014). Several studies have analyzed the variables influencing the performance of golf putting, mainly focusing on the measures of product performance (Dias et al. computational intelligence and decision making—trends and applications, from intelligent systems, control and automation: science and engineering book series, 2013; Couceiro et al. J Motor Behav 45:3753, 2013). The majority of this research has been carried out in a laboratory context, where the green is usually emulated using a carpet (e.g., Delay et al. Hum Mov Sci 16:597619, 1997; Coello et al. Int J Sport Phychol 31:2446, 2000), with circular targets, or holes of different sizes (Dias and Mendes Rev Bras Cienc Esporte 24:545553, 2010; Dias et al. computational intelligence and decision making—trends and applications, from intelligent systems, control and automation: science and engineering book series, 2013). Most of the methodologies adopted by these studies describe the quantification of the motor performance error based on the final location of the ball in relation to the center of the hole, commonly known as radial error (Couceiro et al. J Motor Behav 45:3753, 2013; Dias et al. computational intelligence and decision making—trends and applications, from intelligent systems, control and automation: science and engineering book series, 2013). Despite their usefulness, this and similar techniques are not sufficient to describe a player’s performance as a whole, bearing in mind that they only consider the ‘cause and effect’ linear actions resulting from an athlete’s actions or match situations. For instance, one needs to better understand the meaning of having a golf ball finishing before, after, or in the vicinity of the hole, and to what extent this is truly meaningful and important for the putting performance. Moreover, it should be noted that current research is scarce around this topic and does not clarify in any way, the difference between a golf ball that stays in the ±90° lines towards the hole, or another that stays in the ±180° lines towards the hole. In other words, the literature does not provide any theoretical support for this research question, thus reinforcing the evaluation methodology proposed here. This chapter introduces alternative methods to further assess and understand the performance of golf putting in terms of product, i.e., in terms of end result. It is noteworthy that we do not aim at replacing the more traditional measures, but on complimenting the information they may provide.
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References
Bart K (1992) Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Pren-tice-Hall, Englewood Cliffs
Coello Y, Delay D, Nougier V, Orliaguet JP (2000) Temporal control of impact movement: the “time from departure” control hypothesis in golf putting. Int J Sport Psychol 31(1):24–46. ISSN 0047-0767
Couceiro MS, Dias G, Martins FML, Luz JM (2012) A fractional calculus approach for the evaluation of the golf lip-out. Signal Image Video P 6(3):437–443. doi:10.1007/s11760-012-0317-1
Couceiro MS, Dias G, Mendes R, Araújo D (2013) Accuracy of pattern detection methods in the performance of golf putting. J Motor Behav 45(1):37–53. doi:10.1080/00222895.2012.740100
Couceiro MS, Martins FML, Clemente F, Dias G, Mendes R (2014) On a fuzzy approach for the evaluation of golf players. Maejo Int J Sci Technol 8(01):86–99. ISSN 1905-7873
Delay D, Nougier V, Orliaguet JP, Coello Y (1997) Movement control in golf putting. Hum Mov Sci 16 (5):597–619. doi:10.1016/S0167-9457(97)00008-0
Dias G, Mendes R (2010) Efeitos do contínuo de níveis de interferência contextual na aprendizagem do “putt” do golfe. Rev Bras Cienc Esporte 24(4):545–553. doi:10.1590/S1807-55092010000400011
Dias G, Mendes R, Couceiro MS, Figueiredo C, Luz JMA (2013) On a Ball’s trajectory model for putting’s evaluation, computational intelligence and decision making—trends and applications, from intelligent systems, control and automation: science and engineering book series. Springer, London
Dias G, Couceiro MS, Barreiros J, Clemente FM, Mendes R, Martins FM (2014) Distance and slope constraints: adaptation and variability in golf putting. Motor Control 18(3):221–243. doi:10.1123/mc.2013-0055
Kolev B, Chountas P, Petrounias I, Kodogiannis V (2005) An application of intuitionistic fuzzy relational databases in football match result predictions. Adv Soft Comput 2:281–289
Mackenzie SJ, Evans, DB (2010) Validity and reliability of a new method for measuring putting stroke kinematics using the TOMI1 system. J Sports Sci 28(8):1–9
Pelz D (2000) Putting bible: the complete guide to mastering the green. Publication Doubleday, New York
Stewart J (2007) Calculus: early transcendental. McGraw Hill, New York
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Dias, G., Couceiro, M.S. (2015). Performance Metrics for the Putting Product. In: The Science of Golf Putting. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-14880-9_4
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DOI: https://doi.org/10.1007/978-3-319-14880-9_4
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