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Using a Screen-Based Task to Explore Expertise-Based Differences in Green Reading
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AI
DOI：10.1177/00315125261439030.png)
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Abstract
En 中文
Background: Green reading is a critical skill in golf putting. Purpose: This study examines how golfers across a continuum of skill levels interpret static 2D images and videos to predict ball roll and putting outcomes. Methods: Eighty participants of varying skill (average putts per round ranging from 29 to 54, with an average of 40.8 putts) completed a screen-based task. Firstly, a series of two putts were presented as static images. Participants first provided an initial read, then reviewed up to six different viewing positions before confirming or revising their decision and finally watched a video of a professional executing the putt before confirming or revising their read. Participants also completed two additional video tasks, firstly watching six videos of occluded putts and judging whether the ball would go in or miss. The second task had three separate putts. Participants viewed each putt from three camera angles (behind, side, and side-delayed) and were asked to identify the read. Results: Golfers are more accurate in predicting the read from a video than a 2D static image. Higher skilled golfers benefit more from additional visual information or a video, predicting outcomes better than lesser skilled golfers. Conclusions: These findings highlight both the potential and limitations of screen-based green reading.
Keywords:
golf putting
performance
expertise
perceptual-cognitive
training
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