Visual information processing characteristics of tactical decisions of soccer players in 3V3 offense scenarios

OBJECTIVE: To quantitatively access the visual search of soccer players in the 3V3 offensive tactical situation and explore the eye movement characteristics and information processing mechanisms of high-level athletes under multi-target tracking. METHODS: Using the expert-novice research paradigm, eight second-level soccer players were selected as the expert group, and eight university students majoring in soccer were selected as the novice group. Real match 3V3 offensive tactical scenarios were used as stimulus materials. The Dikablis Professional head-mounted eye tracker with D-lab software was used to record and process the participants’ eye movement data and decision-making agility data. Experts were invited to independently score the reasonableness of offensive tactical decision-making data. Independent samples t-tests were conducted for intergroup comparison analysis on SPSS 25.0 software. RESULTS: The tactical decision-making agility and rationality of the expert group were significantly higher than those of the novice group (P < 0.01). The reason for this difference in behavior is that the expert group took more fixations and saccades to deeply visually process the position and size of the gap area. At the same time, to gain opportunities to score goals, they prioritized the extraction and processing of information from the far offensive player area, which is closer to the goal. They used a following fixation mode to search for both offensive and defensive players, repeatedly observing the positional characteristics and changes in the gaps created by both sides. They used fewer fixations for visual search of the goal area and defensive player area, forming a visual allocation principle of far offensive players > near offensive players > gap > marking players > pressing players > covering players > goal. CONCLUSION: The expert group can effectively balance decision-making agility and tactical rationality, effectively identify key information, deeply process the extracted information, and adopt different search strategies to handle key and non-key information, forming an economical and effective visual allocation principle to complete efficient perceptual prediction.