Peer-reviewed article
The acute effect of expert video modelling on tennis serve performance in 7- to 9-year-old players.
Eya Kachouri, Ghaith Ezeddine, Hiba Kachouri, Cain C T Clark, Bessem Mkaouer et al. ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This study examined whether watching expert tennis videos helped 7- to 9-year-old boys improve their serve technique compared to traditional training. Seventeen players performed serves under three conditions: normal training, video modelling once, and video modelling twice. Both video conditions led to better serve accuracy and ball contact than traditional training, with the second video condition showing the greatest improvement. Ball speed did not change across conditions.
Key points
- Video modelling conditions significantly outperformed traditional control training for serve success
- The second video modelling condition (VMC2) showed the greatest benefits for successful serve performance and ball impact
- Video modelling improved accuracy across all service court areas tested
- No significant effect of video modelling was found on ball speed
- All pairwise comparisons between conditions showed significant differences after Bonferroni correction
Limitations
This study is a small, short-term experiment with only 17 participants testing acute effects over single sessions. It cannot tell us about long-term skill development, retention of learning, or how video modelling compares to other learning approaches in real-world coaching settings.
Abstract
PURPOSE: This study aimed to determine the acute impact of employing expert modelling on tennis serve performance in 7- to 9-year old boys. METHODS: Seventeen players undertook three conditions sequentially: control condition (traditional training), video modelling condition 1, and condition 2. Each participant performed 16 serves per condition (8 per diagonal), with 1 week between each condition. Serve performance outcomes were evaluated under each condition. RESULTS: Repeated-measure analyses of variance revealed significant main effects for conditions (F (2, 32) = 85.61, p < 0.001, η 2 p = 0.843) and point areas (F (2, 32) = 274.76, p < 0.001, η 2 p = 0.945), with a significant interaction (F (4, 64) = 7.08, p < 0.001, η 2 p = 0.307). Both video modelling conditions outperformed the control condition, with VMC2 showing the greatest benefits for successful serve (F (2, 32) = 85.61, p < 0.001, η 2 p = 0.843) and ball impact (F (2, 32) = 70.29, p < 0.001, η 2 p = 0.815), while no effect was observed for ball speed (p = 0.116-0.458). Significant condition × test interactions were observed for point areas 1, 3, and 5 (p < 0.001, η 2 p = 0.614-0.720). Pairwise comparisons with Bonferroni correction revealed significant differences between all conditions (p < 0.001), with VMC2 producing the largest gains across all point areas. CONCLUSION: Our findings support that the adoption of expert video modelling for coaching/learning tennis serves to expedite the process of technique refinement and facilitate performance enhancement.