New research from Cornell shows that the biggest threats to putting green quality aren't weather or turf type - it's the shoes golfers wear and how they walk. A machine learning study reveals what golf course managers should focus on next.
As rounds of golf surged post-2020, so did wear and tear on putting greens - especially near the hole, where nearly half of all shots are played. A new multi-year study out of Cornell University has identified the key contributors to putting surface disruption (PSD), using machine learning to analyze data from 19 trials conducted between 2019 and 2023.
Researchers evaluated over 50 golf shoe models across different surface types and weather conditions to determine what really causes visible turf damage and reduced ball roll quality. The result? The biggest impact came not from weather or turf type, but from shoe tread design and the individual player's walking style.
Using a random forest algorithm, the study found that visual turf damage could be moderately predicted (R2 = 0.48), but ball roll distance changes were largely unpredictable (R2 = 0.16) - even with over 100 variables considered, including firmness, moisture, and climate data.
Notably, once player and shoe factors were removed from the model, predictive power collapsed, highlighting a major challenge for turf managers: the biggest influencers of damage are largely outside their control.
The findings suggest a new research direction: shifting focus from weather and surface alone to deeper biological and structural properties of turf - such as plant density, leaf rigidity, and maintenance practices like topdressing and mowing.
Why it matters: As golf continues to grow, course managers may need to rethink green maintenance strategies - not just by improving turf resilience, but by influencing golfer behavior and footwear choices.
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This article summarizes reporting originally published by onlinelibrary.wiley.com
