Golf Research Update: Advancements and Insights
From Smart Clubs to Swing Biomechanics - Key Insights in Golf Research
Below are summaries of three recent academic publications related to golf performance.
Paper #1 - Location Matters-Can a Smart Golf Club Detect Where the Club Face Hits the Ball?
What: The researchers investigated whether a motion sensor attached to a golf club could accurately detect the point of impact with the ball.
How: An athlete performed 282 swings using a 7-iron, and each swing's impact location was tracked and labeled using a TrackMan. Simultaneously, the club's motion was captured using an Inertial Measurement Unit mounted on the club's shaft. These data points were then fed into a neural network, an advanced machine learning model that simulates human brain processing.
Results: The neural network successfully classified the impact location with a 93.8% accuracy.
Implications: This research opens the door to the potential for capturing critical biomechanical parameters in golf swings outside of a laboratory setting, using a mobile and personalized measurement system that can provide extensive information on golf-specific performance metrics.
Paper #2 — Kinematic, Kinetic, and Temporal Metrics Associated With Golf Proficiency
What: Various golf swing components were collected from driver swings executed by 33 male golfers, who were classified into three skill levels: proficient, average, or unskilled.
How: High-speed motion analysis was employed to collect kinematic data, while dual force plates were used to measure ground reaction forces (GRF).
Results: Key metrics varied significantly across different skill levels of golfers, providing insights into factors that distinguish proficiency. Proficient golfers showed a notably higher 'x-factor' at ball impact, with a 9° greater separation in trunk and pelvis rotation compared to average golfers and 15° greater than unskilled golfers.
Additionally, these golfers exhibited 83% more trunk deceleration from peak velocity to ball impact than average golfers and 220% more than unskilled golfers, indicating a more controlled and powerful swing. In contrast, unskilled golfers had a lower x-factor at the top of the backswing and lower peak x-factor values.
Implications: These findings highlight the critical role of body rotation control and the sequencing of movements in achieving golf proficiency. This detailed understanding of specific biomechanical metrics offers potential pathways for targeted training and skill development in golfers of various levels.
Paper #3 - Validity and Reliability of the FlightScope Mevo+ Launch Monitor for Assessing Golf Performance
What: The study evaluated the FlightScope Mevo+ launch monitor's accuracy and consistency compared to the TrackMan 4.
How: It involved 29 youth golfers executing shots with a driver and 6-iron, measuring eight metrics across both devices.
Results: There was near-perfect correlations for clubhead speed, ball speed, carry distance, and smash factor. However, variables like spin rate, launch angle, and attack angle showed significant variation.
Implications: The study affirms the FlightScope Mevo+ as a reliable tool for certain key golf performance metrics. However, caution should be taken when using spin rate, launch angle, and/or angle of attack for feedback in coaching and/or training.
The first paper is super-interesting. I've thought this would be possible for a bit - although I have to question why only one subject as the training data will be relatively fixed to one swing rather than across multiple, especially as this won't produce (much) deviance in path and also will have a relatively fixed form of kinematics (smooth versus harsh transitions etc). I'd expect a NN to perform well of one player's data, but I wonder how well a model would generalise...