I recently completed a Biomechanics Research Masters which focused on golf driving performance. Today’s blog post will explore some of my findings and how I utilized TrackMan.
This research unbeknown to me at the time started in a Cardiff School of Sport year 1 undergrad lecture where (like most golfers) I was deep in concentration not on the content of the lecture itself but how I could shave a few strokes off my game. I had stumbled across a question,
‘Do I actually hit the ball further when I try to hit it harder?’
This would underpin my final year dissertation where I had golfers hit shots under two conditions; a control group to represent their normal performance and a set of shots that had one aim, go further than the shots hit in the control condition!
This initial study was performed indoors using TrackMan for the ball data and a 3D automated system (Codamotion) to analyze the swing. In a 2011 ISBS paper abstract, I reported the findings of a 3 hcp golfer who I had studied. The segment separation of the shoulder and hips (X-Factor and X-Factor stretch) and maximum endpoint velocity of the left hip, shoulder, and elbow showed a significant increase in the ‘hit it further’ condition.
Ball velocity increased by 2 mph although neither this or vertical launch produced a significant difference. The player had created the ability to hit the ball further but was unable to exhibit noticeably increased distance.
Having completed my Bsc (Hon) Sport and Exercise Sciences degree I was fortunate to get the opportunity to continue this research into an MPhil thesis. I’d initially examined past research centered on the swing mechanisms that produced club head/ball speed, it was clear I needed to establish a better understanding of what the ball was doing and produce a tighter definition of what I was actually testing.
I found this in Newell’s (1986) Constraints-Led approach model. Constraints-Led Approach considers that the organism, task and environment constraints are in interaction and it is this interaction that determines the optimum movement pattern required. The presence of wind affecting the required swing and club selection is a great example of an environmental constraint.
The golfer’s physical capabilities may affect the shots that they can perform; this is presented as an Organism constraint. The Task constraint is more specific to the performance context set by the goals rather than the already stated effect of the environment; this gives consideration to the rules and playing area.
With the weather always changing, the diversity in physical characteristics of the golfing population and with each golf hole being unique, Golf more than any other sport has more varying interaction and flow through this model for success to be achieved.
‘Variability can be functional?’
The constraints-led approach gave my initial distance question a framework to work from and allowed exploration that variability can be functional, allowing elite players to create their high level of performance rather than hinder it.
This is contrary to many coaching models which preach reducing the degrees of freedom and repeatability. The previous statement is not aimed to invalidate swing models but to further add a catalyst with which to aid their effectiveness to their number one objective, lowering scores. This was central to my thesis and through a number of studies using both launch monitors and automated 3D systems (Vicon) their findings informed the adaption of the original model that highlights my belief that coaching knowledge needs to interact with these other mechanisms to gain a full understanding of performance (figure 1).
It was essential to first establish whether these task constraints could alter outcome performance on the course and if a greater understanding could aid both coaching and performance, TrackMan data was needed.
First Study using TrackMan
Focusing on the Task constraints elements of the Newell (1986) model I expanded my research question to include an accuracy constraint to the previous distance and standard drives (figure 2). The testing protocol for this study excluded the golfer’s body movements and focused solely on the ball using the TrackMan.
17 golfers were used in the study, (age 29 ± 10 yrs, height 1.8 ± 0.1 m, mass 81 ± 6 kg) 11 professionals and 6 elite amateurs. Each golfer hit 60 drivers split between the 3 task constraints. These were performed in blocks of fives to prevent order effects and no advice on how to satisfy the tasks was given to prevent experimental bias. The shots were collected in calm conditions with the data normalized in the TrackMan software.
The group findings (Table 1) found significant differences between all three constraints in the carry and total distance, ball velocity, and peak height. The aim to hit the ball further did increase the distance produced and associated ball velocity was greater.
The accuracy constraint produced opposing findings with a reduction in both ball velocity and achieved distance. The ball flight of the distance constraint produced a significantly higher launch and peak height against the other constraints with the accuracy drive producing a lower peak height in comparison to the distance drive only.
One of the biggest differences was the landing angle of the accuracy constraint which was 5 degrees flatter than the other constraints. These findings show additional distance could be found and when trying to satisfy the constraints both ball velocity and flight characteristics altered.
‘Ball Velocity and Carry/Total Distance increased’
Table 1: Mean results of the variables studied for the three task constraints
| Standard | Distance | Accuracy | |
| Target Line Carry Distance (m) | 221 ± 9 | 228* ± 10 | 214* ± 10 |
| Target Line Total Distance (m) | 238 ± 11 | 244* ± 11 | 233* ± 10 |
| Carry Absolute Lateral Dispersion (m) | 15 ± 11 | 16 ± 12 | 13** ± 9** |
| Total Absolute Lateral dispersion (m) | 16 ± 12 | 18 ± 13 | 15** ± 11** |
| Angle offline (degrees) | 3.8 ± 2.7 | 4.2 ± 3.0 | 3.5 ± 2.5 |
| Initial Ball Velocity (MPH) | 157 ± 2 | 161* ± 2 | 154* ± 2 |
| Peak height (m) | 28 ± 4 | 31* ± 5* | 24* ± 4 |
| Vertical launch angle (deg) | 9 ± 1 | 10* ± 2 | 8** ± 1 |
| Ball Spin (RPM) | 3024 ± 548 | 3035 ± 546 | 2997 ± 541 |
| Absolute Spin Axis (deg) | 5 ± 3 | 5 ± 4 | 5 ± 4 |
| Angle of descent (deg) | -40 ± 5 | -40 ± 6 | -35* ± 6 |
*p = 0.05 significance with Standard. ** p = 0.05 significance with Distance only (ANOVA)
In regards to dispersion, the accuracy constraint produced significantly straighter drives compared to the Distance constraint. The significance did not, however, transfer to the angle offline measurement nor did it apply to the standard constraints performance.
The offline angle was used as it factors distance whereas total lateral distance does not. Although the accuracy constraint showed it could have a positive effect on dispersion, it appears to have come at the cost of total distance.
In Mark Broadie’s book ‘Every Shot Counts’ Mark explains from a stroke gained perspective distance is more important than dispersion when analyzing a Tour Pro’s effectiveness off the tee. As the Accuracy constraint has reduced total distance it would have to be considerably straighter to outperform the distance constraint and as its performance was not significantly straighter, this doesn’t seem viable on course strategy in its current form.
The ball flight of the accuracy constraint is however considerably different with a lower flight/land angle associated with more roll, therefore, it could potentially become viable if the ground conditions allowed (environmental constraint).
This initial TrackMan based study was essential to build a foundation for further work on this topic. The distance constraint displayed the ability to outperform the standard driving performance. In this blog post, I have reported group findings only; you should expect individual differences and these need to be considered.
16 of the 17 participants had a significantly higher ball velocity with 6 gaining a 9 m or greater total distance; the biggest gain was 18 m. The simple nature of the testing protocol makes it easy to apply to a coaching session; I’d recommend only 5 shots of each constraint and collect the student’s data.
Having extensively studied the task constraint effect on driving performance it is my calculated assessment if used for multiple sessions the distance constraint will further improve and highlight that accuracy only strategies leave you vulnerable on the course, especially in the heat of battle!
Steve Buzza
PGA TPI Professional
Golf Digest Best Young Teachers in America
MPhil Sports Biomechanics
Twitter: @stevebuzzagolf
Instagram: @BuzzaGolf
YouTube: BuzzaGolf
Email: buzzagolf@gmail.com
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