Day job: Golf Data Science & Biomechanics. Sports science/performance consultant and researcher on the side.
Raleigh, NC
Joined February 2016
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To⬆️clubhead speed (CHS) with training
- ⬆️force potential (rate & magnitude) of the muscles
- ⬆️time available to apply force and/or distance through which the force is applied (e.g., longer hand path via better rotation).
- Develop skill of using this within the golf swing
Running a golfer’s Trackman data through some models to identify what launch conditions may be beneficial, then simulating the effects across many golf shots.
This helps identify promising areas to target. But the next step is actually making the change and retesting to confirm
Playing around with some methods for modeling a golf ball’s trajectory based on launch and environmental conditions.
A few simple examples attached here, but definitely a lot of ways this type of modeling can be useful.
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I've been trying to model characteristics that predict longevity on the PGA Tour. Some initial results:
1. Good ball striking is protective against losing status
2. Performance decay across ~3 years is useful for predicting who will lose status vs current performance alone
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Top 25 golfers on the PGA Tour over time, categorized by distance and accuracy vs the field
You can get it done in a variety of ways, but being good at one and at least solid at the other is a good combo. We're seeing less short golfers in the Top 25 golfers now than previously
Some trends from top 100 PGA Tour players by year.
All age categories are hitting it further and faster over time. But there are progressively less older players in the top 100.
It's a change in who makes up the PGA Tour, not just changes at the individual player level.
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It’s easy to fall into the trap of collecting data because it’s there.
There’s many ways to collect data. But without a plan it can add noise or take attention away from what matters
A useful question to ask:
Is this useful and actionable or just interesting and accessible?
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In an industry full of tech and data, transparency can be a powerful differentiator for golf tech companies/products.
Many coaches and golfers are naturally skeptical. So the gain in trust can often outweigh any fear of “being exposed” for not being 100% perfect at everything.
I like to make some data collection an embedded part of training vs just formal structured test sessions
In this case, athletes did a few max effort jumps at the end of warm-ups. Main goal was feedback to drive some intent & competition
On the backend: we track trends over time
Some cool historical snippets from a nearly 60 year-old thesis (Wenzel, 1968
1. Frank Stranahan stating he'd quit golf before he quit lifting weights
2. The Oregon golf team lifting weights in the 1960's
Both at times when golf was not exactly bought into strength training.
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Not to mention this thesis was one of the first formal studies on golf fitness/S&C, and found some positive results from an isometric + dynamic strength training program.
Link to the thesis: commons.und.edu/theses/3715/
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The minimum speed required to compete on the PGA TOUR is increasing.
Here's a plot showing the average (black) and slowest ball speed (blue) for Top 50 golfers each year.
Top 50 players current average in the mid 170's, and no Top 50 player is below mid 160's.
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Flywheel training has been a big part of my day-to-day for the last couple of years. So it’s been great being able to contribute to some research in that space.
More to come from this PhD project
🚨Published Today🚨
Flywheel Resistance Training for Anterior Cruciate Ligament Rehabilitation
By Margaret Michalak, Chloe Ryan, Alex Ehlert and John Cronin
📲Hit the link below to read the full article for 🆓⬇️🔓
journal.iusca.org/index.php/…
There’s been 4 seasons since 1987 where a golfer averaged 20+ yards longer than the Tour average while also hitting above average fairways.
Tiger had 3 of those 4 seasons from 1997 to 2000
Access to data is not an issue in golf.
Domain expertise and an understanding of the applied setting (including common challenges and pain points associated with leveraging data/tech) is a differentiator for both product development and effective use of current products/tech
I created a simple tool to calculate a measure of jump impulse from measures of jump height and body weight + some scoring measures based off a few comparison groups.
This basic version of the tool is helpful for showing some key things about jump testing for golfers (a thread)
Some updates to the launch monitor tool.
GC Quad imports + Club face strike locations. Now can connect the dots between club delivery, strike location and ball flight/shot outcome across multiple shots and individual shots.
Some screenshots of specific examples below
Frank Stranahan was an interesting character. Competitive powerlifter while playing pro golf, during a time when hardly any golfers lifted weights.
Excerpt from Wenzel (1968).
commons.und.edu/cgi/viewcont…
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The more I work with golf data, the more useful I’ve found customized tools that complement the product softwares already out there
Here’s a very simple version of a launch monitor tool. Just upload the raw exports from launch monitors, and it populates customizable dashboards
I've posted two new newsletter articles in recent weeks:
1. Putting golf swing demands into perspective
alexs-newsletter-a98584.beeh…
2. Better use of data in golf
alexs-newsletter-a98584.beeh…
I will be trying to post more often. Let me know if there are topics you'd like to see!
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If you want to compare jump height to values reported for pro golfers, first consider:
1. Jump height alone has downsides in golf (extra size can be an advantage for distance, but disadvantage for jump height).
2. How you test jump height will affect the results
More below
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