Building a model that visualizes strategic golf
golfcoursewiki.substack.com
golfcoursewiki.substack.com
The point of what I'm doing here is pointing that strokes gained approach at the golf course instead at the player. Ideally, I'd like to continue working on it to build something that can help clubs make minimal, inexpensive changes while maximally improving the strategic interest if the way the course plays.
Yes, that's why I mentioned that this gives you at least one possible measure of 'strategicness' which can be computed automatically. Measure the variance across policies (skill level, which maybe you could define physically as some sort of 'mean absolute circular error' in strokes?), and now you can do quite simple optimization routines to modify courses. Like take a course, randomly flip some squares to sand/water/grass/etc, compute the new strategicness, and keep it if it's higher. Random search, simulated annealing, CMA-ES, novelty search, lots of easy possibilities I bet even a LLM could implement for you these days which would let you take an existing golf course and search for new modified layouts with higher strategy, to inspire a human expert in modifying a course.
Holy crap, this is a really good idea. I’ve already shown how this is done by adding and removing bunkers, but single pixels is extremely smart.
>would let you take an existing golf course and search for new modified layouts with higher strategy, to inspire a human expert in modifying a course.
I’ve seen so many folks misunderstand what I’m doing here. I applaud you here. Finding the minimum viable (cheapest) way to improve the strategic elements of a golf course across handicaps is more important than ever, and is one of the goals of this exercise as a potential product.
Some info for non-golfers: every golf course has a “course rating” and a “slope.” Course rating is essentially the “true par” of the course for a “scratch golfer” (a golfer who normally shoots par). The “slope” is a measure of how much worse less-talented golfers score compared to a scratch golfer. These numbers are used to compute a golfer’s “handicap,” which lets them compete fairly against more- or less-skilled players in tournaments.
Course ratings are currently assigned by people measuring distances from the tee box to various points of trouble and then to the green. This makes course length the dominant factor by far in terms of course rating. If we adopted something like scoofy’s inverse strokes gained metric, course ratings would become far more accurate, and they would become much cheaper (asymptotically approaching free) for courses to obtain. (Currently courses must pay to have their course rated.)
My previous best idea for fixing this was just to use the scores reported by players every day to nudge the rating toward its “true” value. But that would be subject to a lot of conflation that this simulated approach is not.
I see you mention looking for topographical data at a good resolution. I'll follow that with interest. There are US public datasets (IIRC) at more like 3m or 5m resolution... not bad but not really good enough for golf, especially greenside. I couldn't figure out how to pull that info programmatically in the past, though.
Overall this is super cool work, will be watching for more!
The maps, importantly, don't tell you how to play the hole. They just show where the hole is easier to play from, if you're already in that location. Whether or not you ought to attempt to reach those areas is the choice the player makes, and it's going to be different based on what the strategy the player uses is. I allude to this in later image of Talking Stick O'odham #2, which has the internal aiming system tuned up to be aggressive (say, for a skins game), and another image where it is tuned down for safety (say, for a derby or defending a lead in a stroke play tournament).
The maps really just kind of "show the idea" behind the strategic design. The best use case would be for helping golf course architects communicate the changes they want to make to potential memberships, who might be hesitant to change they don't understand.
It's very much not a system like Decade, or ones that companies like Arccos can provide to improve performance.
Are you making an assumption that you need to have the same resolution at every point on the course? Maybe there are broad areas of fairway that have similar scores (so lower res is fine) and specific "sharp" areas (you'd probably need finer resolution on the hole-wards side of bunkers vs the side that's farther away from the hole?).
There's lots of techniques in computer graphics for figuring out when you can downsample, and you're going to have lots of opportunities to tune those techniques to the problem of golf course strategy analysis (trees, as you mention, probably cast "shadows" of uncertainty and those shadows would need better sampling).
(disclaimer: I golf <1x / year so I don't even know all the words you used in the article)
I do think, however, that there should be some relationship between resolution and the "stickiness" of the surface (with higher friction variable, e.g. heavy rough). Given the higher friction areas, there will be less movement in rollout. Less movement in rollout means that the net effect of the contouring is less significant, which means that the resolution is less important, and we can probably save time in these areas.
I'll really have to think about this. It's a good idea.
I'll eventually spend time fixing the issue, but it works fine if you just refresh the page. I really should a "you broke reddit, please refresh" page to the 500 error page.
If you need to contact me, just use this tally link: https://tally.so/r/7R2Bla
cheers