OK, just to reserve a little space for later, all will be revealed between 2 different ways of going about the same thing.
Just quickly:
The result that we get here tells you how far the ball will travel out of a specific rough for a specific input shot power (which I will from now on convert in to distances), so say you hit the ball out of 30-40% rough with 90% power - or with 45yards on the shot bar - these tables will tell you how far the ball will go for that. You scroll down the tables to find the required power you need for the distance at-hand.
My predictor on the other hand will tell you directly the input power you will need to go the distance you specified in the user control panel for the rough you also specified in the user control panel - it does the scrolling down the table bit for you.
These 2 subtly different metrics can be inter-converted between one another, and I do this myself to compare models.....
Screwed up earlier, let me do this inter-conversion thing again - hey I only did this late last night before bed, nobody is purrrfect, lols:
From the illustration below you will see that I have taken the measured data for Punch 30/40% rough and derived an equation for it - a linear line of best fit, this enables me to create a model which can be assimilated in to my existing predictor.
Now as it stands in the following equation (red arrow in the illustration):
y = 1.27x -19.11
The distance out for a shot power in (the way it is done here with the tables), makes 'y' the subject, so:
y = (1.27 * 35) -19.11
this brings about the 25 yard figure you will see from the tables if you look-up 70% shot power (35 yards).
With my way of doing it (i.e. given a desired input distance tell me what I need to hit), I make 'x' the subject, so:
35 = (1.27 * x )- 19.11
This brings about the 42.6 yards figure in the bottom table (circled in green, rounded to 43), this figure then has to go through the selector (circled in black) to satisfy certain rough and distance limits if it is to be compared to my prediction for the same shot.
So 42.6 yards input power corresponds to 85% in the table and that ties in with the desired figure of 35yards, everything comes around full circle.
So to convert between the different methods we just change the subject (which I am sure most in here now wish I would change the subject, lols), between 'x' or 'y'.
But thats not where the differences end....
In my approach I split the rough compensation and the club compensation in to separate parts, this means I have to take my rough compensation figure from my rough table and then add an additional club compensation factor which is referenced from another table. The club compensations were derived by punching on the fairway of hole number 10 pinehurst.
Club compensations are intrinsic to these look-up tables as the author took the shots out of actual rough using the actual club - he does it all in one, but there are advantages as you will see later in splitting these 2 aspects (rough and club compensation) up.
So my method says to go 35 yards out of 35% rough add 5yards (bottom table circled in green), then reference the club compensation table for 35+5 = 40 yards and add this in too.
Lo-and-behold the club compensation factor for a shot of 40 yards = 3.5yards, so the total figure I get is 35 + 5 + 3.5 = 43.5 yards, here you can see that part of my club compensation table:
You can see this in the illustration as 43.8yds which would corrospond to 87.5% in the tables making me slightly higher at about 36.5 yards.
This is already very close an outcome between the 2 models and I can tweak my rough factor so the 2 are identical, but thats not the point here because my predictor is tailored to my ball.
The point is that both predictions yield results that are in the same ballpark when compared like-for-like, my predictor validates his data and his data validates my club compensation factors.
they go hand-in-hand which is a nice ending :-)
The thing to say was that by splitting the rough and club compen. in to 2 stages it means I can use club compen. generically for any rough - I don't have to go on course and shoot balls every time - the linear relationships of all 3 tables of tabulated data means the roughs can be represented by a single scaling factor at each % range and this is what I do, like many of you.
if I want sand - I guess the scaling factor then bolt on the club compensation factor, same goes with fescue e.t.c.....then refine the guess in game, again like we all do.
Thx,
deena.
Additional:
I also bolted on my wind predictor to this assimilated method so I can have both figures pop up for full in-game parameters, sometimes there will be no figure at all - this in most instances will mean that the shot requirements are out of range, e.g: If I decide to take on a 40 yard punch in 25% rough with spin there will be no corresponding figure because the guy who compiles these tables went no further than 35 yards for full backspin in that rough % - the selector sorts out all that and will return a null result if parameters are exceeded.
Additional additional:
Of course there is no need to find myself having exceeded shot parameters so easily - because I have the formula for the relationship between shot input power and yardage I can simple extend this to cover the full range of the club and this is the point about taking data and turning it in to a model - it's not JUST to be a clever dick, it's also because we can then take just a very small handful of on-course measurements and convert them in to an accurate prediction of what happens over the whole range of values. I am using this method in order to provide an additional club compensation for every single iron in my bag.