My immediate thought is that OP is reinventing dynamic programming/RL from first principles. The final visualization looks exactly like a standard value estimate heatmap. Golf is a MDP over all the physical points on the course, with stochastic probabilities of transition to each one based on golfer skill and physical randomness. Strokes are the cost to be minimized, the colors are the value estimate at each state, and his difficulties with the different maps is because a value function is defined as the expected value of being in that state assuming you will follow a particular policy thereafter (ie. be a golfer of a particular skill level, playing optimally for that skill). This lets you formalize 'strategicness' of a golf course: it is how much the value estimates differ on average across the full range of golf skills; a non-strategic course looks identical for the beginner and pro, while an incredibly strategic course might have completely different values for every point for every bracket of skill. (You could probably automate creation of pathological golf courses this way, where even a slight increase in skill makes the new strategy different.)