Introduction to Monte Carlo

Last year I read the Mark Broadie book - Every Shot Counts. A superb book with some amazing insights into amatuer and professional golf. During the book he made numerous references to running monte carlo simulations as a way of optimising strategy. He didnt share the simulator, and a google search did not yield any golf specific monte carlo simulators. I therefore decided to try and build my own prototype.

Stage 1 was working out how to model the result of a shot (where will it go). To do this I needed distance and dispersion data, namely the mean and standard deviation for each. At the time I was practising on a Trackman every few weeks, and Trackman gives you incredibly accurate distance and dispersion data. Trackman is very helpful by including consistency (s.d.) in its exports. On Skytrak you need to calculate this from the raw data (not difficult).

I put this data into Excel and using the NORM.INV function I was able to generate simulated shots based on a small sample of real data. This model is very simplistic, but it meant I could easily plot where my shots were likely to land. By capturing data for different clubs I was able to work out how far and how wide I was likely to be.

Sample File:
Monte Carlo Golf Model 1

The sample excel file demonstrates this for some data I captured last year. The only inputs are the green boxes. From it I can see the following:
For a 2H I am likely to hit it 235 yards, with 90% of shots ending up 228-242 yards. I am likely to land 15ft left of target with 90% of shots dispersed within 24 yards.

For a driver I am likely to hit it 278 yards, with 90% of shots ending up 233-321 yards. I am likely to land 12ft left of target with 90% of shots dispersed within 47 yards.

Based on this information I am quite likely to hit fairway with my 2H, albeit at a reduced distance. With my driver I am far less likely to hit fairway but on most holes I will still be safe.