# Baysian Data Analysis of PGA Golf Scores with Py-Stan

Hi Everyone,

The link below is a survey of some analysis and prediction of golf scores using py-stan. I use three different models to understand and predict scores. It’s not ground breaking research, but could be good for a Stan conference presentation. The readme contains and embedded slide presentation with overview, analysis, and results.

Best,
Jamie

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Cool. You might be interested in presenting this at StanCon in January. You can go faster if you replace Stan constructs like

``````for (n in 1:N) {
y[n] ~ normal(alpha[t[n]] + tau[p[n]], sigma[p[n]]);
}
``````

with the single line

``````y ~ normal(alpha[t] + tau[p], sigma[p]);
``````

Since `t` is an integer array with size `N`, `alpha[t]` copies the elements of `alpha` the appropriate number of times so that the total size of `alpha[t]` is also `N`.

I’m totally into people posting sports examples! They usually come with cool plots.

The intervals in this plot: https://github.com/jamiebernardin/bayesian_golf#-4 , are these like mins and maxes of round scores for each player? Or 50% intervals?

And do the orange dots here (https://github.com/jamiebernardin/bayesian_golf#-11) come from generated quantities and the green line the original data?

I was looking at “Different tournaments/different courses have different coefficients that fit SG to score”. Does that factor into the regression here: https://github.com/jamiebernardin/bayesian_golf#-19 ? I think adding comments to the model there would be good (I wasn’t exactly sure what N_T is… Is it number of tournaments?)

Is it possible to plot the AR coefficients along with the score predictions for a few players?

Fun stuff!

makes sense… wasn’t sure I could do that with the mapping array. thank you!

thanks for the feedback, will definitely do a second pass at legends and more explanation. great idea for AR coef.

doesn’t seem to work, fyi.

No matches for:

real[] + real[]

Oh, if you can define `alpha` and `tau` to be vectors and then you can add them.

Either that or you can use `to_vector(alpha) + to_vector(tau)`. Arrays and vector/matrix things are kept distinct in Stan (arrays are std::vectors, and vector/matrix things are Eigen types).

Thanks for pointing that out. Yes, indeed… took like 40% the time of
the non-vectorized version.

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