Everyone sees something in data based on what they do the most. If I were faced with this dataset, I would consider some sort of repeated measures using a generalized linear model. For instance, if you were looking for estimates for each player, as a conditional value over time, you might try:
data long;
set topnfl;
year=20;value=s20/100;output;
year=19;value=s19/100;output;
year=18;value=s18/100;output;
year=17;value=s17/100;output;
year=16;value=s16/100;output;
year=15;value=s15/100;output;
keep player year value;
run;
proc glimmix data=long; class player year; nloptions maxiter=1000; model value=player year/dist=bin ddf=32; random year/residual type=ar(1) group=player; lsmeans player/diff ilink; run;
I set the denominator degrees of freedom to 32, which is what the 'skeleton' ANOVA table (no RANDOM statement) provides in PROC GLM. I used the binomial distribution because the measures are the result of an undetermined number of Bernoulli trials (either made or missed).
SteveDenham
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