A few thoughts:
-Your minutes projections for the Spurs' stars seem low. Last year, Duncan, Kawhi, and Parker each saw large increases in min/g in the postseason, ending at around 36 each. Some of that was due to the Spurs playing a total of 7 overtimes in their 21 games (adding 1.4 minutes per game, or 7 additional player-minutes) or due to them having slightly less depth last year, but I'd bump up the minutes of each of those three, as well as Ginobili. Popovich spoke about Kawhi in particular here
“We want to up his minutes,” Popovich said. “He’s going to play more minutes (in the playoffs) than Tim Duncan does probably, more minutes than Manu Ginobili probably. This is his stretch run and he needs to be in shape for it. He’s never really been able to do this because it was a lockout season or we had to limit his minutes last year. This is the first time he’s been able to lay it out there.”
I'd project Kawhi at no less than 37 min/g and probably more, especially considering that if seeds hold, the Spurs' road would go through James Harden, Kevin Durant, and LeBron James. The extra minutes for the stars would come from Belinelli, Diaw, Mills, and Splitter at the back end of the rotation, I'd expect. Good lord, the Spurs are deep.
-Jerryd Bayless is no longer with the Grizzlies, having been traded to Boston in the deal that acquired Courtney Lee. I'd expect most of his projected minutes to instead go Mike Miller's way.
-Adding xRAPM values up like that may make sense, but calling them SRS values isn't passing the smell test. The Spurs, Clippers, and Warriors all would come in as the #1 overall team of all time were they to keep those values up for an entire season, and we'd have 10 teams with an SRS north of +6. If each value was multiplied by .6, the magnitudes would seem more reasonable, but it seems like the "effect of being up X" variable is having quite a large effect. Maybe there's some function we can apply to these totals, so that we know that when the Spurs are given a score of 13.4, due to the extra difficulty in adding each additional point of efficiency differential, that translates out to the difference between 0 and +8, or +9 or +7 or whatever it is.
-It also seems strange that Warriors-Clippers ended up that close in your model. How did you set that up? If you used the 12.4 and 11.9 values from before, a half-point difference is not insignificant, and should result in a swing of more than 3 games per 10,000, right? Am I missing something there? It may be that the model just minimizes differences as these scores grow. Strange behavior with large inputs like that wouldn't surprise me much considering how extreme of an outlier these are when interpreted as SRS scores, but I don't see how that would have that effect if you just generated predicted PPG for each team and plugged it into Pythagorean W/L. I might just have misunderstood what your inputs are, though.