Warning: This post contains math and basically just uses stats.
Thought I'd take a quick run at trying to see where this team projects going into this year using a few statistical methods. The primary rotation seems to be pretty set at this point, and any third string PG or backup C won't skew these numbers too much, I don't think.
For all of these methods, I need to make assumptions on the minutes distribution. To do this, I first set an ideal rotation based on my personal preferences (which I think are reasonable). See below.
PG: Kyle Lowry (30), Greivis Vasquez (18)
SG: DeMar DeRozan (30), Vasquez (6), Louis Williams (12)
SF: Terrence Ross (30), DeRozan (6), James Johnson (12)
PF: Amir Johnson (18), Patrick Patterson (30)
C: Jonas Valanciunas (32), Amir (10), Chuck Hayes (6)
Leaving the following minutes breakdown:
But that is a perfect scenario. The reality is, games will be missed due to injury, and our backups and third stringers will see an increased role. So the next step was estimating the number of games to be missed. This is a complete crapshoot of course. But to get a good guess, I took the average number of games played over the past 3 seasons (yes, correcting for the 66 game season in 2011-12) and used that as the number of games played for the primary rotation. Then I assigned the backup minutes accordingly to fill in the gaps (assigned entirely to 3rd stringers, to be conservative). The result can be seen below.
The third strings like Fields and Hansbrough end up with a lot of minutes due to my assigning them all the injury minutes. I built this using WS at first, and this was the most conservative approach (and the simplest) so I stuck with it. You'll be able to see the impact in the win totals in each section. BeBe and Bruno I just used for cleaning up minutes from rounding elsewhere - they contribute 0 wins in all the models (except xRAPM, where they contribute average wins).
So, using last year's WS/48 numbers, and the total minutes I approximated above, we can get a WS projection for this coming season.
Total WS predicted: 53 wins. 53-29 record. Very nice.
Again, using last year's WP/48 numbers, and the total minutes I approximated above, we can get a WP projection for this coming season.
Total WP predicted: 55 wins. 55-27 record. Even nicer.
A little more complicated this one. First, we grab all the offensive and defensive xRAPM numbers for each player (that's an adjusted plus-minus in terms of impact on the team regardless of teammates, opponents, team effects). They are presented in a points per 100 possessions impact. So a net impact on offense, and one on defense. We then apply to these the pace the Raptors played at, so get each player's impact over the length of a game (48 minutes). The Raptors played at a 91.5 pace last year. So that suppresses the numbers somewhat. Then you take that points/48 minutes number, and add the impact to the league average points per game (101). That gives each player an ORTG and DRTG based on xRAPM. Then we can apply pythagorean wins to predict the wins generated by each player per game.
Total RAPM wins predicted: 47 wins. 47-35 record. Not as nice as the other two. But still a very nice low water mark.
Final prediction: I'll just use a simple average of the three systems. That means 51-31. 50 wins here we come.