FanPost

An Early Projection for the Raptors 2015-16 Season

Kim Klement-USA TODAY Sports

With most of the big moves of free agency having settled down, and even a lot of role players off the market with a few notable exceptions, I thought I'd take a look at how the season projects out.

I'm using RPM here, ESPN's player impact statistic, and then using net points as a predictor for winning percentage using Pythagorean wins.

Quick walk through: first, I took the RPM value of every player in the league from last season. Didn't adjust them in any way, didn't project improvement or decay. Just last year's value.

Then I placed all the players on their current teams based on the off-season so far, and all the rumours I could track. Any players still unsigned I've left off completely for now. Most of them are either low impact or low minutes players so they shouldn't skew this too much.

I made no adjustments to minutes, or guesses on how playing time will change. I simply listed the players and their MPG from last season for each team. I tried using total minutes, but it became too difficult to adjust for players who missed large chunks of last season. By using MPG that fixes most of those issues (though not all, I'll touch on this later).

Then, having compiled all the players, their RPM and their minutes based entirely on last season, I created a MPG-weighted average RPM for every player on every team. This creates an automatic adjustment - I'm not taking an approach like WS and simply summing the WS of new players, as if their minutes need to be adjusted those numbers will obviously change. This approach simply assumes that if a team, based on their total MPG from last season, has too few minutes to go around, every player will have their minutes adjusted downwards proportionally. Probably not true, but there aren't any instances where this effect is extreme, so I think this is a reasonable approach.

This yields, for each team, an average RPM (I actually did dRPM and oRPM separately just for additional information). Those RPM values are then multiplied by 5 (as at any time, a team will effectively have 5 of their average player on the floor impacting the game). That value is added (or subtracted, for dRPM) from the league average ORTG last year (103) to give a predicted ORTG and DRTG for the team.

Then using the Pythagorean approach, win percentage is calculated (I used a power of 14.5, though 16 has been used as well). Multiply by 82 and you've got your number of wins.

Here is what the Raptors looked like, for example.

Player Team |
MPG |
ORPM |
DRPM |
O-impact |
D-impact
DeMarre Carroll TOR 31.3 0.96 -0.25 30.05 -7.83
Bismack Biyombo TOR 19.4 -2.58 1.59 -50.05 30.85
Luis Scola TOR 20.5 -0.32 0.87 -6.56 17.84
Cory Joseph TOR 18.3 0.04 0.96 0.73 17.57
Kyle Lowry TOR 34.5 2.55 1.27 87.98 43.82
James Johnson TOR 19.6 0.33 1.66 6.47 32.54
Patrick Patterson TOR 26.6 2.16 -1.82 57.46 -48.41
DeMar DeRozan TOR 35 0.21 -0.36 7.35 -12.60
Jonas Valanciunas TOR 26.2 -1.72 1.05 -45.06 27.51
Terrence Ross TOR 25.5 1.75 -3.77 44.63 -96.14
Lucas Nogueira TOR 3.8 -0.92 -0.34 -3.50 -1.29
Bruno Caboclo TOR 2.9 -1.43 -1.63 -4.15 -4.73
Total 263.6 125.335 -0.881
Per player average RPM 0.475 -0.003
Net impact per 5 players 2.377 -0.017
ORTG and DRTG 105.4 103.0
Win % 58%
Wins 47.7

So, I did that for every team. And ended up with these rankings. *** EDIT: updated to reflect completely forgetting the Lance Stephenson LAC/CHA trade the first time through (although somehow getting Barnes to the right team). Plus adding the impact of the Lawson deal for HOU/DEN. ***

Place |
East |
ORTG |
DRTG |
Wins
1 ATL 106.4 98.9 60.8
2 CLE 107.7 101.6 57.5
3 CHI 104.8 101.5 50.3
4 TOR 105.4 103.0 47.7
5 WAS 101.1 99.6 45.4
6 BOS 104.1 102.7 45.2
7 IND 103.2 104.0 38.7
8 CHA 101.1 102.2 37.8
9 MIA 103.6 105.3 36.2
10 DET 103.7 106.5 33.4
11 MIL 97.7 102.2 28.0
12 ORL 100.5 107.0 23.6
13 BKN 99.3 107.2 20.2
14 NY 100.2 109.9 16.9
15 PHI 94.6 106.6 12.3
Place West ORTG DRTG Wins
1 GS 108.9 97.4 68.5
2 SA 108.1 97.8 66.5
3 HOU 108.5 102.2 57.7
4 DAL 109.4 103.4 56.8
5 MEM 105.1 101.0 52.4
6 OKC 106.9 102.8 52.2
7 NO 106.5 103.6 49.2
8 LAC 106.4 103.9 48.1
9 SAC 103.8 102.0 46.4
10 PHX 98.6 98.1 42.5
11 UTAH 101.7 102.4 39.0
12 POR 101.1 101.9 38.6
13 DEN 97.4 106.5 17.7
14 LAL 100.9 110.4 17.5
15 MIN 98.6 108.2 16.9

Those results seem reasonable to me.

Some corrections I thought to make (they are not implemented above, that's the raw data there). Paul George returning to Indy using his RPM and MPG from 2013-14 would mean a jump of 7 wins, up to 5th right behind TOR. Chris Bosh returning to his 2013-14 RPM and MPG would mean a 3 win jump for MIA, which only moves them one spot up the standings unless you don't do the Paul George adjustment. Kevin Durant doing the same would add 4 wins to OKC's total, moving them up one spot ahead of MEM. I didn't adjust for Melo, as he had a reasonable number of games and minutes and had his usual impact last season.

So, a couple promising things for Raptors fans: first, the Raptors project to have a top 10 offence again. And they project to improve to a league average defence. Hopefully they can make even larger strides based on improved systems, but it is heartening that purely on the strength of the personnel they are looking to make a 6-7 spot jump defensively. And it appears that the prediction of a struggling Raptors offence might be a little premature.

Another very promising consideration: the Raptors own the less valuable of the Knicks and Nuggets first round picks this coming draft. And this system places those two teams 3rd and 7th 5th (!) in the lottery. A 7th 5th overall pick would be a pretty nice return for Andrea Bargnani.

I hope you liked this little early projection game. I tried to keep my own assumptions and biases completely out of it. Any questions are very welcome.