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Hi all,
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......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
(Sorry about the formatting there just can't get the table embedding to work).
So, I did that for every team. And ended up with these rankings.
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 MIA 103.6 105.3 36.2
9 DET 103.7 106.5 33.4
10 CHA 100.0 102.6 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
Pos West ORTG DRTG Wins
1 GS 108.9 97.4 68.5
2 SA 108.1 97.8 66.5
3 DAL 109.4 103.4 56.8
4 MEM 105.1 101.0 52.4
5 OKC 106.9 102.8 52.2
6 LAC 107.7 103.6 52.2
7 NO 106.5 103.6 49.2
8 HOU 104.0 101.2 48.9
9 SAC 103.8 102.0 46.4
10 PHX 98.6 98.1 42.5
11 UTA 101.7 102.4 39.0
12 POR 101.1 101.9 38.6
13 DEN 101.6 108.0 24.1
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 doesn't move them 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 in the lottery. A 7th 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.
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......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
(Sorry about the formatting there just can't get the table embedding to work).
So, I did that for every team. And ended up with these rankings.
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 MIA 103.6 105.3 36.2
9 DET 103.7 106.5 33.4
10 CHA 100.0 102.6 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
Pos West ORTG DRTG Wins
1 GS 108.9 97.4 68.5
2 SA 108.1 97.8 66.5
3 DAL 109.4 103.4 56.8
4 MEM 105.1 101.0 52.4
5 OKC 106.9 102.8 52.2
6 LAC 107.7 103.6 52.2
7 NO 106.5 103.6 49.2
8 HOU 104.0 101.2 48.9
9 SAC 103.8 102.0 46.4
10 PHX 98.6 98.1 42.5
11 UTA 101.7 102.4 39.0
12 POR 101.1 101.9 38.6
13 DEN 101.6 108.0 24.1
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 doesn't move them 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 in the lottery. A 7th 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.
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