Oklahoma City Thunder: After Two Games

All of the stats have been updated through last night’s games, and the Oklahoma City Thunder are officially dead last—30th out of 30 teams in offensive efficiency at 87.6.

Offensive efficiency is how many points are scored per 100 possessions. The 100 possessions evens the playing field, so we can compare how efficiently we put the ball in the hole as compared to other teams. Well, now you know.

It doesn’t surprise me at all that we are last.  What surprises me is that we got there after two games. If you’ve been reading this blog, I’ve been pointing out that we lost a really good shooter last year when we traded Wally Szczerbiak, and essentially replaced him with cap room and hopeful optimism thinking that Green and Westbrook would fill the void.

We are playing predictably fast, with a pace factor of 93.6 possessions per 48 minutes, which equates to 11th fastest in the league. Our defensive rating is not so bad—99.9 points allowed per 100 possessions. That works out to ninth out of 30 teams.

Do the math here—we only score 87.6 points per 100 trips up the court, but we allow the opponent to score 99.9. We are a jump-shooting team that doesn’t shoot that well, and we have no post up game and no three-point shooting. I predicted 26 wins, and I will stick with that.

On a different subject, I’ve been using win score a lot lately and I hope I am not confusing anyone. It’s essentially a formula to measure the overall efficiency of a player and or a team.

The formula makes sense and here’s why: the formula itself is  Pts + Rebs + Stls + .5Ass + .5Blks - FGAs - TOs - .5FTAs - .5PFs / MP x 48 - Avg WS at player’s position. It takes all of the positive contributions of a player, subtracts the negative contributions, divides that by the minutes played, multiplies that by 48 minutes (to make a fair comparison), then subtracts the average win score for the players position.

When this is all done, you get a number that directly points to the player's positive or negative contribution to the team. A win score of .000 would be exactly equivalent to the average player at that position. If you had 5 players on your team that played all the minutes of every game, and they all had win scores of .000, that team would go 41-41 for the season. See how this works?

I’ve finished crunching the numbers for the Houston-OKC game last night and here’s the way they shake out:

Player     Win score        Player          Win score
Petro       2.89                Yao                -.53
Durant     7.51               T-mac             1.276
Watson   -3.29              Alston             -5.67
Green      -.67                Artest              -4.69
Collison  -7.90               Scola              .978
Wilcox    -24.3               Landry            14.76
Mason    -11.24               Barry              -3.54
Smith      -5.81
Westbrk. -7.71               Brooks             -1.83

See all those negative signs on the left column? Remember, they are the player’s win score above or below the average player at that same position. Only Petro and Durant exceeded the average players at their respective positions. Green just about made it, which is encouraging. Wilcox was—well, never mind.

For the enemy, Yao was above average—yet still below the overall contribution of Petro, if you can believe it. Scola had a nice game, and Landry was out-of-his-mind, insanely efficient.

When you contrast Landry’s contribution as the big man off the bench with our big men in Smith and Wilcox, you can see why we were outrebounded and sent the Rockets to the line 31 times.

Next up, a winnable game against a young T-wolves team.