Basically, APP is a formula I use to bottle a player’s performance into one number without the bias of team pace. Using this formula, Chris Paul performed just slightly better than LeBron James did during the season so he would be my vote for MVP. One big criticism about this method of analysis is that it doesn’t factor wins into how well a player is perceived to have done for the year. But I have several arguments against that critique: Firstly, basketball is a team sport and everyone on the team – from the franchise player to the scrub at the end of the bench – is attributed each individual win or loss. Sure, some players are so good they are capabale of “taking over a game,” but that dominating performance would most definitely appear in his individual stats which is what APP is based in.

My second argument is really a question. Does the team with the most wins at the end of the regular season have the MVP? What if it’s consisted of two Top 10 players? What if it just has above average players at each position? It’s hard to determine who gets what portion of the win pie depending on what type of team has been constructed (although it’s a fascinating subject I’d like to try to figure out someday ). Even voters have proven that, while extremely relevant, wins aren’t the only deciding factor in who should be MVP. Since the beginning of MVP awards in 1956, the NBA has awarded the MVP to a player on the best (or tied for best) team, determined by win percentage, 63% of the time. An MVP has even been picked from as low as the ninth best team (Kareem Abdul-Jabbar in 1976).

So wins don’t seem super-important to me. I figure that players who perform extremeley well, enough to be considered elite level and thus, an MVP candidate, will naturally be playing on these high-win teams, anyway. Though not necessarily. Anyway, here are the Top 10 players based on APP from the 2009 NBA Season. Using this method, Mr. Chris Paul would be holding the trophy rather than ‘Bronny. But, ah well.

Top 10 MVP Candidates (Ordered by Difference in APP)
#	Player	Team	Pos.	PPG	RPG	APG	FG%	3P%	FT%	APP
1	Chris Paul	G		22.8	5.5	11.0	.503	.362	.868	36.74
2	LeBron James	F		28.4	7.6	7.2	.489	.344	.780	36.69
3	Dwyane Wade	G		30.2	5.0	7.5	.491	.317	.765	34.33
4	Dwight Howard	C-F		20.6	13.8	1.4	.572	.000	.594	29.59
5	Tim Duncan	F-C		19.3	10.7	3.5	.504	.000	.692	27.71
6	Dirk Nowitzki	F		25.9	8.4	2.4	.479	.359	.890	27.04
7	Chris Bosh	F-C		22.7	10.0	2.5	.487	.245	.817	26.81
8	Al Jefferson	F		23.1	11.0	1.6	.497	.000	.738	26.77
9	Pau Gasol	F		18.9	9.6	3.5	.567	.500	.781	26.49
10	Brandon Roy	G		22.6	4.7	5.1	.480	.377	.824	26.39

One thing I’ve taken note of recently is positional averages. Basically, I desired finding the league average APP for guards, forwards and centers. Now, if a player was listed as playing multiple positions (care of Basketball Reference), his APP would be used for both positions. Then, I would find the difference between a player’s APP and his positional average APP. Once again, if a player was a multiple-position baller, I would rate him against the higher of the two positional averages. Essentially, I wanted to experiment to see if comparing players against just their positional counterparts would change anything from a blanket comparison against every NBA player. First, the positional APP averages and then the results:

Random Table Style: 1979 Chicago Bulls

Okay here are the results after ordering by difference in APP above positional APP:

Top 10 MVP Candidates (Ordered by Difference above Positional APP)
#	Player	Team	Pos.	PPG	RPG	APG	FG%	3P%	FT%	APP	Pos. APP	Diff.
1	Chris Paul	G		22.8	5.5	11.0	.503	.362	.868	36.74	9.93 (G)	26.81
2	LeBron James	F		28.4	7.6	7.2	.489	.344	.780	36.69	10.31 (F)	26.38
3	Dwyane Wade	G		30.2	5.0	7.5	.491	.317	.765	34.33	9.93 (G)	24.40
4	Dwight Howard	C-F		20.6	13.8	1.4	.572	.000	.594	29.59	10.31 (F)	19.28
5	Tim Duncan	F-C		19.3	10.7	3.5	.504	.000	.692	27.71	10.31 (F)	17.40
6	Dirk Nowitzki	F		25.9	8.4	2.4	.479	.359	.890	27.04	10.31 (F)	16.73
7	Chris Bosh	F-C		22.7	10.0	2.5	.487	.245	.817	26.81	10.31 (F)	16.50
8	Brandon Roy	G		22.6	4.7	5.1	.480	.377	.824	26.39	9.93 (G)	16.46*
9	Al Jefferson	F		23.1	11.0	1.6	.497	.000	.738	26.77	10.31 (F)	16.46**
9	Pau Gasol	F		18.9	9.6	3.5	.567	.500	.781	26.49	10.31 (F)	16.18

* Roy’s difference out to 4 digits is 16.4623
** Jefferson’s difference out to 4 digits is 16.4586

While nothing super crazy happened with this experiment (the same 10 players appear in both methods) there were a few minor shake ups. Brandon Roy moved up from 10th spot to 8th, while Pau Gasol moved down to 10th. The All-NBA teams are a complete wash … regardles of which method you use all three teams come out with the same results. Here they are though, in case you don’t believe me:

APP – Adjusted Pace Performance
A weighted formula for combining everything a player does on the basketball court into one number. This number is then adjusted based upon the players’ team pace to provide a balanced way to compare players’ performances.

APP for an average player in the league for 2009 Season: 9.88
Stats through 4/15/09