Introduction to Adjusted Scoring Margin

This year as I was preparing to fill out an NCAA bracket, I wanted to use something beyond the typical, "I saw North Carolina play, and they look pretty good." As most people who follow sports statistics know, scoring margin is the best predictor of future success. However, with the college game there is so much of a variance in the strength of schedule that pure scoring margin seems like a silly way to evaluate teams.

I decided to use a formula to determine how well teams performed against a given team based on how the other teams performed against that team. For example, let's take a situation where Team A beats Team B 80-70. Let's also say that Team B typically scores 65 points a game and gives up 85 points per game. Team A scored 80 points, but since Team B typically gives up 85 points, Team A actually performed 5 points worse than the "average" college offense (Offensive Quotient). On defense, Team A gave up 70 points, but since Team B typically only scores 65 points, Team A again performed 5 points worse than the average college defense (Defensive Quotient). By just looking at the game, Team A would get a +10 scoring margin, but with the adjusted scoring margin, Team A gets a -10 rating (the offensive quotient plus the defensive quotient). If you add a team's adjusted scoring margin ("ASM") for each game and divide by the number of games you can rate the teams accordingly. Obviously, a team with a zero ASM is average (negative is bad and positive is good).

So I threw the numbers together and found that Kansas was the best team followed by Memphis, *cough* Duke *cough*, North Carolina, and UCLA. So it seemed that a Final Four with the four number one seeds was likely (not a surprise that the numbers would bear this out). Once the Final Four came around and there were four number one seeds left, I calculated the outcomes of the games for one of my buddies who has a hypothetical gambling problem (to quote the Sports Guy, "if gambling were legal"). I told my buddy that the hypothetical lines were off and to take a Memphis-Kansas final. He made a lot of hypothetical money. Then he had me look at the final and I discovered that Kansas should have been a one point favorite. At the time Memphis was at -2 (hypothetically). So I told him to take Kansas. Shortly thereafter Rose disclosed that he was hurt and the line changed to favor Kansas. My buddy hypothetically took Kansas anyways because I said that they were better. One Mario Chalmers shot later (it was a close game as the numbers demonstrated) and a good overtime for Kansas and my buddy made a lot more hypothetical money.

My eventual design was to translate this to the NBA because it would be more effective since each team plays every other team at least twice. So I use the same system only I calculate how many points an average team would score or give up based on offensive and defensive efficiency. This method is particularly helpful early in the season when strength of schedule varies much more. Using offensive and defensive efficiency to determine strength of schedule is better than W/L record because records are often based on flukes (I will tackle this topic independently some day). The NBA ASM allows me to tell who has played the most difficult schedules (both on offense and defense), the best offensive teams, the best defensive teams and an expected outcome of any game.

I will begin doing a team-by-team assesment of the 2007-2008 season based on these numbers very soon.