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Stats Corner: San Diego State and their #1 Ranking

Review what the NET ranking system is

San Diego State v Houston Photo by Jeff Gross/Getty Images

Mountain West Conference, we have the number 1 ranked team basketball team. Congratulations to San Diego State for being ranked first in the nation under the NET system. As the conference portion of the basketball season starts, we are going to review how the NET system works before spending the next few Stats Corner looking at the individual team rankings.

Last year the NCAA used the NET Ranking to offer suggestions, as an “organizational piece” (NCAA official website quote), to help the committee decide who gets into the tournament. Other quotes from the NCAA described the NET ranking as “not a deciding factor” and “not going to determine if a team is in or out of the bracket.”

Dan Gavitt, NCAA senior vice-president for basketball, said that no decision has been made to reveal how NET works to the public. He did say that “formulas are archaic like the RPI was” and artificial intelligence algorithms are not easy for people to understand. That could also be read as “we are smart, you are stupid, don’t challenge the Forces that be.” Because we like to challenge the Forces that be, and we don’t like being told just to trust the people who set up a system to keep Boise State, Fresno State, and UCF out of the college football playoffs, we are just going do the opposite of what we were told.

NET is an acronym for NCAA Evaluation Tool and is a predictive-learning model. Predictive learning is a method of machine “learning”. The machine attempts to create a model by simulating different outcomes in situations. These situations are numerous, with the computer comparing expected results with actual results and adjusting as needed. Think of it as a small child trying to solve a problem. They try something it doesn’t work, so they adjust based on what they have just observed and experienced. NET is not learning in the human sense of the word, the purpose is to use the known effects of actions and create planning operators, or in non-geek, it is more like discovering which models produce the most realistic results by comparing them to actual results. It is not artificial intelligence; the robotic overloads are not coming (yet).

The NCAA was gracious enough to release 5 items which will influence the model. The most influential is Team Value Index. In short, results: did you win or lose, where you did play (i.e. home or away), and who did you play, strength of schedule does play a role. Team efficiency or points scored and allowed per possession for both offense and defense. Run and shoot games (no defense) who have high points scored for offense but could have a lower points per possession than a slow and grind game. The major significance for this item is the predictive element. The network will be able to predict scores and efficiency of team, not players, and then compare expected to real results, and then adjust. Wins, obviously important, will be considered “not-insignificant” whatever that means. However, only wins against Division 1 schools will count. Playing Division 2 cupcakes towards the end of the season like SEC football, will not help the NET score. Last year, N.C. State played 9 non-conference teams who were ranked below 200. After NC State was left out of the NCAA tournament last year, Athletic Director Debbie Yow release a statement which included “The NCAA NET calculation was introduced as the new ranking system to replace the RPI as the primary sorting tool for evaluating teams…Based on the metrics sited above that the NCAA indicated they would use to evaluate team performance, we are disappointed for our athletes, coaches and fans that our total body of work was not rewarded with selection to the NCAA Tournament.” TCU was also left out and coach Jamie Dixon said during his press conference “You look at the NET, there are six teams that had a lower NETs than us. They created a tool and talked about it and then there are six teams there that are lower than us that are in.” The big dogs don’t like being left out.

The five influential items for the NET ranking are: Team Value Index, Team Efficiency, Scoring Margin (maxed out at 10 points), Wins, and Road vs. Home venues. Once again, only games against Division 1 teams count towards the NET ranking. The NCAA has created team sheets for each team, see all team sheets here. These contain all the information used to create the rankings. Since there is not an algorithm, there is no formula for humans to recreate the rankings; the predictive-learning model does it and we cannot verify the results. The team sheet divides the games into 4 quadrants for rankings. Quadrant 1 is the top opponents. If the team is playing at home, opponents who are ranked 1-30 are included, neutral courts are 1-50, and away from home includes 1-75. For quadrant 2 it is home 31-75, neutral 51-100, and away 76-135. Quadrant 3 is home 76-160, neutral 101-200, and away 136-240. Quadrant 4 is the easiest opponents and home over 161, neutral over 201, and away is over 241. For example, Utah State is currently ranked 73rd, therefore when the Aztecs play in Logan it is considered at Q1 game for San Diego State, but when the Aggies come to visit the game is now Q2.

Interestingly, which team is played does not factor in the item. For this item, a road win is more valuable than a home win, regardless of who you are playing. A victory counts +1.4 on the road, +1 on a neutral court, and +.6 at home. Losses will count -.6 on the road, -1 on the neutral court, and -1.4 at home. So if you are going to play a cupcake make sure it is a D1 team and do it on the road, it counts more. Scoring margin: remember when BCS models included “style points” and a team would run up the score to look better, well it is back to a point, actually to 10 points. Win big, and it helps, however, all double-digit wins are the same as the points are capped at 10, right now the Aggies are loving this and the Rebels are complaining. This also is a big predictive model. Gavin relayed “Ten was the number that was the most optimal to getting the most level of accuracy without going so far that we started to influence the behavior of coaches. In other words, the NCAA doesn’t like when teams pour it on just improve the chances of getting into the NCAA tournament. Overtimes games are given special treatment, the winning team is automatically assigned a +1 score and the losing side -1 score no matter what the final score is. This is to show that at the end of 40 minutes it was a close game and not penalize a team who loses in overtime after being tied at the end of the regulation game.

As confusing as this is, the Aztecs have currently mastered the model. Unfortunately, the Aggies are the only Q1 game left of their schedule, but that is a topic for another week.