The NCAA announced on Wednesday that they have eliminated the RPI as the primary sorting tool to be used during the NCAA tournament selection process.
This system was approved in late July and included input from not only the NABC and the Division I men’s basketball committee but “top basketball analytics experts” as well as Google Cloud Professional Services.
“What has been developed is a contemporary method of looking at teams analytically, using results-based and predictive metrics that will assist the Men’s Basketball Committee as it reviews games throughout the season,” said Dan Gavitt, senior vice president of basketball for the NCAA. “While no perfect rankings exist, using the results of past tournaments will help ensure that the rankings are built on an objective source of truth.”
The new metric, which is called the NCAA Evaluation Tool (or NET), will rely on “game results, strength of schedule, game location, scoring margin, net offensive and defensive efficiency, and the quality of wins and losses.”
There is a lot to take in here, and to avoid getting to into the weeds when it comes to the nerdy part of the analytics, this is what you need to know: This metric will be impacted both by the predictive nature of metrics like KenPom as well as purely results-based metrics like the RPI. The difference is subtle but important. Predictive metrics are generally based on things like efficiency and are not as impacted by something like a buzzer-beater going in and changing the outcome. Results-based metrics are, obviously, as they change the result of the game even if it shouldn’t impact how good you think either team is.
Why is it important to include both?
Because we want those buzzer-beaters to matter, right? That’s why it’s worth getting so excited when they go in. Winning needs to matter, otherwise there’s no point in playing the game. But losing a nail-biter is not the same as getting whipped by 25. That should matter, too. I’m glad both will be factored in.
It’s also worth noting here that while scoring margin is factored in, it’s impact will be capped at 10 points to prevent running up the score.
If there is one concerning element about this new metric, it’s that the NCAA is as of yet undecided on whether or not to peel back the current and show us how the sausage is made, so to speak. Gavitt told CBS Sports that the algorithm will be powered by artificial intelligence and that it will not be “readable”, and that should be mildly concerning. Even with the archaic formula for the RPI public, it took years to convince the NCAA that a new metric was needed. A public understanding of how the numbers that will play such a pivotal role in determining seeding and inclusion into the biggest sporting event in the United States should be of paramount importance for the NCAA.
And even if there is no precise formula that can be laid out, the inputs that will be used to create the model should be clearly and precisely defined. When one team is left out of the NCAA tournament because their NET is 20 spots lower than the last at-large team in the field, we need to know why it’s lower. Anything less than total transparency here is wrong.
College basketball coaches should have an understanding of the best way to put together a schedule to maximize their chance to get into the tournament. Smart coaches figured out how to game the RPI — don’t play teams with astronomically high RPIs, find all-reward-no-risk road games against elite (top 10 or 15) teams and load up on teams that should do well in the best mid-major leagues. This is their livelihood. They should be given the chance to schedule the right way.
I also believe that the NCAA should retroactively run NET on the past two or three seasons and compare the results to the teams that were put in and left out of the NCAA tournament field. Frankly, it would be silly not to. That’s the easiest way to figure out where there are bugs in the system, and it’s the best way for everyone to understand how this thing will play out.
They listened to us by eliminating the RPI as the metric used for sorting, and hopefully they’ll listen to me now.
Speaking of sorting, this is the second straight season where the NCAA has made a major change in the way that they determine tournament resumes. Last year, the standard of top 50 RPI wins was eliminated, instead reverting to a quadrant system than controlled for where the game was played. Put another way, a top 30 RPI win at home was equivalent to a top RPI 75 win on the road; both were labelled Quadrant 1 wins.
This year, the selection committee will use the same sorting method, only the groups will be based on NET, not the RPI. In other words, a top 30 NET win at home now equals a top 75 NET win on the road.
In addition, team sheets were also adjusted to include both predictive and results-based metrics that are commonplace in college basketball coverage, from KenPom and Sagarin rankings to the KPI and ESPN’s results-based metric, strength of record.
What will be fascinating is to see how all of this is going to impact the way that the brackets get put together. For the first time, I’m actually excited to see the first peak at the rankings, when the NCAA unveils the top four seed lines in mid-February.
“The NCAA Men’s Basketball Committee has had helpful metrics it has used over the years, and will continue to use the team sheets,” Gavitt said, “but those will now be sorted by the NCAA Evaluation Tool. As has always been the case, the committee won’t solely focus on metrics to select at-large teams and seed the field. There will always be a subjective element to the tournament selection process, too.”