I was just using KenPom since everyone is most familiar with his methodology. And yeah, I agree about recruiting rankings, the source of uncertainty is completely different.
Yeah, that’s kind of my point. The point estimates are closer together and the margins of error (although KenPom doesn’t report them) are sure to be larger because the teams are just more volatile game-to-game. So the further down the rankings you go, the less certain you can be that a higher ranking actually means a better team.
Why are teams more volatile game-to-game further down the rankings? KenPom’s site has the actual Adj Eff numbers and also “luck” which is essentially volatility in performance versus results.
I just don’t follow why KenPom is somehow less effective in different ranges of the rankings.
I was making an assumption there that good teams mostly play well, bad teams mostly play badly, and middle of the road teams tend to bounce between good and bad games. I don’t actually have anything to back that up. I don’t think luck is at all the same as what I’m talking about. Luck is about random swings in teams’ past results, not the statistical uncertainty of making inferences about future results based on a limited sample of past results.
I don’t know if what I’m about to suggest is accurate or merely an impression, but it’s the presence of Kenpom and other data-driven methodologies that have led the NCAA to adopt its own, so my impression is that whatever the stated purpose, there’s a good degree of venn-diagram-like overlap in what these things do and to what end.
The overlap should be that predictive metrics better help understand how good a win or loss is. You need something that decides how good one team is relative to another to then evaluate a resume on. A team can be top 25 good but have a bad resume (ex penn st this year). You would use the predictive metrics to justify that wisconsin losing on the road at penn st isn’t a terrible loss. But by the same tool you can say that penn st’s resume isn’t good enough to get into the tournament. You still evaluate seeding teams on the same results based system you had before, but now you don’t over value a road loss to a decent team just because that team has a bad overall record.
The Committee uses NET in a drastically different manner than people use Kenpom. They’re not just seeding 1-36 straight up with NET. They use NET to compare team resumes to each other.
I really can’t say for sure, but I think it’s important to think about and not just a sports thing. People are trying to measure things and that’s good, and so much better than before we had the ability to work with big data, but sometimes if you pick at data-driven conclusions out there in the working world you discover that they are really just hottakes because somebody made a dump assumption about data.
My intuition tells me that you are correct that there is more volatility as you move down the rankings. There are 350 division 1 schools. We can group them in 7 buckets of 50 teams. What feels like the safer bet? 1 vs 50 or 301 versus 350?
Edit: actually the more I think about it there is probably more consistency at the extremes and the volatility is in the middle. The worst basketball team out of 350 is probably as extremely bad as the #1 team is probably extremely good.
What I am waiting for is: the B1G will schedule us to play every other day for 2 1/2 weeks to make up games and then demand we play in the B1G tournament and coach Howard says “wait a minute, I am not ok with this schedule, it’s not in the best interests of our student/athletes”. And see what happens!
OSU received considerations in football can we expect/demand them as well?
F around and find out. What are you going to do if one of your two biggest cash cows opts out in the name of student-athlete health? You won’t do a thing