Scoring is the best it's been in men's college basketball in 30 years. Why?
Questions with questionably easy answers
Around this time every year, we get a rush of articles, all with a similar title: what happened to college basketball? None of these articles specify that they mean men’s college basketball, but it’s implied. This past year, it was more popular than ever, as women’s college basketball began to easily outpace the men’s side in Final Four TV ratings. The WBB title game had four million more viewers on a Sunday afternoon than the MBB title game had in primetime between the two best teams of last season.
I won’t argue with any qualms one might have about college basketball’s marketing prowess or lack thereof. I’m on record in our predictions column as stating the networks seemingly have no interest in promoting the sport beyond five or six programs.
As evidence, last year’s champion, UConn, played six games against ranked in the AP Poll at the time of the game. Five were played on ESPNU, ESPN, ESPN2 (2x), FS1. One - a February 17 battle with Marquette - got on national TV (FOX). Meanwhile, Iowa, the biggest draw in women’s basketball, played four games against top 25 teams on FOX or NBC before the conference tournament even began. The actual champion, South Carolina, played three games on ABC and got standalone slots on main ESPN (who the SEC’s contract is with) eight times.
So, sure, men’s college basketball has a marketing problem. This blog isn’t qualified to break down why that’s so. One thing college basketball does not have is a scoring problem. I wrote about this on Monday and it’s only gotten more solidified since:
But I do think it’s worth listing out. This is a copy of a post I’ve made on the various socials this week, just updated for the time of writing (Thursday evening). All stats through 10 days unless otherwise noted:
Best offensive efficiency (1.043 PPP) on record through 10 days.
Highest eFG% (50.3%).
Lowest TO% (18.1%).
T-highest OREB% (30%) since 2015-16.
Highest 2PT% (51.1%).
Highest FT% (70.3%).
Lowest non-steal TO% (8%).
Highest PPG (74.0 PPG) since 2017-18 and the second-highest since 1994-95.
This is a continuation of a trend that began last year, one that saw the then-highest offensive efficiency in a full season (1.052 PPP), highest 2PT%, highest PPG since 2017-18, highest Steal% since 2005-06 but second-lowest TO% ever. The quality of basketball on the average night is the best it’s been in arguably 30 years, based on the stats we have available. At very minimum, the sport is its most conventionally attractive since 2017-18, when Villanova ruled the earth and offense was back in style.
If you create a rudimentary watchability rating - PPG times offensive efficiency - this is the most purely watchable start to a college basketball season in the 29-year era of KenPom. Even going by full season numbers, the rudimentary watchability score says that this is the best, most watchable college basketball has been since 1991. As a Young Person, I am told that 1991 was a pretty good time.
The sport goes in cycles, but this one seems here to stay for a bit. As noted by Ken Pomeroy, the first week of the season gave us the third-highest single day of scoring seen in 30 years:
I like finding answers, or at least attempting to find answers, for why this might be so. So: I have six(ish) theories we’ll try and uncover here. These are in rough order for how responsible I think they might be. Also: subscribe, because only the first one of these is free.
On to #1.
THEORY #1: Continued fallout from the revised block/charge rule, as evidenced by the lower-than-ever unforced error rate.
This is my personal favorite theory and the one that’s easiest to point to. After the 2022-23 season, the NCAA seriously reworked the block/charge rule, which more or less eliminated the Jaylin Williams play. Since the moment this got taken out, there’s been noticeable statistical effects, which Ken outlined last fall:
We can get more granular, though. And when we do we find out that the decrease in non-steal turnovers probably understates the decrease in charges. Modern play-by-play listings record offensive fouls, and the offensive foul is indeed eligible for the endangered species list. Here are offensive fouls5 per game over the past four seasons (for which we have more extensive play-by-play data):
Season Off fouls/game Flop T's/game
2024 2.77 0.03
2023 4.06 0.36
2022 4.33 0.05
2021 4.50 0.02Offensive fouls are down 32% from last year and 38% from the 2021 season. That’s it. That’s the story of the start to this season.
All I can add is that this has continued to be the case. After sitting at a non-steal TO% of 8.4% last year through ten days, it’s fallen even further to 8% despite TO% as a whole being pretty stable. This has coincided with a small, but noteworthy 1.2% rise in Free Throw Rate. Basically, defensive fouls are up a tad, but it’s leading to better offense and a much more scoring-friendly game.
A good example is this charge from the 2022-23 season early on. Chris Beard’s defense was well-known for a vast, vast majority of his career as being really excellent at taking charges because of how the rules were formerly written. In November 2022, this is an offensive foul:
In November 2024, because this is no longer allowed, Beard’s secondary defender must now challenge the shot instead of attempting to draw a charge that won’t come under these rules. The primary defender instead must attempt to draw the charge, and those are falling rapidly, too. This has led to Beard’s first full season at Ole Miss producing far and away the lowest TO% of his career despite his second-highest Block%. This year likely won’t be much different.
You can even see Sean Pedulla point in the direction of a charge, hoping for a call that doesn’t come. Even with a full year of adjusting, you can still expect to see this as offensive fouls continue to dip and blocking calls rise. It’s a step in the right direction for a sport that had swung too far in favor of the charge.
BEHIND THE WALL ($): Coaches are younger, but players are older, plus other theories