2025 Draft Class Difference of Top100 vs All Opp

There were 355 Div 1 NCAA men’s basketball teams in 2025. Using Barttorvik data, the data was filtered to top 100 opponents teams, which would include most NCAA tournament teams.

This filters the prospect data against better competition as the NCAA team schedule can vary greatly depending on conference.

As expected, the prospect stats filtering for only top 100 opponents led to worse advanced statistics.

The prospects outliers in the draft class that had a large % decrease or increase in bpm (overall catchall measure) are identified below to determine if warrants consideration in evaluating the prospect.

Large Top100 bpm Decrease

1. Cedric Coward

Coward’s data in 2025 is an incredibly small sample size compared to other prospects. Coward only played 6 NCAA games and only 1 of those games was vs top 100 opponents. The top100 game went poorly for Coward. Considering the small NCAA game sample size, the level of competition and the poor outcome in the top100 game, there aren’t any positive conclusions that can be parsed from Coward’s 2025 NCAA data. Further analysis in Coward’s previous NCAA years in 2023 and 2024 will be necessary.

2. Egor Demin

Demin played 33 NCAA with 26 of them vs top100 opponents. It’s quite remarkable the 7 non top100 games increased Demin’s bpm by that large an amount.

Notable stat differences when filtering for top100 opponents:

1. bpm decreased by 43.6%: 5.05 => 2.85

2. Assist to turnover ratio decreased by 12.4%: 1.86 => 1.63

3. Offensive rebounding % decreased by 46.2%: 1.3% => 0.7%

4. Block % decreased by 47.1%: 1.7% => 0.9%

5. Steal % decreased by 20%: 2.5% => 2%

6. dunksmade/40 decreased by 24.5%: 0.53 => 0.40

7. 2 point % decreased: 0.55 => 0.53

8. 3 point % decreased: 0.27 => 0.22

Demin’s appealing advantages (playmaking, wing size at 6' 8.25'') were dampened by top 100 opponents.

1. His playmaking, while still a positive, was worse (ast/tov)

2. The traditional wing role player stats (ORB_per, blk_per, stl_per) decreased significantly

3. Already low scoring efficiency stats were much worse vs top 100 opponents.

Overall the top100 opponent data suggests Demin’s NBA potential as a lead playmaker, role-playing wing or scorer need to be questioned.

3. Thomas Sorber

Sorber played 24 NCAA games with 13 games vs top 100 opponents.

Notable stat differences when filtering for top100 opponents:

1. bpm decreased by 22.5%: 7.56 => 5.86

2. 2 point % decreased: 0.60 => 0.53

3. 3 point % decreased: 0.16 => 0.12

4. rim scoring % decreased: 0.69 => 0.59

5. dunksmade/40 decreased by 24.1%: 1.12 => 0.85

6. overall counting stats/40 (oreb/40, dreb/40, ast/40, stl/40, blk/40, pts/40)

Overall, Sorber top100 opponent data suggests a potential NBA scoring convern especially in the paint (rimmade/(rimmade+rimmiss) and dunksmade/40) vs better competition. Sorber measurements (6'9.25'' height, but a 7'6.00'' wingspan) means that he is slightly undersized as a C at the NBA level, especially if the 3 point shot doesn’t improve (only 16% vs all opponents and 12% vs top100 opponents). While the overall counting stats decreased, he still provided good role-player production vs better competition.

Large Top100 bpm Increase

1. Liam McNeeley

McNeeley played 27 NCAA games with 19 top100 games.

Notable stat differences when filtering for top100 opponents:

1. bpm increased by 76.9%: 1.08 => 1.91

2. assist to turnover ratio increased by 13.1%: 1.22 => 1.38

3. 2 point percentage decreased by 6.8%: 0.44 => 0.41

While McNeeley had the largest bpm % increase when accounting for top100 data, since his overall bpm was so low to begin with at 1.08, this only increases to 1.91. The playmaking improved (ast/tov), but the already low 2 point percentage dropping even lower is concerning. Overall, the bpm increase is not noteworthy.

2. Ace Bailey

Bailey played 30 NCAA games with 22 games against top100 opponents. Paradoxically, Bailey’s bpm greatly increased when removing the 8 non-top 100 games.

Notable stat differences when filtering for top100 opponents:

1. bpm increased by 43.5%: 3.70 => 5.31

2. assist to turnover ratio increased by 12.9%: 0.62 => 0.70

3. assist % increased by 14.5%: 8.30 => 9.50

4. steal % increased by 11.8%: 1.70 => 1.90

Bailey’s large bpm increase vs top100 opponents was due to the playmaking (ast/tov and AST_per) and defensive stats (stl_per) and not scoring efficiency (TS_per, twoP_per, TP_per) were relatively constant. However the fact that Bailey had better non-scoring statistical output vs better competition is potentially noteworthy to consider Bailey’s potential as a role-playing wing (6' 7.50'' height and 7' 0.50'' wingspan)

3. Tre Johnson

Johnson played 33 NCAA games with 26 games vs top100 opponents.

Notable stat differences when filtering for top100 opponents:

1. bpm increased by 36.1%: 3.68 => 5.01

2. off rebound % increased by 18.2%: 1.10 => 1.30

3. assist % increased by 7.9%: 16.50 => 17.80

4. dunksmade/40 decreased by 12.5%: 0.24 => 0.21

5. true shooting % decreased: 55.67 => 53.90

Similar to Bailey, Johnson’s bpm increase was not driven by scoring efficiency (TS actually decreased), but from playmaking and role playing statistics. The Off rebound % increased, but is still low for the large SG dimensions of (6' 4.75'' height and 6' 10.25'' wingspan).