Nobody Is Recruiting D-II

Louisiana's Problem

Louisiana went 5-26. They scored 58.6 points per game and allowed 74.2, a margin of -15.5 per game. Their shot distribution:

The easy read is shot selection: 16.8 midrange shots per game at 27.6%, that's bad process. But across the league, shot selection barely correlates with winning (r=0.159). Shot accuracydoes (r=0.743). Louisiana isn't losing because they're taking the wrong shots. They're losing because they don't have the personnel to make them.

Here's where the shots are going:

Two players account for 26.2 shots per game. Neither shoots above 24% from three. The team's most efficient players, Daniel (0.910 pts/att) and Mosley (0.927), combine for just 13.6 attempts. The players generating the most value per attempt are getting the fewest opportunities. That's not a scheme problem. It's a roster problem.

What Louisiana needs is straightforward: someone who can shoot from the perimeter and handle a starter's workload. That player doesn't exist on this roster. But she exists in D-II.

The Transfer Gap

We matched 34 confirmed D-II, JUCO, and NAIA transfers who played D1 minutes in 2025-26 against their pre-transfer stats. The question: does the shooting translate?

D2 stats from school athletics sites and ESPN. D1 stats from ESPN play-by-play. Dashes indicate insufficient attempts or unavailable data.

PPG drops roughly 50% across the board, but that's a role problem, not a talent problem. These players are transferring to teams that already have scorers. They land on rosters where the starting spots are spoken for and settle into bench roles getting 6-8 shots per game. The volume drops. The shooting doesn't.

In aggregate, the 34 transfers shot 39.0% FG in D1 against a league average of 40.8%. Only 29% shot above the D1 average. But the standouts tell the real story: Chesterfield went from 54.2% to 56.0%. Callaway improved her three-point shooting from 35.0% to 39.8%. Bowman jumped from 47.3% to 53.3%. Rim finishers and real shooters translate. Volume scorers don't.

The teams that actually need scoring are not recruiting D-II. The teams that are recruiting D-II are burying those players on the bench. That's the gap.

Four Players

We identified four D-II players who fit the profile: high-efficiency shooters on winning teams, with the kind of production that suggests they could handle a D1 starter's workload.

All stats from the 2025-26 season.

Four different players, four different profiles.

Jones is the purest shooter. Her three-point percentage jumped from 32.3% as a freshman to 41.5% as a sophomore on 212 attempts. PBC All-Conference First Team. Her team went 31-3.

Rowland is the most complete. 18.4/6.1/3.8 with 1.7 steals per game. She tore her ACL three games into her sophomore year, came back, and led Colorado Mesa to the D-II Final Four. She put up 27 points and 10 rebounds in the semifinal. RMAC Freshman of the Year before the injury.

Baldessari is the volume scorer. 21.0 PPG on 44.7% shooting with 8.5 rebounds. First Team All-RMAC, D2CCA All-Region. She scored 45 points in a game against Nelson, the most by any player in D-II this season, and had six games of 30+. Her 2P% is 51.8%, which is elite.

Vaaler is the breakout. She went from 8.6 PPG as a freshman to 17.7 as a sophomore, with her minutes nearly doubling. She pulls down 7.9 rebounds per game and shoots 87.0% from the line. The three-point percentage (33.5%) is the lowest of the four, but she does it on 6.7 attempts per game.

The Math

We modeled each player on Louisiana with 15 field goal attempts per game. That's a starting role. That's the point. The 15 FGA come from redistributing the team's lowest-efficiency shot attempts. Each player's D-II shooting percentages are discounted for D1 competition: 2P% reduced by 3-5 points, 3P% by 4-7 points, free throw rate scaled down 15%.

Net PPG gain over baseline. Louisiana's current margin: -15.5. Conservative = 3P% −7, 2P% −5. Moderate = 3P% −4, 2P% −3. Optimistic = 3P% −2, 2P% −1.

Rowland and Baldessari are the highest-impact additions, for different reasons. Rowland gets there through free throws: she draws 5.3 FTA per game in D-II and converts at 85.7%. Baldessari gets there through elite finishing: her 2P% is 51.8%, and she also gets to the line at a high rate. Jones has the best three-point shooting but a lower free throw rate, which limits her ceiling in the model. Vaaler has the lowest pure scoring impact but brings 7.9 RPG, which translates to extra possessions the model doesn't capture.

To put the margins in context, here's the bottom of the Sun Belt this season:

Louisiana is an outlier. The gap between them and the next-worst team (Georgia State at -5.3) is 10 points. One player doesn't turn a 5-win team into a contender. But at the moderate projection, Rowland or Baldessari moves Louisiana's margin from -15.5 to roughly -9, which puts them in the range of teams winning 10-11 games. That's not a championship. It's a foundation.

And Louisiana is just the example. Every conference has a bottom third. Programs losing by 5-10 points per game with rosters that can't shoot from the perimeter. The argument isn't specific to one team or one player. It's structural: if your problem is personnel, and you're not looking at D-II, you're ignoring a talent pool that the data says can help.

Who Should Care

Programs ranked 8th-12th in their conference.You're not landing the top 50 portal players. Everyone knows who they are, and the bidding war for them is already over. D-II has proven shooters and complete players going overlooked. The supply of talent is real and the demand is low.

Mid-major coaches trying to compete up.If you're a Sun Belt or Conference USA program, you have the same access to the D-II portal as anyone. Scouting it seriously, with actual film review and shooting data, gives you an edge that costs nothing except attention.

NIL collectives looking for ROI. The math is the same as softball: the marginal win in D-II talent is dramatically cheaper than fighting over D1 portal players everyone already knows about.

Caveats

This is a small sample. 34 transfers, one season. The transfer performance table is the best we have, and it shows clear patterns, but N=34 is not definitive. Conclusions should be held loosely.

D-II competition is weaker. A player shooting 41.5% from three on 212 attempts against D-II defenders is doing it on real volume, but the defensive quality gap between divisions is real. The conservative scenario tries to account for that with a 7-point discount on 3P%, but the true adjustment factor is unknown.

We can't model defensive adjustment, coaching scheme fit, pace differences, or the psychological jump to a new program. Basketball is not a spreadsheet. But the efficiency data is real, and the core observation stands: teams at the bottom of D1 need shooters, D-II has shooters, and the transfer rate between the two is surprisingly low.

Methodology

Shot data sourced from ESPN play-by-play API via sportsdataverse. 907,000 shots across 363 D1 teams, 2025-26 season. Shot zones classified by distance from basket centroid: rim (<6 ft), paint (6-14 ft), midrange (14 ft to 3PT line), three (beyond arc). Expected value = FG% × point value. Transfer list compiled from WBB Blog. D-II player stats from school athletics sites. Projection model: each player given 15 FGA/game redistributed from Louisiana's lowest pts/att players. D-II shooting rates discounted for D1 (conservative: 3P% −7, 2P% −5; moderate: 3P% −4, 2P% −3; optimistic: 3P% −2, 2P% −1). FT rate scaled to 85% of D-II rate. Model captures direct shot replacement only; secondary effects (assists, rebounding, extra possessions) are noted but not quantified.