2026 AUSL College Draft Recap: How Did the Board Perform?
May 2026 · Draft Chalkboard
The 2026 AUSL College Draft is in the books. Seventeen players were selected across four rounds, headlined by Carolina's pick of Tennessee flamethrower Karlyn Pickens at #1 overall. Now we can look back at how our prospect board stacked up against the real thing.
The headline number: 16 of 17 picks (94%) were on our board.
The only complete miss was Oregon's Amari Harper, taken 14th by Oklahoma City. More on that below.
Round 1: Near Perfect
| Pick | Player | School | Our Rank | Our Tier | Verdict |
|---|---|---|---|---|---|
| 1 | Karlyn Pickens | Tennessee | #3 | Elite | Hit |
| 2 | NiJaree Canady | Texas Tech | #2 | Elite | Hit |
| 3 | Maya Johnson | Belmont | #45 | Tier 3 | Miss |
| 4 | Megan Grant | UCLA | #4 | Elite | Hit |
| 5 | Jocelyn Erickson | Florida | #5 | Elite | Hit |
| 6 | Jordan Woolery | UCLA | #9 | Tier 1 | Hit |
Five of six first-round picks were in our top 9. The Elite tier exists to identify the consensus top of the class, and it did — Pickens, Canady, Grant, Erickson, and Atwood (pick 7) all went in the first seven selections.
The miss: Maya Johnson, 3rd overall.A left-handed pitcher from Belmont — a mid-major program whose conference awards we don't currently scrape. We had her at #45, buried in Tier 3. She likely had OVC-level accolades that would have bumped her significantly if we'd ingested them. A narrow data coverage gap, not a model failure — one mid-major player was selected in the entire draft.
Round 2: Solid
| Pick | Player | School | Our Rank | Our Tier | Verdict |
|---|---|---|---|---|---|
| 7 | Reese Atwood | Texas | #6 | Elite | Hit |
| 8 | Leighann Goode | Texas | #21 | Tier 2 | Hit |
| 9 | Sydney Stewart | Arizona | #14 | Tier 1 | Hit |
| 10 | Peja Goold | Mississippi State | #27 | Tier 3 | Soft miss |
| 11 | Taryn Kern | Stanford | #12 | Tier 1 | Hit |
| 12 | Taylor Tinsley | UCLA | #8 | Tier 1 | Hit |
The board nailed the round. Atwood at #6 going 7th, Tinsley at #8 going 12th, Kern at #12 going 11th — all within a few slots of where they actually landed.
The soft miss: Peja Goold, pick 10.We had her ranked #27, Tier 3 — but her WAR projection was 6.11, the highest of any Tier 3 player on the board. The tier system (award-driven) undervalued her while the WAR model (stats-driven) had her pegged as elite. When your own numbers disagree with your own labels, it's worth paying attention. Goold going 10th overall validates the statistical projection over the award signal.
Rounds 3-4: Expected Variance
| Pick | Player | School | Our Rank | Our Tier | Verdict |
|---|---|---|---|---|---|
| 13 | Ailana Agbayani | Oklahoma | #44 | Tier 3 | Hit |
| 14 | Amari Harper | Oregon | — | Not ranked | Miss |
| 15 | Kenzie Brown | Arizona State | #13 | Tier 1 | Hit |
| 16 | Dakota Kennedy | Arkansas | #16 | Tier 1 | Hit |
| 17 | Kenleigh Cahalan | Florida | #32 | Tier 3 | Hit |
Brown at #13 falling to pick 15 and Kennedy at #16 going 16th — almost exact matches. Later rounds track team-specific needs more than pure talent ranking, and two Tier 1 talents falling to round 3 is evidence of that (team fit > board position).
The miss: Amari Harper, pick 14.A utility player from Oregon who we didn't have ranked at all. A Power 5 utility player who should have been in our data — likely a gap in the roster scan. One miss in 17 picks from a coverage gap is acceptable but fixable.
What We Got Right
- The Elite tier is calibrated.Five of our six Elite-tier players went in the first seven picks. The sixth, Jordy Frahm, opted to stay at Nebraska to chase a College World Series title. When the best player on the board chooses not to go pro, that's not a miss — that's an eligibility filter we didn't apply.
- Tier 1 landed where expected.Woolery (#9 board → pick 6), Tinsley (#8 → pick 12), Kern (#12 → pick 11), Brown (#13 → pick 15), Kennedy (#16 → pick 16). The entire Tier 1 cluster landed in the first three rounds, which is exactly what Tier 1 should mean.
- The order was close. Of the 16 players we had ranked, the average difference between our board position and actual draft position was just 4.3 spots. Removing the Maya Johnson outlier, it drops to 2.8 spots.
What We Missed and Why
1. Mid-major coverage (Maya Johnson, Belmont)
Johnson was the only mid-major player selected in the entire draft. We don't currently scrape mid-major conference awards (OVC, MVC, etc.), so her tier was based on incomplete signal. She likely had conference-level accolades that would have moved her up significantly through the same tier logic that works for Power 5 players.
Fix:Add mid-major conference awards to the scraper and run those players through the existing tier system. The model isn't wrong — it just didn't have the data.
2. WAR vs Tier disagreements (Peja Goold, Mississippi State)
Goold had a 6.11 WAR projection but was slotted Tier 3 because her award portfolio was thin relative to Power 5 peers. The board's own statistical model disagreed with its own tier label. When a player's projected WAR is top-5 caliber but their tier says Tier 3, that should trigger a manual flag.
Fix:Add a “WAR override” rule — any player whose WAR projection exceeds the median of the tier above gets automatically bumped.
3. Roster coverage gap (Amari Harper, Oregon)
The only player we completely missed. A Power 5 utility player who should have been in our data. Likely a gap in the roster scan — either she was classified under a position we filtered, or her stats didn't meet our initial thresholds for inclusion.
Fix: Lower inclusion thresholds for Power 5 players. Better to have 200 prospects ranked with some noise than miss a player who goes in the first 17 picks.
By the Numbers
| Metric | Result |
|---|---|
| Picks on board | 16/17 (94%) |
| Round 1 accuracy | 5/6 on board, 4/6 in correct tier |
| Elite tier hit rate | 5/6 Elite players drafted in top 7 |
| Avg position difference | 4.3 spots (2.8 excl. Johnson) |
| Complete misses | 1 (Amari Harper) |
| Major tier misses | 1 (Maya Johnson: Tier 3 → pick 3) |
| Not drafted | Jordy Frahm (#1, opted to stay in school) |
Looking Ahead
The board's strength is clear: when players come from Power 5 programs and have accumulated awards, our signal model identifies and ranks them with high accuracy. The weakness is equally clear: non-Power 5 arms and players whose statistical profiles outpace their award portfolios get systematically undervalued.
For 2027, three adjustments:
- Eligibility tracking— flag players who've declared vs. those who may return to school
- WAR override threshold— let the stats correct the tiers when disagreements are large
- Expanded Power 5 inclusion— cast a wider net to avoid coverage gaps on players like Harper
Sixteen of seventeen. We'll take it.