| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | Youngstown Phantoms | USHL | 12 | 7 | 4 | 11 | 0.917 | 0.5408 | 0.6080 | 2.7507 | 3.0926 |
| 2024-25 | Youngstown Phantoms | USHL | 62 | 20 | 24 | 44 | 0.710 | 0.4187 | 0.4494 | 2.1296 | 2.2856 |
| 2025-26 | Youngstown Phantoms | USHL | 53 | 27 | 34 | 61 | 1.151 | 0.6789 | 0.6959 | 3.4535 | 3.5398 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2026-27 | Ohio State | D1 | BigTen | FR | 0 | 0 | 0 | 0 | 0.000 |
How to read this: NCAAe and D3e factors convert a player's junior PPG into expected NCAA scoring at the D1 or D3 level. Harder conferences → lower projected PPG for the same player. A strong junior player (e.g. USHL 0.90 PPG) will project much higher in NESCAC than Big Ten because the D3 scoring environment is lower-difficulty.
Strength factor: conferences above 1.0 are harder than average; below 1.0 are easier. The formula is: Base NCAAe PPG ÷ Conference Strength = Projected PPG.