| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Omaha Lancers | USHL | 3 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2022-23 | Youngstown Phantoms | USHL | 56 | 3 | 13 | 16 | 0.286 | 0.1685 | 0.1766 | 0.8581 | 0.8992 |
| 2023-24 | Youngstown Phantoms | USHL | 60 | 4 | 9 | 13 | 0.217 | 0.1278 | 0.1276 | 0.6508 | 0.6497 |
| 2024-25 | Youngstown Phantoms | USHL | 29 | 3 | 3 | 6 | 0.207 | 0.1221 | 0.1157 | 0.6214 | 0.5886 |
| 2025-26 | Youngstown Phantoms | USHL | 57 | 12 | 35 | 47 | 0.825 | 0.4864 | 0.4372 | 2.4766 | 2.2262 |
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.