| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Youngstown Phantoms | USHL | 6 | 4 | 1 | 5 | 0.833 | 0.4916 | 0.5466 | 2.5027 | 2.7829 |
| 2023-24 | Youngstown Phantoms | USHL | 52 | 10 | 11 | 21 | 0.404 | 0.2382 | 0.2530 | 1.2128 | 1.2884 |
| 2024-25 | Youngstown Phantoms | USHL | 49 | 10 | 18 | 28 | 0.571 | 0.3371 | 0.3409 | 1.7161 | 1.7354 |
| 2025-26 | Moncton Wildcats | QMJHL | 58 | 25 | 35 | 60 | 1.034 | 0.5082 | 0.4922 | 2.7624 | 2.6752 |
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.