| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | USHL | 36 | 0 | 9 | 9 | 0.250 | 0.1537 | 0.1679 | 0.7366 | 0.8047 |
| 2023-24 | Youngstown Phantoms | USHL | 50 | 8 | 16 | 24 | 0.480 | 0.2951 | 0.3077 | 1.4142 | 1.4746 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Denver | D1 | NCHC | SO | 6 | 0 | 1 | 1 | 0.167 |
| 2024-25 | Denver | D1 | NCHC | — | 40 | 0 | 1 | 1 | 0.025 |
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.