| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | — | USHL | 61 | 5 | 18 | 23 | 0.377 | 0.2317 | 0.2534 | 1.1107 | 1.2145 |
| 2022-23 | — | USHL | 56 | 3 | 35 | 38 | 0.679 | 0.4171 | 0.4344 | 1.9993 | 2.0822 |
| 2023-24 | Youngstown Phantoms | USHL | 48 | 7 | 32 | 39 | 0.812 | 0.4994 | 0.4953 | 2.3938 | 2.3742 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | North Dakota | D1 | NCHC | SO | 38 | 1 | 5 | 6 | 0.158 |
| 2024-25 | North Dakota | D1 | NCHC | — | 30 | 2 | 2 | 4 | 0.133 |
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.