| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Dauphin Kings | MJHL | 53 | 6 | 27 | 33 | 0.623 | 0.1199 | 0.1239 | 0.3926 | 0.4057 |
| 2022-23 | Dubuque Fighting Saints | USHL | 62 | 6 | 19 | 25 | 0.403 | 0.2378 | 0.2275 | 1.2110 | 1.1586 |
| 2023-24 | Dubuque Fighting Saints | USHL | 53 | 7 | 10 | 17 | 0.321 | 0.1892 | 0.1716 | 0.9635 | 0.8739 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | North Dakota | D1 | NCHC | SO | 15 | 0 | 2 | 2 | 0.133 |
| 2024-25 | North Dakota | D1 | NCHC | — | 37 | 2 | 4 | 6 | 0.162 |
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.