| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | — | MJHL | 46 | 8 | 12 | 20 | 0.435 | 0.1182 | 0.1285 | 0.2740 | 0.2980 |
| 2022-23 | Waywayseecappo Wolverines | MJHL | 42 | 6 | 8 | 14 | 0.333 | 0.0906 | 0.0943 | 0.2100 | 0.2186 |
| 2023-24 | Waywayseecappo Wolverines | MJHL | 57 | 29 | 23 | 52 | 0.912 | 0.2481 | 0.2452 | 0.5749 | 0.5682 |
| 2024-25 | Elmira Aviators | NAHL | 28 | 7 | 13 | 20 | 0.714 | 0.2830 | 0.2701 | 0.7499 | 0.7156 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Minot State University | ACHA_D1 | — | — | 13 | 3 | 9 | 12 | 0.923 |
| 2025-26 | SUNY Oswego | D3 | SUNYAC | FR | 2 | 0 | 0 | 0 | 0.000 |
| 2024-25 | Minot State University | ACHA_D1 | — | — | 13 | 3 | 9 | 12 | 0.923 |
| 2023-24 | Minot State University | ACHA_D1 | — | — | 13 | 3 | 9 | 12 | 0.923 |
| 2022-23 | Minot State University | ACHA_D1 | — | — | 13 | 3 | 9 | 12 | 0.923 |
| 2021-22 | Minot State University | ACHA_D1 | — | — | 13 | 3 | 9 | 12 | 0.923 |
| 2020-21 | Minot State University | ACHA_D1 | — | — | 13 | 3 | 9 | 12 | 0.923 |
| 2009-10 | St. Lawrence | D1 | ECAC-W | SR | 35 | 8 | 17 | 25 | 0.714 |
| 2008-09 | St. Lawrence | D1 | ECAC-W | JR | 38 | 8 | 13 | 21 | 0.553 |
| 2007-08 | St. Lawrence | D1 | ECAC-W | SO | 35 | 3 | 11 | 14 | 0.400 |
| 2006-07 | St. Lawrence | D1 | ECAC-W | FR | 38 | 1 | 13 | 14 | 0.368 |
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.