| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Houston Bulls | NAHL | 2 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2023-24 | Whitecourt Wolverines | AJHL | 27 | 2 | 11 | 13 | 0.481 | 0.1598 | 0.1602 | 0.4463 | 0.4474 |
| 2024-25 | — | NAHL | 56 | 6 | 32 | 38 | 0.679 | 0.2689 | 0.2612 | 0.7125 | 0.6922 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Northern Michigan | D1 | CCHA | — | 17 | 0 | 0 | 0 | 0.000 |
| 2018-19 | Williams | D1 | — | JR | 19 | 1 | 0 | 1 | 0.053 |
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.