| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Hearst Lumberjacks | NOJHL | 43 | 18 | 26 | 44 | 1.023 | 0.1457 | 0.1487 | 0.4246 | 0.4333 |
| 2023-24 | Renfrew Wolves | CCHL | 51 | 24 | 41 | 65 | 1.274 | 0.2764 | 0.2690 | 0.9866 | 0.9601 |
| 2024-25 | Amarillo Wranglers | NAHL | 29 | 6 | 13 | 19 | 0.655 | 0.2327 | 0.2200 | 0.6879 | 0.6503 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Curry | D3 | CNE | — | 12 | 6 | 4 | 10 | 0.833 |
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.