| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Soo Greyhounds | OHL | 62 | 4 | 11 | 15 | 0.242 | 0.1399 | 0.1529 | 0.6216 | 0.6792 |
| 2023-24 | Soo Greyhounds | OHL | 66 | 4 | 11 | 15 | 0.227 | 0.1315 | 0.1374 | 0.5840 | 0.6102 |
| 2024-25 | Niagara IceDogs | OHL | 68 | 4 | 23 | 27 | 0.397 | 0.2297 | 0.2281 | 1.0203 | 1.0134 |
| 2025-26 | Moncton Wildcats | QMJHL | 62 | 4 | 15 | 19 | 0.306 | 0.1506 | 0.1433 | 0.8184 | 0.7785 |
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.