| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Sudbury Wolves | OHL | 49 | 2 | 11 | 13 | 0.265 | 0.1540 | 0.1657 | 0.6798 | 0.7316 |
| 2022-23 | Sudbury Wolves | OHL | 67 | 10 | 28 | 38 | 0.567 | 0.3291 | 0.3399 | 1.4535 | 1.5013 |
| 2023-24 | Sudbury Wolves | OHL | 45 | 4 | 19 | 23 | 0.511 | 0.2966 | 0.2921 | 1.3097 | 1.2900 |
| 2024-25 | Flint Firebirds | OHL | 66 | 7 | 38 | 45 | 0.682 | 0.3956 | 0.3692 | 1.7471 | 1.6307 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Michigan | D1 | BigTen | FR | 15 | 1 | 0 | 1 | 0.067 |
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.