| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2019-20 | Winona | USHS-MN | 27 | 2 | 4 | 6 | 0.222 | 0.0598 | 0.0598 | 0.0540 | 0.0540 |
| 2022-23 | Aurora Tigers | OJHL | 1 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2023-24 | Sarnia Sting | OHL | 49 | 11 | 9 | 20 | 0.408 | 0.2436 | 0.2739 | 1.0573 | 1.1888 |
| 2024-25 | Sarnia Sting | OHL | 68 | 16 | 25 | 41 | 0.603 | 0.3598 | 0.3860 | 1.5616 | 1.6752 |
| 2025-26 | — | OHL | 67 | 22 | 41 | 63 | 0.940 | 0.5612 | 0.5738 | 2.4356 | 2.4903 |
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.