| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Waterloo Black Hawks | USHL | 9 | 1 | 1 | 2 | 0.222 | 0.1311 | 0.1422 | 0.6668 | 0.7232 |
| 2023-24 | Waterloo Black Hawks | USHL | 24 | 0 | 2 | 2 | 0.083 | 0.0491 | 0.0508 | 0.2500 | 0.2587 |
| 2024-25 | Brooks Bandits | BCHL | 51 | 13 | 24 | 37 | 0.726 | 0.2795 | 0.2825 | 1.0552 | 1.0665 |
| 2025-26 | Kelowna Rockets | WHL | 65 | 10 | 23 | 33 | 0.508 | 0.2470 | 0.2305 | 1.2402 | 1.1571 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2026-27 | Minnesota Duluth | D1 | NCHC | FR | 0 | 0 | 0 | 0 | 0.000 |
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.