| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Corpus Christi IceRays | NAHL | 2 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2022-23 | Corpus Christi IceRays | NAHL | 44 | 8 | 22 | 30 | 0.682 | 0.2701 | 0.2862 | 0.7158 | 0.7585 |
| 2023-24 | Chicago Steel | USHL | 59 | 11 | 15 | 26 | 0.441 | 0.2709 | 0.2618 | 1.2984 | 1.2547 |
| 2024-25 | Cedar Rapids RoughRiders | USHL | 57 | 13 | 10 | 23 | 0.404 | 0.2480 | 0.2270 | 1.1888 | 1.0881 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Princeton | D1 | ECAC | FR | 2 | 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.