| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | — | BCHL | 53 | 21 | 20 | 41 | 0.774 | 0.2882 | 0.2940 | 1.1272 | 1.1498 |
| 2022-23 | — | BCHL | 54 | 21 | 31 | 52 | 0.963 | 0.3587 | 0.3483 | 1.4032 | 1.3626 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2024-25 | Brown | D1 | ECAC | — | 32 | 9 | 19 | 28 | 0.875 |
| 2023-24 | Brown | D1 | ECAC | — | 30 | 6 | 12 | 18 | 0.600 |
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.