| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Connecticut Jr. Rangers | USPHL-Premier | 42 | 5 | 13 | 18 | 0.429 | 0.0483 | 0.0509 | 0.1458 | 0.1535 |
| 2023-24 | Connecticut Jr. Rangers | NCDC | 13 | 0 | 1 | 1 | 0.077 | 0.0178 | 0.0180 | 0.0622 | 0.0629 |
| 2024-25 | Connecticut Jr. Rangers | USPHL-Premier | 40 | 4 | 24 | 28 | 0.700 | 0.0790 | 0.0748 | 0.2381 | 0.2253 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Post | D2 | NE10 | — | 27 | 1 | 5 | 6 | 0.222 |
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.