| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Connecticut Jr. Rangers | USPHL-Premier | 42 | 21 | 26 | 47 | 1.119 | 0.3688 | 0.3679 | 0.3807 | 0.3798 |
| 2023-24 | Connecticut Jr. Rangers | USPHL-Premier | 33 | 45 | 67 | 112 | 3.394 | 1.1186 | 1.0620 | 1.1546 | 1.0962 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Saint Mary's (MN) | D3 | MIAC | SO | 26 | 21 | 25 | 46 | 1.769 |
| 2024-25 | Saint Mary's (MN) | D3 | MIAC | FR | 23 | 5 | 14 | 19 | 0.826 |
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.