| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | New Jersey Rockets | NCDC | 17 | 1 | 0 | 1 | 0.059 | 0.0136 | 0.0141 | 0.0475 | 0.0494 |
| 2023-24 | Northern Cyclones | NCDC | 32 | 5 | 7 | 12 | 0.375 | 0.0867 | 0.0852 | 0.3032 | 0.2980 |
| 2024-25 | Northern Cyclones | NCDC | 51 | 13 | 13 | 26 | 0.510 | 0.1178 | 0.1105 | 0.4122 | 0.3867 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Saint Anselm | D2 | NE10 | — | 21 | 4 | 8 | 12 | 0.571 |
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.