| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Maryland Black Bears | NAHL | 36 | 0 | 4 | 4 | 0.111 | 0.0395 | 0.0396 | 0.1166 | 0.1169 |
| 2023-24 | — | NAHL | 41 | 5 | 4 | 9 | 0.220 | 0.0780 | 0.0745 | 0.2305 | 0.2200 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Amherst | D3 | NESCAC | SO | 24 | 2 | 2 | 4 | 0.167 |
| 2024-25 | Amherst | D3 | NESCAC | — | 23 | 3 | 4 | 7 | 0.304 |
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.