| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Pittsburgh Vengeance | USPHL-Premier | 13 | 6 | 5 | 11 | 0.846 | 0.1107 | 0.1187 | 0.2872 | 0.3081 |
| 2023-24 | Fairbanks Ice Dogs | NAHL | 47 | 5 | 18 | 23 | 0.489 | 0.1798 | 0.1774 | 0.5166 | 0.5098 |
| 2024-25 | Fairbanks Ice Dogs | NAHL | 56 | 10 | 31 | 41 | 0.732 | 0.2690 | 0.2513 | 0.7727 | 0.7220 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Chatham | D3 | UCHC | FR | 27 | 5 | 13 | 18 | 0.667 |
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.