| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | Bay State Bobcats | NA3HL | 15 | 2 | 1 | 3 | 0.200 | 0.0251 | 0.0271 | 0.0631 | 0.0682 |
| 2018-19 | Brooklyn Aviators | USPHL-Premier | 30 | 5 | 6 | 11 | 0.367 | 0.0480 | 0.0472 | 0.1245 | 0.1223 |
| 2019-20 | Hudson Havoc | USPHL-Premier | 24 | 14 | 5 | 19 | 0.792 | 0.1036 | 0.1036 | 0.2687 | 0.2687 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | King's | D3 | MAC | — | 7 | 0 | 0 | 0 | 0.000 |
| 2021-22 | King's | D3 | MAC | — | 6 | 0 | 0 | 0 | 0.000 |
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.