| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Charlotte Rush | USPHL-Premier | 42 | 13 | 40 | 53 | 1.262 | 0.1699 | 0.1697 | 0.4296 | 0.4291 |
| 2023-24 | Tampa Bay Juniors | USPHL-Premier | 37 | 20 | 23 | 43 | 1.162 | 0.1564 | 0.1487 | 0.3956 | 0.3760 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | SUNY Brockport | D3 | — | SO | 25 | 1 | 5 | 6 | 0.240 |
| 2024-25 | SUNY Brockport | D3 | — | FR | 22 | 3 | 2 | 5 | 0.227 |
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.