| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Austin Bruins | NAHL | 38 | 0 | 6 | 6 | 0.158 | 0.0626 | 0.0658 | 0.1658 | 0.1743 |
| 2023-24 | Austin Bruins | NAHL | 57 | 2 | 22 | 24 | 0.421 | 0.1668 | 0.1674 | 0.4421 | 0.4437 |
| 2024-25 | Austin Bruins | NAHL | 47 | 10 | 20 | 30 | 0.638 | 0.2529 | 0.2405 | 0.7366 | 0.6681 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Brown | D1 | ECAC | FR | 17 | 1 | 0 | 1 | 0.059 |
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.