| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Smiths Falls Bears | CCHL | 52 | 9 | 22 | 31 | 0.596 | 0.1293 | 0.1246 | 0.4615 | 0.4446 |
| 2023-24 | Smiths Falls Bears | CCHL | 54 | 20 | 41 | 61 | 1.130 | 0.2450 | 0.2230 | 0.8744 | 0.7958 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Neumann | D3 | MAC | SO | 27 | 7 | 16 | 23 | 0.852 |
| 2024-25 | Neumann | D3 | MAC | — | 26 | 12 | 22 | 34 | 1.308 |
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.