| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | SHL-J20 | 24 | 9 | 3 | 12 | 0.500 | 0.2761 | 0.2983 | 0.6673 | 0.7210 |
| 2023-24 | — | SuperElit | 33 | 6 | 3 | 9 | 0.273 | 0.1068 | 0.1102 | 0.3350 | 0.3456 |
| 2024-25 | Lincoln Stars | USHL | 36 | 22 | 22 | 44 | 1.222 | 0.7513 | 0.7400 | 3.6008 | 3.5467 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Wisconsin | D1 | BigTen | — | 20 | 2 | 3 | 5 | 0.250 |
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.