| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Lincoln Stars | USHL | 49 | 4 | 7 | 11 | 0.225 | 0.1380 | 0.1440 | 0.6614 | 0.6903 |
| 2022-23 | Lincoln Stars | USHL | 60 | 16 | 15 | 31 | 0.517 | 0.3176 | 0.3150 | 1.5223 | 1.5097 |
| 2023-24 | Lincoln Stars | USHL | 62 | 22 | 22 | 44 | 0.710 | 0.4363 | 0.4110 | 2.0909 | 1.9698 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Penn State | D1 | BigTen | — | 0 | 0 | 0 | 0 | 0.000 |
| 2024-25 | Penn State | D1 | BigTen | — | 35 | 2 | 4 | 6 | 0.171 |
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.