| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Dubuque Fighting Saints | USHL | 53 | 11 | 8 | 19 | 0.358 | 0.2115 | 0.2330 | 1.0767 | 1.1863 |
| 2022-23 | Dubuque Fighting Saints | USHL | 61 | 13 | 25 | 38 | 0.623 | 0.3675 | 0.3858 | 1.8711 | 1.9643 |
| 2023-24 | Youngstown Phantoms | USHL | 59 | 13 | 21 | 34 | 0.576 | 0.3400 | 0.3401 | 1.7309 | 1.7312 |
| 2024-25 | Youngstown Phantoms | USHL | 60 | 11 | 7 | 18 | 0.300 | 0.1770 | 0.1680 | 0.9010 | 0.8552 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Northern Michigan | D1 | CCHA | FR | 31 | 5 | 3 | 8 | 0.258 |
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.