| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2020-21 | — | USHL | 42 | 6 | 7 | 13 | 0.309 | 0.1902 | 0.1902 | 0.9118 | 0.9118 |
| 2021-22 | Youngstown Phantoms | USHL | 49 | 14 | 16 | 30 | 0.612 | 0.3763 | 0.3531 | 1.8037 | 1.6925 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Cornell | D1 | ECAC | SR | 18 | 0 | 0 | 0 | 0.000 |
| 2024-25 | Cornell | D1 | ECAC | JR | 0 | 0 | 0 | 0 | 0.000 |
| 2023-24 | Cornell | D1 | ECAC | SO | 24 | 1 | 1 | 2 | 0.083 |
| 2022-23 | Cornell | D1 | ECAC | FR | 23 | 3 | 5 | 8 | 0.348 |
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.