| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2018-19 | — | USHL | 9 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2019-20 | — | USHL | 48 | 8 | 29 | 37 | 0.771 | 0.4738 | 0.4738 | 2.2709 | 2.2709 |
| 2020-21 | — | USHL | 48 | 19 | 40 | 59 | 1.229 | 0.7556 | 0.7556 | 3.6215 | 3.6215 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Ohio State | D1 | BigTen | — | 40 | 4 | 28 | 32 | 0.800 |
| 2021-22 | Ohio State | D1 | BigTen | — | 31 | 4 | 25 | 29 | 0.935 |
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.