| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | — | NTDP-U18 | 57 | 9 | 20 | 29 | 0.509 | 0.4047 | 0.4117 | 1.9056 | 1.9385 |
| 2017-18 | U.S. National U18 Team | NTDP-U18 | 38 | 11 | 32 | 43 | 1.132 | 0.9001 | 0.8699 | 4.2381 | 4.0958 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Ohio State | D1 | BigTen | — | 40 | 12 | 27 | 39 | 0.975 |
| 2021-22 | Ohio State | D1 | BigTen | — | 35 | 10 | 18 | 28 | 0.800 |
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.