| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | Youngstown Phantoms | USHL | 49 | 5 | 11 | 16 | 0.327 | 0.1926 | 0.2099 | 0.9806 | 1.0685 |
| 2017-18 | Youngstown Phantoms | USHL | 58 | 17 | 21 | 38 | 0.655 | 0.3865 | 0.4020 | 1.9678 | 2.0466 |
| 2018-19 | — | USHL | 57 | 13 | 19 | 32 | 0.561 | 0.3312 | 0.3274 | 1.6861 | 1.6667 |
| 2024-25 | Jukurit | Liiga | 25 | 8 | 10 | 18 | 0.720 | 1.8000 | 1.9589 | 6.1892 | 6.7355 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Notre Dame | D1 | BigTen | JR | 39 | 16 | 12 | 28 | 0.718 |
| 2020-21 | Notre Dame | D1 | BigTen | SO | 24 | 5 | 11 | 16 | 0.667 |
| 2019-20 | Notre Dame | D1 | BigTen | FR | 8 | 1 | 0 | 1 | 0.125 |
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.