| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | WHL | 6 | 0 | 2 | 2 | 0.333 | 0.1622 | 0.1864 | 0.8167 | 0.9384 |
| 2023-24 | Tri-City Americans | WHL | 62 | 8 | 21 | 29 | 0.468 | 0.2275 | 0.2502 | 1.1460 | 1.2604 |
| 2024-25 | Tri-City Americans | WHL | 68 | 11 | 43 | 54 | 0.794 | 0.3863 | 0.4042 | 1.9458 | 2.0361 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Penn State | D1 | BigTen | — | 35 | 11 | 15 | 26 | 0.743 |
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.