| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Des Moines Buccaneers | USHL | 9 | 0 | 2 | 2 | 0.222 | 0.1311 | 0.1438 | 0.6674 | 0.7318 |
| 2023-24 | Des Moines Buccaneers | USHL | 62 | 2 | 14 | 16 | 0.258 | 0.1523 | 0.1594 | 0.7752 | 0.8115 |
| 2024-25 | Des Moines Buccaneers | USHL | 62 | 1 | 9 | 10 | 0.161 | 0.0952 | 0.0948 | 0.4844 | 0.4824 |
| 2025-26 | Ottawa 67's | OHL | 65 | 1 | 20 | 21 | 0.323 | 0.1869 | 0.1765 | 0.8302 | 0.7842 |
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.