| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Corpus Christi IceRays | NAHL | 5 | 1 | 3 | 4 | 0.800 | 0.2842 | 0.3175 | 0.8399 | 0.9384 |
| 2022-23 | Des Moines Buccaneers | USHL | 54 | 6 | 10 | 16 | 0.296 | 0.1748 | 0.1791 | 0.8730 | 0.8944 |
| 2023-24 | Des Moines Buccaneers | USHL | 49 | 6 | 18 | 24 | 0.490 | 0.2889 | 0.2816 | 1.4430 | 1.4066 |
| 2024-25 | Muskegon Lumberjacks | USHL | 60 | 11 | 24 | 35 | 0.583 | 0.3441 | 0.3179 | 1.7185 | 1.5875 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Alaska Fairbanks | D1 | WCHA | FR | 31 | 3 | 4 | 7 | 0.226 |
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.