| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Cushing Academy | NE-Prep | 29 | 9 | 21 | 30 | 1.034 | 0.2049 | 0.2049 | 0.4713 | 0.4713 |
| 2023-24 | Cushing Academy | NE-Prep | 32 | 22 | 29 | 51 | 1.594 | 0.3157 | 0.3157 | 0.7261 | 0.7261 |
| 2024-25 | Lone Star Brahmas | NAHL | 49 | 3 | 7 | 10 | 0.204 | 0.0750 | 0.0762 | 0.2154 | 0.2187 |
| 2025-26 | Johnstown Tomahawks | NAHL | 57 | 19 | 39 | 58 | 1.018 | 0.3738 | 0.3611 | 1.0740 | 1.0376 |
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.