| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Amarillo Wranglers | NAHL | 58 | 2 | 6 | 8 | 0.138 | 0.0490 | 0.0510 | 0.1448 | 0.1508 |
| 2022-23 | Amarillo Wranglers | NAHL | 60 | 4 | 8 | 12 | 0.200 | 0.0710 | 0.0705 | 0.2100 | 0.2084 |
| 2023-24 | Amarillo Wranglers | NAHL | 58 | 3 | 18 | 21 | 0.362 | 0.1286 | 0.1215 | 0.3802 | 0.3591 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Trine | D3 | NCHA | SO | 19 | 0 | 1 | 1 | 0.053 |
| 2024-25 | Trine | D3 | NCHA | — | 23 | 0 | 5 | 5 | 0.217 |
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.