| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Amarillo Wranglers | NAHL | 46 | 8 | 6 | 14 | 0.304 | 0.1118 | 0.1182 | 0.3212 | 0.3395 |
| 2022-23 | Amarillo Wranglers | NAHL | 23 | 8 | 6 | 14 | 0.609 | 0.2236 | 0.2253 | 0.6425 | 0.6475 |
| 2023-24 | Amarillo Wranglers | NAHL | 31 | 1 | 3 | 4 | 0.129 | 0.0474 | 0.0455 | 0.1362 | 0.1307 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2024-25 | Buffalo State | D3 | SUNYAC | — | 17 | 3 | 0 | 3 | 0.176 |
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.