| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Pueblo Bulls | USPHL-Premier | 32 | 2 | 0 | 2 | 0.062 | 0.0070 | 0.0072 | 0.0213 | 0.0221 |
| 2023-24 | Lake Tahoe Lakers | USPHL-Premier | 40 | 4 | 10 | 14 | 0.350 | 0.0395 | 0.0390 | 0.1191 | 0.1176 |
| 2024-25 | Bridgewater Jr. Bandits | EHL | 42 | 8 | 11 | 19 | 0.452 | 0.0662 | 0.0637 | 0.2218 | 0.2135 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Framingham State | D3 | MASCAC | — | 21 | 2 | 5 | 7 | 0.333 |
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.