| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Newmarket Hurricanes | OJHL | 53 | 24 | 25 | 49 | 0.924 | 0.2777 | 0.2936 | 0.6328 | 0.6691 |
| 2023-24 | Austin Bruins | NAHL | 59 | 15 | 27 | 42 | 0.712 | 0.2821 | 0.2925 | 0.7474 | 0.7750 |
| 2024-25 | Austin Bruins | NAHL | 55 | 36 | 46 | 82 | 1.491 | 0.5907 | 0.5815 | 1.5653 | 1.5409 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Union | D1 | ECAC | FR | 32 | 4 | 15 | 19 | 0.594 |
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.