| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Newmarket Hurricanes | OJHL | 45 | 0 | 7 | 7 | 0.156 | 0.0381 | 0.0408 | 0.1065 | 0.1141 |
| 2023-24 | Newmarket Hurricanes | OJHL | 39 | 4 | 9 | 13 | 0.333 | 0.0817 | 0.0833 | 0.2281 | 0.2325 |
| 2024-25 | Newmarket Hurricanes | OJHL | 55 | 2 | 27 | 29 | 0.527 | 0.1292 | 0.1250 | 0.3609 | 0.3493 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Middlebury | D3 | NESCAC | — | 9 | 0 | 1 | 1 | 0.111 |
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.