| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | — | OJHL | 49 | 11 | 17 | 28 | 0.571 | 0.1596 | 0.1632 | 0.3943 | 0.4033 |
| 2017-18 | — | OJHL | 50 | 18 | 12 | 30 | 0.600 | 0.1676 | 0.1634 | 0.4141 | 0.4036 |
| 2018-19 | Markham Royals | OJHL | 51 | 17 | 21 | 38 | 0.745 | 0.2082 | 0.1927 | 0.5142 | 0.4760 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | SUNY Cortland | D3 | SUNYAC | JR | 19 | 1 | 6 | 7 | 0.368 |
| 2019-20 | SUNY Cortland | D3 | — | FR | 20 | 4 | 8 | 12 | 0.600 |
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.