| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | Collingwood Blues | OJHL | 46 | 22 | 33 | 55 | 1.196 | 0.2931 | 0.2802 | 0.8232 | 0.7870 |
| 2017-18 | Collingwood Blues | OJHL | 51 | 30 | 33 | 63 | 1.235 | 0.3028 | 0.2749 | 0.8505 | 0.7721 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2019-20 | SUNY Potsdam | D3 | SUNYAC | SO | 27 | 10 | 11 | 21 | 0.778 |
| 2018-19 | SUNY Potsdam | D3 | SUNYAC | FR | 8 | 4 | 1 | 5 | 0.625 |
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.