| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2015-16 | Richmond Generals | USPHL-Elite | 40 | 22 | 32 | 54 | 1.350 | 0.1246 | 0.1265 | 0.5278 | 0.5374 |
| 2016-17 | Kirkland Lake Gold Miners | NOJHL | 54 | 26 | 33 | 59 | 1.093 | 0.1825 | 0.1737 | 0.4548 | 0.4330 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2018-19 | Aurora | D3 | NCHA | — | 0 | 0 | 0 | 0 | 0.000 |
| 2017-18 | Aurora | D3 | NCHA | — | 23 | 5 | 5 | 10 | 0.435 |
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.