| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | Rochester Monarchs | USPHL-Elite | 43 | 16 | 31 | 47 | 1.093 | 0.1009 | 0.1029 | 0.2503 | 0.2553 |
| 2017-18 | Markham Royals | OJHL | 53 | 5 | 8 | 13 | 0.245 | 0.0601 | 0.0573 | 0.1689 | 0.1610 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Nazareth | D3 | UCHC | — | 23 | 3 | 3 | 6 | 0.261 |
| 2020-21 | Nazareth | D3 | UCHC | — | 11 | 0 | 1 | 1 | 0.091 |
| 2018-19 | Utica | D3 | UCHC | — | 19 | 3 | 3 | 6 | 0.316 |
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.