| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2014-15 | Green Bay Gamblers | USHL | 4 | 0 | 1 | 1 | 0.250 | 0.1475 | 0.1552 | 0.7509 | 0.7902 |
| 2015-16 | — | BCHL | 53 | 9 | 18 | 27 | 0.509 | 0.1963 | 0.2014 | 0.7413 | 0.7606 |
| 2016-17 | Jersey Hitmen | USPHL-Premier-Classic | 26 | 7 | 13 | 20 | 0.769 | 0.2159 | 0.2173 | 0.6324 | 0.6366 |
| 2017-18 | Jersey Hitmen | NCDC | 47 | 17 | 27 | 44 | 0.936 | 0.3817 | 0.3599 | 0.7525 | 0.7096 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Lindenwood | D1 | CCHA | — | 29 | 6 | 14 | 20 | 0.690 |
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.