| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2014-15 | New York Bobcats | EHL | 16 | 3 | 9 | 12 | 0.750 | 0.1610 | 0.1589 | 0.3673 | 0.3626 |
| 2015-16 | Northern Cyclones | EHL | 41 | 13 | 22 | 35 | 0.854 | 0.1832 | 0.1728 | 0.4181 | 0.3944 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2018-19 | Canton | D3 | — | JR | 24 | 3 | 10 | 13 | 0.542 |
| 2017-18 | Canton | D3 | — | SO | 23 | 6 | 11 | 17 | 0.739 |
| 2016-17 | Canton | D3 | — | FR | 23 | 9 | 13 | 22 | 0.957 |
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.