| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2017-18 | New York Apple Core | EHL | 37 | 6 | 11 | 17 | 0.460 | 0.0986 | 0.1007 | 0.2250 | 0.2297 |
| 2018-19 | New York Apple Core | EHL | 44 | 6 | 14 | 20 | 0.455 | 0.0975 | 0.0947 | 0.2226 | 0.2161 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | SUNY Potsdam | D3 | SUNYAC | SR | 22 | 3 | 13 | 16 | 0.727 |
| 2021-22 | SUNY Potsdam | D3 | SUNYAC | JR | 16 | 0 | 2 | 2 | 0.125 |
| 2019-20 | SUNY Potsdam | D3 | — | FR | 24 | 2 | 9 | 11 | 0.458 |
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.