| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | Buffalo Jr. Sabres | OJHL | 6 | 1 | 2 | 3 | 0.500 | 0.1397 | 0.1441 | 0.3451 | 0.3560 |
| 2017-18 | Buffalo Jr. Sabres | OJHL | 53 | 11 | 22 | 33 | 0.623 | 0.1740 | 0.1711 | 0.4297 | 0.4226 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | SUNY Cortland | D3 | SUNYAC | SR | 26 | 8 | 16 | 24 | 0.923 |
| 2019-20 | SUNY Cortland | D3 | — | SO | 12 | 0 | 4 | 4 | 0.333 |
| 2018-19 | SUNY Cortland | D3 | — | FR | 23 | 5 | 7 | 12 | 0.522 |
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.