| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2015-16 | Collingwood Blues | OJHL | 39 | 15 | 19 | 34 | 0.872 | 0.2436 | 0.2304 | 0.6016 | 0.5689 |
| 2016-17 | Collingwood Blues | OJHL | 48 | 27 | 32 | 59 | 1.229 | 0.3434 | 0.3094 | 0.8483 | 0.7643 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2019-20 | SUNY Geneseo | D3 | — | JR | 27 | 6 | 16 | 22 | 0.815 |
| 2018-19 | SUNY Geneseo | D3 | — | SO | 29 | 9 | 4 | 13 | 0.448 |
| 2017-18 | SUNY Geneseo | D3 | — | FR | 21 | 5 | 7 | 12 | 0.571 |
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.