| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2014-15 | Brampton Steelheads | OHL | 11 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2015-16 | Toronto Patriots | OJHL | 43 | 8 | 11 | 19 | 0.442 | 0.1235 | 0.1245 | 0.3050 | 0.3074 |
| 2016-17 | — | OJHL | 50 | 12 | 14 | 26 | 0.520 | 0.1453 | 0.1400 | 0.3589 | 0.3457 |
| 2017-18 | North York Rangers | OJHL | 54 | 9 | 21 | 30 | 0.556 | 0.1552 | 0.1420 | 0.3834 | 0.3509 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | San Diego State University | ACHA_D1 | — | — | 17 | 2 | 8 | 10 | 0.588 |
| 2024-25 | San Diego State University | ACHA_D1 | — | — | 17 | 2 | 8 | 10 | 0.588 |
| 2023-24 | San Diego State University | ACHA_D1 | — | — | 17 | 2 | 8 | 10 | 0.588 |
| 2022-23 | Canton | D3 | — | SR | 24 | 3 | 5 | 8 | 0.333 |
| 2022-23 | San Diego State University | ACHA_D1 | — | — | 17 | 2 | 8 | 10 | 0.588 |
| 2021-22 | Canton | D3 | — | SR | 20 | 1 | 2 | 3 | 0.150 |
| 2021-22 | San Diego State University | ACHA_D1 | — | — | 17 | 2 | 8 | 10 | 0.588 |
| 2020-21 | San Diego State University | ACHA_D1 | — | — | 17 | 2 | 8 | 10 | 0.588 |
| 2019-20 | Canton | D3 | — | FR | 15 | 6 | 2 | 8 | 0.533 |
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.