| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2013-14 | Calgary Canucks | AJHL | 38 | 6 | 5 | 11 | 0.289 | 0.0967 | 0.1069 | 0.2687 | 0.2971 |
| 2014-15 | Fort McMurray Oil Barons | AJHL | 17 | 0 | 1 | 1 | 0.059 | 0.0196 | 0.0206 | 0.0546 | 0.0575 |
| 2015-16 | — | SJHL | 37 | 2 | 9 | 11 | 0.297 | 0.0859 | 0.0876 | 0.2238 | 0.2282 |
| 2016-17 | Kindersley Klippers | SJHL | 52 | 8 | 21 | 29 | 0.558 | 0.1611 | 0.1563 | 0.4198 | 0.4072 |
| 2017-18 | Cowichan Valley Capitals | BCHL | 45 | 6 | 23 | 29 | 0.644 | 0.2508 | 0.2286 | 0.9397 | 0.8564 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Adrian | D3 | NCHA | SR | 22 | 0 | 3 | 3 | 0.136 |
| 2020-21 | Adrian | D3 | NCHA | JR | 19 | 0 | 1 | 1 | 0.053 |
| 2019-20 | Adrian | D3 | NCHA | SO | 16 | 1 | 7 | 8 | 0.500 |
| 2018-19 | Adrian | D3 | NCHA | FR | 3 | 0 | 0 | 0 | 0.000 |
| 2017-18 | SUNY Cortland | D3 | — | SR | 24 | 3 | 10 | 13 | 0.542 |
| 2016-17 | SUNY Cortland | D3 | — | JR | 24 | 9 | 5 | 14 | 0.583 |
| 2015-16 | SUNY Cortland | D3 | — | SO | 25 | 7 | 6 | 13 | 0.520 |
| 2014-15 | SUNY Cortland | D3 | — | FR | 25 | 6 | 11 | 17 | 0.680 |
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.