| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2015-16 | Lloydminster Bobcats | AJHL | 1 | 0 | 1 | 1 | 1.000 | 0.3340 | 0.3570 | 0.9283 | 0.9922 |
| 2016-17 | — | SJHL | 31 | 1 | 2 | 3 | 0.097 | 0.0280 | 0.0289 | 0.0729 | 0.0753 |
| 2017-18 | Kindersley Klippers | SJHL | 56 | 8 | 13 | 21 | 0.375 | 0.1083 | 0.1069 | 0.2823 | 0.2786 |
| 2018-19 | Kindersley Klippers | SJHL | 55 | 11 | 17 | 28 | 0.509 | 0.1471 | 0.1376 | 0.3833 | 0.3584 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2019-20 | Bryn Athyn | D3 | — | FR | 21 | 6 | 6 | 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.