| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2006-07 | Selkirk Steelers | MJHL | 3 | 0 | 1 | 1 | 0.333 | 0.0642 | 0.0720 | 0.2102 | 0.2357 |
| 2008-09 | Powell River Kings | BCHL | 53 | 1 | 41 | 42 | 0.792 | 0.3054 | 0.3108 | 1.1533 | 1.1735 |
| 2009-10 | Powell River Kings | BCHL | 51 | 8 | 34 | 42 | 0.824 | 0.3173 | 0.3055 | 1.1984 | 1.1540 |
| 2018-19 | Torpedo Nizhny Novgorod | KHL | 39 | 3 | 13 | 16 | 0.410 | 1.0257 | 0.9594 | 5.6534 | 5.2879 |
| 2019-20 | — | SHL | 44 | 2 | 11 | 13 | 0.295 | 0.7388 | 0.7388 | 3.8559 | 3.8559 |
| 2020-21 | ERC Ingolstadt | DEL | 30 | 3 | 19 | 22 | 0.733 | 1.8332 | 1.8332 | 6.2171 | 6.2171 |
| 2021-22 | ERC Ingolstadt | DEL | 39 | 3 | 16 | 19 | 0.487 | 1.2180 | 1.0047 | 4.1306 | 3.4072 |
| 2022-23 | ERC Ingolstadt | DEL | 49 | 4 | 21 | 25 | 0.510 | 1.2755 | 1.0146 | 4.3256 | 3.4410 |
| 2023-24 | ERC Ingolstadt | DEL | 50 | 4 | 26 | 30 | 0.600 | 1.5000 | 1.1162 | 5.0869 | 3.7854 |
| 2024-25 | ERC Ingolstadt | DEL | 40 | 2 | 18 | 20 | 0.500 | 1.2500 | 0.8728 | 4.2391 | 2.9598 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2013-14 | Union | D1 | ECAC | — | 40 | 8 | 31 | 39 | 0.975 |
| 2012-13 | Union | D1 | ECAC | — | 35 | 6 | 18 | 24 | 0.686 |
| 2011-12 | Union | D1 | ECAC | — | 39 | 8 | 21 | 29 | 0.744 |
| 2010-11 | Union | D1 | ECAC | — | 40 | 6 | 26 | 32 | 0.800 |
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.