| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2008-09 | Winnipeg Blues | MJHL | 4 | 1 | 2 | 3 | 0.750 | 0.1444 | 0.1637 | 0.4730 | 0.5361 |
| 2009-10 | Winnipeg Blues | MJHL | 53 | 29 | 32 | 61 | 1.151 | 0.2215 | 0.2411 | 0.7258 | 0.7901 |
| 2010-11 | Penticton Vees | BCHL | 58 | 29 | 43 | 72 | 1.241 | 0.4783 | 0.4932 | 1.8066 | 1.8630 |
| 2017-18 | Shanghai Dragons | KHL | 25 | 0 | 2 | 2 | 0.080 | 0.2000 | 0.2189 | 1.1023 | 1.2067 |
| 2018-19 | KooKoo | Liiga | 3 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2021-22 | Düsseldorfer EG | DEL | 56 | 22 | 31 | 53 | 0.946 | 2.3660 | 2.2240 | 8.0238 | 7.5422 |
| 2022-23 | Düsseldorfer EG | DEL | 12 | 6 | 8 | 14 | 1.167 | 2.9168 | 2.6560 | 9.8915 | 9.0071 |
| 2023-24 | Düsseldorfer EG | DEL | 34 | 20 | 17 | 37 | 1.088 | 2.7205 | 2.3377 | 9.2260 | 7.9278 |
| 2024-25 | Düsseldorfer EG | DEL | 52 | 20 | 28 | 48 | 0.923 | 2.3077 | 1.8770 | 7.8262 | 6.3655 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2014-15 | North Dakota | D1 | NCHC | SR | 40 | 13 | 8 | 21 | 0.525 |
| 2013-14 | North Dakota | D1 | NCHC | JR | 35 | 7 | 11 | 18 | 0.514 |
| 2012-13 | North Dakota | D1 | WCHA-orig | SO | 29 | 2 | 8 | 10 | 0.345 |
| 2011-12 | North Dakota | D1 | WCHA-orig | FR | 17 | 5 | 1 | 6 | 0.353 |
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.