| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2006-07 | Chicago Steel | USHL | 14 | 0 | 5 | 5 | 0.357 | 0.2107 | 0.2246 | 1.0725 | 1.1430 |
| 2007-08 | Chicago Steel | USHL | 58 | 19 | 35 | 54 | 0.931 | 0.5492 | 0.5572 | 2.7962 | 2.8368 |
| 2015-16 | Augsburger Panther | DEL | 45 | 15 | 31 | 46 | 1.022 | 2.5555 | 2.8045 | 8.6664 | 9.5107 |
| 2016-17 | Augsburger Panther | DEL | 49 | 11 | 23 | 34 | 0.694 | 1.7348 | 1.8707 | 5.8830 | 6.3437 |
| 2017-18 | Augsburger Panther | DEL | 48 | 11 | 32 | 43 | 0.896 | 2.2395 | 2.3757 | 7.5948 | 8.0566 |
| 2018-19 | Augsburger Panther | DEL | 52 | 11 | 34 | 45 | 0.865 | 2.1635 | 2.0985 | 7.3370 | 7.1166 |
| 2019-20 | Augsburger Panther | DEL | 42 | 11 | 38 | 49 | 1.167 | 2.9168 | 2.9168 | 9.8915 | 9.8915 |
| 2020-21 | Augsburger Panther | DEL | 38 | 10 | 20 | 30 | 0.789 | 1.9737 | 1.9737 | 6.6935 | 6.6935 |
| 2021-22 | Augsburger Panther | DEL | 50 | 5 | 15 | 20 | 0.400 | 1.0000 | 0.7905 | 3.3913 | 2.6809 |
| 2022-23 | Augsburger Panther | DEL | 56 | 13 | 19 | 32 | 0.571 | 1.4285 | 1.0872 | 4.8444 | 3.6870 |
| 2023-24 | Iserlohn Roosters | DEL | 50 | 8 | 11 | 19 | 0.380 | 0.9500 | 0.6743 | 3.2217 | 2.2867 |
| 2024-25 | Dresdner Eislöwen | DEL2 | 43 | 9 | 25 | 34 | 0.791 | 1.0592 | 0.6183 | 1.4188 | 0.8282 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2012-13 | St. Cloud State | D1 | WCHA-orig | SR | 42 | 13 | 37 | 50 | 1.190 |
| 2011-12 | St. Cloud State | D1 | WCHA-orig | SR | 10 | 2 | 10 | 12 | 1.200 |
| 2010-11 | St. Cloud State | D1 | WCHA-orig | JR | 38 | 13 | 26 | 39 | 1.026 |
| 2009-10 | St. Cloud State | D1 | NCHC | SO | 43 | 6 | 25 | 31 | 0.721 |
| 2008-09 | St. Cloud State | D1 | NCHC | FR | 38 | 8 | 7 | 15 | 0.395 |
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.