| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2011-12 | Cedar Rapids RoughRiders | USHL | 59 | 19 | 13 | 32 | 0.542 | 0.3200 | 0.3353 | 1.6290 | 1.7071 |
| 2012-13 | — | USHL | 66 | 34 | 26 | 60 | 0.909 | 0.5363 | 0.5335 | 2.7304 | 2.7159 |
| 2018-19 | Växjö Lakers HC | SHL | 31 | 3 | 9 | 12 | 0.387 | 0.9677 | 1.0389 | 5.0511 | 5.4225 |
| 2019-20 | Eisbären Berlin | DEL | 52 | 18 | 22 | 40 | 0.769 | 1.9230 | 1.9230 | 6.5214 | 6.5214 |
| 2020-21 | TPS | Liiga | 16 | 3 | 3 | 6 | 0.375 | 0.9375 | 0.9375 | 3.2235 | 3.2235 |
| 2021-22 | EHC München | DEL | 51 | 25 | 25 | 50 | 0.980 | 2.4510 | 2.5240 | 8.3120 | 8.5595 |
| 2022-23 | EHC München | DEL | 56 | 27 | 28 | 55 | 0.982 | 2.4552 | 2.4562 | 8.3264 | 8.3296 |
| 2023-24 | EHC München | DEL | 51 | 21 | 14 | 35 | 0.686 | 1.7158 | 1.6285 | 5.8186 | 5.5224 |
| 2024-25 | Adler Mannheim | DEL | 11 | 1 | 5 | 6 | 0.545 | 1.3638 | 1.2317 | 4.6249 | 4.1770 |
| 2025-26 | Dresdner Eislöwen | DEL | 52 | 21 | 18 | 39 | 0.750 | 1.8750 | 1.7812 | 6.3586 | 6.0404 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | Nebraska Omaha | D1 | NCHC | SR | 38 | 20 | 27 | 47 | 1.237 |
| 2015-16 | Nebraska Omaha | D1 | NCHC | JR | 35 | 21 | 15 | 36 | 1.029 |
| 2014-15 | Nebraska Omaha | D1 | NCHC | SO | 39 | 20 | 17 | 37 | 0.949 |
| 2013-14 | Nebraska Omaha | D1 | NCHC | FR | 35 | 9 | 10 | 19 | 0.543 |
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.