| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2011-12 | Düsseldorfer EG U18 | DNL | 34 | 15 | 19 | 34 | 1.000 | 0.1543 | 0.1685 | 0.6416 | 0.7008 |
| 2012-13 | Jungadler Mannheim U18 | DNL | 36 | 26 | 46 | 72 | 2.000 | 0.3086 | 0.3227 | 1.2832 | 1.3417 |
| 2013-14 | Wenatchee Wild | NAHL | 49 | 24 | 25 | 49 | 1.000 | 0.3674 | 0.3898 | 1.0569 | 1.1212 |
| 2014-15 | Sioux Falls Stampede | USHL | 52 | 19 | 15 | 34 | 0.654 | 0.3857 | 0.3766 | 1.9619 | 1.9155 |
| 2015-16 | Sioux Falls Stampede | USHL | 59 | 30 | 24 | 54 | 0.915 | 0.5399 | 0.5021 | 2.7465 | 2.5540 |
| 2020-21 | Eisbären Berlin | DEL | 38 | 11 | 11 | 22 | 0.579 | 1.4472 | 1.4472 | 4.9069 | 4.9069 |
| 2021-22 | — | DEL | 48 | 5 | 6 | 11 | 0.229 | 0.5730 | 0.6346 | 1.9428 | 2.1515 |
| 2022-23 | Straubing Tigers | DEL | 51 | 12 | 14 | 26 | 0.510 | 1.2745 | 1.3739 | 4.3212 | 4.6582 |
| 2023-24 | Straubing Tigers | DEL | 30 | 8 | 13 | 21 | 0.700 | 1.7500 | 1.7967 | 5.9334 | 6.0918 |
| 2024-25 | Kölner Haie | DEL | 51 | 8 | 17 | 25 | 0.490 | 1.2255 | 1.2019 | 4.1551 | 4.0751 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2019-20 | Minnesota State | D1 | WCHA | SR | 37 | 14 | 23 | 37 | 1.000 |
| 2018-19 | Minnesota State | D1 | WCHA | JR | 36 | 14 | 26 | 40 | 1.111 |
| 2017-18 | Minnesota State | D1 | WCHA | SO | 40 | 9 | 28 | 37 | 0.925 |
| 2016-17 | Minnesota State | D1 | WCHA | FR | 39 | 8 | 10 | 18 | 0.462 |
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.