| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2013-14 | Jungadler Mannheim U18 | DNL | 28 | 13 | 18 | 31 | 1.107 | 0.1709 | 0.1777 | 0.7106 | 0.7390 |
| 2014-15 | Jungadler Mannheim U19 | DNL | 16 | 12 | 14 | 26 | 1.625 | 0.2509 | 0.2492 | 1.0431 | 1.0358 |
| 2015-16 | Jungadler Mannheim U19 | DNL | 38 | 29 | 43 | 72 | 1.895 | 0.2925 | 0.2747 | 1.2162 | 1.1422 |
| 2016-17 | Des Moines Buccaneers | USHL | 51 | 19 | 19 | 38 | 0.745 | 0.4395 | 0.4195 | 2.2378 | 2.1362 |
| 2017-18 | Des Moines Buccaneers | USHL | 60 | 19 | 19 | 38 | 0.633 | 0.3736 | 0.3381 | 1.9021 | 1.7213 |
| 2023-24 | Löwen Frankfurt | DEL | 34 | 11 | 10 | 21 | 0.618 | 1.5440 | 1.7021 | 5.2361 | 5.7722 |
| 2024-25 | Löwen Frankfurt | DEL | 52 | 11 | 21 | 32 | 0.615 | 1.5385 | 1.6254 | 5.2175 | 5.5121 |
| 2025-26 | Iserlohn Roosters | DEL | 49 | 10 | 21 | 31 | 0.633 | 1.5817 | 1.7451 | 5.3642 | 5.9184 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Minnesota State | D1 | CCHA | — | 40 | 18 | 31 | 49 | 1.225 |
| 2020-21 | Minnesota State | D1 | WCHA | JR | 27 | 10 | 18 | 28 | 1.037 |
| 2019-20 | Minnesota State | D1 | WCHA | SO | 35 | 9 | 16 | 25 | 0.714 |
| 2018-19 | Minnesota State | D1 | WCHA | FR | 41 | 8 | 13 | 21 | 0.512 |
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.