| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2014-15 | Lincoln Stars | USHL | 53 | 6 | 9 | 15 | 0.283 | 0.1669 | 0.1738 | 0.8500 | 0.8853 |
| 2015-16 | Green Bay Gamblers | USHL | 60 | 6 | 33 | 39 | 0.650 | 0.3834 | 0.3815 | 1.9522 | 1.9426 |
| 2022-23 | Amur Khabarovsk | KHL | 53 | 7 | 13 | 20 | 0.377 | 0.9435 | 0.9799 | 5.2001 | 5.4007 |
| 2023-24 | Amur Khabarovsk | KHL | 37 | 2 | 11 | 13 | 0.351 | 0.8785 | 0.8632 | 4.8418 | 4.7576 |
| 2024-25 | Amur Khabarovsk | KHL | 57 | 2 | 15 | 17 | 0.298 | 0.7455 | 0.7105 | 4.1088 | 3.9159 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2019-20 | Western Michigan | D1 | NCHC | — | 26 | 3 | 18 | 21 | 0.808 |
| 2018-19 | Western Michigan | D1 | NCHC | — | 37 | 7 | 19 | 26 | 0.703 |
| 2017-18 | Western Michigan | D1 | NCHC | — | 36 | 6 | 18 | 24 | 0.667 |
| 2016-17 | Western Michigan | D1 | NCHC | — | 38 | 2 | 12 | 14 | 0.368 |
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.