| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2014-15 | Sioux City Musketeers | USHL | 10 | 1 | 0 | 1 | 0.100 | 0.0590 | 0.0624 | 0.3003 | 0.3175 |
| 2015-16 | Johnstown Tomahawks | NAHL | 51 | 10 | 14 | 24 | 0.471 | 0.1729 | 0.1807 | 0.4967 | 0.5190 |
| 2016-17 | Johnstown Tomahawks | NAHL | 46 | 11 | 25 | 36 | 0.783 | 0.2875 | 0.2848 | 0.8260 | 0.8182 |
| 2020-21 | Krefeld Pinguine | DEL | 38 | 3 | 3 | 6 | 0.158 | 0.3948 | 0.3948 | 1.3387 | 1.3387 |
| 2021-22 | Dinamo Riga | KHL | 37 | 1 | 2 | 3 | 0.081 | 0.2028 | 0.2301 | 1.1175 | 1.2677 |
| 2023-24 | Västerviks IK | Allsvenskan | 52 | 9 | 8 | 17 | 0.327 | 0.8173 | 0.7866 | 3.0669 | 2.9519 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2020-21 | Arizona State | D1 | NCHC | SR | 2 | 0 | 0 | 0 | 0.000 |
| 2019-20 | Arizona State | D1 | NCHC | JR | 32 | 4 | 8 | 12 | 0.375 |
| 2018-19 | Arizona State | D1 | NCHC | SO | 33 | 5 | 5 | 10 | 0.303 |
| 2017-18 | Arizona State | D1 | NCHC | FR | 22 | 1 | 0 | 1 | 0.045 |
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.