| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2013-14 | Tri-City Storm | USHL | 3 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2014-15 | Chicago Steel | USHL | 59 | 3 | 18 | 21 | 0.356 | 0.2099 | 0.2185 | 1.0689 | 1.1126 |
| 2015-16 | — | USHL | 57 | 4 | 10 | 14 | 0.246 | 0.1449 | 0.1441 | 0.7376 | 0.7335 |
| 2016-17 | Omaha Lancers | USHL | 60 | 5 | 16 | 21 | 0.350 | 0.2065 | 0.1948 | 1.0512 | 0.9917 |
| 2017-18 | Youngstown Phantoms | USHL | 58 | 1 | 22 | 23 | 0.397 | 0.2340 | 0.2091 | 1.1911 | 1.0645 |
| 2024-25 | IK Oskarshamn | Allsvenskan | 52 | 20 | 18 | 38 | 0.731 | 1.8270 | 1.6171 | 6.8563 | 6.0686 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Western Michigan | D1 | NCHC | SR | 39 | 8 | 24 | 32 | 0.821 |
| 2020-21 | Western Michigan | D1 | NCHC | JR | 25 | 4 | 10 | 14 | 0.560 |
| 2019-20 | Western Michigan | D1 | NCHC | SO | 36 | 2 | 15 | 17 | 0.472 |
| 2018-19 | Western Michigan | D1 | NCHC | FR | 20 | 2 | 2 | 4 | 0.200 |
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.