| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2014-15 | Omaha Lancers | USHL | 58 | 10 | 17 | 27 | 0.466 | 0.2746 | 0.2699 | 1.3981 | 1.3743 |
| 2015-16 | Omaha Lancers | USHL | 58 | 16 | 32 | 48 | 0.828 | 0.4882 | 0.4572 | 2.4856 | 2.3278 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | San Diego State University | ACHA_D1 | — | — | 8 | 1 | 2 | 3 | 0.375 |
| 2024-25 | San Diego State University | ACHA_D1 | — | — | 8 | 1 | 2 | 3 | 0.375 |
| 2023-24 | San Diego State University | ACHA_D1 | — | — | 8 | 1 | 2 | 3 | 0.375 |
| 2022-23 | San Diego State University | ACHA_D1 | — | — | 8 | 1 | 2 | 3 | 0.375 |
| 2021-22 | San Diego State University | ACHA_D1 | — | — | 8 | 1 | 2 | 3 | 0.375 |
| 2020-21 | San Diego State University | ACHA_D1 | — | — | 8 | 1 | 2 | 3 | 0.375 |
| 2019-20 | Minnesota State | D1 | WCHA | SR | 37 | 5 | 8 | 13 | 0.351 |
| 2018-19 | Minnesota State | D1 | WCHA | JR | 41 | 8 | 8 | 16 | 0.390 |
| 2017-18 | Minnesota State | D1 | WCHA | SO | 32 | 1 | 1 | 2 | 0.062 |
| 2016-17 | Minnesota State | D1 | WCHA | FR | 39 | 0 | 6 | 6 | 0.154 |
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.