| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2012-13 | Chicago Steel | USHL | 52 | 5 | 7 | 12 | 0.231 | 0.1361 | 0.1501 | 0.6932 | 0.7643 |
| 2013-14 | — | USHL | 49 | 8 | 9 | 17 | 0.347 | 0.2046 | 0.2160 | 1.0419 | 1.1000 |
| 2014-15 | — | USHL | 39 | 3 | 8 | 11 | 0.282 | 0.1664 | 0.1675 | 0.8473 | 0.8529 |
| 2015-16 | Nanaimo Clippers | BCHL | 53 | 14 | 21 | 35 | 0.660 | 0.2545 | 0.2495 | 0.9611 | 0.9421 |
| 2016-17 | Janesville Jets | NAHL | 41 | 12 | 27 | 39 | 0.951 | 0.3495 | 0.3286 | 1.0040 | 0.9438 |
| 2023-24 | Starbulls Rosenheim | DEL2 | 28 | 10 | 12 | 22 | 0.786 | 1.0525 | 1.0337 | 1.4099 | 1.3847 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | American International | D1 | AHA | GR | 36 | 13 | 16 | 29 | 0.806 |
| 2020-21 | American International | D1 | AHA | SR | 19 | 7 | 11 | 18 | 0.947 |
| 2019-20 | American International | D1 | AHA | JR | 34 | 3 | 6 | 9 | 0.265 |
| 2018-19 | American International | D1 | AHA | SO | 34 | 3 | 11 | 14 | 0.412 |
| 2017-18 | American International | D1 | AHA | FR | 11 | 0 | 1 | 1 | 0.091 |
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.