| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2009-10 | St. Louis Bandits | NAHL | 1 | 0 | 1 | 1 | 1.000 | 0.3674 | 0.3960 | 1.0555 | 1.1376 |
| 2010-11 | Green Bay Gamblers | USHL | 57 | 9 | 17 | 26 | 0.456 | 0.2691 | 0.2660 | 1.3699 | 1.3541 |
| 2011-12 | — | USHL | 58 | 21 | 28 | 49 | 0.845 | 0.4983 | 0.4693 | 2.5373 | 2.3897 |
| 2012-13 | — | USHL | 64 | 13 | 30 | 43 | 0.672 | 0.3964 | 0.3522 | 2.0180 | 1.7932 |
| 2017-18 | SaiPa | Liiga | 60 | 10 | 19 | 29 | 0.483 | 1.2083 | 1.2249 | 4.1545 | 4.2117 |
| 2018-19 | SaiPa | Liiga | 58 | 13 | 15 | 28 | 0.483 | 1.2070 | 1.1785 | 4.1502 | 4.0521 |
| 2019-20 | Mora IK | Allsvenskan | 11 | 3 | 1 | 4 | 0.364 | 0.9090 | 0.9090 | 3.4113 | 3.4113 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | Penn State | D1 | BigTen | SR | 39 | 11 | 27 | 38 | 0.974 |
| 2015-16 | Penn State | D1 | BigTen | JR | 38 | 11 | 27 | 38 | 1.000 |
| 2014-15 | Penn State | D1 | BigTen | SO | 36 | 15 | 19 | 34 | 0.944 |
| 2013-14 | Penn State | D1 | BigTen | FR | 34 | 7 | 11 | 18 | 0.529 |
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.