icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2014-15 | Green Bay Gamblers | USHL | 57 | 5 | 9 | 14 | 0.246 | 0.1510 | 0.1582 | 0.7236 | 0.7581 |
| 2015-16 | Green Bay Gamblers | USHL | 60 | 12 | 12 | 24 | 0.400 | 0.2459 | 0.2462 | 1.1785 | 1.1800 |
| 2024-25 | Selber Wölfe | DEL2 | 26 | 13 | 17 | 30 | 1.154 | 0.4977 | 0.4838 | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Miami | D1 | NCHC | — | 36 | 9 | 10 | 19 | 0.528 |
| 2024-25 | Quinnipiac | D1 | ECAC | — | 33 | 2 | 3 | 5 | 0.151 |
| 2019-20 | Maine | D1 | HockeyEast | SR | 34 | 4 | 6 | 10 | 0.294 |
| 2019-20 | Stonehill | D1 | AHA | SR | 6 | 0 | 4 | 4 | 0.667 |
| 2018-19 | Maine | D1 | HockeyEast | JR | 28 | 6 | 3 | 9 | 0.321 |
| 2018-19 | Stonehill | D1 | AHA | JR | 5 | 0 | 0 | 0 | 0.000 |
| 2018-19 | Wisconsin-River Falls | D1 | BigTen | FR | 3 | 0 | 0 | 0 | 0.000 |
| 2017-18 | Castleton | D3 | — | SR | 25 | 1 | 10 | 11 | 0.440 |
| 2017-18 | Maine | D1 | HockeyEast | — | 31 | 4 | 2 | 6 | 0.194 |
| 2016-17 | Castleton | D3 | — | JR | 22 | 3 | 8 | 11 | 0.500 |
| 2016-17 | Maine | D1 | HockeyEast | — | 33 | 4 | 6 | 10 | 0.303 |
| 2015-16 | Castleton | D3 | — | SO | 20 | 0 | 5 | 5 | 0.250 |
| 2014-15 | Castleton | D3 | — | FR | 13 | 1 | 4 | 5 | 0.385 |
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.