icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2013-14 | Lone Star Brahmas | NAHL | 2 | 1 | 0 | 1 | 0.500 | 0.1981 | 0.2035 | 0.5250 | 0.5393 |
| 2014-15 | Green Bay Gamblers | USHL | 55 | 11 | 7 | 18 | 0.327 | 0.2012 | 0.1897 | 0.9643 | 0.9091 |
| 2021-22 | Ässät | Liiga | 56 | 18 | 14 | 32 | 0.571 | 1.4285 | 1.3322 | — | — |
| 2022-23 | Ässät | Liiga | 58 | 15 | 13 | 28 | 0.483 | 1.2070 | 1.0988 | — | — |
| 2023-24 | Vityaz Moscow Region | KHL | 68 | 10 | 25 | 35 | 0.515 | 1.2868 | 1.1373 | — | — |
| 2024-25 | Vityaz Moscow Region | KHL | 68 | 21 | 22 | 43 | 0.632 | 1.5810 | 1.3506 | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2018-19 | Mercyhurst | D1 | AHA | SR | 38 | 15 | 22 | 37 | 0.974 |
| 2017-18 | Mercyhurst | D1 | AHA | JR | 37 | 15 | 23 | 38 | 1.027 |
| 2016-17 | Mercyhurst | D1 | AHA | SO | 39 | 9 | 28 | 37 | 0.949 |
| 2015-16 | Mercyhurst | D1 | AHA | FR | 36 | 10 | 22 | 32 | 0.889 |
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.