icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2010-11 | Jungadler Mannheim U18 | DNL | 36 | 9 | 23 | 32 | 0.889 | 0.1373 | 0.1530 | 0.5698 | 0.6351 |
| 2011-12 | Jungadler Mannheim U18 | DNL | 36 | 6 | 22 | 28 | 0.778 | 0.1202 | 0.1286 | 0.4986 | 0.5334 |
| 2012-13 | Muskegon Lumberjacks | USHL | 50 | 3 | 22 | 25 | 0.500 | 0.3184 | 0.3343 | 1.4984 | 1.5731 |
| 2013-14 | — | USHL | 56 | 13 | 24 | 37 | 0.661 | 0.4207 | 0.4220 | 1.9799 | 1.9860 |
| 2019-20 | Kölner Haie | DEL | 51 | 9 | 18 | 27 | 0.529 | — | — | — | — |
| 2020-21 | Kölner Haie | DEL | 37 | 12 | 23 | 35 | 0.946 | — | — | — | — |
| 2021-22 | EHC München | DEL | 45 | 12 | 37 | 49 | 1.089 | — | — | — | — |
| 2022-23 | EHC München | DEL | 55 | 6 | 22 | 28 | 0.509 | — | — | — | — |
| 2023-24 | Eisbären Berlin | DEL | 50 | 12 | 26 | 38 | 0.760 | — | — | — | — |
| 2024-25 | Eisbären Berlin | DEL | 50 | 14 | 24 | 38 | 0.760 | — | — | — | — |
| 2025-26 | Eisbären Berlin | DEL | 52 | 9 | 38 | 47 | 0.904 | — | — | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | Western Michigan | D1 | NCHC | JR | 37 | 9 | 12 | 21 | 0.568 |
| 2015-16 | Western Michigan | D1 | NCHC | SO | 36 | 7 | 10 | 17 | 0.472 |
| 2014-15 | Western Michigan | D1 | NCHC | FR | 32 | 11 | 10 | 21 | 0.656 |
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.