icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2007-08 | Chicago Steel | USHL | 60 | 23 | 38 | 61 | 1.017 | 0.6250 | 0.6005 | 2.9954 | 2.8778 |
| 2016-17 | Jokerit | KHL | 55 | 16 | 20 | 36 | 0.654 | 1.6362 | 1.5285 | — | — |
| 2017-18 | Jokerit | KHL | 44 | 14 | 16 | 30 | 0.682 | 1.7045 | 1.5195 | — | — |
| 2018-19 | Jokerit | KHL | 62 | 13 | 45 | 58 | 0.935 | 2.3388 | 1.9813 | — | — |
| 2019-20 | Jokerit | KHL | 56 | 19 | 29 | 48 | 0.857 | 2.1427 | 2.1427 | — | — |
| 2020-21 | Jokerit | KHL | 53 | 12 | 42 | 54 | 1.019 | 2.5473 | 2.5473 | — | — |
| 2021-22 | Jokerit | KHL | 41 | 9 | 33 | 42 | 1.024 | 2.5610 | 1.7490 | — | — |
| 2024-25 | Luleå HF | SHL | 51 | 13 | 25 | 38 | 0.745 | 1.8627 | 0.9503 | — | — |
| 2025-26 | Luleå HF | SHL | 50 | 11 | 25 | 36 | 0.720 | 1.8000 | 1.8000 | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2011-12 | Yale | D1 | ECAC | SR | 35 | 21 | 25 | 46 | 1.314 |
| 2010-11 | Yale | D1 | ECAC | JR | 36 | 20 | 26 | 46 | 1.278 |
| 2009-10 | Yale | D1 | ECAC | SO | 34 | 16 | 29 | 45 | 1.323 |
| 2008-09 | Yale | D1 | ECAC | FR | 33 | 12 | 14 | 26 | 0.788 |
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.