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 | HIFK U20 | SM-Liiga-Jr | 31 | 8 | 15 | 23 | 0.742 | 0.4021 | 0.3918 | 1.1019 | 1.0737 |
| 2014-15 | Janesville Jets | NAHL | 33 | 3 | 9 | 12 | 0.364 | 0.1292 | 0.1267 | 1.4731 | 1.3975 |
| 2015-16 | Jokerit U20 | SM-Liiga-Jr | 49 | 17 | 50 | 67 | 1.367 | 0.7411 | 0.6495 | 2.0307 | 1.7798 |
| 2016-17 | Sport | Liiga | 27 | 4 | 7 | 11 | 0.407 | 1.0185 | 1.2615 | — | — |
| 2017-18 | Sport | Liiga | 59 | 9 | 22 | 31 | 0.525 | 1.3135 | 1.5366 | — | — |
| 2018-19 | Sport | Liiga | 45 | 15 | 16 | 31 | 0.689 | 1.7222 | 1.9502 | — | — |
| 2019-20 | Sport | Liiga | 58 | 7 | 19 | 26 | 0.448 | 1.1207 | 1.1207 | — | — |
| 2020-21 | KooKoo | Liiga | 53 | 6 | 20 | 26 | 0.491 | 1.2265 | 1.2265 | — | — |
| 2021-22 | KooKoo | Liiga | 48 | 8 | 7 | 15 | 0.312 | 0.7812 | 0.7332 | — | — |
| 2022-23 | Kristianstads IK | Allsvenskan | 49 | 14 | 14 | 28 | 0.571 | 1.4285 | 1.2586 | — | — |
| 2023-24 | Nybro Vikings IF | Allsvenskan | 52 | 7 | 8 | 15 | 0.288 | 0.7212 | 0.6160 | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | Alaska Anchorage | D1 | WCHA | FR | 18 | 0 | 4 | 4 | 0.222 |
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.