icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2009-10 | Frölunda HC U20 | SHL-J20 | 4 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2010-11 | Frölunda HC U20 | SHL-J20 | 20 | 1 | 1 | 2 | 0.100 | 0.0556 | 0.0597 | 0.1332 | 0.1431 |
| 2011-12 | Frölunda HC U20 | SuperElit | 45 | 13 | 6 | 19 | 0.422 | 0.1666 | 0.1732 | 0.5734 | 0.5962 |
| 2012-13 | Frölunda HC U20 | SuperElit | 41 | 29 | 17 | 46 | 1.122 | 0.4427 | 0.4377 | 1.5238 | 1.5065 |
| 2018-19 | IK Pantern | Allsvenskan | 48 | 15 | 11 | 26 | 0.542 | 1.3542 | 1.3512 | 5.0822 | 5.0708 |
| 2019-20 | Almtuna IS | Allsvenskan | 52 | 12 | 21 | 33 | 0.635 | 1.5865 | 1.5865 | 5.9538 | 5.9538 |
| 2020-21 | Linköping HC | SHL | 5 | 1 | 0 | 1 | 0.200 | 0.5000 | 0.5000 | 2.6097 | 2.6097 |
| 2021-22 | Södertälje SK | Allsvenskan | 42 | 9 | 11 | 20 | 0.476 | 1.1905 | 1.0339 | 4.4677 | 3.8799 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | Michigan State | D1 | BigTen | SR | 35 | 7 | 8 | 15 | 0.429 |
| 2015-16 | Michigan State | D1 | BigTen | JR | 31 | 7 | 8 | 15 | 0.484 |
| 2014-15 | Michigan State | D1 | BigTen | SO | 35 | 5 | 5 | 10 | 0.286 |
| 2013-14 | Michigan State | D1 | BigTen | FR | 35 | 8 | 5 | 13 | 0.371 |
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.