icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2012-13 | Fargo Force | USHL | 4 | 0 | 1 | 1 | 0.250 | 0.1537 | 0.1620 | 0.7366 | 0.7762 |
| 2013-14 | Fargo Force | USHL | 47 | 3 | 9 | 12 | 0.255 | 0.1569 | 0.1580 | 0.7522 | 0.7574 |
| 2017-18 | Kärpät | Liiga | 44 | 4 | 16 | 20 | 0.455 | 1.1362 | 1.3394 | — | — |
| 2018-19 | Kärpät | Liiga | 60 | 9 | 21 | 30 | 0.500 | 1.2500 | 1.4268 | — | — |
| 2022-23 | TPS | Liiga | 58 | 5 | 15 | 20 | 0.345 | 0.8620 | 0.7977 | — | — |
| 2023-24 | Malmö Redhawks | SHL | 29 | 6 | 6 | 12 | 0.414 | 1.0345 | 0.9526 | — | — |
| 2024-25 | Malmö Redhawks | SHL | 43 | 2 | 12 | 14 | 0.326 | 0.8140 | 0.7019 | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | Colorado College | D1 | NCHC | — | 36 | 2 | 8 | 10 | 0.278 |
| 2015-16 | Colorado College | D1 | NCHC | — | 36 | 3 | 12 | 15 | 0.417 |
| 2014-15 | Colorado College | D1 | NCHC | — | 35 | 5 | 6 | 11 | 0.314 |
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.