icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1995-96 | Des Moines Buccaneers | USHL | 34 | 11 | 17 | 28 | 0.824 | 0.4858 | 0.4663 | 2.4262 | 2.3289 |
| 2007-08 | Neepawa Titans | MJHL | 56 | 13 | 15 | 28 | 0.500 | 0.0963 | 0.1080 | 0.3151 | 0.3534 |
| 2008-09 | Tri-City Storm | USHL | 58 | 10 | 14 | 24 | 0.414 | 0.2441 | 0.2583 | 1.2191 | 1.2899 |
| 2009-10 | Sioux Falls Stampede | USHL | 59 | 14 | 33 | 47 | 0.797 | 0.4699 | 0.4671 | 2.3469 | 2.3331 |
| 2017-18 | Mora IK | SHL | 52 | 10 | 6 | 16 | 0.308 | 0.7692 | 0.7479 | — | — |
| 2018-19 | Mora IK | SHL | 41 | 12 | 6 | 18 | 0.439 | 1.0975 | 1.0125 | — | — |
| 2019-20 | ERC Ingolstadt | DEL | 48 | 9 | 16 | 25 | 0.521 | 1.3020 | 1.3020 | — | — |
| 2020-21 | Södertälje SK | Allsvenskan | 38 | 2 | 10 | 12 | 0.316 | 0.7895 | 0.7895 | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2013-14 | Alaska Anchorage | D1 | WCHA | SR | 38 | 20 | 18 | 38 | 1.000 |
| 2012-13 | Alaska Anchorage | D1 | — | JR | 36 | 7 | 12 | 19 | 0.528 |
| 2011-12 | Alaska Anchorage | D1 | — | SO | 34 | 10 | 7 | 17 | 0.500 |
| 2010-11 | Alaska Anchorage | D1 | — | FR | 30 | 10 | 10 | 20 | 0.667 |
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.