icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2011-12 | U.S. National U17 Team | NTDP-U18 | 55 | 26 | 22 | 48 | 0.873 | 0.6941 | 0.7017 | 3.2684 | 3.3044 |
| 2012-13 | U.S. National U18 Team | NTDP-U18 | 67 | 29 | 30 | 59 | 0.881 | 0.7004 | 0.6715 | 3.2980 | 3.1620 |
| 2018-19 | ERC Ingolstadt | DEL | 50 | 13 | 25 | 38 | 0.760 | — | — | — | — |
| 2019-20 | IK Oskarshamn | SHL | 51 | 13 | 15 | 28 | 0.549 | — | — | — | — |
| 2020-21 | IK Oskarshamn | SHL | 48 | 8 | 21 | 29 | 0.604 | — | — | — | — |
| 2021-22 | Rögle BK | SHL | 34 | 4 | 6 | 10 | 0.294 | — | — | — | — |
| 2022-23 | — | Liiga | 50 | 10 | 19 | 29 | 0.580 | — | — | — | — |
| 2023-24 | Brynäs IF | Allsvenskan | 25 | 11 | 14 | 25 | 1.000 | — | — | — | — |
| 2024-25 | Djurgårdens IF | Allsvenskan | 33 | 14 | 10 | 24 | 0.727 | — | — | — | — |
| 2025-26 | MoDo Hockey | Allsvenskan | 45 | 14 | 21 | 35 | 0.778 | — | — | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | New Hampshire | D1 | HockeyEast | SR | 40 | 24 | 39 | 63 | 1.575 |
| 2015-16 | New Hampshire | D1 | HockeyEast | JR | 37 | 10 | 36 | 46 | 1.243 |
| 2014-15 | New Hampshire | D1 | HockeyEast | SO | 39 | 18 | 24 | 42 | 1.077 |
| 2013-14 | New Hampshire | D1 | HockeyEast | FR | 37 | 5 | 11 | 16 | 0.432 |
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.