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 | Hawkesbury Hawks | CCHL | 61 | 5 | 27 | 32 | 0.525 | 0.1675 | 0.1692 | 0.4061 | 0.4102 |
| 2014-15 | Amarillo Wranglers | NAHL | 58 | 12 | 25 | 37 | 0.638 | 0.2527 | 0.2474 | 0.6697 | 0.6556 |
| 2022-23 | — | Allsvenskan | 51 | 7 | 26 | 33 | 0.647 | 1.6178 | 1.4225 | — | — |
| 2023-24 | MoDo Hockey | SHL | 52 | 4 | 13 | 17 | 0.327 | 0.8173 | 0.7437 | — | — |
| 2024-25 | Tappara | Liiga | 58 | 9 | 28 | 37 | 0.638 | 1.5948 | 1.3524 | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2017-18 | Minnesota | D1 | BigTen | — | 40 | 10 | 25 | 35 | 0.875 |
| 2017-18 | Minnesota State | D1 | WCHA | JR | 40 | 10 | 25 | 35 | 0.875 |
| 2016-17 | Minnesota | D1 | BigTen | — | 31 | 8 | 23 | 31 | 1.000 |
| 2016-17 | Minnesota State | D1 | WCHA | SO | 31 | 8 | 23 | 31 | 1.000 |
| 2015-16 | Minnesota | D1 | BigTen | — | 36 | 2 | 9 | 11 | 0.306 |
| 2015-16 | Minnesota State | D1 | WCHA | FR | 36 | 2 | 9 | 11 | 0.306 |
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.