icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2015-16 | Thayer | NE-Prep-Girls | 23 | 4 | 9 | 13 | 0.570 | 0.3537 | 0.3537 | — | — |
| 2016-17 | Thayer | NE-Prep-Girls | 29 | 11 | 15 | 26 | 0.900 | 0.5585 | 0.5585 | — | — |
| 2017-18 | Thayer | NE-Prep-Girls | 27 | 10 | 6 | 16 | 0.590 | 0.3661 | 0.3661 | — | — |
| 2018-19 | Thayer | NE-Prep-Girls | 15 | 3 | 6 | 9 | 0.600 | 0.3723 | 0.3723 | — | — |
| 2019-20 | Thayer Academy | USHS-W | 22 | 4 | 1 | 5 | 0.227 | 0.0683 | 0.0683 | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2024-25 | Endicott | D3 | NEHC | — | 28 | 15 | 13 | 28 | 1.000 |
| 2023-24 | Endicott | D3 | NEHC | — | 26 | 11 | 14 | 25 | 0.962 |
| 2022-23 | Plattsburgh | D3 | — | JR | 24 | 1 | 0 | 1 | 0.042 |
| 2022-23 | Plattsburgh State | D3 | — | — | 24 | 1 | 0 | 1 | 0.042 |
| 2022-23 | SUNY Plattsburgh | D3 | — | — | 24 | 1 | 0 | 1 | 0.042 |
| 2021-22 | Plattsburgh | D3 | — | SO | 22 | 2 | 2 | 4 | 0.182 |
| 2021-22 | Plattsburgh State | D3 | — | — | 24 | 2 | 2 | 4 | 0.167 |
| 2021-22 | SUNY Plattsburgh | D3 | — | — | 24 | 2 | 2 | 4 | 0.167 |
| 2020-21 | Plattsburgh State | D3 | — | — | 0 | 0 | 0 | 0 | 0.000 |
| 2020-21 | SUNY Plattsburgh | D3 | — | — | 0 | 0 | 0 | 0 | 0.000 |
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.