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 | Toronto Furies | CWHL | 22 | 5 | 10 | 15 | 0.682 | — | — | — | — |
| 2016-17 | Toronto Furies | CWHL | 24 | 4 | 3 | 7 | 0.292 | — | — | — | — |
| 2018-19 | Toronto Furies | CWHL | 15 | 2 | 2 | 4 | 0.267 | — | — | — | — |
| 2019-20 | Toronto Furies | CWHL | 15 | 2 | 2 | 4 | 0.267 | — | — | — | — |
| 2020-21 | Toronto Furies | CWHL | 15 | 2 | 2 | 4 | 0.267 | — | — | — | — |
| 2021-22 | Toronto Furies | CWHL | 15 | 2 | 2 | 4 | 0.267 | — | — | — | — |
| 2022-23 | Toronto Furies | CWHL | 15 | 2 | 2 | 4 | 0.267 | — | — | — | — |
| 2023-24 | Toronto Furies | CWHL | 15 | 2 | 2 | 4 | 0.267 | — | — | — | — |
| 2025-26 | Toronto Furies | CWHL | 15 | 2 | 2 | 4 | 0.267 | — | — | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2014-15 | Cornell | D1 | ECAC-W | SR | 32 | 21 | 27 | 48 | 1.500 |
| 2013-14 | Cornell | D1 | ECAC-W | JR | 34 | 21 | 22 | 43 | 1.265 |
| 2012-13 | Cornell | D1 | ECAC-W | SO | 28 | 9 | 12 | 21 | 0.750 |
| 2011-12 | Cornell | D1 | ECAC-W | FR | 32 | 7 | 12 | 19 | 0.594 |
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.