icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2012-13 | Toronto Furies | CWHL | 24 | 8 | 17 | 25 | 1.042 | — | — | — | — |
| 2014-15 | Calgary Inferno | CWHL | 24 | 17 | 20 | 37 | 1.542 | — | — | — | — |
| 2015-16 | Calgary Inferno | CWHL | 4 | 4 | 2 | 6 | 1.500 | — | — | — | — |
| 2016-17 | Calgary Inferno | CWHL | 20 | 7 | 15 | 22 | 1.100 | — | — | — | — |
| 2017-18 | Calgary Inferno | CWHL | 0 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2018-19 | Calgary Inferno | CWHL | 27 | 15 | 24 | 39 | 1.444 | — | — | — | — |
| 2019-20 | Calgary Inferno | CWHL | 27 | 15 | 24 | 39 | 1.444 | — | — | — | — |
| 2020-21 | Calgary Inferno | CWHL | 27 | 15 | 24 | 39 | 1.444 | — | — | — | — |
| 2021-22 | Calgary Inferno | CWHL | 27 | 15 | 24 | 39 | 1.444 | — | — | — | — |
| 2022-23 | Calgary Inferno | CWHL | 27 | 15 | 24 | 39 | 1.444 | — | — | — | — |
| 2023-24 | Calgary Inferno | CWHL | 27 | 15 | 24 | 39 | 1.444 | — | — | — | — |
| 2025-26 | Calgary Inferno | CWHL | 27 | 15 | 24 | 39 | 1.444 | — | — | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2011-12 | Cornell | D1 | ECAC-W | SR | 33 | 30 | 31 | 61 | 1.849 |
| 2010-11 | Cornell | D1 | ECAC-W | JR | 33 | 26 | 24 | 50 | 1.515 |
| 2008-09 | Cornell | D1 | ECAC-W | SO | 26 | 25 | 20 | 45 | 1.731 |
| 2007-08 | Cornell | D1 | ECAC-W | FR | 26 | 16 | 16 | 32 | 1.231 |
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.