icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2019-20 | Philadelphia Jr Flyers 16U | JWHL-U16 | 28 | 1 | 9 | 10 | 0.357 | 0.0778 | 0.0778 | — | — |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Plattsburgh | D3 | SUNYAC | SR | 28 | 7 | 15 | 22 | 0.786 |
| 2025-26 | SUNY Plattsburgh | D3 | SUNYAC | SR | 28 | 7 | 15 | 22 | 0.786 |
| 2024-25 | Plattsburgh | D3 | SUNYAC | JR | 28 | 1 | 7 | 8 | 0.286 |
| 2024-25 | Plattsburgh State | D3 | — | — | 28 | 1 | 7 | 8 | 0.286 |
| 2024-25 | SUNY Plattsburgh | D3 | — | — | 28 | 1 | 7 | 8 | 0.286 |
| 2023-24 | Plattsburgh | D3 | SUNYAC | SO | 26 | 2 | 4 | 6 | 0.231 |
| 2023-24 | Plattsburgh State | D3 | — | — | 28 | 2 | 4 | 6 | 0.214 |
| 2023-24 | SUNY Plattsburgh | D3 | — | — | 28 | 2 | 4 | 6 | 0.214 |
| 2022-23 | Plattsburgh | D3 | — | FR | 29 | 2 | 7 | 9 | 0.310 |
| 2022-23 | Plattsburgh State | D3 | — | — | 29 | 2 | 7 | 9 | 0.310 |
| 2022-23 | SUNY Plattsburgh | D3 | — | — | 29 | 2 | 7 | 9 | 0.310 |
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.