icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | Pittsburgh Penguins Elite 13U | 13u-aaa | 57 | 7 | 22 | 29 | 0.509 | — | — | — | — |
| 2023-24 | Pittsburgh Penguins Elite 14U | 14u-aaa | 65 | 4 | 17 | 21 | 0.323 | — | — | — | — |
| 2024-25 | Pittsburgh Penguins Elite 15U | 15u-aaa | 62 | 1 | 20 | 21 | 0.339 | — | — | — | — |
| 2025-26 | Oakland Jr. Grizzlies 16U AAA | maha-16u | 18 | 3 | 6 | 9 | 0.500 | — | — | — | — |
| 2026-27 | Oklahoma Warriors | NAHL | 4 | 0 | 2 | 2 | 0.500 | 0.1837 | 0.2027 | 0.5284 | 0.5830 |
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.