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 | Youngstown Phantoms | USHL | 1 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2023-24 | Maryland Black Bears | NAHL | 51 | 3 | 20 | 23 | 0.451 | 0.1657 | 0.1865 | — | — |
| 2024-25 | Maryland Black Bears | NAHL | 23 | 4 | 6 | 10 | 0.435 | 0.1597 | 0.1713 | 0.4595 | 0.4930 |
| 2025-26 | Tri-City Storm | USHL | 45 | 1 | 7 | 8 | 0.178 | 0.1049 | 0.1029 | 0.5335 | 0.5231 |
| 2026-27 | Tri-City Storm | USHL | 6 | 1 | 1 | 2 | 0.333 | 0.1966 | 0.1966 | 1.0001 | 1.0001 |
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.