icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Lincoln Stars | USHL | 2 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2022-23 | Lincoln Stars | USHL | 4 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2023-24 | New Mexico Ice Wolves | NAHL | 48 | 3 | 15 | 18 | 0.375 | 0.1378 | 0.1427 | 0.3958 | 0.4100 |
| 2024-25 | New Mexico Ice Wolves | NAHL | 53 | 7 | 17 | 24 | 0.453 | 0.1664 | 0.1636 | 0.4779 | 0.4699 |
| 2025-26 | Brooks Bandits | BCHL | 54 | 7 | 12 | 19 | 0.352 | 0.1356 | 0.1260 | 0.5121 | 0.4759 |
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.