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 | Spruce Grove Saints | AJHL | 5 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2022-23 | Spruce Grove Saints | AJHL | 44 | 6 | 20 | 26 | 0.591 | 0.1982 | 0.2121 | 0.5468 | 0.5852 |
| 2023-24 | Langley Rivermen | BCHL | 53 | 2 | 11 | 13 | 0.245 | 0.0945 | 0.0969 | 0.3570 | 0.3660 |
| 2024-25 | Waterloo Black Hawks | USHL | 59 | 2 | 10 | 12 | 0.203 | 0.1200 | 0.1138 | 0.6109 | 0.5791 |
| 2025-26 | Green Bay Gamblers | USHL | 45 | 2 | 6 | 8 | 0.178 | 0.1049 | 0.0944 | 0.5340 | 0.4805 |
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.