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 | Sherwood Park Crusaders | AJHL | 11 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2023-24 | Calgary Hitmen | WHL | 65 | 19 | 26 | 45 | 0.692 | 0.3368 | 0.3499 | 1.6911 | 1.7571 |
| 2024-25 | Calgary Hitmen | WHL | 63 | 10 | 16 | 26 | 0.413 | 0.2008 | 0.1979 | 1.0081 | 0.9936 |
| 2025-26 | Regina Pats | WHL | 56 | 30 | 29 | 59 | 1.054 | 0.5126 | 0.4815 | 2.5736 | 2.4174 |
| 2026-27 | Regina Pats | WHL | 4 | 4 | 2 | 6 | 1.500 | 0.7298 | 0.7298 | 3.6640 | 3.6640 |
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.