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 | Waterloo Black Hawks | USHL | 13 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2023-24 | Waterloo Black Hawks | USHL | 25 | 3 | 2 | 5 | 0.200 | 0.1180 | 0.1261 | 0.6001 | 0.6413 |
| 2024-25 | Sherwood Park Crusaders | BCHL | 28 | 9 | 11 | 20 | 0.714 | 0.2752 | 0.2874 | 1.0389 | 1.0850 |
| 2025-26 | Kingston Frontenacs | OHL | 64 | 14 | 15 | 29 | 0.453 | 0.2621 | 0.2533 | 1.1864 | 1.1465 |
| 2026-27 | Newfoundland Regiment | QMJHL | 7 | 6 | 1 | 7 | 1.000 | 0.4913 | 0.4913 | 2.6696 | 2.6696 |
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.