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 | Lakeville South | USHS-MN | 29 | 8 | 9 | 17 | 0.586 | 0.0742 | 0.0742 | 0.1423 | 0.1423 |
| 2023-24 | Lakeville South | USHS-MN | 26 | 11 | 19 | 30 | 1.154 | 0.1461 | 0.1461 | 0.2801 | 0.2801 |
| 2024-25 | Lincoln Stars | USHL | 1 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2025-26 | Lincoln Stars | USHL | 50 | 6 | 4 | 10 | 0.200 | 0.1180 | 0.1185 | 0.6001 | 0.6025 |
| 2026-27 | Lincoln Stars | USHL | 4 | 1 | 0 | 1 | 0.250 | 0.1475 | 0.1475 | 0.7502 | 0.7502 |
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.