| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2001-02 | Dauphin Kings | MJHL | 52 | 12 | 20 | 32 | 0.615 | 0.1741 | 0.1743 | 0.3878 | 0.3883 |
| 2002-03 | Dauphin Kings | MJHL | 61 | 14 | 33 | 47 | 0.770 | 0.2180 | 0.2095 | 0.4855 | 0.4667 |
| 2003-04 | Dauphin Kings | MJHL | 44 | 17 | 23 | 40 | 0.909 | 0.2572 | 0.2352 | 0.5728 | 0.5239 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2004-05 | SUNY Plattsburgh | D3 | — | FR | 14 | 2 | 3 | 5 | 0.357 |
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.