| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | La Ronge Ice Wolves | SJHL | 35 | 0 | 4 | 4 | 0.114 | 0.0293 | 0.0304 | 0.0847 | 0.0879 |
| 2023-24 | La Ronge Ice Wolves | SJHL | 54 | 9 | 7 | 16 | 0.296 | 0.0759 | 0.0751 | 0.2196 | 0.2174 |
| 2024-25 | La Ronge Ice Wolves | SJHL | 55 | 21 | 24 | 45 | 0.818 | 0.2096 | 0.1963 | 0.6064 | 0.5678 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Middlebury | D3 | NESCAC | — | 10 | 0 | 1 | 1 | 0.100 |
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.