| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Weyburn Red Wings | SJHL | 46 | 10 | 8 | 18 | 0.391 | 0.1003 | 0.1146 | 0.2950 | 0.3371 |
| 2022-23 | Weyburn Red Wings | SJHL | 56 | 25 | 32 | 57 | 1.018 | 0.2608 | 0.2850 | 0.7673 | 0.8386 |
| 2023-24 | Brooks Bandits | AJHL | 34 | 17 | 16 | 33 | 0.971 | 0.3255 | 0.3358 | 0.8982 | 0.9267 |
| 2024-25 | Waterloo Black Hawks | USHL | 53 | 8 | 9 | 17 | 0.321 | 0.1892 | 0.1810 | 0.9635 | 0.9218 |
| 2025-26 | Waterloo Black Hawks | USHL | 57 | 24 | 20 | 44 | 0.772 | 0.4553 | 0.4136 | 2.3183 | 2.1058 |
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.