| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | USHL-Style-Czech | 30 | 10 | 21 | 31 | 1.033 | 0.2983 | 0.3390 | 0.9541 | 1.0844 |
| 2023-24 | — | USHL-Style-Czech | 29 | 8 | 21 | 29 | 1.000 | 0.2887 | 0.3062 | 0.9234 | 0.9793 |
| 2024-25 | — | USHL | 56 | 17 | 42 | 59 | 1.054 | 0.6709 | 0.7021 | 3.1573 | 3.3040 |
| 2025-26 | — | OHL | 48 | 28 | 49 | 77 | 1.604 | 0.9574 | 0.9528 | 4.1552 | 4.1353 |
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.