| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Newmarket Hurricanes | OJHL | 36 | 5 | 12 | 17 | 0.472 | 0.1418 | 0.1510 | 0.3232 | 0.3442 |
| 2022-23 | Newmarket Hurricanes | OJHL | 54 | 11 | 37 | 48 | 0.889 | 0.2670 | 0.2707 | 0.6085 | 0.6170 |
| 2023-24 | Shreveport Mudbugs | NAHL | 60 | 21 | 27 | 48 | 0.800 | 0.3170 | 0.3149 | 0.8399 | 0.8344 |
| 2024-25 | Shreveport Mudbugs | NAHL | 40 | 15 | 36 | 51 | 1.275 | 0.5052 | 0.4754 | 1.3386 | 1.2597 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Canisius | D1 | AHA | FR | 18 | 2 | 4 | 6 | 0.333 |
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.