icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | — | NAHL | 36 | 15 | 14 | 92.3% | 2.56 | 4 | 0.9843 | 85.2% |
| 2023-24 | Chicago Steel | USHL | 9 | 5 | 3 | 89.6% | 3.11 | 1 | 0.9980 | 78.2% |
| 2022-23 | — | NAHL | 15 | 5 | 5 | 90.9% | 2.87 | 0 | 0.9843 | 89.9% |
| 2021-22 | — | NA3HL | 19 | 18 | 1 | 92.5% | 1.48 | 5 | 0.9400 | 91.3% |
| 2021-22 | — | NAHL | — | — | — | — | — | — | 0.9843 | — |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Northeastern | D1 | 4 | 2 | 2 | 92.0% | 2.72 | 0 |
| 2024-25 | Northeastern | D1 | 6 | 1 | 3 | 92.0% | 2.13 | 1 |
Historical goalies with similar age-adjusted SVe profiles who went on to play NCAA D1.
| Name | Junior League | Junior SV% | Adj SVe | College | NCAA SV% | NCAA GAA |
|---|---|---|---|---|---|---|
| Brandon Milberg | NAHL | 92.4% | 85.0% | Sacred Heart | 86.3% | 3.89 |
| Matt Ladd | NAHL | 92.2% | 84.4% | Canisius | 86.8% | 3.97 |
| Beni Halasz | NAHL | 92.1% | 85.2% | Northern Michigan | 91.9% | 2.32 |
| Gergely Orosz | NAHL | 91.9% | 85.4% | Alaska Anchorage | 91.5% | 2.75 |
| Adam Manji | BCHL | 90.4% | 82.2% | American International | 92.1% | 2.70 |
Historical goalies with similar age-adjusted SVe profiles who went on to play NCAA D2/D3.
| Name | Junior League | Junior SV% | Adj SVe | College | Div | SV% | GAA |
|---|---|---|---|---|---|---|---|
| Max Macchioni | USPHL-Premier | 95.3% | 85.1% | Fitchburg State | D3 | 92.2% | 2.61 |
| Max Macchioni | NCDC | 91.1% | 84.7% | Fitchburg State | D3 | 92.2% | 2.61 |
| Antoine Robidoux-Hurtubise | EHL | 90.6% | 85.3% | Misericordia | D3 | 84.5% | 5.01 |
| Nick Wiencek | NCDC | 91.8% | 84.9% | Lake Forest | D3 | 94.4% | 1.50 |
| Braedyn McIntosh | NCDC | 93.2% | 86.8% | Aurora | D3 | 91.7% | 3.09 |
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.