icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | West Kelowna Warriors | BCHL | 36 | 27 | 8 | 92.5% | 1.98 | 6 | 0.9990 | 89.1% |
| 2024-25 | — | SJHL | 37 | 25 | 11 | 91.9% | 2.63 | 5 | 0.9700 | 88.9% |
| 2023-24 | — | SJHL | 17 | 7 | 7 | 91.4% | 3.19 | 0 | 0.9700 | 92.0% |
| 2023-24 | — | AJHL | 5 | 2 | 2 | 85.3% | 3.72 | 0 | 0.9700 | 85.0% |
| 2022-23 | — | BCHL | 21 | 6 | 6 | 86.0% | 4.40 | 0 | 0.9990 | 91.3% |
| 2021-22 | — | BCHL | 1 | 1 | 0 | 93.1% | 2.00 | 0 | 0.9990 | 97.0% |
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 |
|---|---|---|---|---|---|---|
| Jackson Fuller | NAHL | 92.9% | 88.7% | Northern Michigan | — | — |
| Beni Halasz | NAHL | 92.1% | 87.8% | Northern Michigan | 91.9% | 2.32 |
| Brandon Milberg | NAHL | 92.4% | 87.8% | Sacred Heart | 86.3% | 3.89 |
| Quentin Sigurdson | NAHL | 92.3% | 87.9% | Northeastern | 92.0% | 2.13 |
| Matt Ladd | NAHL | 92.2% | 87.4% | Canisius | 86.8% | 3.97 |
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% | 88.4% | Fitchburg State | D3 | 92.2% | 2.61 |
| Braedyn McIntosh | NCDC | 93.2% | 89.7% | Aurora | D3 | 91.7% | 3.09 |
| Cameron Smith | OJHL | 91.4% | 88.8% | Curry | D3 | 87.0% | 2.11 |
| Nick Wiencek | NCDC | 91.8% | 88.0% | Lake Forest | D3 | 94.4% | 1.50 |
| Max Macchioni | NCDC | 91.1% | 87.6% | Fitchburg State | D3 | 92.2% | 2.61 |
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.