icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Alberni Valley Bulldogs | BCHL | 13 | 7 | 6 | 91.1% | 2.77 | 0 | 0.9990 | 90.3% |
| 2025-26 | Sioux City Musketeers | USHL | 25 | 14 | 7 | 87.8% | 3.83 | 0 | 0.9983 | 84.5% |
| 2024-25 | — | BCHL | 17 | 7 | 7 | 90.6% | 3.15 | 0 | 0.9990 | 92.3% |
| 2022-23 | — | NCDC | 1 | 0 | 1 | 87.8% | 5.00 | 0 | 0.9400 | 91.6% |
| 2022-23 | — | USPHL-Elite | — | — | — | — | — | — | 0.9400 | — |
| 2022-23 | — | USPHL-Premier | — | — | — | — | — | — | 0.9400 | — |
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 |
|---|---|---|---|---|---|---|
| Deivs Rolovs | NAHL | 91.9% | 90.2% | Niagara | 88.3% | 3.49 |
| Nathan Airey | BCHL | 92.5% | 90.4% | Minnesota | 86.5% | 3.23 |
| Cole Moore | NAHL | 91.9% | 90.3% | Bowling Green | 91.8% | 2.87 |
| Aidan Porter | BCHL | 90.8% | 89.0% | Princeton | 86.2% | 3.99 |
| Oliver Auyeung-Ashton | BCHL | 90.9% | 89.0% | Northern Michigan | 93.0% | 2.67 |
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 |
|---|---|---|---|---|---|---|---|
| Andrew Palena | NCDC | 91.4% | 90.9% | Amherst | D3 | 94.9% | 1.40 |
| Brody Haynes | NCDC | 92.5% | 92.3% | Elmira | D3 | 94.8% | 1.22 |
| Cameron Kuntar | NCDC | 89.3% | 89.1% | Chatham | D3 | 92.4% | 2.62 |
| Russ Decoste | USPHL-Premier | 93.1% | 88.5% | Westfield State | D3 | 93.3% | 2.38 |
| Thomas Walker | EHL | 92.5% | 91.5% | Albertus Magnus | D3 | 85.7% | 5.54 |
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.