icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2024-25 | Lincoln Stars | USHL | 43 | 29 | 12 | 91.1% | 2.40 | 3 | 0.9983 | 84.5% |
| 2023-24 | Lincoln Stars | USHL | 41 | 20 | 18 | 90.2% | 3.05 | 0 | 0.9983 | 87.0% |
| 2022-23 | — | MHL-RU | 21 | 5 | 11 | 92.6% | 2.88 | 2 | 0.9600 | 89.2% |
| 2021-22 | — | KHL | — | — | — | — | — | — | 1.0100 | — |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | St. Cloud State | D1 | 20 | 6 | 12 | 89.7% | 3.08 | 2 |
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 |
|---|---|---|---|---|---|---|
| Jan Spunar | USHL | 90.7% | 84.1% | North Dakota | 91.9% | 1.91 |
| Chad Veltri | USHL | 90.2% | 83.9% | Niagara | 93.1% | 2.10 |
| Ben Bonisteel | OJHL | 92.6% | 84.6% | Canisius | 89.3% | 3.10 |
| Jack Williams | USHL | 90.8% | 84.5% | Notre Dame | 88.9% | 2.90 |
| Jacob Steinman | QMJHL | 92.3% | 85.4% | Bowling Green | 92.1% | 2.14 |
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 |
|---|---|---|---|---|---|---|---|
| Diego D'Alessandro | CCHL | 89.2% | 84.9% | King's | D3 | 86.5% | 4.68 |
| Owen Swanbon | USPHL-Premier | 90.4% | 83.4% | Framingham State | D3 | 83.3% | 8.53 |
| Jeffrey Reda | USPHL-Premier | 90.6% | 83.7% | Franklin Pierce | D2 | 85.9% | 5.85 |
| Michael Cammasso | USPHL-Premier | 92.4% | 85.1% | Johnson & Wales | D3 | 82.2% | 8.99 |
| Josh Koziol | NA3HL | 93.3% | 83.8% | Augsburg | D3 | 71.4% | 5.76 |
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.