icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | NOJHL | 34 | 22 | 9 | 91.3% | 2.71 | 1 | 0.9700 | 84.5% |
| 2021-22 | — | USPHL-Premier | 23 | 12 | 8 | 91.3% | 2.52 | 4 | 0.9400 | 85.9% |
| 2021-22 | — | NAHL | — | — | — | — | — | — | 0.9843 | — |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Suffolk | D3 | 3 | — | — | — | — | 0 |
| 2024-25 | Suffolk | D3 | — | — | — | — | — | — |
| 2023-24 | Suffolk | D3 | 9 | — | — | 89.7% | 3.99 | 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 |
|---|---|---|---|---|---|---|
| Ethan Dahlmeir | USHL | 90.0% | 84.4% | Miami | 87.0% | 4.04 |
| Mathis Rousseau | QMJHL | 90.5% | 84.7% | Maine | 89.6% | 2.59 |
| Patriks Berzins | USHL | 90.1% | 84.4% | Maine | 90.0% | 1.79 |
| Connor Murphy | CCHL | 91.7% | 84.9% | Northeastern | 68.8% | 7.04 |
| Malachi Klassen | MJHL | 91.7% | 85.4% | Holy Cross | — | 3.04 |
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 |
|---|---|---|---|---|---|---|---|
| Tyler Roy | USPHL-Premier | 91.1% | 84.8% | Neumann | D3 | 87.2% | 3.68 |
| Tyler Sayger | USPHL-Premier | 90.8% | 84.5% | King's | D3 | 90.5% | 4.40 |
| Alexander Timmons | USPHL-Premier | 90.5% | 84.4% | Framingham State | D3 | 84.9% | 5.20 |
| Jeff Zero | NA3HL | 95.0% | 85.9% | Elmira | D3 | 100.0% | — |
| Eric Daniel | USPHL-Premier | 92.0% | 85.7% | King's | D3 | 86.9% | 6.55 |
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.