icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | NCDC | 12 | 9 | 2 | 92.6% | 3.01 | 1 | 0.9400 | 82.8% |
| 2022-23 | — | NAHL | 20 | 1 | 13 | 88.6% | 4.02 | 0 | 0.9843 | 82.5% |
| 2021-22 | — | NAHL | 37 | 19 | 9 | 90.1% | 2.72 | 1 | 0.9843 | 90.0% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Aurora | D3 | 29 | — | — | 92.9% | 2.12 | 0 |
| 2024-25 | Aurora | D3 | 7 | — | — | 96.2% | 0.77 | 0 |
| 2023-24 | Aurora | D3 | 13 | — | — | 91.0% | 2.97 | 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 |
|---|---|---|---|---|---|---|
| Malachi Klassen | MJHL | 91.7% | 82.2% | Holy Cross | — | 3.04 |
| Ben Charette | AJHL | 92.5% | 82.7% | Harvard | 90.9% | 2.77 |
| Rorke Applebee | BCHL | 90.2% | 83.3% | Lake Superior State | 90.7% | 3.00 |
| John Hawthorne | BCHL | 91.8% | 83.9% | Northern Michigan | 88.2% | 3.10 |
| Connor Murphy | CCHL | 91.7% | 81.3% | Northeastern | 68.8% | 7.04 |
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 Sayger | NCDC | 89.4% | 83.4% | King's | D3 | 90.5% | 4.40 |
| Duncan Rolleman | USPHL-Premier | 92.5% | 83.2% | Wilkes | D3 | 90.0% | 3.84 |
| Jeff Zero | NA3HL | 95.0% | 83.7% | Elmira | D3 | 100.0% | — |
| Eric Daniel | USPHL-Premier | 92.0% | 82.8% | King's | D3 | 86.9% | 6.55 |
| Alexander Timmons | USPHL-Premier | 90.5% | 81.5% | Framingham State | D3 | 84.9% | 5.20 |
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.