icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2024-25 | — | NAHL | 33 | 3 | 25 | 89.2% | 4.46 | 1 | 0.9843 | 85.5% |
| 2023-24 | — | CCHL | 33 | 4 | 19 | 88.4% | 4.53 | 0 | 0.9700 | 85.2% |
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 |
|---|---|---|---|---|---|---|
| Liam Beerman | USHL | 91.2% | 85.7% | Lindenwood | 90.3% | 3.24 |
| Ben Charette | AJHL | 92.5% | 86.0% | Harvard | 90.9% | 2.77 |
| Malachi Klassen | MJHL | 91.7% | 85.4% | Holy Cross | — | 3.04 |
| Carson Dorfman | NAHL | 90.1% | 86.6% | RPI | 100.0% | — |
| Rorke Applebee | BCHL | 90.2% | 86.5% | Lake Superior State | 90.7% | 3.00 |
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 |
|---|---|---|---|---|---|---|---|
| Eric Daniel | USPHL-Premier | 92.0% | 85.7% | King's | D3 | 86.9% | 6.55 |
| Kannon Flageolle | NOJHL | 91.3% | 84.9% | Suffolk | D3 | 89.7% | 3.99 |
| Tyler Roy | USPHL-Premier | 91.1% | 84.8% | Neumann | D3 | 87.2% | 3.68 |
| Jeff Zero | NA3HL | 95.0% | 85.9% | Elmira | D3 | 100.0% | — |
| Tyler Sayger | NCDC | 89.4% | 86.1% | King's | D3 | 90.5% | 4.40 |
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.