icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2018-19 | — | EHL | 13 | 5 | 6 | 91.3% | 3.07 | 1 | 0.9400 | 82.0% |
| 2018-19 | — | USPHL-Premier | 12 | 4 | 5 | 91.1% | 3.17 | 0 | 0.9400 | 79.4% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2022-23 | Manhattanville | D3 | 11 | 2 | 6 | 89.4% | 3.99 | — |
| 2021-22 | Manhattanville | D3 | 16 | 7 | 6 | 89.9% | 3.45 | — |
| 2020-21 | Neumann | D3 | 1 | 0 | 1 | 91.2% | 3.08 | 0 |
| 2019-20 | Neumann | D3 | 6 | 2 | 3 | 87.2% | 3.68 | 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 |
| Connor Murphy | CCHL | 91.7% | 81.3% | Northeastern | 68.8% | 7.04 |
| Liam Beerman | USHL | 91.2% | 80.8% | Lindenwood | 90.3% | 3.24 |
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 |
|---|---|---|---|---|---|---|---|
| Kannon Flageolle | NOJHL | 91.3% | 81.7% | Suffolk | D3 | 89.7% | 3.99 |
| Tyler Sayger | USPHL-Premier | 90.8% | 81.4% | King's | D3 | 90.5% | 4.40 |
| Alexander Timmons | USPHL-Premier | 90.5% | 81.5% | Framingham State | D3 | 84.9% | 5.20 |
| Eric Daniel | USPHL-Premier | 92.0% | 82.8% | King's | D3 | 86.9% | 6.55 |
| Tyler Sayger | NCDC | 89.4% | 83.4% | 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.