icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | — | MJHL | 36 | 24 | 8 | 91.7% | 2.16 | 4 | 0.9700 | 82.2% |
| 2022-23 | — | MJHL | 37 | 23 | 10 | 92.0% | 2.47 | 2 | 0.9700 | 89.0% |
| 2021-22 | — | MJHL | 18 | 10 | 6 | 92.0% | 2.29 | 2 | 0.9700 | 94.8% |
| 2018-19 | — | MJHL | 2 | 0 | 1 | 77.8% | 8.17 | 0 | 0.9700 | 86.8% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2024-25 | Holy Cross | D1 | 1 | 0 | 0 | — | 3.04 | 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 |
|---|---|---|---|---|---|---|
| Connor Murphy | CCHL | 91.7% | 81.3% | Northeastern | 68.8% | 7.04 |
| Ben Charette | AJHL | 92.5% | 82.7% | Harvard | 90.9% | 2.77 |
| Beckham Dempsey | NCDC | 90.4% | 80.7% | Colorado College | — | — |
| 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 |
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 |
|---|---|---|---|---|---|---|---|
| Alexander Timmons | USPHL-Premier | 90.5% | 81.5% | Framingham State | D3 | 84.9% | 5.20 |
| Tyler Roy | USPHL-Premier | 91.1% | 81.8% | Neumann | D3 | 87.2% | 3.68 |
| Tyler Sayger | USPHL-Premier | 90.8% | 81.4% | King's | D3 | 90.5% | 4.40 |
| Kannon Flageolle | NOJHL | 91.3% | 81.7% | Suffolk | D3 | 89.7% | 3.99 |
| 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.