icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | Des Moines Buccaneers | USHL | 48 | 21 | 23 | 90.0% | 2.96 | 0 | 0.9980 | 79.5% |
| 2022-23 | — | NAHL | 29 | 13 | 11 | 91.6% | 2.35 | 3 | 0.9843 | 91.6% |
| 2021-22 | — | NAHL | 22 | 10 | 5 | 89.6% | 2.56 | 4 | 0.9843 | 95.7% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Minnesota Duluth | D1 | 8 | 5 | 2 | 91.0% | 2.33 | 0 |
| 2024-25 | Miami | D1 | 14 | 1 | 1 | 87.0% | 4.04 | 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 |
|---|---|---|---|---|---|---|
| Patriks Berzins | USHL | 90.1% | 79.5% | Maine | 90.0% | 1.79 |
| Mathis Rousseau | QMJHL | 90.5% | 80.1% | Maine | 89.6% | 2.59 |
| Beckham Dempsey | NCDC | 90.4% | 80.7% | Colorado College | — | — |
| Kyle Chauvette | USHL | 89.2% | 79.5% | Union | 90.5% | 2.87 |
| Charlie Schenkel | OHL | 89.7% | 78.8% | Robert Morris | 90.9% | 2.80 |
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 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 Roy | USPHL-Premier | 91.1% | 81.8% | Neumann | D3 | 87.2% | 3.68 |
| Dane Couture | MJHL | 86.5% | 80.8% | Concordia | D3 | 87.3% | 2.94 |
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.