icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Des Moines Buccaneers | USHL | 34 | 21 | 11 | 90.9% | 2.41 | 3 | 0.9987 | 88.3% |
| 2024-25 | Fargo Force | USHL | 40 | 20 | 17 | 89.5% | 2.97 | 1 | 0.9987 | 90.1% |
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 |
|---|---|---|---|---|---|---|
| Jared Moe | USHL | 90.7% | 88.2% | Minnesota | 91.5% | 2.46 |
| Evan Fear | USHL | 90.8% | 88.1% | Quinnipiac | 77.4% | 6.56 |
| Jacob Biron | CCHL | 90.9% | 87.1% | Army | 92.3% | 1.68 |
| Ryan Bischel | USHL | 90.5% | 87.8% | Notre Dame | 91.5% | 2.52 |
| Calvin Vachon | USHL | 89.6% | 86.9% | Alaska Fairbanks | 89.7% | 3.11 |
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 |
|---|---|---|---|---|---|---|---|
| Ryan Kenny | USPHL-Premier | 93.5% | 89.9% | Stevenson | D3 | 89.6% | 2.95 |
| Jarret Bovarnick | EHL | 88.3% | 87.5% | Suffolk | D3 | 87.2% | 4.52 |
| Michael Paterson-Jones | USPHL-Premier | 92.0% | 88.9% | Wilkes | D3 | 89.5% | 3.08 |
| Tyler Fromolz | NA3HL | 92.0% | 86.3% | Marian | D3 | 88.3% | 3.85 |
| Ryan Nolan | NAHL | 88.9% | 90.6% | St. Olaf | D3 | 93.5% | 2.31 |
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.