icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2018-19 | — | USHL | 44 | 25 | 14 | 87.4% | 3.22 | 2 | 0.9980 | 80.6% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2023-24 | Sacred Heart | D1 | 17 | 6 | 9 | 89.5% | 2.95 | 0 |
| 2022-23 | Sacred Heart | D1 | 2 | 1 | 1 | 83.3% | 4.20 | 0 |
| 2021-22 | Sacred Heart | D1 | 28 | 10 | 12 | 90.9% | 2.38 | — |
| 2020-21 | Arizona State | D1 | 3 | 1 | 2 | 83.6% | 6.23 | 0 |
| 2019-20 | Arizona State | D1 | 1 | 0 | 0 | 92.3% | 4.00 | 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 |
|---|---|---|---|---|---|---|
| Conor Callaghan | USHL | 88.4% | 81.0% | Princeton | 85.3% | 5.02 |
| Dylan Silverstein | USHL | 88.8% | 81.0% | Quinnipiac | 90.3% | 2.25 |
| Remington Keopple | USHL | 89.3% | 82.4% | Cornell | 85.2% | 3.10 |
| Troy Kobryn | USHL | 89.5% | 82.1% | Merrimack | 89.3% | 3.30 |
| Melvin Strahl | USHL | 90.3% | 83.0% | Michigan | 92.2% | 2.27 |
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 |
|---|---|---|---|---|---|---|---|
| Brandon Shantz | NA3HL | 89.7% | 82.6% | Plymouth State | D3 | 92.1% | 3.00 |
| Clément Labillois | SJHL | 88.9% | 84.4% | Assumption | D2 | 93.6% | 2.08 |
| Carson Ironside | AJHL | 87.8% | 84.2% | Albertus Magnus | D3 | 89.3% | 2.76 |
| Dylan Dewatcher | OJHL | 86.3% | 81.7% | Western Connecticut | D3 | 85.8% | 5.91 |
| Ford DeLoss | USPHL-Premier | 92.0% | 85.8% | Stevenson | D3 | 50.0% | 3.85 |
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.