icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2018-19 | — | BCHL | 42 | 27 | 11 | 91.2% | 2.95 | 2 | 0.9990 | 81.1% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2023-24 | Clarkson | D1 | 25 | 12 | 11 | 90.2% | 2.47 | 1 |
| 2022-23 | Providence | D1 | 5 | 2 | 3 | 87.0% | 2.89 | 1 |
| 2021-22 | Nebraska Omaha | D1 | 10 | 5 | 3 | 91.8% | 2.53 | 0 |
| 2020-21 | Nebraska Omaha | D1 | 5 | 2 | 0 | 91.4% | 2.83 | 1 |
| 2019-20 | Nebraska Omaha | D1 | 11 | 4 | 6 | 90.7% | 2.87 | 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 |
|---|---|---|---|---|---|---|
| Joshua Seeley | NAHL | 90.0% | 81.6% | Bentley | 82.6% | 8.58 |
| David Fessenden | NAHL | 91.5% | 82.6% | Alabama-Huntsville | 86.9% | 4.44 |
| Ryan Keyes | NCDC | 92.6% | 79.7% | Merrimack | 92.0% | 2.22 |
| Noah Pak | OJHL | 94.5% | 81.2% | Yale | 87.2% | 3.86 |
| Salvatore Evola | NAHL | 91.7% | 82.9% | Bowling Green | 86.2% | 4.25 |
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 |
|---|---|---|---|---|---|---|---|
| Jacob Gorinsky | OJHL | 89.9% | 80.7% | Norwich | D3 | 83.3% | 5.00 |
| Nicholas Von Kaufmann | OJHL | 88.9% | 79.9% | Marian | D3 | 88.6% | 3.25 |
| Josh Koziol | NA3HL | 93.3% | 81.2% | Augsburg | D3 | 71.4% | 5.76 |
| Logan Palmer | USPHL-Premier | 92.1% | 80.5% | Western New England | D3 | 90.9% | 3.11 |
| Dean Hahn | USPHL-Premier | 91.6% | 80.0% | Salem State | D3 | 88.9% | 3.95 |
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.