icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | — | USPHL-Premier | 15 | 13 | 1 | 94.0% | 1.12 | 3 | 0.9400 | 84.0% |
| 2020-21 | — | USPHL-Premier | 19 | 16 | 3 | 92.3% | 1.84 | 1 | 0.9400 | 86.8% |
| 2019-20 | — | USPHL-Premier | 22 | 18 | 4 | 92.2% | 1.77 | 4 | 0.9400 | 86.7% |
| 2018-19 | — | USPHL-Premier | 17 | 8 | 6 | 92.2% | 2.43 | 2 | 0.9400 | 91.8% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Alvernia | D3 | 1 | — | — | 86.4% | 3.11 | 0 |
| 2024-25 | Alvernia | D3 | 11 | 4 | 4 | 88.8% | 3.46 | 0 |
| 2023-24 | Rivier | D3 | 10 | 3 | 4 | 92.0% | 2.97 | — |
| 2022-23 | Rivier | D3 | 6 | 3 | 1 | 91.5% | 2.97 | — |
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% | 84.9% | Bentley | 82.6% | 8.58 |
| Alexander Tracy | USHL | 89.6% | 83.0% | Minnesota | 87.9% | 2.24 |
| Jan Spunar | USHL | 90.7% | 84.1% | North Dakota | 91.9% | 1.91 |
| Ryan Keyes | NCDC | 92.6% | 83.2% | Merrimack | 92.0% | 2.22 |
| Chad Veltri | USHL | 90.2% | 83.9% | Niagara | 93.1% | 2.10 |
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 |
|---|---|---|---|---|---|---|---|
| Josh Koziol | NA3HL | 93.3% | 83.8% | Augsburg | D3 | 71.4% | 5.76 |
| Dean Hahn | USPHL-Premier | 91.6% | 84.1% | Salem State | D3 | 88.9% | 3.95 |
| Jeffrey Reda | USPHL-Premier | 90.6% | 83.7% | Franklin Pierce | D2 | 85.9% | 5.85 |
| Logan Palmer | USPHL-Premier | 92.1% | 84.5% | Western New England | D3 | 90.9% | 3.11 |
| Owen Swanbon | USPHL-Premier | 90.4% | 83.4% | Framingham State | D3 | 83.3% | 8.53 |
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.