icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2024-25 | Lethbridge Hurricanes | WHL | 54 | 26 | 18 | 89.9% | 3.15 | 2 | 1.0047 | 81.5% |
| 2023-24 | Moose Jaw Warriors | WHL | 54 | 35 | 15 | 90.8% | 3.08 | 3 | 1.0047 | 89.1% |
| 2022-23 | — | WHL | 33 | 15 | 16 | 86.7% | 4.19 | 0 | 1.0047 | 91.1% |
| 2021-22 | — | WHL | 18 | 8 | 7 | 89.1% | 3.60 | 1 | 1.0047 | 97.0% |
| 2020-21 | — | WHL | — | — | — | — | — | — | 1.0047 | — |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Colorado College | D1 | 9 | 1 | 4 | 91.1% | 2.53 | 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 |
|---|---|---|---|---|---|---|
| Jake Barczewski | USHL | 90.4% | 82.3% | Canisius | 90.5% | 2.92 |
| Stephen Peck | USHL | 90.9% | 82.5% | Michigan | 91.1% | 2.61 |
| Christian Manz | USHL | 88.0% | 79.9% | Augustana | 89.4% | 2.84 |
| Joey Lamoreaux | USHL | 89.7% | 82.0% | St. Cloud State | 89.3% | 3.25 |
| Dylan Silverstein | USHL | 88.8% | 81.0% | Quinnipiac | 90.3% | 2.25 |
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 |
|---|---|---|---|---|---|---|---|
| Dylan Dewatcher | OJHL | 86.3% | 81.7% | Western Connecticut | D3 | 85.8% | 5.91 |
| Sean Guerin | NOJHL | 90.2% | 81.9% | Southern New Hampshire | D2 | 90.7% | 3.28 |
| Owen Carlson | NA3HL | 92.1% | 83.0% | Lawrence | D3 | 90.2% | 3.41 |
| Caleb Chabot | CCHL | 89.1% | 84.2% | Albertus Magnus | D3 | 93.4% | 1.79 |
| Gustave Bylin | AJHL | 90.4% | 85.5% | Tufts | D3 | 89.8% | 3.01 |
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.