icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
Los Angeles Kings
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | Fargo Force | USHL | 33 | 28 | 3 | 92.3% | 1.86 | 5 | 0.9980 | 93.3% |
| 2022-23 | — | NTDP-U18 | 2 | 0 | 0 | 100.0% | — | 0 | 0.9200 | 89.9% |
| 2022-23 | — | USHL | 1 | 0 | 0 | 100.0% | — | 0 | 0.9980 | 97.0% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Western Michigan | D1 | 39 | 27 | 11 | 91.5% | 2.30 | 4 |
| 2024-25 | Western Michigan | D1 | 25 | 19 | 5 | 92.2% | 1.90 | 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 |
|---|---|---|---|---|---|---|
| Mathis Langevin | QMJHL | 91.2% | 92.0% | Miami | 93.8% | 2.00 |
| Kaidan Mbereko | USHL | 89.8% | 91.4% | Colorado College | 92.5% | 2.30 |
| Ben Kraws | USHL | 89.5% | 91.2% | Miami | 87.1% | 4.12 |
| Matthew Thiessen | USHL | 88.9% | 89.7% | Maine | 50.0% | 25.59 |
| Carsen Musser | USHL | 90.5% | 90.7% | Colorado College | 87.9% | 3.94 |
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 |
|---|---|---|---|---|---|---|---|
| Brody Haynes | NCDC | 92.5% | 92.3% | Elmira | D3 | 94.8% | 1.22 |
| Cooper Rautenstrauch | NCDC | 90.6% | 93.8% | Colby | D3 | 93.4% | 2.08 |
| Cooper Rautenstrauch | NAHL | 88.9% | 94.0% | Colby | D3 | 93.4% | 2.08 |
| Vaughn Makar | NAHL | 90.9% | 95.6% | St. Norbert | D3 | 93.2% | 1.73 |
| Brayden Ingram | EHL | 91.2% | 94.4% | Assumption | D2 | 91.4% | 2.71 |
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.