icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2007-08 | Brooks Bandits | AJHL | 1 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2008-09 | Lethbridge Hurricanes | WHL | 32 | 1 | 6 | 7 | 0.219 | 0.1064 | 0.1049 | 0.5369 | 0.5293 |
| 2009-10 | Lethbridge Hurricanes | WHL | 37 | 4 | 5 | 9 | 0.243 | 0.1183 | 0.1106 | 0.5967 | 0.5578 |
| 2010-11 | Lethbridge Hurricanes | WHL | 70 | 15 | 15 | 30 | 0.429 | 0.2085 | 0.1851 | 1.0517 | 0.9338 |
| 2011-12 | Univ. of Calgary | USports-M | 28 | 0 | 4 | 4 | 0.143 | 0.0754 | 0.0792 | 0.4188 | 0.4399 |
| 2012-13 | Univ. of Calgary | USports-M | 26 | 7 | 5 | 12 | 0.462 | 0.2435 | 0.2445 | 1.3527 | 1.3584 |
| 2013-14 | Univ. of Calgary | USports-M | 23 | 5 | 4 | 9 | 0.391 | 0.2064 | 0.1975 | 1.1469 | 1.0974 |
| 2014-15 | Univ. of Calgary | USports-M | 28 | 1 | 9 | 10 | 0.357 | 0.1884 | 0.1705 | 1.0467 | 0.9473 |
| 2015-16 | Univ. of Calgary | USports-M | 28 | 5 | 13 | 18 | 0.643 | 0.3392 | 0.2903 | 1.8843 | 1.6125 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2017-18 | Fredonia | D3 | SUNYAC | JR | 12 | 0 | 2 | 2 | 0.167 |
| 2016-17 | Fredonia | D3 | SUNYAC | SO | 3 | 0 | 0 | 0 | 0.000 |
| 2015-16 | Fredonia | D3 | SUNYAC | FR | 11 | 0 | 0 | 0 | 0.000 |
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.