| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2014-15 | Chicago Steel | USHL | 60 | 13 | 20 | 33 | 0.550 | 0.3244 | 0.3236 | 1.6504 | 1.6462 |
| 2015-16 | — | USHL | 58 | 12 | 12 | 24 | 0.414 | 0.2441 | 0.2321 | 1.2417 | 1.1809 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2018-19 | Cornell | D1 | ECAC | — | 27 | 2 | 4 | 6 | 0.222 |
| 2017-18 | Cornell | D1 | ECAC | — | 1 | 0 | 0 | 0 | 0.000 |
| 2016-17 | Cornell | D1 | ECAC | — | 10 | 1 | 1 | 2 | 0.200 |
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.