| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2014-15 | Lincoln Stars | USHL | 28 | 2 | 1 | 3 | 0.107 | 0.0632 | 0.0687 | 0.3217 | 0.3498 |
| 2015-16 | Lincoln Stars | USHL | 56 | 4 | 7 | 11 | 0.196 | 0.1159 | 0.1206 | 0.5899 | 0.6141 |
| 2016-17 | Chicago Steel | USHL | 59 | 28 | 14 | 42 | 0.712 | 0.4199 | 0.4157 | 2.1381 | 2.1165 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2019-20 | Harvard | D1 | ECAC | JR | 31 | 6 | 8 | 14 | 0.452 |
| 2018-19 | Harvard | D1 | ECAC | SO | 33 | 8 | 12 | 20 | 0.606 |
| 2017-18 | Harvard | D1 | ECAC | FR | 33 | 10 | 7 | 17 | 0.515 |
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.