Name | Height Inches no Shoes | Height Inches w/shoes | Weight | Body Fat | Hand Length | Hand Width | Wingspan Inches | Reach in Inches | No Step Vert Reach in Inches | Max Vert Reach in inches | No Step Vert | Max Vert | Bench | Agility | Sprint |
C.J. McCollum | 74.5 | 75.5 | 197 | 8.6% | 8 | 9.5 | 78.5 | 96.5 | 129.25 | 135 | 32 | 38.5 | 11.02 | 3.32 |
Name | Weight > than standard (in lbs) | Body Fat < than Standard (as a % of BF) | Reach > than standard (in inches) | Wingspan > than standard (in inches) | Vertical > than standard (in inches) | Speed > than standard (in sec) | Agility > than standard (in sec) | # additional bench reps than expected |
C.J. McCollum | 8 | 1.13% | -2.08 | 0.06 | 2.03 | -0.06 | 0.29 | |
C.J. McCollum' weight to height indicates he weighs a little more than average. His reach is 2.08" less than average, but his wingspan is average for a player of his size. He is a good leaper, vertical is 2" more than average, of average speed, and a little above average agility. In aggregate he would not be described as long, but is a good athlete. | ||||||||
Below are the top 20 Comp seasons to C.J. McCollum at age 18 and 19. The 4th column is only statistical similarity, and does not include any combine measurements. | |||
C.J. McCollum | Senior season age 21 | ||
Player | Age | Conference | Statistical Similarity |
Damian Lillard | 21 | Big Sky | 94.3% |
Rodney Stuckey | 19 | Big Sky | 94.2% |
Willie Green | 21 | Horizon League | 94.1% |
Orlando Johnson | 21 | Big West | 95.2% |
Stephen Curry | 19 | Southern | 92.7% |
Courtney Lee | 22 | Sun Belt | 94.5% |
Randy Foye | 22 | Big East | 93.0% |
Khalid Reeves | 21 | Pacific 12 | 95.6% |
Jordan Crawford | 21 | Atlantic 10 | 94.8% |
Jeremy Lin | 20 | Ivy Group | 89.4% |
Damian Lillard | 19 | Big Sky | 91.8% |
Charles Smith (UoNM) | 21 | Mountain West | 92.2% |
Charles Smith (UoNM) | 20 | Mountain West | 93.1% |
Ronnie Price | 21 | Great West | 94.0% |
Gary Neal | 22 | Colonial AA | 94.3% |
Stephen Curry | 18 | Southern | 92.5% |
Marcus Thornton | 21 | Southeastern | 94.5% |
Lucious Harris | 22 | Big West | 94.7% |
Rodney Stuckey | 20 | Big Sky | 91.7% |
Orlando Johnson | 22 | Big West | 93.5% |
C.J. McCollum | Junior season age 20 | ||
Player | Age | Conference | Statistical Similarity |
Jeremy Lin | 20 | Ivy Group | 92.4% |
Rodney Stuckey | 20 | Big Sky | 92.7% |
Damian Lillard | 21 | Big Sky | 91.9% |
Rodney Stuckey | 19 | Big Sky | 92.8% |
Charles Smith (UoNM) | 20 | Mountain West | 92.4% |
Orlando Johnson | 21 | Big West | 91.5% |
Randy Foye | 22 | Big East | 92.8% |
Courtney Lee | 20 | Sun Belt | 92.7% |
Jeremy Lin | 21 | Ivy Group | 94.3% |
Damian Lillard | 19 | Big Sky | 92.3% |
Courtney Lee | 22 | Sun Belt | 93.2% |
Willie Green | 21 | Horizon League | 93.7% |
Randy Foye | 21 | Big East | 91.4% |
Emanual Davis | 22 | Mid-Eastern Athletic | 91.6% |
Emanual Davis | 20 | Mid-Eastern Athletic | 91.4% |
Rodney Buford | 20 | Missouri Valley | 95.2% |
Iman Shumpert | 20 | Atlantic Coast | 91.8% |
Charles Smith (UoNM) | 21 | Mountain West | 91.3% |
Tierre Brown | 21 | Southland | 90.9% |
Norris Cole | 22 | Horizon League | 94.7% |
Very Best Case Comps | Stephan Curry | 5% | |
Likely Best Case | Damian Lillard, Jeremy Lin, | 18% | |
Most Likely | Rodney Stuckey, Iman Shumpert, Courtney Lee, Randy Foye, Marcus Thornton | 48% | |
Likely Worst Case | Charles Smith, Norris Cole, Kahlid Reeves, Jordan Crawford, Ronnie Price | 28% | |
Absolute Worst Case | Tierre Brown | 3% | |
C.J. McCollum has a really nice set of comps, and is clearly a lower risk, but solid return type player. He has a 70%+ probability of being no worse than Rodney Stuckey, Courtney Lee, Randy Foye, Marcus Thornton. Every one of his comps played 3 yers (I am assuming Lillard and Orlando Johnson make it 3 years), and his only absolute worst case Tierre Brown played 3, albiet unproductive, years. | |||
C.J. McCollum is a perfect illustration of one of the enhancements I made since I posted at mid-season regarding Damian Lillard. If you look at McCollum's comps you will note that only 4 players came from Big 6 conference schools (Khalid Reeves, Marcus Thornton, Randy Foye, and Iman Shumpert). I have included conference as a discreet variable in the model. I want to place each players performance into the context they played in. Damian Lillard was a big time scorer in college, but he played in the Big Sky, so how does that translate into the NBA? I absolutely DID NOT want to find an algorithm that would adjust any players stats up or down to account for different contexts. What I wanted to do was place every player into their appropriate context, and use their comps as a proxy for that player, and use the eventual performance of their comps to project what the target player would do in the NBA. Now if you look through the comps the very best statistical comp is Khalid Reeves, and that to me is instructive. I have only 4 bits of physycal data for Reeves to compare to McCollum, so physycal similarity doesn't explain why he is a comp. They played in widely divergent conferences. For Reeves to be a comp he must be extremely similar statistically, which he is. So in summary, I have tried to protect the integrity of the comp process, and I try always to avoid using algorithms to adjust for the difference in context. Find the best comps, and then allow the comps to describe the player, and allow the results that come from that to speak for themselves. | |||
The players that most directly jump out at me is this analysis are Steph Curry, Damian Lillard, Jeremy Lin, Rodney Stuckey, and Gary Neal. All of these players were tremendous college players and they all played in small conferences, and all have had very good to great success in the NBA. | |||
Average Adjusted Stat Line for Target Player | |||||||||||||||
Season | Shooting Statistics Totals | ||||||||||||||
FG | FGA | FG% | 2P | 2PA | 2P% | 3P | 3PA | 3P% | FT | FTA | FT% | TS% | eFG% | ||
C.J. McCollum | 1 | 359 | 817 | 43.8% | 266 | 591 | 45.0% | 93 | 227 | 40.7% | 146 | 179 | 81.9% | 53.0% | 49.5% |
C.J. McCollum | 2 | 320 | 723 | 44.1% | 256 | 562 | 45.5% | 63 | 161 | 38.9% | 144 | 175 | 82.4% | 52.4% | 48.4% |
C.J. McCollum | 3 | 342 | 810 | 42.2% | 273 | 623 | 43.8% | 70 | 187 | 37.3% | 149 | 185 | 80.8% | 50.4% | 46.5% |
3 Year Average | 341 | 785 | 43.4% | 265 | 593 | 44.8% | 76 | 192 | 39.0% | 146 | 180 | 81.7% | 51.9% | 48.1% | |
Season | Accumulation Stats Totals | ||||||||||||||
ORB | DRB | TRB | AST | STL | BLK | TOV | A/TO | PF | PTS | ||||||
C.J. McCollum | 1 | 40 | 180 | 221 | 257 | 71 | 15 | 147 | 1.75 | 152 | 957 | ||||
C.J. McCollum | 2 | 46 | 167 | 212 | 224 | 69 | 12 | 121 | 1.86 | 149 | 846 | ||||
C.J. McCollum | 3 | 42 | 168 | 210 | 220 | 71 | 18 | 119 | 1.85 | 153 | 904 | ||||
3 Year Average | 43 | 172 | 214 | 234 | 70 | 15 | 129 | 1.82 | 151 | 904 | |||||
Season | Shooting Statistics per 36 Minutes | ||||||||||||||
FG | FGA | FG% | 2P | 2PA | 2P% | 3P | 3PA | 3P% | FT | FTA | FT% | TS% | eFG% | ||
C.J. McCollum | 1 | 6.70 | 15.28 | 43.8% | 4.96 | 11.04 | 45.0% | 1.73 | 4.26 | 40.7% | 2.75 | 3.36 | 81.8% | 53.0% | 49.5% |
C.J. McCollum | 2 | 6.36 | 14.40 | 44.1% | 5.10 | 11.19 | 45.5% | 1.25 | 3.20 | 39.0% | 2.87 | 3.49 | 82.3% | 52.4% | 48.4% |
C.J. McCollum | 3 | 6.45 | 15.27 | 42.2% | 5.15 | 11.73 | 43.8% | 1.32 | 3.53 | 37.3% | 2.80 | 3.47 | 80.8% | 50.4% | 46.5% |
3 Year Average | 6.5 | 15.0 | 43.4% | 5.1 | 11.3 | 44.8% | 1.4 | 3.7 | 39.0% | 2.8 | 3.4 | 81.6% | 51.9% | 48.1% | |
Season | Accumulation Stats per 36 Minutes | ||||||||||||||
ORB | DRB | TRB | AST | STL | BLK | TOV | A/TO | PF | PTS | ||||||
C.J. McCollum | 1 | 0.75 | 3.37 | 4.12 | 4.81 | 1.34 | 0.28 | 2.74 | 1.75 | 2.86 | 17.87 | ||||
C.J. McCollum | 2 | 0.91 | 3.33 | 4.24 | 4.46 | 1.38 | 0.24 | 2.40 | 1.86 | 2.98 | 16.83 | ||||
C.J. McCollum | 3 | 0.80 | 3.15 | 3.95 | 4.15 | 1.32 | 0.33 | 2.25 | 1.85 | 2.89 | 17.05 | ||||
3 Year Average | 0.8 | 3.3 | 4.1 | 4.5 | 1.3 | 0.3 | 2.5 | 1.82 | 2.9 | 17.3 | |||||
Season | Usage Stats | ||||||||||||||
ORB% | DRB% | TRB% | AST% | STL% | BLK% | TOV% | USG% | ||||||||
C.J. McCollum | 1 | 2.5% | 10.5% | 6.5% | 20.1% | 1.9% | 0.6% | 14.8% | 22.7% | ||||||
C.J. McCollum | 2 | 2.7% | 10.9% | 6.7% | 19.5% | 1.9% | 0.5% | 13.5% | 22.5% | ||||||
C.J. McCollum | 3 | 2.6% | 10.3% | 6.3% | 18.8% | 1.9% | 0.7% | 12.4% | 23.0% | ||||||
3 Year Average | 2.6% | 10.6% | 6.5% | 19.5% | 1.9% | 0.6% | 13.6% | 22.7% | |||||||
Season | Advanced Measurements | ||||||||||||||
PER | ORtg | DRtg | OWS | DWS | WS | WS/48 | |||||||||
C.J. McCollum | 1 | 14.85 | 104.2 | 101.5 | 2.34 | 1.39 | 3.65 | 0.080 | |||||||
C.J. McCollum | 2 | 15.07 | 104.2 | 103.6 | 1.91 | 1.41 | 3.24 | 0.077 | |||||||
C.J. McCollum | 3 | 14.86 | 103.1 | 103.5 | 1.41 | 1.42 | 2.76 | 0.070 | |||||||
3 Year Average | 14.92 | 103.8 | 102.9 | 5.7 | 4.2 | 9.65 | 0.076 | ||||||||
Overall for his first 3 years C.J. McCollum projects to be league average in PER, Ortg/Drtg delta, and a little below an average NBA player according to Win Shares. Compared to Damian Lillard, McCollum projects a lower PER, WS/48, and a better Ortg/DRtg delta. This stat line though is quite similar to Damian Lillard's projected stat line. Lillard exceeded his stat line most specifically because he truely went to the best situation for himself, and then he performed at his rate stat porjections, while increasing his usage rate, and somewhat less so his assist rate. In many ways you could say the same thing about Steph Curry. On the other hand Jeremy Lin was forced to take a couple of years to find his ideal situation and when he did, he took off. If McCollum went to the right situation for himself he could have a fabulous rookie year, but I doubt that will happen, because it doesn't happen all that often. | |||||||||||||||
C.J. McCollum projects to be a better defender than Damian Lillard, and in fact for a PG he projects to be quite good. Offensively though he is not the same player as Lillard, with an Ortg of 104. He is not the shooter that Lillard or Curry are, but he is an OK shooter. As a passer McCollum projects to be below average for a PG, so he is clearly a shoot first PG, which is consistent with his usage rate of 22.7% which is very high at this stage. | |||||||||||||||