The 2020 NBA Draft concluded on Wednesday as the new crop of players were introduced to the NBA. The draft class this year was unique as the number one overall pick was being disputed up until the pick was made. Even though the top choice was the most talked about among fans and analysts of the NBA, the later lottery picks and even second round picks are how teams can improve their teams while staying cheap and good. Every year there are surprise players and busts in the NBA draft, and using stats can help to find which players will blossom into superstars and which ones will struggle in the NBA.
The way that I chose to analyze the draft was to use logistic regressions to find the probability of a player becoming an all star, an all defense player, or a good player. A good player was defined as someone with a career adjusted game score of 15 or greater. The adjusted game score was found by this calculation: PTS + 0.4 * REB + STL + 0.7 * AST + 0.7 * BLK. This is the similar as the actual game score computation, but it only involves points, rebounds, assists, steals, and blocks, which were the only stats included in my dataset. Additionally, I found the predicted three point percentage in order to find out how good a shooter the players were. I applied these models to all the players whose stats I could find, using a combination of advanced stats like Box Plus Minus and basic per 36 counting stats like points and steals.
High Upside Players
- Anthony Edwards, MIN (1st)
- Onyeka Okongwu, ATL (6th)
- Killian Hayes, DET (7th)
- Aleksej Pokusevski, OKC (17th)
- Jalen Smith, PHX (10th)
The highest upside players were found using my model for predicting the players most likely to become all stars. The main inputs (in order of most important to least important) were age, draft pick, box plus minus, minutes played per game, usage rate, and finally individual defensive rating. Since advanced stats were not available for players who played internationally, adjusted models were made for them, using slightly different inputs.
Anthony Edwards had the highest probability of being an all star among the all the draft picks, with a 27% chance. However, his 28% chance was the lowest of all number one picks in the 2010’s, just behind Anthony Bennett’s 29%. This can be explained by his low BPM of just 5.5, lowest among all number one picks of the 2010’s as well. On the other hand, Onyeka Okongwu was boosted by his high BPM while still being young and drafted highly. His 25% chance of being an all star was 2nd among the 2020 draft class. Jalen Smith also was highly regarded for similar reasons, with a college BPM of 12 in addition to a large role for Maryland.
International players Killian Hayes and Aleksej Pokusevski were both good picks with high upside. One of the weaknesses of Hayes while being scouted was his low 29% three point percentage in the German league. However, he had a very high free throw percentage (87%) will likely mean that he can develop into a good shooter, as free throw percentage actually correlates better with NBA three point percentage than college/international three point percentage. Pokusevki has the potential to develop into a great player for similar reasons with a free throw percentage of 78%, meaning he can develop into a good shooter with playmaking ability while also being 7 feet tall. He also blocked 2.8 shots and stole 2 balls per 36 minutes, signifying potential defensive capability (provided he puts more weight on his skinny 201 pound frame).
Low Upside Players
- Deni Avdija, WAS (9th)
- Saddiq Bey, DET (19th)
- Josh Green, DAL (18th)
- Isaac Okoro, CLE (5th)
- Aaron Nesmith, BOS (14th)
Deni Avdija, drafted 9th by the Wizards, is unlikely to be an all star in the NBA. Using his international stats provided by Tankathon.com, Avdija has a bad shooting outlook in the NBA, shown by his low free throw percentage (59%). Moreover, he had a small role, averaging just 21.7 minutes per game. Saddiq Bey and Aaron Nesmith have low NBA ceilings because of their high draft ages, something they were unable to make up for with their college BPMs. This does not mean that they cannot become good NBA players, though, as Nesmith projects as a good shooter and has high potential for being a contributing player. The primary reason for Isaac Okoro’s low ceiling was his poor BPM in college. Moreover, he is unlikely to be a good shooter in the NBA and did not have a very large role in college.
Good Defensive Potential
- Xavier Tillman, SAC (35th)
- Onyeka Okongwu, ATL (6th)
- Tyrese Haliburton, SAC (12th)
- Aleksej Pokusevski, OKC (17th)
The logistic regression for predicting all defensive players were steals per 36 minutes, defensive box plus minus, draft pick, minutes played per game, and weight. Xavier Tillman had the highest college defensive BPM of the 2020 draft class, with a value of 6.2. This have him an all defensive probability of about 10%. Okongwu also had a high defensive box plus minus. Additionally, he had 3.2 blocks and 1.4 steals per 36 minutes, meaning he could develop into a strong defensive presence in the NBA. Haliburton and Pokusevski were both boosted by their high number of steals at Iowa State and the Greek league, respectively. However, while there were some good defensive players in this draft, there were not many compared to other drafts since 2011. For example, every other draft since 2011 had at least one player with a probability of making an all defense team of at least 14%. No surefire defensive players such as Zion Williamson, Matisse Thybulle, Anthony Davis, or Ben Simmons were present in this draft.
Best Shooting Potential
- Tyrese Haliburton, SAC (12th)
- Aaron Nesmith, BOS (14th)
- Anthony Edwards, MIN (1st)
- Isaiah Joe, PHI (49th)
- Kira Lewis Jr, NOP (13th)
- LaMelo Ball, CHA (3rd)
The model for projected NBA three point percentage was found using five variables: draft pick, free throw percentage, minutes per game, free throw rate, and three point attempt rate. All of the players listed above had very high free throw percentages and shot three pointers frequently in college. Three point percentage in college was not even a significant variable in this regression, showing that a good free throw percentage will likely result in good three point shooting in the NBA. Therefore, NBA scouts should not focus all too much on three point shooting in college and rather focus more on free throw shooting.
Highest Risk
- Aleksej Pokusevski, OKC (17th)
- Malachi Flynn, TOR (29th)
- Precious Achiuwa, MIA (20th)
- Jalen Smith, PHX (10th)
- Obi Toppin, NYK (8th)
The high risk players were those who had a relatively high chance of becoming an all star, but a relatively low chance of becoming a contributing player. Both of these models used similar inputs, but the coefficients of the variables differed. For example, BPM was more important for predicting all stars, while usage was better for predicting contributing players. These high ceiling, low floor players were great for teams that wanted to take a risk and possibly secure a great player. The Thunder, in particular, made a good move in taking Pokusevski as if he turns out to be an all star, he can help lead them for the future. However, if he doesn’t, the Thunder still have numerous picks over the next few years to make up for it.
Lowest Risk
- Leandro Bolmaro, NYK (23rd)
- RJ Hampton, DEN (24th)
- Isaac Okoro, CLE (5th)
- Patrick Williams, CHI (4th)
- Cole Anthony, ORL (15th)
The low risk players of the draft had a high chance of being a contributor, but a low chance of becoming an all star. The selections of Patrick Williams and Isaac Okoro were questionable picks for the top five, since they both had a low chance of becoming a great player. Teams should strive to find higher upside players if they are picking in the top five as it likely means that their teams are not very talented. On the other hand, good teams that want to solidify their lineups should be perfectly content with picking a low risk player, since they are likely to become a rotation player.
Conclusion
This year’s NBA Draft featured sixty new players drafted into the league. As always, some of them will outplay their draft slot, while others will struggle to live up to their expectations. While they are not perfect, advanced and simple college and international stats can allow us to better predict which players will have success than by just using scouting alone. It is likely that the models I created will miss on several draft prospects, but they should predict how well many will do in the NBA.
Data Sources:
Best Ball Stats 