Predicting the 2018-19 NBA Season

Just in time for the NBA season to kick off tomorrow, I have projected win totals for each team. Below you will find my predictions for each team alongside latest Vegas odds. I’ve also included the difference between my predictions and the Vegas line, as well as the absolute value of the differences. Below the tables you’ll find my team by team thoughts. Here’s to hoping I do better than last season, with a median difference between prediction and actual wins of 6.

Western Conference

  1. Golden State Warriors (55) I’m nervous with how low this prediction is, but there are two important factors to consider. First, the West is going to be a bloodbath this season. Only the Kings are outright terrible. Second, we have seen this team prioritize the postseason which makes sense given the injury history of stars like Curry and Durant.
  2. Houston Rockets (53) Their roster got a bit weaker. Also, last year they were gunning to show their dominance and win Harden an MVP. This year, look for them to also prioritize postseason health.
  3. Utah Jazz (49) The Jazz looked great last postseason and sophomore guard Donovan Mitchell should only improve. 
  4. San Antonio Spurs (48) Yes, I factored in Dejounte Murray’s season-ending injury. The question for the Spurs is what can they get out of DeMar DeRozan? 
  5. Oklahoma City Thunder (47) Simply getting rid of Carmelo should be a net positive for the Thunder. However, they have a few reasons for concern. First, Russell Westbrook relies heavily on athleticism but is turning 30 next month. Second, Andre Roberson has apparently suffered a setback in his injury recovery. Having Roberson on the court is essential to their defense which is essential to the Thunder being an elite team. 
  6. Minnesota Timberwolves (45) I took Jimmy Butler off the Timberwolves but I did not put him on another team, which will almost certainly create problems at season’s end. I also gave Wiggins a small boost to his production in the season before Butler came to Minnesota.
  7. Denver Nuggets (44) This is a nice young team with plenty of players poised to improve: Nikola Jokic, Jamal Murray, Gary Harris. Having Paul Millsap back from injury will be a nice boost to possibly get them into the playoffs this year. If Jokic does not improve on defense, they could be in trouble.
  8. New Orleans Pelicans (43) The Pelicans looked phenomenal in the playoffs against the Trailblazers. They are basically bringing back that squad with an added Julius Randle, who should be a great addition.
  9. Los Angeles Lakers (42) Yes, right now I have a LeBron team missing the playoffs. By a game.  I could see them as high as fourth if you look at how tight these win projections are. I think we’re all curious how this team will look given how differently they are built than the LeBron Cavaliers. I think their recent pickups of Rajon Rondo, Lance Stephenson, and Javale McGee are suspect.
  10. Portland Trailblazers (41) I think Damian Lillard had a phenomenal season but it was also a career year. Look for him to regress a bit without any strong steps forward from the rest of this squad.
  11. Dallas Mavericks (38) Luca Doncic is going to be an NBA star, but this team is a few years away from competing. 
  12. Los Angeles Clippers (34) I’m still not sure how the Clippers performed as well as did last season and it’s possible I’m missing something here. But I don’t think this team is quite good enough to compete in an excellent Western Conference. 
  13. Memphis Grizzlies (33) The Grizzlies seem to think they’ll be competitive with Mike Conley and Marc Gasol healthy but I’m not so sure. My money is on this team struggling and then the front office breaks things up.
  14. Phoenix Suns (26) Another team operating under delusions of grandeur, they have some nice young players but will struggle for a few more seasons. Look for them to shoot themselves in the foot by making an ill-advised trade for a veteran point guard.
  15. Sacramento Kings (25) The Kings are bad. Marvin Bagley might be a good NBA player but I doubt he’ll be better than Doncic.

Eastern Conference

  1. Toronto Raptors (62) Take a 59 win team, swap out DeMar DeRozan for Kawhi Leonard, and remove LeBron from the conference. That sounds like 3 more wins to me. If Leonard’s injury is not resolved this won’t happen, but all signs indicate he will be ready to go this week. 
  2. Boston Celtics (55) One of my biggest problems last season was accurately predicting the performance of young players improving their skills. I attempted to tackle that with these young Celtics stars but I will not be shocked if they outperform this projection.
  3. Philadelphia 76ers (54) See my above comment given the number of young stars in Philadelphia. I’m not expecting great things from Markelle Fultz although I think he’ll be a positive contributor. It’s always fair to question the health of Joel Embiid, too.
  4. Indiana Pacers (51) So long as Victor Oladipo doesn’t serously regress, this team should be real good. Tyreke Evans is a great addition for them.
  5. Milwaukee Bucks (50) I am really struggling with how to appropriately factor in the huge coaching upgrade from Jason Kidd to Mike Budenholzer. Based on preseason games, which we should take with a grain of salt, Giannis Antetokounmpo looks like a leading MVP candidate.
  6. Washington Wizards (48) If there is one thing I feel confident predicting, it is that the Wizards will have locker room troubles. We have seen it before from teams lead by John Wall and it is basically guarantee when Dwight Howard comes to town. I’m curious to see how Thomas Satoransky compared to last year as a fill-in starter when Wall was injured. 
  7. Miami Heat (43) I am tempted to project the Heat trading for Jimmy Butler but that’s not looking like a done deal. The Heat look stuck in just-above-mediocrity. They have lots of good players, but not a single very good one. 
  8. Brooklyn Nets (39) I am feeling a bit uneasy about how I have the Nets. But it’s worth considering how most analysts love the Nets style, particularly their shooting profile, they just have lacked the talent to date. In a weak Eastern Conference, they have a chance to be competitive. 
  9. Detroit Pistons (35) Andre Drummond and Blake Griffin looked surprisingly good on the court together. But its still not a very effective roster makeup and they’re going to struggle to score. I’m not giving a boost for regular season master coach Dwayne Casey, who could push them at least into the playoffs.
  10. Charlotte Hornets (35) I want to see Kemba Walker on a roster where the second best player is better than Jeremy Lamb! 
  11. Cleveland Cavaliers (30) I don’t see how Kevin Love is a successful first option in today’s NBA. Minnesota Kevin Love was bigger and able to bully guys around in the post. After getting into shape to be a corner 3 threat for LeBron, this is going to be a rough adjustment. They also like a decent point guard which has a multiplying negative effect.
  12. Orlando Magic (27) The Magic are struggling. Who will win games for them? D.J. Augustin? Nikola Vucevic? They’ve got Aaron Gordon I guess. Oh and this factors in Mo Bamba as a decent rookie.
  13. Chicago Bulls (27) The Bulls have some reasons for optimism, just a few years into the future. They have made a number of poor decisions recently, such as picking up Jabari Parker and signing Zach Lavine for too much money.
  14. New York Knicks (27) I would have them a bit higher if Kristaps Porzingis was healthy. Until then, they’ll be relying on Enes Kanter (who looked surprisingly good last season) and Tim Hardaway Jr. Just not good enough. 
  15. Atlanta Hawks (25) Trae Young will likely have a handful of games where he goes crazy but for the most part he will struggle. I have John Collins and Alex Len as their best players. Yeah, they are going to be bad.

Moneyball

If you’re wondering where I got my love for sports analytics, the answer is Moneyball. Published in 2003, Moneyball explains Sabermetrics through the story of Oakland Athletics General Manager Billy Beane. As defined by Wikipedia, Sabermetrics is the empirical analysis of baseball, especially baseball statistics that measure in-game activity“.

Moneyball generated a lot of controversy. These stats nerds were ruining the game with complicated algorithms. At the time, Beane thought players who excelled in on-base percentage were undervalued compared to those with a high batting average. Old school analysts like Joe Morgan mocked those who valued on-base percentage for wanting to clog up the basepaths.

The Oakland Athletics haven’t come within a 10-foot pole of a World Series. Why is Moneyball so lauded?

I evaluated whether on-base percentage is a better statistic than batting average. If you think that’s a poor way to evaluate the value of Sabermetrics, I agree. But it is probably the most popularized insight from Moneyball and makes for a simple test.

Using MLB data from 2003 to 2018, I calculated a multiple linear regression between batting average, on-base percentage, and wins. Here was the result:

Wins = (OBP * 560) – (BA * 204) – 49

This means to estimate how many games a team will win, you multiply their on-base percentage by 560, subtract their batting average multiplied by 204, and subtract 49. After controlling for OBP, having a higher BA is associated with a smaller chance of winning.

How could this be? Hits make you less likely to win? Not exactly. On-base percentage includes hits, walks, and hits by pitch. Since batting average includes hits, on-base percentage captures everything captured by batting average and then some.

To confirm this, I calculated two simple linear regressions, that between on-base percentage & winning and that between batting average & winning.

Wins = (OBP * 409) – 52
Mean Squared Error = 98

Wins = (BA * 340) – 7
Mean Squared Error = 113

First, we see the coefficient is stronger for on-base percentage than for batting average, 409 compared to 340. Second, we see the mean squared error is smaller for on-base percentage meaning the relationship is clearer. Conceptually, on-base percentage is more valuable to me than batting average. But if you’re skeptical and wanted the data, there it is.

The debate between on-base percentage and batting average is only one example between the statistics nerds and the old guard. Over a decade after Moneyball was published, on-base percentage is now properly valued. The promise of using statistics and economics to recognize undervalued assets in sports remains as alluring as ever.

Thanks to Kyle Safran for helping me prepare this post.

Projects

Here are some projects I’m currently working on and/or thinking about kickstarting:
A computer version of the Parker Brothers World Flag Game About The United Nations. This is a classic game for the Needham family, especially at our cottage. I’m working on this game as a chance to practice the Angular framework and possibly the full MEAN stack. I wanted to use a board game that, as far as I know, has not been digitized before.

An NBA game log tool. I watch a lot of the NBA and I try to go beyond watching solely the best teams. Two seasons ago I used a Google Sheet to track what games I watched to ensure I was getting a decent spread of all 30 teams. I want to make a tool to easily log which games I watch, how much of a game I watch, and my notes on what I observe. Additional features would include tagging and looking up notes based on individual players as well as a login system for multiple users. My instinct is to make this with the Flask Python framework.

An NBA lineup quick view tool. The official NBA website has a great tool for analyzing different lineups. I want to make a handy reference where people can quickly see the best and worst lineups in the league and for each team on the basis of offensive success, defensive success, and net success. You could in theory do this with the NBA.com site, but it requires a number of steps. I want to create a website that has this data already prepared for you. This would likely be a Flask project as well.

Reach out to Scott Sumner and discuss building a cryptocurrency linked to NGDP as he proposed here. The step I need to take between here and now is building a just-for-fun cryptocurrency as proof of competency.

Calculate which NBA player had the highest game score against each NBA team last season. I did this for the 2016-2017 season.
Track how accurate Pythagorean win projections were throughout the previous NBA season. This was my original goal in starting a weekly NBA blog. I have the data ready to analyze, I just haven’t followed through on it yet!

If you read this post and are interested in collaborating on any of these projects, email me