The Phoenix Suns Gorilla, the dean of NBA mascots, has been entertaining Suns fans since 1980 and is now a worldwide ambassador for the Suns and the NBA.
Here’s why, statistically speaking, Suns look good heading to NBA Finals
The Phoenix Suns advanced to the NBA Finals after a hard-fought series with the Los Angeles Clippers, taking them in a 4-2 game series Wednesday night. It’s the first time in more than a quarter-century the Suns have been back to the championship series.
At the beginning of the season, no one could have predicted the Suns’ amazing run, especially Las Vegas oddsmakers, who gave the team a 2% chance of winning a title. Not even Daniel McIntosh, a senior lecturer with the W. P. Carey School of Business at Arizona State University, could have predicted how far the Suns have come.
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McIntosh worked seven years with the NBA, tracking data protocols and developing a cutting-edge methodology for 15 different teams. This gives him an insight into the game most others don’t have.
ASU News spoke with McIntosh about the Suns’ spectacular season and why they’re playing so well from a statistical point of view.
Question: You seem like a person who looks at the NBA game differently — would that be accurate?
Answer: That’s probably a fair assessment. What I would say is that I think a lot of people are now starting to look at sports in a different way. Maybe that’s inner, self-conscious me speaking, still trying to fit in with the cool jocks and not be weird but, truthfully, I’ve always sort of looked at the data side of sports. I remember listening to a Bobby Knight interview. He was talking about how he didn’t understand why his players got so excited after a dunk. One of his players is hooting and hollering and Bobby deadpanned and said, “What? Was it worth more than two points?” That resonated with me and started me down the path of thinking about how data and sports intersect. Specifically, how I could gain an edge by working smarter, not harder.
Unsurprisingly, as a young postgraduate, on the prowl for opportunities, I thought it would be fun to try and make money on this intersection. The easiest way — well, maybe not easiest, but certainly most fun way — would be to make money gambling on sports. I had a background in basketball, played in my youth, coached at the high school level and was able to serve in a support role for Coach (Charli) Turner Thorne at the USA Basketball training facility during her time coaching in their Olympic development program. I figured if I could make a model work, it would be in basketball. I cobbled together as much data as I could get my hands on, built out predictive models, leveraged every statistical trick and cutting-edge methodology out there in hopes of making money watching sports … and failed, miserably. Every back test came in negative.
I was convinced that my math was correct, but that I was limited by the data publicly available. Around this time, the NBA rolled out their SportVu player tracking data protocols and installed four cameras in every NBA arena measuring each player 25 times per second. I knew if I could get access to this data before anyone else did, I could beat Las Vegas. Well, long story short, I was able to get access to the data but was told strictly that I couldn’t use it for betting.
Quite frankly, the teams and league didn’t know what to do with this data, and so I was able to carve out a niche as a consultant working with over a dozen NBA teams and the league office exploring this data. We started looking at position, speeds, accelerations and a variety of other metrics to evaluate player performance and develop management strategies. If you are familiar with the now-infamous concept of “load management,” that was the genesis of that idea. We looked at how to put players in the best position to succeed, how to keep them healthy and how to return them to full performance if they did happen to sustain an injury.
Q: This sounds like the concept for the book and movie “Moneyball,” except it’s being applied to basketball?
A: That’s exactly right! The NBA has taken the analytics baton from baseball and run with it. Baseball has “Moneyball” and Bill James, while basketball has “Basketball on Paper” and Dean Oliver. Not quite as catchy of a title but the same thinking. The neat thing is that the broader public is starting to appreciate these ideas. For the NBA, this mindset entered the public consciousness when NBA teams started tanking and were told to “trust the process” and emphasize the three-point shot, first with the Golden State Warriors and then to an even more extreme Daryl Morey and the Rockets. The basic idea is simple. A 21-foot shot can be worth two points, but a 22-foot shot can be worth three points. Why not take that little step back and get 50% more reward? That fundamentally changed how teams think about offense.
The problem is that these advantages aren’t sustainable. In other words, once teams saw that shooting three-pointers was mathematically more efficient, everyone else did the same thing. It’s a copycat league. It was in this context that teams were very excited to look at new and proprietary sources of data like XYZ player tracking and the metrics we were developing. It was fun to look at a team and say, “There’s no way they keep this up.” Sadly, this often applied to my hometown teams.
Q: Going into this season, what were your expectations of the Suns?
A: It’s a great question. You’re asking how well do our models do when they are applied to real-world settings. So first, let me say I was wrong about the Suns. I’ve already had to eat crow about the signing of Chris Paul. I expected the Suns would likely be a playoff team but max out around a five seed. I had them as a slightly above-average team entering the season.
In looking at the team, they were young, unproven and had no playoff experience for the core returning players. The team last made the playoffs in 2010 and last made the NBA Finals in 1993. This level of success is rare for great teams and even rarer for the Suns specifically. Adding a 36-year-old point guard with a fairly rich injury history didn’t seem to be a winning formula.
Q: What are some of the factors that are contributing to the court success of this year’s team?
A: I can point to three things that have significantly helped this team. First, they’ve stayed healthy compared to their elite peers. Man-Games Lost is a website that tracks the number of games missed due to injury by a team. In their metrics, the Suns had the third-lowest injuries for the year. Then, look at the playoffs and it is all about matchups. The Suns have faced three teams who have had significant injuries to star players. The Lakers dealt with injuries to Anthony Davis and LeBron James. The Nuggets had Jamal Murray out, and the Clippers had Kawhi Leonard and Serge Ibaka out. On the opposite side of the bracket, Brooklyn had major injuries to James Harden and Kyrie Irving, and Atlanta with Trae Young and Milwaukee with Giannis (Antetokounmpo). That level of elite-level production is nearly impossible to replace, and the Suns have been the beneficiaries so far.
Second, they’ve benefitted from a home-court advantage in the first two rounds of the playoffs. The Lakers were only able to have around 45% of their stadium full throughout their series with the Suns. The Suns, in Game 5, had 90% of their stadium full. In a series where teams might be separated by a few points, that loss of home-court advantage is significant.
Third, the Suns have benefited from a unique ability to work against the current NBA defensive strategy. As I mentioned earlier, the three-point shot is the most efficient in the game. NBA teams know this and now build defenses around stopping three-point shots and limiting layups and dunks. The Suns are constructed in such a way that they have two great shooters that can exploit this strategy. Chris Paul and Devin Booker are both elites when it comes to shooting mid-range jumpers. The Suns ranked No. 1 in closely guarded field goal percentage this year. They were one of four teams to rank in the top 10 in the NBA in both offensive and defensive efficiency. They are a very good basketball team. That in combination with the fortunate matchups and injuries has led to the regular- and postseason success.
Q: What are some other important factors we can’t see that are important in this incredible run?
A: I don’t think people have adequately accounted for the strangeness of this season. The pandemic created a nightmare scenario for the league. Usually, a season ends in July and starts in October. There’s a nice three-month break to recover and prepare for the upcoming season. This past year, the season ended in October and started back up in December. That’s 30 fewer days to get a body ready for the rigors of an NBA season. That’s huge from a ramp-up, ramp-down, injury-management perspective.
Factor in that the Suns were a young team, had the short eight-game bubble success to build on but no playoff grind, and you have a built-in advantage to help this team. The growth of Deandre Ayton and Devin Booker then puts them in the place they are today: competing in their first NBA finals since 1993.
Q: We’ve touched on the “Moneyball” aspect of today’s game. Where do you see the future of the NBA?
A: Predicting the future is difficult, but I can speak to some interesting trends. First, the G League is becoming a viable alternative to college basketball. If that were to continue, with more money and endorsement opportunities, you’d have closer to a European model than the current collegiate model. What happens in the name, image and likeness space plus the new CBA
all will impact how this shakes out. But the ramifications of this change would be how teams develop players. We don’t have youth academies in the U.S. We have AAU (Amatuer Athletic Union). I think one of the biggest changes will be how we scout, identify and sign talent.
Then on the court, we are seeing the use of analytics explode. I’ve mentioned XYZ player data, and that data is broken down into performance and tactical. Performance looks at things like speeds and accelerations. Tactical looks at things like defenses, sets and outcomes. How did we do against zone offense in these situations? The divide between these two is tough to cross, but new data sources like sleep-tracking data, increased wearable technologies and things like AI are working to fill in the gaps to make our models better. As I mentioned, the NBA is a copycat league. Much of what teams are working on is how do we develop things in-house that are harder for other teams to copy.
Q: Statistically speaking, should I make a trip to Las Vegas and place my entire life savings on the Suns to win the championship?
A: Oh, man! I knew this was coming. … As someone that has lost his fair share of money to Las Vegas, my answer would be an emphatic no. There’s a reason million-dollar hotels are popping up in the middle of the desert. They are very good at what they do, and they have access to models, data and wager histories that you and I don’t. Add in that they’ve also created a built-in edge with how they set their lines, and it’s a losing proposition. If you look at gambling as entertainment, like spending $20 to go to a movie or $20 on the Suns to win, I think it serves its purpose. If you start putting your life savings on it, that’s probably a pretty bad idea. But there’re stories of it working, so paraphrasing Dirty Harry, “Do you feel lucky?”