Continuing the debate of the value of three-point shooting in today’s NBA, my article analyzing this issue from a mathematical perspective has now been published on the arXiv, check it out!
Howard Beck had an interesting article today on Bleacher Report, basically suggesting that the NBA finals, in particular, the current style of play embodied by The Golden State Warriors is somehow a vindication of D’Antoni’s basketball philosophies: “Shoot a lot of threes”, “Shoot in 7 seconds or less”, “Play small lineups”, etc…
While the Warriors have certainly embodied some of these philosophies, my personal opinion is that D’Antoni’s style of play can only be vindicated if there is a clear trend in championship teams that reflect these philosophies. As I show below, this is simply not the case.
I looked at the last 15 NBA Champions (from 2000-2014), and tried to see if there were any clear patterns in common between the teams. This is essentially what I found:
Two things that are immediately clear are:
1. There is very little that championship teams have in common!
2. The overwhelming thing that they do have in common is that 14 of the last 15 NBA champions have all been ranked in the Top 10 for Defensive Rating, something that Mike D’Antoni’s coaching philosophy has never really included throughout his years in Phoenix, New York, and Los Angeles.
This, I believe is the grand point that no one seems to be interested in making, perhaps, because according to the “mainstream”, defensive-oriented basketball, which, by definition is “less-flashy” still is the overwhelming common characteristic amongst championship-winning teams.
Perhaps, the Warriors will win this year, but as I said above, I do not believe that one year is anywhere near enough to establish a trend and a vindication of D’Antoni’s basketball philosophies.
Further, there were some other things in Beck’s article that I found to be a bit concerning:
He claimed “Today, coaches speak enthusiastically about “positionless” basketball—whereas 10 years ago, D’Antoni had to sell Marion and Stoudemire on the concept.”
This is not actually true. The triangle offense is the de facto example of “positionless” basketball, and has been around since the 1940s when Sam Barry introduced it at USC. Phil Jackson and Tex Winter’s Bulls and Lakers teams embodied the concept of positionless basketball. In fact, as can be seen from the diagram below (taken from http://khamel83.tripod.com/intro.htm), players don’t have set positions in the triangle offense. Rather, there are regions based on optimality and spacing:
The NBA finals are exactly five days away, and I wanted to present an analysis breaking down the matchup between The Golden State Warriors and Cleveland Cavaliers.
I used machine and statistical learning techniques to generate the most probable scenarios for the outcome of each game, and this is what I found.
Note that the probabilities listed above are not the probabilities for a team to win a specific game, they are the probabilities of a specific scenario occurring. Also, multiple scenarios can occur in a single game, so the probability of multiple scenarios occurring would be the sum of the individual ones.
The Model Results So Far (Updated: June 11, 2015)
Game 1: Scenario Outcomes: 1 and 2 – GSW win
Game 2: Scenario Outcome: 9 – CLE win
Game 3: Scenario Outcomes: 5, 8 – CLE win
Thoughts so far: Despite GSW being down right now 2-1, I still believe that Cleveland’s wins were statistical anomalies. Cleveland’s Game 2 and Game 3 wins according to our model only had 1.07%, 9.34%, and 1.765% chances of occurring in this series. Whereas, the GSW Game 1 win had a 44% chance of occurring in this series.
Game 4: Scenario Outcome: 2 – GSW win
Updated: June 14, 2015
Game 5: Scenario Outcomes: 1,2 – GSW win
Thoughts: All of GSW wins have been the dominant scenarios in this series, i.e., Outcomes 1 and 2. All of CLE wins in this series have been statistical anomalies/outliers. This pattern continued in Game 5.
Updated: June 17, 2015
Game 6: Scenario Outcomes: 1,2 – GSW win
Another GSW win through the dominant scenarios in the series, as expected.
It is without question that the greatest team in NBA history was the 1995-1996 Chicago Bulls. They went 72-10 that year and went on to win the NBA Championship against a top-notch Seattle Supersonics team.
Phil Jackson’s system and first-class coaching were the major reasons why the Bulls were so good, but I wanted to analyze their reason for winning using data science methodologies.
The results that I found were very interesting. First, I mined through each individual game’s data to obtain patterns in the Bulls wins and losses, and this is what I found:
One sees that the Bulls were a defensive nightmare, and if you look at these results in detail, it makes sense that the Sonics were really the only team that ever posed a threat to them. This shows that to beat the Bulls, the opposing team would have to simultaneously:
- Ensure Ron Harper had a FG% less than 44.95% in a game,
- Ensure Dennis Rodman would have less than 17 total rebounds in a game,
- Ensure Luc Longley had less than 2 blocks in a game,
- Ensure Michael Jordan had a FG% less than 46.55% in a game.
If any one of these conditions were not met, the Bulls would win!
This analysis on some level also dispels the notion espoused by several sports analysts like Skip Bayless of ESPN who continually claim that the Bulls’ sole reason for success was Michael Jordan. Ron Harper’s contributions although of paramount importance are rarely mentioned nowadays.
This analysis also shows that the key to the success of the Bulls was not necessarily the number of points that Jordan scored, but the incredible efficiency with which he scored them.
A boosting algorithm also allows us to deduce the most important characteristics in the Bulls’ quality of play and whether they would win or lose a game. The results are as follows:
It is quite interesting that this analysis shows that winning a championship is not about one player, sure, every team needs great players, but the Bulls were a great team, consisting of many great components working together.
Based on several internal statistical models that my colleagues and I developed, we all have concluded that the Raptors losing the way they did in the first round was somewhat of a statistical anomaly. Through an extensive analysis, I present evidence below that shows it was due to several coaching breakdowns in strategy that lead to the Raptors’ collapse.
Optimal preparedness would have been to prepare and utilize an extensive analysis of the Washington Wizards’ style of play. Using advanced machine learning techniques, we generated two results, first based on tree boosting, and the other based on classification trees that found the weak points in the Wizards’ system that would have greatly helped the Raptors in this series.
First, one should be interested in the most important commonalities and characteristics in the Wizards’ play. This result is as follows:
One can immediately see that out of several factors, the two most important factors in determining whether the Wizards will win or lose a game is their team FG% and the number of points their opponent score in a game. From this analysis, we obtain that to beat the Wizards, the Raptors should have focused on particularly strong interior defense, and in particular, stopping penetration. From an offensive point of view, the Raptors should have played a strong and slow half-court game focused on getting close-to-the-basket, high-percentage shots, instead of “high-octane” running up and down the court as they seemed to do very frequently.
Going deeper in this analysis, one also has as a result the following classification tree:
In this tree, “W” and “L” denote whether the Wizards will win or lose a game, “FG.” denotes the Wizards’ FG%, “OFG.%” denotes the Raptors’ field goal percentage, and “OPTS” denotes the number of points in a game the Raptors should score. One sees that for the Wizards to lose games, the coaching strategy should have been designed to ensure that the Wizards would shoot below 45.25%, while the Raptors should have shot at least 40.3% each game. Complementary to the above analysis, one notes that since three point shots are not fundamental to the Wizards’ offense, to accomplish this, the Raptors should have had strong half-court defensive schemes (including traps and trapping zones), combined with slow-paced, interior offensive schemes.
In conclusion, it is important to note that these analytical results and ideas were available well in advance of the NBA playoffs, and the Raptors would have tremendously benefited from using these ideas. I would also like to point out that I have only offered a preview of the results I obtained. I have also developed several results pertaining to optimal offensive and defensive schemes that would not only change the way the Raptors play, but would make them significantly better.
Much has been said about the effect that Kobe has had on the Lakers this season. Byron Scott has been limiting his minutes at times, and at times has played him almost the entire game. There have been times this season where analysts and fans of the Lakers have claimed that the team actually plays better without Kobe. We decided to look at these ideas from a statistical perspective.
We looked at a whole bunch of data of Kobe’s play this season (courtesy of Basketball-Reference.com), and compared his individual play to whether the Lakers win games or not. This is what we found.
In this first classification tree, note that ‘Y’ denotes when the Lakers are expected to win, and ‘N’ denotes when they are expected to lose. What we found is any time that Kobe shoots at least 44.95%, the Lakers can be expected to win. If he shoots less than this percentage, then the only way the Lakers can win with Kobe still in the game is if he has less than 3 personal fouls, shoots less than 36.65% from the 3PFG% line and attempts more than 6-7 shots in the game.
From a statistical perspective, the Lakers can win many more games if Byron Scott optimizes the Lakers offense to get Kobe the ball in high-percentage shooting areas of the floor, i.e., closer to the basket than further away from it. Certainly, from a statistical perspective, Byron Scott’s way of allowing Kobe to play “freestyle” basketball is hurting the Lakers’ chances at winning games.
The second classification tree analysis that we did was to look at the whole debate over how many minutes is optimal for Kobe to play. What we found was that if Kobe plays less than 31 minutes in a game, the Lakers can expect to lose that game, while he is on the roster. If he plays more than 31 minutes, and has more than 7-8 assists, the Lakers can expect to win. The only other possibility for the Lakers to win games in this context is if he plays more than 31 minutes, has less than 7-8 assists, makes more than 6-7 of his shots, and plays less than 34-35 minutes a game.
Our previous analysis showed that the Lakers have the best chance of winning consistently when Kobe shoots a high percentage. This analysis shows that it is optimal for him to play between 31-35 minutes a game if he has less than 7 assists, but anytime he has more than 7 assists in a game, the Lakers can be expected to win. Therefore, from an offensive strategy perspective, the Lakers need to play more team-oriented basketball centered around Kobe. In hindsight, which is supported statistically, Kobe and the Lakers would be much better off in a post-oriented offense that promotes distributing the ball, high-percentage shots, and a slow pace. All of these three seem to be completely opposite to how Byron Scott has managed this team this year, and we feel that is why the Lakers have the record that they do!