An Analysis of The 2015 NBA Finals Matchup

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. 

Three-Point Shooting Teams and The 2014-2015 NBA Playoffs

Major Update: June 22, 2015.  I have now published a formal article on the arXiv proving many of the assertions made earlier in this blog post. It can be found here:

Some controversy was stirred up today when Knicks President and Basketball coaching legend Phil Jackson made the following tweets regarding three-point shooting teams not doing so well in the second round of the playoffs:

Data Analytics and The 1995-1996 Chicago Bulls

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:

  1.  Ensure Ron Harper had a FG% less than 44.95% in a game,
  2. Ensure Dennis Rodman would have less than 17 total rebounds in a game,
  3. Ensure Luc Longley had less than 2 blocks in a game,
  4. 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:

We see that a key feature of the Bulls’ quality of play depends on how efficient Ron Harper in terms of his FG%.

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.

Data Analytics and The Raptors 2015 Loss

          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.