## Ranking NBA Championship Teams

The first thing to note is that just by looking at Basketball-Reference.com there are 62 factors that uniquely classify a team: MP FG FGA FG% 3P 3PA 3P% 2P 2PA 2P% FT FTA FT% ORB DRB TRB AST STL BLK TOV PF PTS OMP OFG OFGA OFG% O3P O3PA O3P% O2P O2PA O2P% OFT OFTA OFT% OORB ODRB OTRB OAST OSTL OBLK OTOV OPF OPTS PW PL MOV SOS SRS ORtg DRtg Pace FTr 3PAr eFG% TOV% ORB% FT/FGA eFG% TOV% DRB% FT/FGA, where OFGA indicates a given team’s opponent’s FGA per game average for a specific season.
The reason it is not meaningful to look at a specific statistic or a pair of statistics such as “three-point attempt rate” is that,

$\boxed{\frac{62!}{2! 60!} = 1891}$ possible comparisons can be made.

Because of this, what is required is a detailed statistic learning approach. I looked at the full season statistics for the last twenty NBA champions from the 1995-1996 Chicago Bulls to the 2014-2015 Golden State Warriors.

I employed principle compoent analysis (PCA) to reduce the number of dimensions to see which variables contribute most to the variance of the data set. I found that the first 7 of 20 principle compoents explained 88.52% of the variance. Therefore, we can effectively reduce the dimension of the data set from 63 to 7.  This can be seen in the scree plot below:

A visualization of the 63-variable data set is as follows:

The power of principle components analysis reduced this high-dimensional dataset to a more manageable (but, perhaps still complicated) 7-dimensional data set, visualized as follows:

Next, I computed the Euclidean distance metric to perform hierarchical clustering on these seven principle components. I obtained the following result:

We notice immediately that:

1. The 2015 Golden State Warriors were very similar to the 2014 San Antonio Spurs.
2. Not surprisingly, Phil Jackson’s 2000 and 2002 Lakers teams were very similar to each other but not to any other championship team, and similarly for his 2009 and 2010 Lakers teams.
3. Interestingly, the two teams that stand out which are truly dissimilar to any other championship team are the 2008 Boston Celtics and the 1998 Chicago Bulls.

This analysis also eliminates the notion that a team has to play a specific style, for example “modern-day play” to win a championship. In principle, there are many possible ways and styles that lead to a championship and an analysis such as this deeply probing the data shows this to be the case.

## Ranking NBA Players

The 2015-2016 NBA season is dawning upon us, and as usual, ESPN has been doing their usual #NBArank, where they are ranking players based on the following non-rigorous methodology:

We asked, “Which player will be better in 2015-16?” To decide, voters had to consider both the quality and quantity of each player’s contributions to his team’s ability to win games. More than 100 voters weighed in on nearly 30,000 pairs of players.

Of course, while I suspect this type of thing has to be just for fun , it has generated a great deal of controversy with many arguments ensuing between fans. For example, Kobe Bryant being ranked 93rd overall in the NBA this year gained a fair deal of criticism from Stephen A. Smith on ESPN First Take.

In general, at least to me, it does not make any sense to rank players from different positions that bring different strengths to a team sport such as basketball. That is, what does it really mean for Tim Duncan to be better than Russell Westbrook (or vice-versa), or Kevin Love to be better than Mike Conley (or vice-versa), etc…

From a mathematical/data science perspective, the only sensible thing to do is to take all the players in the league, and apply a clustering algorithm such as K-means clustering to group players of similar talents and contributions into groups. This is not a trivial thing to do, but it is the sort of thing that data scientists do all the time! For this analysis, I went to Basketball-Reference.com, and pulled out last season’s (2014-2015) per game averages of every player in the league, looking at 25 statistical factors from FGA, FG% to STL, BLK, and TOV. One can see that this is a 25-dimensional problem.

Our goal then is to consider the problem where denoting $C_{1}, ... C_{K}$ as sets containing the observations in each cluster, we want to solve the optimization problem:

$\mbox{minimize}_{C_{1},...C_{k}} \left\{\sum_{k=1}^{K} W(C_{k})\right\}$,

where $W$ is our distance measure. We use the squared Euclidean distance to define the within-cluster variation, and then solve:

The first thing to do is to decide how many clusters we want to use in our solution. This is done by looking at the within sum of squares (WSS) plot:

First, we will use 3 clusters in our K-means solution. In this case, the between sum of squares versus total sum of squares ratio was 77.0%, indicating a good “fit”). We use three clusters to begin with, because based on visual inspection, the data clusters very nicely into 3 clusters. The plots obtained were as follows:

The three clusters of players can be found in the following PDF File. Note that the blue circles represent Cluster 1, the red circles represent Cluster 2, and the green circles represent Cluster 3.

Next, we dramatically increase the number of clusters to 20 in our K-means solution.

Performing the K-means clustering, we obtain the following sets of scatter plots. (Note that, it is a bit difficult to display a 25×25 plot on here, so I have split them into a series of plots. Note also, that the between sum of squares versus total sum of squares ratio was 94.8 %, indicating a good “fit”):

The cluster behaviour can be seen more clearly in three dimensions. We now display some examples:

The 20 groups of players we obtained can be seen in the PDF file linked below:

nbastatsnewclusters

The legend for the clusters obtained was:

Two sample group clusters from our analysis are displayed below in the table. It is interesting that the analysis/algorithm provided that Carmelo Anthony and Kobe Bryant  belong in one group/cluster while LaMarcus Aldridge, Lebron James, and Dwyane Wade belong in another cluster.

 Group 16 Group 19 Arron.Afflalo.1 Steven.Adams Carmelo.Anthony LaMarcus.Aldridge Patrick.Beverley Bradley.Beal Chris.Bosh Andrew.Bogut Kobe.Bryant Jimmy.Butler Jose.Calderon DeMarre.Carroll Michael.Carter.Williams.1 Michael.Carter.Williams Darren.Collison Mike.Conley Goran.Dragic.1 DeMarcus.Cousins Langston.Galloway Anthony.Davis Kevin.Garnett DeMar.DeRozan Kevin.Garnett.1 Mike.Dunleavy Jeff.Green.2 Rudy.Gay George.Hill Eric.Gordon Jrue.Holiday Blake.Griffin Dwight.Howard Tobias.Harris Brandon.Jennings Nene.Hilario Enes.Kanter.1 Jordan.Hill Michael.Kidd.Gilchrist Serge.Ibaka Brandon.Knight.1 LeBron.James Kevin.Martin Al.Jefferson Timofey.Mozgov.2 Wesley.Johnson Rajon.Rondo.2 Brandon.Knight Derrick.Rose Kawhi.Leonard J.R..Smith.2 Robin.Lopez Jared.Sullinger Kyle.Lowry Thaddeus.Young.1 Wesley.Matthews Luc.Mbah.a.Moute Khris.Middleton Greg.Monroe Donatas.Motiejunas Joakim.Noah Victor.Oladipo Tony.Parker Chandler.Parsons Zach.Randolph Andre.Roberson Rajon.Rondo P.J..Tucker Dwyane.Wade Kemba.Walker David.West Russell.Westbrook Deron.Williams

If we use more clusters, players will obviously be placed into smaller groups. The following clustering results can be seen in the linked PDF files.

1. 50 Clusters – (between_SS / total_SS =  97.4 %) – PDF File
2. 70 Clusters – (between_SS / total_SS =  97.8 %) – PDF File
3. 100 Clusters – (between_SS / total_SS =  98.3 %) – PDF File
4. 200 Clusters (extreme case) – (between_SS / total_SS =  99.1 %) – PDF File

I did not include the visualizations for these computations because they are quite difficult to visualize.

Looking at the 100 Clusters file, we see two interesting results:

• In Cluster 16, we have: Carmelo Anthony, Chris Bosh, Kobe Bryant and Kevin Martin
• In Cluster 74, we have: LaMarcus Aldridge, Anthony Davis, Rudy Gay, Blake Griffin, LeBron James and Russell Westbrook

CONCLUSIONS:

We therefore see that is does not make much mathematical/statistical sense to compare and two pairs of players. In my opinion, the only logical thing to do when ranking players is to decide on rankings within clusters. So, based on the above analysis, it makes sense to ask for example whether Carmelo is a better player than Kobe or whether Lebron is a better player than Westbrook, etc… But, based on last season’s statistics, it doesn’t make much sense to ask whether Kobe is a better player than Westbrook, because they have been clustered differently. I think ESPN could benefit tremendously by using a rigorous approach to these sorts of things which spark many conversations because many people take them seriously.

## Do More Gun Laws Prevent Gun Violence?

Abstract:

1. The number and quality of gun-control laws a state has drastically effects the number of gun-related deaths.
2. Other factors like mean household income play a smaller role in the number of gun-related deaths.
3. Factors like the amount of money a state spends on mental-health care has a negligible effect on the number of gun-related deaths. This point is quite important as there are a number of policy-makers that consistently argue that the focus needs to be on the mentally ill and that this will curb the number of gun-related deaths.

Contents:

1. Critique of Recent Gun-Control Opposition Studies
2. A more correct way to look at the Gun Deaths data using data science methodologies.

A Critique of Recent Gun-Control Opposition Studies

In light of the recent tragedy in Oregon which is part of a disturbing trend in an increase in gun violence in The United States, we are once again in the aftermath where President Obama and most Democrats are advocating for more gun laws that they claim would aid in decreasing gun violence while their Republican counterparts are as usual arguing the precise opposite. Indeed, there have been two very simplified  “studies” presented in the media thus far that have been cited frequently by gun advocates:

I have singled out these two examples, but most of the studies claiming to “do statistics” follow a similar suit and methodology, so I have listed them here. It should be noted that these studies are extremely simplified, as they compute correlations, while in reality they only look at two factors (the gun death rate and a state’s “Brady grade”). As we show below, the answer to the question of interest and one that allows us to determine causation and correlation must depend on several state-dependent factors and hence, requires deeper statistical learning methodologies, of which NONE of the second amendment advocates seem to be aware of.

The reason why one cannot deduce anything significant from correlations as is done in Volokh’s article is correlation coefficients are good “summary statistics” but they hardly tell you anything deep about the data you are working with. For example, in Volokh’s article, he uses MS Excel to compute the correlations between a pair of variables, but Excel itself uses the Pearson correlation coefficient, which essentially is a measure of the linearity between two variables. If the underlying data exhibits a nonlinear relationship, the correlation coefficient will return a small value, but this in no way means there is no relationship between the data, it just means it is not linear. Similarly, other correlation coefficient computations make other assumptions about the data such as coming from a normal distribution, which is strange to assume from the onset. (There is also the more technical issue that a state’s Brady grade is not exactly a random variable. So measuring the correlation between a supposed random variable (the number of homicides) and a non-random variable is not exactly a sound idea.)

A simple example of where the correlation calculation fails is to try to determine the relationship between the following set of data. Consider 2 variables, x and y. Let x have the data

x              y
-1.0000  0.2420
-0.9000  0.2661
-0.8000  0.2897
-0.7000  0.3123
-0.6000  0.3332
-0.5000  0.3521
-0.4000  0.3683
-0.3000  0.3814
-0.2000  0.3910
-0.1000  0.3970
0            0.3989
0.1000  0.3970
0.2000  0.3910
0.3000  0.3814
0.4000  0.3683
0.5000  0.3521
0.6000  0.3332
0.7000  0.3123
0.8000  0.2897
0.9000  0.2661
1.0000  0.2420

If one tries to compute the correlation between x and y, one will obtain that the correlation coefficient is zero! (Try it!) A simple conclusion would be that therefore there is no linear causation/dependence between x and y. But, if one now makes a scatter plot of x and y, one gets:

Despite having zero correlation, there is apparently a very strong relationship between x and y. In fact, after some analysis,  one can show that they obey the following relationship:

$y = \frac{1}{\sqrt{2 \pi}} e^{-(x^2)/2}$,

that is, y is the normal distribution. So, in this example and similar examples where there is a strong nonlinear relationship between the two variables, the correlation, in particular, the Pearson correlation is meaningless. Strangely, despite this, Volokh uses a near-zero correlation of his data to demonstrate that there is no correlation between a state’s gun score and the number of gun-related deaths, but this is not what his results show! He is misinterpreting his calculations.

Indeed, looking at Volokh’s specific example of comparing the Brady score to the number of Homicides, one gets the following scatter plot:

Volokh that computes the Pearson correlation between the two variables and obtains a result of 0.0323, that is, quite close to zero, which leads him to conclude that there is no correlation between the two. But, this is not what this result means. What it is saying in this case, is that there is a strong nonlinear relationship between the two. Even a very rough analysis between the two variables, and as I’ve said above, and demonstrate below, looking at two variables for a state is hardly useful, but for argument sake, there is a rough sinusoidal relationship between the two variables:

In fact, the fit of this sum-of-sines curve is an 8-term sine function with a R^2 of 0.5322. So, it’s not great, but there is clearly at least some causal behaviour between the two variables. But, I will say again, that due to the clustering of points around zero on the x-axis above, there will be simply NO function that fits the points, because it will not be one-to-one and onto, that is, there are repeated x-points for the same y-value in the data, and this is problematic. So, looking at two variables is not useful at all, and what this calculation shows is that the relationship if there is one would be strongly nonlinear, so measuring the correlation doesn’t make any sense.

Therefore, one requires a much deeper analysis, which we attempt to provide below.

A more correct way to look at the Gun Homicide data using data science methodologies.

I wanted to analyze using data science methodologies which side is correct. Due to limited time resources, I was only able to look at data from previous years (2010-2014) and looked at state-by-state data comparing:

1. # of Firearm deaths per 100,000 people (Data from: http://kff.org/other/state-indicator/firearms-death-rate-per-100000/)
2. Total State Population (Obtained from Wikipedia)
3. Population Density / Square Mile (Obtained from Wikipedia)
4. Median Household Income (Obtained from Wikipedia)
5. Gun Law Grade: This data was obtained from http://gunlawscorecard.org/, which is The Law Center to Prevent Gun Violence and grades each state based on the number and quality of their gun laws using letter grades, i.e., A,A+,B+,F, etc… To use this data in the data science algorithms, I converted each letter grade to a numerical grade based on the following scale: A+: 90, A-: 90, A: 85, B:73,B-:70,B+:77,C:63,C-:60,C+:67, D:53,D-:50,D+:57,F:0.
6. State Mental Health Agency Per Capita Mental Health Services Expenditures (Obtained from: http://kff.org/other/state-indicator/smha-expenditures-per-capita/#table)
7. Some data was available for some years and not for others, so there are very slight percentage changes from year-to-year, but overall, this should have a negligible effect on the results.

This is what I found.

Using a boosted regression tree algorithm, I wanted to find which are the largest contributing factors to the number of firearm deaths per 100,000 people and found:

(The above numbers were calculated from a gradient boosted model with a gaussian loss function. 5000 iterations were performed.)

One sees right away that the quality and number of gun laws a state has is the overwhelming factor in the number of gun-related deaths, with the amount of money a state spends on mental health services having a negligible effect.

Next, I created a regression tree to analyze this problem further. I found the following:

The numbers in the very last level of each tree indicate the number of gun-related deaths. One sees that once again where the individual state’s gun law grade is above 73.5%, that is, higher than a “B”, the number of gun-related deaths is at its lowest at a predicted 5.7 / 100,000 people. (Note that: the sum of squares error for this regression was found to be 3.838). Interestingly, the regression tree also predicts that highest number of gun-related deaths all occur for states that score an “F”!

In fact, using a Principle Components Analysis (PCA), and plotting the first two principle components, we find that:

One sees from this PCA analysis, that states that have a high gun-law grade have a low death rate.

Finally, using K-means clustering, I found the following:

One sees from the above results, the states that have a very low “Gun Law grade” are clustered together in having the highest firearms death rate. (See the fourth column in this matrix). That is, zooming in:

This question has been raised many times because the gun deaths number above includes the number of self-inflicted gun deaths. The argument has been that if we filter out this data from the gun deaths above, the arguments in this article fall apart. As I now show, this is in fact, not the case. Using the state-by-state firearm suicide rate from (http://archinte.jamanetwork.com/article.aspx?articleid=1661390), I performed this filtering to obtain the following principle components analysis biplot:

One sees that the PCA puts approximately equal weight (loadings) onto population density, gun-law grade, and median household income. It is quite clear that states that have a very high gun-law grade have a low amount of gun murders, and vice-versa.

One sees that the data shows that there is a very large anti-correlation between a state’s gun law grade and the death rate. There is also a very small anti-correlation between how much a state spends on mental health care and the death rate.

Therefore, the conclusions one can draw immediately are:

1. The number and quality of gun-control laws a state has drastically effects the number of gun-related deaths.
2. Other factors like mean household income play a smaller role in the number of gun-related deaths.
3. Factors like the amount of money a state spends on mental-health care has a negligible effect on the number of gun-related deaths. This point is quite important as there are a number of policy-makers that consistently argue that the focus needs to be on the mentally ill and that this will curb the number of gun-related deaths.
4. It would be interesting to apply these methodologies to data from other years. I will perhaps pursue this at a later time.

## Let’s not go overboard with this Trump stuff!

It has certainly become the talk of the town with some of the latest polls showing that Donald Trump is leading Hillary Clinton in a hypothetical 2016 matchup.

I decided to run my polling algorithm to simulate 100,000 election matchups between Clinton and Trump. I calibrated my model using a variety of data sources.

These were the results:

Based on these simulations, I conclude that:

I think in the era of the 24-hour news cycle, too much is made of one poll.

## Hillary Clinton Still Has the Best Chance of Being The Democratic Party Nominee in 2016

A great deal of noise has been made in the previous weeks about the surge in the polls of Donald Trump and Bernie Sanders. This has led some people to question whether Hillary Clinton will actually end up being the Democratic party nominee in 2016. This was further evidenced by the fact that Sanders is now leading Clinton in the latest New Hampshire polls.

However, running an analysis on current polling data, I still believe that even though it is very early, Hillary Clinton still has the best chance of being the Democratic party nominee. In fact, running some algorithms against the current data, I found that:

Hillary Clinton: $\boxed{99.9 \%}$ chance of winning Democratic nomination.

Bernie Sanders: $\boxed{0.01\%}$ chance of winning Democratic nomination.

These numbers were deduced from an algorithm that used non-parametric methods to obtain the following probability density functions.

Thanks to Hargun Singh Kohli for data compilation and research.

## The “Evolution” of the 3-Point Shot in The NBA

The purpose of this post is to determine whether basketball teams who choose to employ an offensive strategy that involves predominantly shooting three point shots is stable and optimal. We employ a game-theoretical approach using techniques from dynamical systems theory to show that taking more three point shots to a point where an offensive strategy is dependent on predominantly shooting threes is not necessarily optimal, and depends on a combination of payoff constraints, where one can establish conditions via the global stability of equilibrium points in addition to Nash equilibria where a predominant two-point offensive strategy would be optimal as well. We perform a detailed fixed-points analysis to establish the local stability of a given offensive strategy. We finally prove the existence of Nash equilibria via global stability techniques via the monotonicity principle. We believe that this work demonstrates that the concept that teams should attempt more three-point shots because a three-point shot is worth more than a two-point shot is therefore, a highly ambiguous statement.



### 1. Introduction

We are currently living in the age of analytics in professional sports, with a strong trend of their use developing in professional basketball. Indeed, perhaps, one of the most discussed results to come out of the analytics era thus far is the claim that teams should shoot as many three-point shots as possible, largely because, three-point shots are worth more than two-point shots, and this somehow is indicative of a very efficient offense. These ideas were mentioned for example by Alex Rucker who said “When you ask coaches what’s better between a 28 percent three-point shot and a 42 percent midrange shot, they’ll say the 42 percent shot. And that’s objectively false. It’s wrong. If LeBron James just jacked a three on every single possession, that’d be an exceptionally good offense. That’s a conversation we’ve had with our coaching staff, and let’s just say they don’t support that approach.” It was also claimed in the same article that “The analytics team is unanimous, and rather emphatic, that every team should shoot more 3s including the Raptors and even the Rockets, who are on pace to break the NBA record for most 3-point attempts in a season.” These assertions were repeated here. In an article by John Schuhmann, it was claimed that “It’s simple math. A made three is worth 1.5 times a made two. So you don’t have to be a great 3-point shooter to make those shots worth a lot more than a jumper from inside the arc. In fact, if you’re not shooting a layup, you might as well be beyond the 3-point line. Last season, the league made 39.4 percent of shots between the restricted area and the arc, for a value of 0.79 points per shot. It made 36.0 percent of threes, for a value of 1.08 points per shot.” The purpose of this paper is to determine whether basketball teams who choose to employ an offensive strategy that involves predominantly shooting three point shots is stable and optimal. We will employ a game-theoretical approach using techniques from dynamical systems theory to show that taking more three point shots to a point where an offensive strategy is dependent on predominantly shooting threes is not necessarily optimal, and depends on a combination of payoff constraints, where one can establish conditions via the global stability of equilibrium points in addition to Nash equilibria where a predominant two-point offensive strategy would be optimal as well. (Article research and other statistics provided by: Hargun Singh Kohli)

### 2. The Dynamical Equations

For our model, we consider two types of NBA teams. The first type are teams that employ two point shots as the predominant part of their offensive strategy, while the other type consists of teams that employ three-point shots as the predominant part of their offensive strategy. There are therefore two predominant strategies, which we will denote as ${s_{1}, s_{2}}$, such that we define

$\displaystyle \mathbf{S} = \left\{s_{1}, s_{2}\right\}. \ \ \ \ \ (1)$

We then let ${n_{i}}$ represent the number of teams using ${s_{i}}$, such that the total number of teams in the league is given by

$\displaystyle N = \sum_{i =1}^{k} n_{i}, \ \ \ \ \ (2)$

which implies that the proportion of teams using strategy ${s_{i}}$ is given by

$\displaystyle x_i = \frac{n_{i}}{N}. \ \ \ \ \ (3)$

The state of the population of teams is then represented by ${\mathbf{x} = (x_{1}, \ldots, x_{k})}$. It can be shown that the proportions of individuals using a certain strategy change in time according to the following dynamical system

$\displaystyle \dot{x}_{i} = x_{i}\left[\pi(s_{i}, \mathbf{x}) - \bar{\pi}(\mathbf{x})\right], \ \ \ \ \ (4)$

subject to

$\displaystyle \sum_{i =1}^{k} x_{i} = 1, \ \ \ \ \ (5)$

where we have defined the average payoff function as

$\displaystyle \bar{\pi}(\mathbf{x}) = \sum_{i=1}^{k} x_{i} \pi(s_{i}, \mathbf{x}). \ \ \ \ \ (6)$

Now, let ${x_{1}}$ represent the proportion of teams that predominantly shoot two-point shots, and let ${x_{2}}$ represent the proportion of teams that predominantly shoot three-point shots. Further, denoting the game action set to be ${A = \left\{T, Th\right\}}$, where ${T}$ represents a predominant two-point shot strategy, and ${Th}$ represents a predominant three-point shot strategy. As such, we assign the following payoffs:

$\displaystyle \pi(T,T) = \alpha, \quad \pi(T,Th) = \beta, \quad \pi(Th, T) = \gamma, \quad \pi(Th,Th) = \delta. \ \ \ \ \ (7)$

We therefore have that

$\displaystyle \pi(T,\mathbf{x}) = \alpha x_{1} + \beta x_{2}, \quad \pi(Th, \mathbf{x}) = \gamma x_{1} + \delta x_{2}. \ \ \ \ \ (8)$

From (6), we further have that

$\displaystyle \bar{\pi}(\mathbf{x}) = x_{1} \left( \alpha x_{1} + \beta x_{2}\right) + x_{2} \left(\gamma x_{1} + \delta x_{2}\right). \ \ \ \ \ (9)$

From Eq. (4) the dynamical system is then given by

$\boxed{\dot{x}_{1} = x_{1} \left\{ \left(\alpha x_{1} + \beta x_{2} \right) - x_{1} \left( \alpha x_{1} + \beta x_{2}\right) - x_{2} \left(\gamma x_{1} + \delta x_{2}\right) \right\}}$,

$\boxed{\dot{x}_{2} = x_{2} \left\{ \left( \gamma x_{1} + \delta x_{2}\right) -x_{1} \left( \alpha x_{1} + \beta x_{2}\right) - x_{2} \left(\gamma x_{1} + \delta x_{2}\right) \right\}}$,

subject to the constraint

$\displaystyle x_{1} + x_{2} = 1. \ \ \ \ \ (10)$

Indeed, because of the constraint (10), the dynamical system is actually one-dimensional, which we write in terms of ${x_{1}}$ as

$\displaystyle \boxed{\dot{x}_{1} = x_{1} \left(-1 + x_{1}\right) \left[\delta + \beta \left(-1 + x_{1}\right) - \delta x_{1} + \left(\gamma-\alpha\right)x_{1}\right]}. \ \ \ \ \ (11)$

From Eq. (11), we immediately notice some things of importance. First, we are able to deduce just from the form of the equation what the invariant sets are. We note that for a dynamical system ${\mathbf{x}' = \mathbf{f(x)} \in \mathbf{R^{n}}}$ with flow ${\phi_{t}}$, if we define a ${C^{1}}$ function ${Z: \mathbf{R}^{n} \rightarrow \mathbf{R}}$ such that ${Z' = \alpha Z}$, where ${\alpha: \mathbf{R}^{n} \rightarrow \mathbf{R}}$, then, the subsets of ${\mathbf{R}^{n}}$ defined by ${Z > 0, Z = 0}$, and ${Z < 0}$ are invariant sets of the flow ${\phi_{t}}$. Applying this notion to Eq. (11), one immediately sees that ${x_1 > 0}$, ${x_1 = 0}$, and ${x_1 < 0}$ are invariant sets of the corresponding flow. Further, there also exists a symmetry such that ${x_{1} \rightarrow -x_{1}}$, which implies that without loss of generality, we can restrict our attention to ${x_{1} \geq 0}$.

### 3. Fixed-Points Analysis

With the dynamical system in hand, we are now in a position to perform a fixed-points analysis. There are precisely three fixed points, which are invariant manifolds and are given by:

$\displaystyle P_{1}: x_{1}^{*} = 0, \quad P_{2}: x_{1}^{*} = 1, \quad P_{3}: x_{1}^{*} = \frac{\beta - \delta}{-\alpha + \beta - \delta + \gamma}. \ \ \ \ \ (12)$

Note that, ${P_{3}}$ actually contains ${P_{1}}$ and ${P_{2}}$ as special cases. Namely, when ${\beta = \delta}$, ${P_{3} = 0 = P_{1}}$, and when ${\alpha = \gamma}$, ${P_{3} = 1 = P_{2}}$. We will therefore just analyze, the stability of ${P_{3}}$. ${P_{3} = 0}$ represents a state of the population where all teams predominantly shoot three-point shots. Similarly, ${P_{3} = 1}$ represents a state of the population where all teams predominantly shoot two-point shots, We additionally restrict

$\displaystyle 0 \leq P_{3} \leq 1 \Rightarrow 0 \leq \frac{\beta - \delta}{-\alpha + \beta - \delta + \gamma} \leq 1, \ \ \ \ \ (13)$

which implies the following conditions on the payoffs:

$\displaystyle \left[\delta < \beta \cap \gamma \leq \alpha \right] \cup \left[\delta = \beta \cap \left(\gamma < \alpha \cup \gamma > \alpha \right) \right] \cup \left[\delta > \beta \cap \gamma \leq \alpha \right]. \ \ \ \ \ (14)$

With respect to a stability analysis of ${P_{3}}$, we note the following. The point ${P_{3}}$ is a: • Local sink if: ${\{\delta < \beta\} \cap \{\gamma > \alpha\}}$, • Source if: ${\{\delta > \beta\} \cap \{\gamma < \alpha\}}$, • Saddle: if: ${\{\delta = \beta \} \cap (\gamma < \alpha -\beta + \delta \cup \gamma > \alpha - \beta + \delta)}$, or ${(\{\delta < \beta\} \cup \{\delta > \beta\}) \cap \gamma = \frac{\alpha \delta - \alpha \beta}{\delta - \beta}}$.

What this last calculation shows is that the condition $\delta = \beta$ which always corresponds to the point $x_{1}^* = 0$, which corresponds to a dominant 3-point strategy always exists as a saddle point! That is, there will NEVER be a league that dominantly adopts a three-point strategy, at best, some teams will go towards a 3-point strategy, and others will not irrespective of what the analytics people say. This also shows that a team's basketball strategy really should depend on its respective payoffs, and not current "trends". This behaviour is displayed in the following plot.



Further, the system exhibits some bifurcations as well. In the neigbourhood of ${P_{3} = 0}$, the linearized system takes the form

$\displaystyle x_{1}' = \beta - \delta. \ \ \ \ \ (15)$

Therefore, ${P_{3} = 0}$ destabilizes the system at ${\beta = \delta}$. Similarly, ${P_{3} = 1}$ destabilizes the system at ${\gamma = \alpha}$. Therefore, bifurcations of the system occur on the lines ${\gamma = \alpha}$ and ${\beta = \delta}$ in the four-dimensional parameter space.

### 4. Global Stability and The Existence of Nash Equilibria

With the preceding fixed-points analysis completed, we are now interested in determining global stability conditions. The main motivation is to determine the existence of any Nash equilibria that occur for this game via the following theorem: If ${\mathbf{x}^{*}}$ is an asymptotically stable fixed point, then the symmetric strategy pair ${[\sigma^{*}, \sigma^{*}]}$, with ${\sigma^{*} = \mathbf{x}^*}$ is a Nash equilibrium. We will primarily make use of the monotonicity principle, which says let ${\phi_{t}}$ be a flow on ${\mathbb{R}^{n}}$ with ${S}$ an invariant set. Let ${Z: S \rightarrow \mathbb{R}}$ be a ${C^{1}}$ function whose range is the interval ${(a,b)}$, where ${a \in \mathbb{R} \cup \{-\infty\}, b \in \mathbb{R} \cup \{\infty\}}$, and ${a < b}$. If ${Z}$ is decreasing on orbits in ${S}$, then for all ${\mathbf{x} \in S}$,

$\boxed{\omega(\mathbf{x}) \subseteq \left\{\mathbf{s} \in \partial S | \lim_{\mathbf{y} \rightarrow \mathbf{s}} Z(\mathbf{y}) \neq \mathbf{b}\right\}}$,

$\boxed{ \alpha(\mathbf{x}) \subseteq \left\{\mathbf{s} \in \partial S | \lim_{\mathbf{y} \rightarrow \mathbf{s}} Z(\mathbf{y}) \neq \mathbf{a}\right\}}$.

Consider the function

$\displaystyle Z_{1} = \log \left(-1 + x_{1}\right). \ \ \ \ \ (16)$

Then, we have that

$\displaystyle \dot{Z}_{1}= x_{1} \left[\delta + \beta \left(-1 + x_{1}\right) - \delta x_{1} + x_{1} \left(\gamma - \alpha\right)\right]. \ \ \ \ \ (17)$

For the invariant set ${S_1 = \{0 < x_{1} < 1\}}$, we have that ${\partial S_{1} = \{x_{1} = 0\} \cup \{x_{1} = 1\}}$. One can then immediately see that in ${S_{1}}$,

$\displaystyle \dot{Z}_{1} < 0 \Leftrightarrow \left\{\beta > \delta\right\} \cap \left\{\alpha \geq \gamma\right\}. \ \ \ \ \ (18)$

Therefore, by the monotonicity principle,

$\displaystyle \omega(\mathbf{x}) \subseteq \left\{\mathbf{x}: x_{1} = 1 \right\}. \ \ \ \ \ (19)$

Note that the conditions ${\beta > \delta}$ and ${\alpha \geq \gamma}$ correspond to ${P_{3}}$ above. In particular, for ${\alpha = \gamma}$, ${P_{3} = 1}$, which implies that ${x_{1}^{*} = 1}$ is globally stable. Therefore, under these conditions, the symmetric strategy ${[1,1]}$ is a Nash equilibrium. Now, consider the function

$\displaystyle Z_{2} = \log \left(x_{1}\right). \ \ \ \ \ (20)$

We can therefore see that

$\displaystyle \dot{Z}_{2} = \left[-1 + x_{1}\right] \left[\delta + \beta\left(-1+x_{1}\right) - \delta x_{1} + \left(-\alpha + \gamma\right) x_{1}\right]. \ \ \ \ \ (21)$

Clearly, ${\dot{Z}_{2} < 0}$ in ${S_{1}}$ if for example ${\beta = \delta}$ and ${\alpha < \gamma}$. Then, by the monotonicity principle, we obtain that

$\displaystyle \omega(\mathbf{x}) \subseteq \left\{\mathbf{x}: x_{1} = 0 \right\}. \ \ \ \ \ (22)$

Note that the conditions ${\beta = \delta}$ and ${\alpha < \gamma}$ correspond to ${P_{3}}$ above. In particular, for ${\beta = \delta}$, ${P_{3} = 0}$, which implies that ${x_{1}^{*} = 0}$ is globally stable. Therefore, under these conditions, the symmetric strategy ${[0,0]}$ is a Nash equilibrium. In summary, we have just shown that for the specific case where ${\beta > \delta}$ and ${\alpha = \gamma}$, the strategy ${[1,1]}$ is a Nash equilibrium. On the other hand, for the specific case where ${\beta = \delta}$ and ${\alpha < \gamma}$, the strategy ${[0,0]}$ is a Nash equilibrium. 5. Discussion In the previous section which describes global results, we first concluded that for the case where ${\beta > \delta}$ and ${\alpha = \gamma}$, the strategy ${[1,1]}$ is a Nash equilibrium. The relevance of this is as follows. The condition on the payoffs thus requires that

$\displaystyle \pi(T,T) = \pi(Th,T), \quad \pi(T,Th) > \pi(Th,Th). \ \ \ \ \ (23)$

That is, given the strategy adopted by the other team, neither team could increase their payoff by adopting another strategy if and only if the condition in (23) is satisfied. Given these conditions, if one team has a predominant two-point strategy, it would be the other team’s best response to also use a predominant two-point strategy. We also concluded that for the case where ${\beta = \delta}$ and ${\alpha < \gamma}$, the strategy ${[0,0]}$ is a Nash equilibrium. The relevance of this is as follows. The condition on the payoffs thus requires that

$\displaystyle \pi(T,Th) = \pi(Th,Th), \quad \pi(T,T) < \pi(Th,T). \ \ \ \ \ (24)$

That is, given the strategy adopted by the other team, neither team could increase their payoff by adopting another strategy if and only if the condition in (24) is satisfied. Given these conditions, if one team has a predominant three-point strategy, it would be the other team’s best response to also use a predominant three-point strategy. Further, we also showed that ${x_{1} = 1}$ is globally stable under the conditions in (23). That is, if these conditions hold, every team in the NBA will eventually adopt an offensive strategy predominantly consisting of two-point shots. The conditions in (24) were shown to imply that the point ${x_{1} = 0}$ is globally stable. This means that if these conditions now hold, every team in the NBA will eventually adopt an offensive strategy predominantly consisting of three-point shots. We also provided through a careful stability analysis of the fixed points criteria for the local stability of strategies. For example, we showed that a predominant three-point strategy is locally stable if ${\pi(T,Th) - \pi(Th,Th) < 0}$, while it is unstable if ${\pi(T,Th) - \pi(Th,Th) \geq 0}$. In addition, a predominant two-point strategy was found to be locally stable when ${\pi(Th,T) - \pi(T,T) < 0}$, and unstable when ${\pi(Th,T) - \pi(T,T) \geq 0}$. There is also they key point of which one of these strategies has the highest probability of being executed. We know that

$\displaystyle \pi(\sigma,\mathbf{x}) = \sum_{s \in \mathbf{S}} \sum_{s' \in \mathbf{S}} p(s) x(s') \pi(s,s'). \ \ \ \ \ (25)$

That is, the payoff to a team using strategy ${\sigma}$ in a league with profile ${\mathbf{x}}$ is proportional to the probability of this team using strategy ${s \in \mathbf{S}}$. We therefore see that a team’s optimal strategy would be that for which they could maximize their payoff, that is, for which ${p(s)}$ is a maximum, while keeping in mind the strategy of the other team, hence, the existence of Nash equilibria. Hopefully, this work also shows that the concept that teams should attempt more three-point shots because a three-point shot is worth more than a two-point shot is a highly ambiguous statement. In actuality, one needs to analyze what offensive strategy is optimal which is constrained by a particular set of payoffs.