# If Brandon Jennings Could Shoot Like Stephen Curry

**Posted:**April 9, 2010

**Filed under:**Statistics Commentary 3 Comments

The NBA season is coming to an end soon. It is around this time of the year that “Awards Watch” talks begin to heat up. Typically the two most relevant and interesting races are the MVP and Rookie-Of-The-Year awards. This year LeBron James has had the MVP award locked up for some time now.

That leaves us to ponder over the top rookie title. Unlike the MVP race, this award has plenty of qualified candidates. The media, who vote for the award, have generally focused on three players as the most likely to win – Tyreke Evans, Stephen Curry, and Brandon Jennings.

What I want to focus on today is the play of Milwaukee Bucks guard Brandon Jennings. More specifically, I want to talk about his ability (more like inability) to put the ball through the basket. In order to win a basketball game it is very important to make the shots you take. This idea is really simple and I think every basketball fan generally knows this to some degree. However when “experts” discuss the ability and productivity of a basketball player, especially in comparison to other players, this idea is generally ignored. The most cited stat of choice, sometimes the only reference of comparison, is Points Per Game. If you score a lot of points in the NBA you are generally considered a good player, no matter how many shot attempts it costs you to achieve that score. To most NBA observers a player scoring 25 PPG is very good, most of the time they’re right. However, what is often ignored is how they amass those points. Does it take them 20 shots or 30 to 40?. There is a big difference and a team has only a limited number of possessions to use. Empty (scoreless) possessions put a team in a hole. All of this brings me to note that making shots is something Brandon Jennings struggles to do. Because of this I don’t think Jennings should be considered the top choice for Rookie Of The Year.

When I say Jennings is struggling, I think I need to put it in perspective. Brandon Jennings field goal percentage (37%) is the lowest of any player, who shoots as often as he does, in the last 30 years (Evidence here). Now Jennings is a good 3 point and free throw shooter, so his low FG% is a bit misleading. However, when you look at his True Shooting Percentage (Which takes into account 3 pointers and free throws), he still comes up short. His TS% is 47.4%, he ranks 32nd of 38 among qualified rookies according to ESPN.

I have established that Jennings struggles making the shots he takes but what does this mean to his team? I’ve taken a long time to get to the point in the title but the question I ask is “What if Brandon Jennings could shoot like Stephen Curry?” I chose Curry as a point of comparison for a few reasons. First, they are both rookie point guards. They both play generally the same role on their team. And they have very similar non-shooting related statistics. Most important, Curry is a very good shooter.

If Jennings could shoot like Curry, what would it mean to the Bucks? This is where statistics come in. Curry scores .96 points per possession while Jennings scores .85. The Milwaukee Bucks as a team have an offensive efficiency of 101.7(Basically, their teams Points Per Possession times 100). Milwaukee as a team uses 91.2 possessions a game. Of these possessions Brandon Jennings uses 17.9 (on average). Using these possessions Brandon Jennings manages to average 15.4 points. If Jennings shot the ball like Curry he would average 17.18 PPG. That is a difference of a whole 1.78 points. That would give the Bucks an Offensive Efficiency of 103.5. If you subtract a teams defensive efficiency from its offensive efficiency you get its efficiency differential (Basically, how badly a team tends to beat their opponent). The Bucks currently sport a differential of 1.2. If BJ shot like Curry it would be 3.0.

Blah, blah, blah…what does this all mean? Well, efficiency differential is the best tool to use in determining which basketball team is the best. As such, it is the best tool for predicting future performance as well. As an example, Duke University led the nation this year in efficiency differential. A team with a zero point differential should finish 41-41 in an NBA season. A typical NBA team with an efficiency differential of 1.2 should win between 44-45 games. A team with an efficiency differential of 3.0 should win about 49 games. (I’m saying this because NBAStuffer.com tells me one point of efficiency differential = 2.7 wins, if this is incorrect please let me know). So if Brandon Jennings could shoot like Stephen Curry the Milwaukee Bucks would be 4 to 5 wins better. That is a significant amount considering it is just one aspect of one players production. So that answers my original question.

As I said Curry is an efficient scorer but what if Brandon Jennings scored like Chauncey Billups, who is one of the most efficient point guards in the NBA (1.08 PPP). If Jennings scored as efficiently as Billups then the Bucks would be looking at a projected record of 54 to 55 wins (a ten win difference!). No wonder a Chauncey Billups led team has reached the conference finals for six consecutive seasons (Now that we know how important making your shots is to winning, who would ever trade Billups for Allen Iverson?).

## Wizards Will Not Be Good In 2010 – Long Winded Explanation

Posted:June 30, 2010 |Author:robbieomalley |Filed under:NBA, Statistics Commentary, Washington Wizards | 2 CommentsOn my blog, when I attempt to describe the contributions of an NBA player, I use something called Wins Produced. It’s a metric developed by professor David Berri. It is derived from efficiency differential, a teams offensive efficiency minus its defensive efficiency. Efficiency differential is typically the best single statistic to go by when determining which team is the best. If you want to read more about Wins Produced, you should read this book.

Now, a lot of people don’t agree with this metric because it doesn’t always agree with popular perception. This is mainly because popular perception (and NBA salary) is almost entirely dependent on a players points per game. This is often regardless of how many shots it takes a player to earn these points. It is important to make the shots you take because there is a finite amount of possessions available in a game (In the case of the Wizards – 94.1 possessions per game). That is why there is a big disconnect between Wins Produced and popular perception, especially in regards to high volume scorers like Allen Iverson.

There is another reason, I think, for why people don’t like to talk individual players being “good” or “bad.” It is because regular NBA fans have trouble separating the performance of an individual player from the performance of his team (Ex: Kobe = 5 rings, LeBron = 0. Therefore Kobe > LeBron). Anyone can see, for the most part, which team is the best. They do this by looking at Win/Loss record and, to a lesser extent, efficiency differential. So there is a stat that is widely available and, more importantly, widely accepted tool for judging how good or bad a team is. This makes discussion of which team is the best much more common, and much more accurate than discussion on individual performance.

Unfortunately, there is not a widely accepted tool that is used to describe the ability of an individual player, independent of his team. While Wins Produced is not as widely accepted, cited, or used as John Hollingers PER, I think it is the best tool for determining an individual players worth. It measures players in “Wins produced” because winning is the definition of being productive in the NBA. It is accurate because it is derived from efficiency differential (offensive minus defensive efficiency), which is the best tool for explaining wins and the strength of a team.

I think I would do better explaining Wins Produced in general terms than from an individual player perspective, at least right now.

In WP, players are judged relative to the average at their position. The average player has a Wins Produced per 48 minutes of .100. Why is this? Let’s start with the average team. Every team plays 82 games over the course of an NBA regular season. A perfectly average team wins 50% of the time, or 41 games. In other words, an average team earns (or produces) a half a win or .5 wins every game they play. During each game an NBA team plays 5 players at a time. There are 5 positions – PG, SG, SF, PF, and C. Therefore you do the math of .5 wins divided by 5 positions and you get .100. So, as you can see, the average player produces .100 wins per 48 minutes (an NBA game is 48 minutes long).

Now that it has been established, indisputably, that an average NBA player produces .100 wins per 48 minutes (because an average team produces .500), I think it is necessary to note another aspect of explaining wins of an NBA team called The Pareto Principle.

“Pareto’s contribution to economics, as the History of Economic Thought website indicates, extends beyond the concept of Pareto Optimality. But despite all he did for economics, Pareto is perhaps best known for the Pareto Principle, a concept rarely discussed in economics (at least not in my classes). Pareto observed that 80% of the income in Italy came from 20% of the population.This observation led to a general rule of thumb: 80% of outcomes come from 20% of the people. So for businesses, 80% of sales come from 20% of clients, or 80% of your problems come from 20% of your workers, etc…

Although outside of economics the Pareto Principle seems fairly popular, I have always thought the 80-20 rule was far too simplistic. And yet, much to my surprise, it seems to apply to the NBA. In 2006-07 there were 1,230 regular season wins. When we look at Wins Produced, we see that 80% of these 1,230 victories were produced by 22.4% of the players.

Looking at a larger sample, since 1990-91 there have been 18,355 regular season wins in the NBA. Across these 16 seasons there have been on average 431 players per season, or 6,907 player observations across the entire time period.When we look at the data we see that 1,507 player observations, or 21.4% of all players, produced 80% of all victories. So it’s not quite 80-20, but it seems close enough to me.” – David Berri referring to the work of Vilfredo Pareto.

The Pareto Principle states that, on average, just 20% of NBA players are responsible for 80% of team wins. This makes intuitive sense when thinking of how important players like LeBron James, Chris Paul, and Dwight Howard are to their teams. 20% of an NBA team (with a roster of 15 players) is 3 players. So, typically, 80% of a teams wins are generally due to their top 3ish players.

Wins Produced and The Washington WizardsLet me put what I explained in terms of my team – The Washington Wizards. The Wizards ended the season with a record of 26 wins 56 losses. Their team produced 28.4 wins, so they were slightly better than their record suggested. This is because Wins Produced matches up with efficiency differential which doesn’t always match up exactly with the wins/loss record. Because efficiency differential is a better predictor of how good a team is, I will go with the 28 wins.

So the Washington Wizards were as good as a 28 win team. The teams winning percentage is 34%. That means during an average game the Wizards, as a team, produced .34 wins. If you take .34 and divide it by 5 (the number of players on the floor) you get .068. The average Wizards player had a Wins Produced Per 48 minutes (WP48) of .068. This mark is significantly below average, just like the team. I’d like to think that idea makes intuitive sense to most people – If you are a below average team, you employ below average players.

Going back the The Pareto Principle – we find that the Wizards top 4 players were responsible for 80% of the teams wins. The top 4 players were Mike Miller (.219 WP48 / 8.24 Wins), Brendan Haywood (.200 / 6.73), Antawn Jamison (.103 / 3.42), and Caron Butler (.79 / 3.05 wins). Together they combined to produce 21.44 wins. Their average WP48 between the four of them was .150. That means the rest of the team produced an amazing total of 6.95 wins. Their average WP48 was .026. If the team had just employed these remaining players all of last season their winning percentage would have been about 13%, totaling around 10.55 wins. None of the Wizards top 4 producers will be returning next season. Even if you don’t trust wins produced, those four players are generally considered by everyone’s “eyeball” tests as being the best on the team this past season. We know the players left are not as productive as the ones that are gone. This would mean that the team most likely will not be better than last season. The team did add some players (Arenas, Wall, Hinrich, Booker, Yi, Seraphin) but they will most likely not out perform what the top four players did last season. Therefore, they should win more than 11 games but less than 26. I’ll go

optimistic, split it down the middle, and predict 18 wins right now.Summary:The Wizards were a far below average team last season. Therefore, the typical Washington Wizards player was below average. The Wizards will not bring back their four best players. Therefore the team leftover (and the players that said team is composed of), not only have to be below average, but far below average. Otherwise, they would have won more games last season. Therefore, it is nearly impossible to make the claim that the players the Wizards retained from last season (Nick Young, JaVale McGee, Andray Blatche, and Al Thornton) are good (above average) players. Knowing what we knew about how the team performed last season (26 wins) and the previous season (19 wins), they almost necessarily have to be considered very bad. I know many Wizards fans who have convinced themselves that Blatche, McGee, and/or Nick Young are relatively good players but (based on the team performance) it just isn’t really possible. The main reason I wrote this is to drive that point home.They lost their best players. They added some new players. They have some players who could improve. They likely will be worse in the 2010/11 season relative to the 2009/10 season. Predicting < 18 wins.

TL;DR: For a shorter, more specific explanation see this post.