The following table reports the regular season efficiency differential (offensive efficiency minus defensive efficiency) of the 37 teams that have won an NBA title since 1974.

Some quick observations from this table:

• these 37 title teams averaged a 6.5 efficiency differential and 59 regular season wins
• 15 team led the league in efficiency differential (offensive efficiency minus defensive efficiency) the year the team won the title
• 26 title teams ranked in the top three in efficiency differential
• No team has ranked 5^th in efficiency differential and won the NBA title (that doesn’t really mean anything, but I thought I would toss that out there… and you will see in a moment which fans this observation might make angry)
• only 7 teams ranked outside the top four in efficiency differential have won a title (including the LA Lakers in 2009-10, whose mark of 4.9 ranked 7^th in the league)

With these numbers in mind, let’s look at each team’s efficiency differential at the All-Star break in 2010-11.

Here are some interesting observations (at least interesting to me) from this table.

• The projected wins are not a forecast of where these teams will finish the 2010-11 season.  This is simply the number of wins the corresponding efficiency differential typically translates into across an 82 game season.  Efficiency differential explains about 95% of team wins and is considered a better measure of a team’s future prospects.
• The top three in efficiency differential this season are the Miami Heat, San Antonio Spurs, and Boston Celtics. 
• The Lakers have improved since last year.
• But the Lakers currently rank 5^th in efficiency differential.
• The Dallas Mavericks have the 4^th best winning percentage in the NBA.  But their efficiency differential suggests this team is not a title contender.  And I am not sure a healthy Dirk Nowitzki is enough to close the gap. 

To illustrate, let’s move from efficiency differential to Wins Produced for the Dallas Mavericks. 

The table above indicates that the Mavericks are led this season by Jason Kidd and a healthy Tyson Chandler.  Dallas did recently struggle without Nowitzki, but look at the productivity of Brian Cardinal (a player who got more minutes when Nowitzki was out). 

The performance of the Mavericks this season is consistent with a team that will win about 51 games across an 82 game season.  Dallas has already won 40 games (about six more than their efficiency differential suggests).  So they will probably win more than 51 games.  But teams with a differential below 4.0 don’t typically win NBA titles.

Then again, teams that are this bad do win more often than the team that ranks 5th in efficiency differential.  So maybe fans of Dallas should be more optimistic than fans of the Lakers (then again, maybe not).

- DJ

P.S. Arturo Galletti has done far more than my simple post in exploring which teams are contenders (or pretenders).  Following this link to just one of his recent stories on this subject.

Editor’s Note: The following is from Ian Levy (of Hickory High), who has created the NBA Anti-Awards.  These are awards for actions that people tend not to focus upon (because often these are actions that don’t earn players much praise and don’t really help teams win games).  What follows is his latest update of the front-runners for these awards this season.

Ian Levy is a Third-Grade teacher by day and amateur basketball analyst by afternoon (he usually sleeps at night). Ian suffers from a rare psychological condition known as Anti-Homeritis which renders him incapable of rooting for hometown teams. He grew up in Upstate New York and has therefore been a lifelong Indiana Pacers fan. He writes his own basketball blog, Hickory High, and is a contributor at IndyCornrows and The Two Man Game.  Ian currently lives in Boise, Idaho, where he roots against the Boise State Broncos.

The Anti-Awards have been a running feature at Hickory High.  These awards recognize some of the most discouraging and disgraceful statistical achievements this season.  And with the All-Star Break upon us it’s time for another update.

The Shawn Bradley Award – This award goes to the player 6’10″ or taller who has had the highest percentage of his shot attempts blocked.

Bulls’ rookie, Omer Asik, has led this category almost the entire season. Playing against Asik can make Zaza Pachulia look like Bill Russell. He’s had 22 of shots blocked this season. This works out to 24.2% of his total attempts.

The Shawn Kemp Award – This award goes to the player who has fouled out of the most games. From 1986 up through the present, Shawn Kemp is the NBA’s leader in foul outs with 115, 35 more than his next closest competitor.

Andris Biedrins, DeMarcus Cousins and Serge Ibaka all have fouled out of 6 games this season. Biedrins has come on strong of late but Cousins would seem to have the inside track. He has the highest foul rate (5.3 per 36 minutes) and plays more minutes than any of his competition.

The Jahidi White Award – This award goes to the player with the lowest ratio of Ast/FGA (minimum 300 minutes). The award is named for White who assisted on just 1.7% of his teammates’ baskets over a 334 game career.

Ike Diogu has made a big push and passed Robin Lopez for this award. In 410 minutes Diogu has posted an Ast/FGA ratio of 0.008. If you’re keeping track at home that’s one assist to 129 field goal attempts. With a stunning lack of passing acumen and a single-minded offensive focus Diogu seems like a lock to take this award home.

The Darrick Martin Award - This award goes to the player with the lowest FG% and a minimum of 200 attempts. The award is named for Darrick Martin, a career 38.2% shooter who played 514 games over 13 NBA seasons.

There have been a lot of changes on the leaderboard for this award throughout the season. Players who shot under 40% tend to find themselves outside the playing rotation. Rasual Butler is currently out front, shooting just 32.1% on 215 attempts. His teammate with the Clippers, Randy Foye, is currently 5^th, shooting 36.3%.

The Jason Kidd Award – This award goes to the player with the most turnovers in a single game. Jason Kidd has had a Hall of Fame career with many terrific positive statistical contributions. He’s also had 3 career games with more than 12 turnovers.

Amare Stoudemire is out in front and looks like a lock to take this award home. His 11 turnover game against Washington on December 10th is currently the highest in the league. But with Stoudemire contributing 3 of the top 10 turnover games this season don’t discount the possibility of him topping his personal “best.”

The Matt Bullard Award – This award goes to the player 6’10″ or taller with the lowest Total Rebound Percentage. (Minimum 300 minutes)

There are several strong candidates for this award and the list reads like a who’s who of soft, fluffy, squishy, finesse players. Danilo Gallinari, Donte Greene, Hedo Turkoglu, Matt Bonner, Rashard Lewis, Andrea Bargnani, Brook Lopez and Vlad Radmanovic are all in the running. Gallinari currently has the lead grabbing just 7.7% of the available rebounds while he’s on the floor. However, this race just has too many big names to project a winner at this point.

The Kobe Bryant Award – This award goes to the player who has missed the most shot attempts in a single game. The award is inspired by Kobe’s performance in Game 7 of the Finals last season.

Kobe’s 21 missed field goals on November 11th against Denver are still in the lead. In a Herculean effort to lock up the award, Kobe also missed 19 shots on November 28th against Indiana, December 28th against San Antonio and November 19th against Minnesota. Derrick Rose appears willing to fight Kobe tooth and nail for this award. Rose has also missed 19 field goals on three separate occasions. Nick Young would have to be considered a dark horse candidate. He demonstrated he was for real, missing 20 shots January 28^th against the Thunder.

The Nick Anderson Award - This award goes to the player who missed the most free throws in a single game. Anderson was actually a decent free throw shooter. But his four missed free throw attempts in the 1995 Finals against Houston kind of stand out in my memory.

Dwight Howard is really just competing with himself in this category. This season he has missed 12 free throws in a game once, 11 free throws once, 10 free throws twice and 8 free throws twice. There are other candidates out there, but this is really Howard’s award to lose.

The Chris Childs Award – This award goes to the player who has posted the highest Turnover Percentage so far this season. It’s named after former New York Knick Chris Childs, who retired with a career Turnover Percentage of 22.8%. (Minimum 300 minutes)

Chris Duhon continues to lead the league, with a Turnover Percentage of 32.9%. Joel Pryzbilla has returned from injury and is sneaking up on Duhon. He now trails by just .2 percentage points. A few months ago this seemed like a foregone conclusion but now it seems like it will be sloppy, cringe-inducing race to the finish.

The Darius Songaila Award – This award goes to the player who has provided his team with the least overall production. I use Wins Produced to determine the winner here. (Minimum 300 minutes)

Toronto’s Andrea Bargnani has this award all but locked up. With a WP48 of -0.106 he’s “contributed” -4.0 wins in 1795 minutes. The next closest competitor is Aaron Brooks with -2.0 wins. Barring a significant injury, Bargnani will finish the season as the least productive player in the league. One more season like this and we’ll be renaming this award after him.

Blogs are essentially imitations of newspaper columns.  And podcasts are essentially radio programs.  Having demonstrated (at least to ourselves) that we can write, members of the Wages of Wins Network have now branched out into the world of podcasts.  Friday afternoon, Mosi Platt of the Miami Heat Index, Devin Dignam of NBAEh?, and Arturo Galletti of Arturo’s Amazing Stats, and I got together for the…

Wages of Wins Network Podcast (for Friday, February 18, 2011). 

Here is a list of topics we touched upon:

• Coaching and Learning (related to this article which builds on something Henry Abbott wrote at True Hoop)
• Comments on blog comments
• Is Derrick Rose an MVP Candidate?  (Arturo says yes, the rest of us seemed to disagree)
• The NBA’s labor problems
• All-Star weekend (and who will win the Rookie-Sophomore game)

It would be great if we could get some feedback on this podcasts. So as you listen, let us know what you think of the number of people discussing these issues (do we need more people or fewer people?), the length of the conversation (this podcast lasts an hour or so), and the topics we discuss (is there something you want us to touch upon?). 

Beyond the podcast, here are some additional links that I think are interesting.

• Jennifer Collins of the Marketplace (NPR) reported on the NBA’s labor troubles on Friday morning.  Her brief story included a quote from Allen Sanderson (at the University of Chicago) and two quotes from some economist at Southern Utah University.  It is my hope to write something for Huffington Post on the labor problems in the NBA.  Hopefully that will be done in the next week.

There have been a number of great stories posted on The Wages of Wins Network this past week: 

• Ben Gulker – of Pistons by the Numbers – posted two excellent comments this past week on Rodney Stuckey: Dumars likes Stuckey more than the numbers do and What to do with Rodney Stuckey
• Devin Dignam – of NBAEh? – has frequently commented on the lack of productivity from Andrea Bargnani (Devin noted this in our podcast).  Now he reports (via Chad Ford at ESPN) that Colangelo Won’t Trade Bargnani.
• Ty Willihnganz – at Courtside Analyst – examines the Marginal Win Score Characteristics of an NBA Champion.  Marginal Win Score – for those who are not reading Ty on a regular basis (and if you are not, why not?) – is Win Score that attempts to incorporate individual defense.
• Andres (Dre) Alvarez – at Nerd Numbers – had three great posts on Wednesday, Friday, and Today. 
• Micro Yay Points! looks at how Yahoo determines the player of each game (this post is going to be re-posted below).
• Overrated Lines and the Pareto Principle looks at how many players don’t get to win an NBA title.

And then Dre and Arturo tag-teamed the topic of who gets to win a title with

• A New Challenger Has Arrived (at Nerd Numbers) &
• The Measure of a Champion (at Arturo’s Amazing Stats)

These three posts on NBA Champions are definitely well worth reading (and something I hope to comment on again in the future).

Before I get to Dre’s “Yay Points!” post, let me also note that Ian Levy – of Hickory High – has sent along a fantastic post that will appear tomorrow.

Okay, here is Dre on “Micro Yay Points!”… 

Jeremy Britton and I recently got into a discussion about overrated players. Jeremy was trying to think of a way to use game by game numbers Wins Produced numbers to find overrated and underrated players. I thought this would be a hard problem. Would I compare it to EFF or PER? How about looking over articles for mentions of players? All of these seemed like more work. Jeremy reminded me about this blog we both read called The Wages of Wins Journal. Apparently points are what drive peoples’ opinion of what makes a player good. Sure I thought, maybe for the season. For one game though, points seemed too simple. However, Jeremy is a smart guy and so I figured I’d at least test this out.

In 2001 a travesty happened. No matter what anyone says, Allen Iverson had no right winning the MVP. Iverson is the epitome of using points to fool people. He led the league with 31.1 points but shot an abysmal 42% from the field. It seemed no one cared. Surely, I thought that has to be the lowest any self-respecting fan would let a player’s shooting drop and still be fooled.

With that I went to Basketball-Reference and used their awesome game finder to look for two simple criteria this season. How many players scored 30 or more points in a game and shot at or below 42% from the field? I then went two steps further. I checked how well the player played in the game according to the WP metric. I then hopped over to Yahoo Sports. Yahoo gives a Top Performer label to the “best” player on each team for every game in the season. I was curious if Yahoo would be fooled by any deceptively bad performances. I think you know where this is going.

So far this season there have been 21 instances of a player getting 30 points on a FG of less than 42%. In 13 of these instances Yahoo picked that player as their team’s Top Performer. Let’s take a look at how well Yahoo picked.

Table 1: Inefficient Yahoo Top Performers that were the Wins Produced Top Performer

Table 2: Inefficient Yahoo Top Performers that were not the Wins Produced top Performer

Table 3: Inefficient scorers not picked as Yahoo Top Performer

Table 4: Yahoo Top Performers picked instead of inefficient scorers.

I know I just threw a ton of data at you but here’s the break down. If you crack the 30 point mark, even if you are shooting inefficiently you have a pretty good shot of making player of the game. It doesn’t matter if you are the most productive player on the team either. If you’re hitting 30 you have a better than even shot (9 to 8) of getting picked as your team’s best player.

What changes Yahoo’s mind? It appears to be one of two things. On 11/10 and 2/4 Russell Westbrook and Nene dethroned Durant and Melo as player of the game. Both of these players shot very well and got close to the same number of points as the inefficient player. You may lose your top player then if another player scores better than you. In the Durant case this is actually amusing because despite his worse shooting Durant was actually the better player in the game. The other thing that seems to work is a 20 point 10 rebound night. Hitting this mark (or very close in the case of Amir Johnson and Elton Brand) seems to work to get you noticed. Of course this requires successfully getting rebounds, which I suspect is much harder than shooting more shots.

Unfortunately the same story gets told again. In a single game a good way to get recognized (at least by Yahoo) is to take lots of shots. Even doing this poorly is still a good bet to being called the best player on your team. I don’t think the end result of this should be taken as an insult at Yahoo. I remember back before I started doing a lot of advanced stats I would check one thing: Did Melo score 20 points? A few simple heuristics are what people use to gauge players. Did they hit 30 points? Did they have a 20-10 or did they get a triple double? The truth is that while these are often good metrics to use they can be deceiving. Maybe reading this article might make you look a little closer at the box score. Here’s hoping.

-Dre

Michael Redd – the highest paid player on the Bucks (he is collecting $18 million this season) – has yet to play this year.  But he is now scheduled to re-join the team (although when he will play again is unclear).  And that leads one to ask, how much of a difference would a healthy Redd make this season?

To answer this question, let’s look at where the Bucks are without Redd.  Last season – with Redd only playing 492 minutes – the Bucks won 46 games with a 1.8 efficiency differential (offensive efficiency minus defensive efficiency). 

A Small Gap Between Boring and Interesting

This season, without Redd playing at all, the Bucks have won only 21 of their first 55 games.  Across an 82 game season, a team with a 0.382 winning percentage will only win about 31 games.  So the Bucks have dropped off about 15 games relative to last year.

When we turn to efficiency differential, though, we see this decline is a bit over-stated.  The team’s differential is currently -1.5.  This mark is consistent with a team that will win 25 out of 55 games, or 37 games across an entire NBA season (so this is less than a 10 game decline). 

When we turn to Wins Produced we can see who is producing these wins.

The team is led by Andrew Bogut, who is nearly twice as good as an average player (an average player posts a WP48 – or Wins Produced per 48 minutes – of 0.100).  Beyond Bogut, though, the team has a collection of players – including Ersan Ilyasova, Luc Mbah a Moute, Carlos Delfino, and Earl Boykins – who are just a bit above average (not one of these players posts a WP48 above 0.132).  And another collection – which includes Keyon Dooling, Corey Maggette, Brandon Jennings, Chris Douglas-Roberts, John Brockman, and Drew Gooden – who are below average (but not very far below average; not one player posts a WP48 below 0.056). 

When we look at this collection, we can see why Ty Willihnganz – of Courtside Analyst – offered the following description of the 2010-11 Milwaukee Bucks:

I just cannot get in to this season’s edition of the Milwaukee Bucks.  They are mediocre, unentertaining, and too old to invest much hope in for the future.  They are just kind of “there”.

So the Bucks are boring (an argument – as Ty notes — I made a few years ago).  One suspects, though, that had these Bucks maintained what we saw last year, this team might inspire a bit more excitement.  As the above table reveals, the Bucks are employing five players – Bogut, Ilyasova, Delfino, Maggette, and Brockman – who posted a WP48 mark that was better than 0.150 in 2009-10. And had all the Bucks’ veteran players maintained what we saw last year, this team would have already won 31 games (and be on pace to win 46 games). 

Unfortunately, the Bucks are not quite as good. The declines – for the most part – are quite modest with the biggest drop-offs seen in the play of Corey Maggette and John Salmons. Each of these players is 31 years of age, so this might be the issue. However, as noted above, even if every player offered the same level of production this team would only be less than ten wins (across the entire season) better than what we have seen in 2010-11.  So apparently there isn’t a big gap between “interesting playoff team” and “boring”.

Would Michael Redd have helped close this gap?

At first glance one might think that given Redd’s injury history (hasn’t played in over a year) and age (he is also 31 years old) that Redd couldn’t make a difference.  But let’s assume that Redd can come back healthy and reclaim what he was before he got hurt.  Wouldn’t he help then?

Just to review… back in 2007-08 Redd posted the following numbers: played in 72 games (and 2,702 minutes) with 22.7 points per game (good for 8^th in the league).

What if Redd could get back to what he was in 2007-08?

Well, if that happened… okay, Redd still wouldn’t be helping much.  To see why, let’s turn to Wins Produced.  Here are Redd’s career numbers:

Like many players the Bucks employ today, Redd was quite close to average in 2007-08.  Yes he could score.  But he didn’t do much else.  To see this point, let’s look at what Redd did – with respect to the box score statistics – in 2007-08.  And for perspective, let’s compare this performance to what we saw in 2002-03.  Why 2002-03?  That season Redd was 23 years of age.  And it was that season Redd offered his career peak performance.

In looking at the above numbers, remember that Redd was a second round draft choice.  So he did not enter the league as a player that people expected to place in the top 10 in scoring (had that been the expectation he would have definitely been taken earlier). 

The 2002-03 season was Redd’s third season in the NBA.  Across his first two seasons he had played less than 1,500 minutes.  So again, he was not thought of as a star.  But this performance that season was similar to what you would expect from a star.  Not only did Redd score (25.7 points per 48 minutes), he also was a very efficient from the field and line, he rebounded, grabbed steals, and avoided turnovers.  Consequently, his WP48 number of 0.233 was quite similar to something you might see from Kobe Bryant.

After that season, though, Redd’s scoring per game increased while his overall production declined. When we look at the 2007-08 season, we see a player who could still score (29.0 points per 48 minutes).  But his shooting efficiency from the field – relative to what we saw in 2002-03 – had declined considerably.  He was also grabbing fewer rebounds, getting fewer steals, and committing more turnovers.  Yes he was getting to the free throw line more often and getting more assists.  But with respect to the non-scoring aspects of the game, Redd was offering much less.  Consequently his overall production declined.

Coaches are often over-heard telling their teams to…

• “keep passing the ball until we get a good shot”, or
• “ we have to hit the boards”, or
• “we have to take care of the ball”.

In other words, coaches tell their teams to focus on shooting efficiency, rebounds, and turnovers; the very factors that we see drive wins in the NBA. 

Players, though, are rewarded for scoring.  Players who take shots – and are able to hit these with a minimum level of efficiency – will be rewarded. 

Early in Redd’s career, it looks like – as a second round pick – that he listened to his coaches.  But when he discovered he could score, his focus seemed to shift. The non-scoring aspects of the game were de-emphasized.  And scoring became the goal.

One should emphasize, Redd’s focus on scoring was well-rewarded.  Once again, he is the highest paid player on the Bucks.  In fact he is the 5^th highest paid player in the NBA this season.  But because Redd stopped filling up the box score –  like stars players such as Kobe Bryant, LeBron James, Dwayne Wade, Manu Ginobili (yes he is a “star”), Chris Paul, etc… — Redd stopped producing wins like a “star”.  And consequently — although the Bucks paid Redd like he was a “star” — Milwaukee didn’t get the wins a “true star” produces.

So would a healthy Redd help the Bucks?  What Redd offered in 2007-08 is quite similar to what the team is getting from Carlos Delfino and a bit better than what the team is getting from John Salmons.  So a healthy Redd would probably not change life for the Bucks in 2010-11.  The team would still be left trying to close that gap between boring and interesting.

- DJ 

Henry Abbott – at TrueHoop – had a nice column Tuesday on the “Punitive Coach”.  The story focused on a specific high school coach and the tactics she used to elicit better performances from her players.   For anyone who has watched sports, the tactics are not surprising.  Yelling and punishments are often used in response to poor performances.  But is this approach effective?

Kahneman on Coaching

An answer to this question can be found in a classic story from behavioral economics.  Daniel Kahneman – who won the Nobel Prize in Economics in 2002 (for his work with respect to behavioral economics) – tells the following story in his autobiography:

I had the most satisfying Eureka experience of my career while attempting to teach flight instructors that praise is more effective than punishment for promoting skill-learning. When I had finished my enthusiastic speech, one of the most seasoned instructors in the audience raised his hand and made his own short speech, which began by conceding that positive reinforcement might be good for the birds, but went on to deny that it was optimal for flight cadets. He said, “On many occasions I have praised flight cadets for clean execution of some aerobatic maneuver, and in general when they try it again, they do worse. On the other hand, I have often screamed at cadets for bad execution, and in general they do better the next time. So please don’t tell us that reinforcement works and punishment does not, because the opposite is the case.” This was a joyous moment, in which I understood an important truth about the world: because we tend to reward others when they do well and punish them when they do badly, and because there is regression to the mean, it is part of the human condition that we are statistically punished for rewarding others and rewarded for punishing them. I immediately arranged a demonstration in which each participant tossed two coins at a target behind his back, without any feedback. We measured the distances from the target and could see that those who had done best the first time had mostly deteriorated on their second try, and vice versa. But I knew that this demonstration would not undo the effects of lifelong exposure to a perverse contingency.

There are two lessons to learn from Kahneman’s story.

Yelling at people who make mistakes is not likely to be an effective reaction.  Improvement observed after yelling is probably just regression to the mean.  Furthermore, yelling – as Henry notes – likely imposes additional costs on the player.  One also suspects that at some point, players just learn how to tune out the yelling (something I have asked student-athletes about in the past).

Slow Learning

Kahneman doesn’t just explain why yelling doesn’t work, he also expresses doubt that teaching the coaches not to do this is a futile task.  Coaches have learned how to coach from other coaches.  And this behavior is part of the coaches’ training.  Undoing what people “know” is extraordinarily difficult.

The difficulty people have with new information is another key finding from behavioral economics.  Contrary to the story told in standard neoclassical economics, behavioral economics teaches that people tend to be slow to adopt new information. 

Simple cost-benefit analysis can explain the problem.  When people are presented with information that contradicts what they “know” they are faced with a choice:

• Accept that what they “knew” in the past was incorrect. This choice then imposes a cost as the person must now learn the new information (and learning requires thinking, and thinking isn’t free).
• Reject the new information.  This choice reduces the cost of learning to zero. 

Given these choices, people tend to choose to reject the new information.  Consequently, learning is difficult. 

It is not that people can’t learn.  It is simply that choosing not to learn keeps costs very low.  Of course, as the cost of not learning gets to be higher, people tend to be more likely to look at new information.  But often the cost of not learning is low.  Therefore people are comfortable believing the same thing today that they believed yesterday. 

Why We Disagree

As I wrote this post it occurred to me that this story about learning can be easy to misinterpret.  We often confront people who have different beliefs.  And since we “know” what we believe is correct, and we often can’t get people to change their beliefs, we can easily see that the story told about slow learning is true.  At least, it is easy to think that people are not listening to you because they are “slow”,

Well, maybe not.  Yes, people are slow to learn.  But that is not the only reason people disagree.  Here are some other explanations for why people disagree. 

• Sometimes people have thought about what you are saying (i.e. they suffered the cost of listening to you) and have decided you are incorrect.  This can happen because people can interpret information differently.  This can reflect differences in values or differences in what they think is “important”.  For example, two people can look at the same estimated effect and disagree on whether the effect is “big” or “small”.  And of course, people can also think you are incorrect because you really are incorrect. 
• Sometimes people lack information necessary to understand what you are saying.  In other words, understanding your argument requires some education and the person arguing with you is not as educated as you would like.  This problem is especially common when one is trying to explain the results of statistical analysis.  Most people have little or no training in statistical analysis.  And even those who think they “know” statistics don’t always “know” as much as they could (there are a number of “bad” studies that can be used to illustrate this point).

So which is it?  If we could answer that question, we probably could all agree on everything (or at least reduced the number of disagreements).  Certainly people tend to prefer to think that when others disagree that the problem is ”slow learning” or lack of education.   On the other hand, sometimes maybe what you are saying isn’t correct. 

Since we don’t “know” why disagreements persist, the best course of action is to simply lay forth your argument.  If people agree, that’s great.  If not, listen to what they say.  If after listening you don’t agree…. well, then move on.  We don’t have to agree on everything.  At least, I think that’s what I “know”. 

- DJ