Marathon Races Shiny App

About a year ago I posted about men's and women's marathon (and longer) distance races from the Arrs.net dataset.  In the meantime, Shiny development and the open source announcement of plot.ly have brought data visualization to the next level.  As an avid (at least former) runner, exploring marathon data is interesting at both the personal and "data science" (or is that personal too?) levels.  Thus, I finished a Shiny app that explores this dataset from 2014.  Unfortunately, 2015 data is not being updated for one reason or another, but 2014 provides a lot of observations about marathon and longer distance races.

Click here to access the app.

The values can be toggled between months for 2014 and a searchable table of all the data is below the graph.  You will notice many of the points are small ultra-marathons around the world.  Plot.ly provides nice graph interactive abilities found when hovering in the upper right corner of the graph.

Thanks to RStudio for all their work on Shiny and to Plot.ly for their plotly package and charting library.

Boston Elite Field 2015

Last year I posted about how chances of a non-African country winning the Boston Marathon seemed to be good because of the widening interval of winning times (more recently there had been some historically "slower" races and some historically "faster" ones) and this actually happened.   Meb Kflezighi ran a remarkable race and was widely celebrated as he represented the US in a race more recently dominated by African countries.  His time for winning the race was obviously the fastest, but others in the field had faster PRs.  Because of the variation in winning times my conclusion has been that this provides opportunities for certain runners representing non-African countries to contest the race well.

The amount of participants from Africa in the elite field clearly increases the likelihood that the winner represents an African country.  The runners in the elite field mostly fall into or below the confidence interval shown in the graph above with the slight exception of Matt Tegenkamp whose PR for the marathon is 2:12 ish, just above where this statistical measurement would encompass.  It is clear that once again the elite field is dominated by African runners who are putting up some really impressive PRs.

And yet, with the difference in PRs, last year there was a similar dynamic.  Dennis Kimetto comes to the race with a 2:03 PR and Meb Kflezighi wins the Boston Marathon having run a 2:09 PR previously.  Thus we have another great story this year.  Incredible athletes, some of whom have in the past run much faster than others.  And yet, who can tell what will happen race day.

But why try?  Why did Meb think he could beat someone who in marathon terms could go somewhere he could not?  More broadly, why do we love these events?  Why should Matt Tegankamp attempt to rival someone who would be 2 miles ahead of him on each of their best days?  Variance.  Within these elite athletes there is the notion that on any given day, the guy next to you could be at his best or worst.  As spectators, we're drawn to variance...we love possibilities of things not turning out predictably, or that there is variation in what we assume to be true.  Athletes place their hopes in this, that they could run their absolute best and others may not.  Confidence intervals tell the story of variance, that statistically we can't know for certain.  I think this year yet again, we could see this same variance play out.  The athlete that doesn't have the fastest PR runs their best despite the odds.  This is what makes a great race and what we could see again tomorrow.

Winning a Marathon (Part 2)

In a previous post I looked at a data set published by the AARRS that provides a lot of data on marathons around the world and specifically the winning times of every* race.  After spending a bit more time with the data there are a few more things we can take from this data that may be more helpful for personal use.

As mentioned before, the data includes ultra-marathons, trail-runs, etc.  In an effort to extract those to get only road races I've filtered the data to include only those races with at least 200 participants (male/female so 200 male participants at least or 200 female participants).  Still there are some non-road races in the data that have 200+ participants, but far less than before.  So, is the data totally "cleaned" of these races, no.  But, I think this gets us closer to the finishing time(s) people are running to win "normal" marathon road races.

In this case the average winning time is about 2:35:00 for male winners.  We can assume that this would come down slightly with a few more of the ultra-races stripped out.  You can see different race names as you put your cursor over the point (thanks plot.ly!).  This is potentially helpful for finding a race to win that's within your race time.  In the past 10 years the times haven't changed dramatically (contrary to the graph that included all marathon and ultra distances).  Certainly more races were available the past few years than those before, but it seems that those races are all run just as fast as the others.  

Female winning times have also stayed consistent over the past 10 years for races with more than 200 finishers.  

The average time for Female winners is around 3:02:00 for the last 10 years.  Again, much lower time than had we included all races in the data set without some filtering.

These graphs were only of races in the US.  In general, without having personal knowledge of the race, (terrain, temperature, organization, etc.) marathon difficulty is difficult to measure objectively.  I don't know of any "difficulty index" for marathons (let me know if you know of one), which is why starting with the winning times of races is a good place to start when considering racing with the potential to win.  

Winning a Marathon

The proliferation and participation in the marathon has increased substantially in recent years.  No longer is the distance an event reserved for the super-athletic, but at least in the US one can from many vantage points on highways or streets see the infamous "26.2" sticker donning a rear windshield.  In a previous post I logged participation in marathons worldwide and as can be seen from the animation, certainly in the US this has increased over time.

As participation becomes more the norm we turn now to the question of actually winning a marathon.  The Association of Road Racing Statisticians (yes there is such a thing) maintains an excellent site with all sorts of data on the marathon event as well as other distances.  I created a large file from their data of all marathons each year in the world with their winners.  Marathon in this dataset is anything that is over or equal to 26.2 miles, so that includes trail races or ultra-marathons.  This will make sense when some of the finishing times are seen below.  I plan on talking more about this dataset in future posts but for now we'll look at winning a marathon in the USA.

According to this dataset, in 2013 there were 1,984 marathon events in the US (wow).  And seemingly Fall is the most popular time to host them (ya know before the Holidays).

So how fast do you need to run to win one of these or at least have a decent shot at winning?  Obviously lots of variance depending on which one - or as may be intuitive race purse/recognition is highly correlated with race speed*.  In general for the past several years in the US, the time needed for a male on average is about 3 hours.  As more races have been created giving opportunity to more people, the average time needed to win a marathon has decreased slightly.  In 2013 you "only" needed to run in the 3:30 range to win a marathon, that is on average across 1,984 races.

Interesting to note that to qualify for the Boston Marathon in 2013 as a male a time of 3 hours was needed (wonder if they based that on average winning times over the last 10 years).  Female winning times look similar in that they too have a slight bump in 2013/2014 in terms of "slower" winning times on average.

More recently across all the marathons in the USA, women are winning marathons at around the 4 hour mark.  Again, this all depends on the race one is entering.  But if you are like some of the people who run multiple marathons a year, hitting these averages gives you a decent chance at winning...especially as you heavily consider the number of participants and/or the purse involved ;-) 

For those interested, most of the code for pulling this data and the graph(s) will be on my github page.

*More challenging races (ultra-distance, trail, etc.) are included in the dataset (not all races were created equal) and perhaps more vetting on this dataset on individual races is needed to fully appreciate the finishing times.  A more vetted dataset would surely yield a lower finishing time for both male/female, however combing every race is beyond the scope of this post...maybe when I have a bit more time.

Marathon Finishers Worldwide

This is an interactive timeline of the top 10 countries' participants who finished a marathon. The data was used/scraped from the Association of Road Racing Statisticians where this data is compiled (great site btw). Those interested in the code will find it on my github page.

Not too surprisingly, the US has the most Marathon finishers of any country. Interesting is Japan's increase in Marathon finishers in more recent years as well as the fluctuation in which countries occupy the top 10 with the most finishers. 2014 is incomplete because the year is not finished. The dip in 2012 is a result of the NYC Marathon being canceled.

Data: running2 • Chart ID: MotionChartID197c73527aa6 • googleVis-0.5.6
R version 3.1.1 (2014-07-10) • Google Terms of Use • Documentation and Data Policy

NBA Drafting

The draft for the NBA is quickly approaching.  Much effort on the part of teams goes into selecting the correct assets in a player to complement what a team needs.  Drafts also come in on much cheaper contracts than their more veteran counterparts and are therefore desirable from a value standpoint.  It becomes increasingly important then what pick a team gets and even more so how well they select their draft pick (No. 1 or No. 2 picks not always dictating a high level of performance).  In a recent interview with Bleacher Report, the head of analytics for the Denver Nuggets spoke a little about how they evaluate draft picks.  He made some interesting comments about numbers his franchise evaluates as they consider their draft picks.  Specifically, rebounds were an important metric that was actually translated as a "hustle stat".  I looked at the numbers to see if what he was saying was actually true over the last few years.  Turns out total rebounds per game is an important metric for increasing a draft's chance of being chosen as a top five pick.

I pulled draft year stats for the past 4 years for the top 30 picks from the good people at Basketball-Reference to see if their were any metrics that increased a players chances of being a top five pick each year.  Only drafts who had college stats were used for this analysis.  I wanted to see which per game metrics changed the likelihood of a top 5 selection in the draft class.  What I found is in the decision tree below.  I divided points per game, field goal attempts per game, minutes per game, and total rebounds per game into quartiles.  I lumped the bottom two quartiles together as the "Lower Quartile" and then the "Middle Quartile" and "Upper Quartile".

Decision Tree for Top Five Draft Pick for 2009-2013

As you can see from the tree above rebounds per game not in the "Upper Quartile" have a top five selection probability of .13.  The probability of being a top five pick is .33 if the draft's points per game are in the "Lower Quartile" or in the lower 50% (average or lower) of their draft class and their rebounds per game are in the "Upper Quartile".  Alternatively, if the draft has rebounds per game in the "Lower Quartile, or less than the 50th percentile, and their points per game is in the top 50th or 75th percentile their probability of being a top five pick is only .19.  Rebounds it seems are even more important than having someone who can score in the "Upper Quartile" of a draft class on a per game basis.

Alternatively, if a draft pick gets playing time per game in the "Upper Quartile" and has "Middle Quartile" or "Lower Quartile" points per game, this also yields a probability of .33 of a top five pick.  I interpret this as, if you don't have hustle but have had a lot of playing time in college, this increases the top five pick probability.  I don't interpret minutes per game in a college season to be as meaningful as rebounds simply because minutes per game is more of a college coaching decision that may or may not be relative to player performance.  Minutes are primarily a function of performance and not a stand alone performance metric.

Rebounds matter for draft picks.  Players not showing a strong "hustle stat" have a lower probability of being a top five pick within the respective draft class, unless they have happen to have played a lot of minutes, then this also had a higher top five pick probability.  The competition, shooting distances, and rules are obviously different in the NBA from college and some of the college stats may not translate into professional performance.  That being said this analysis does indicate that the "hustle stat" or rebounds are meaningful for teams other than just the Nuggets.  High performance specifically in this metric increases the probability of being selected as a top five pick in the NBA draft.

Those interested in the R code can find most of it here.

NBA Playoffs 1st Round Comparison

Most people I've talked to feel like they've watched an entire NBA Playoffs series after seeing the first round of this year's playoffs.  It's been amazing basketball.  I've said before, we are watching players whose numbers resemble that of other "golden eras" of the NBA.  

The first round for the most part over the past few years has meant a few games in overtime each year.  As far as this tournament goes, the first round isn't necessarily the most competitive because of the match-ups.  Teams are "seeded" based on regular season performance in each conference and in general teams that are "seeded" further apart will not have as competitive a match up in the first round, thus the advantage to perform well in the regular season to get the 1 seed.  The western conference has had some amazing games in the first round.  Several of the games have gone into overtime.  In fact, more than the last several years combined.  

Below is a simple network of playoff games that have gone into overtime 2010-2013.  Each line indicates a series that was played and each arrow is a game.  Blue=Western, Red=Eastern, and the width of the line is the margin of victory (gotta looks closely) and the arrow points to the visitor away from the home team.  

NBA Playoffs 1st Round 2010-2013

The margin of victory for instance in the Bulls-Nets game last year was 8 points (also 3OT).  For all these games, the average margin of victory was 6 points.  Most of these games you will notice are Eastern conference teams.

NBA Playoffs 1st Round 2014

2014 has been a different story.  In just the Memphis-Thunder match up we've seen 4 games go into overtime (it's been a grind).  These two teams were very competitive and did not demonstrate in general, the match up expectations we have based on "seeding".  The average margin of victory for all these games was 3 points.  The west has been very competitive in the first round.  There have been more first round match ups that have gone into overtime than the past 4 years combined and then the margin of victory has on average been half of what the margin was 2010-2013.


Stamina.  The players for the teams that have advanced are going to need it and most of the people I've talk to need it just to watch the games.  So if you feel like you've watched an entire series, those feelings are legitimate...and we're only out of the first round.

MVP and Speech

Kevin Durant delivered an amazing MVP acceptance speech Tuesday in Oklahoma City.  The award clearly meant much to "Mr. Reliable" as he's now called in multiple media outlets.  He went player by player through his teammates discussing some of their attributes and how they have contributed to him winning the award and making him a better player.   In general, the MVP speech isn't something that I would think in general there is a lot of expectation on.  Meaning, the public feels like you can accept the award, thank a few people, and get back to business.  That's why KDs speech was so unusual.  We can compare it to Lebron's speech this last year.  The sheer difference in length is the first difference you may notice between the two MVPs.

It is interesting to note what words were used.  I chose only the top 11 words from each player to get a sense of how they focused their speech.  Both players mentioned "guys" a lot as well as "team" which gives a sense that they were both giving credit to their teammates.  Graphed against each other, the two speeches show a bit more interesting parts in terms of the frequency of words.

"Love" for instance was a big part of Durant's speech, his communicating the way he felt about his team and those around him was really important.  His speech was about thanking people, showing his appreciation for those around him.  We can also see Durant was also concerned about being "better" and can infer from the text that those around him were the people that made him better since appreciation, love, and team were mentioned so much.  If we scale the graph to only show those words mentioned 13 times or less, we can see where some words were used several times by either player and not by the other.

Here we see "team" mentioned over 10 times by Kevin Durant and only 1 time by Lebron.  Lebron referred to his team as "guys" and we can't discern just from these words that his speech wasn't about his team.  But, it is clear just from looking at word usage that Kevin's speech was clearly more about team than Lebron's without having to listen to the speeches themselves.  Those of you who watched/listened Durant's speech understand that going player by player was a clear objective for his accepting the award and that communicating the importance of his team in the journey was a priority.

As mentioned before, this speech isn't something to have high expectations for, which is why what Durant said and how he said it was so remarkable.  

Mr. Reliable

Earlier this week a headline came out in the Oklahoman newspaper which most know by now.  The "Mr. Unreliable" headline grabbed national attention and it was perceived this small Oklahoma City market was deriding the (arguably) No. 1 player in the NBA and likely MVP of 2014 of their own team.  The writer of the article came out and apologized for the headline he was not responsible for, and dismissed the implication that OKC perceived Kevin Durant as an "unreliable" player.

I'm not sure what the headline was getting at when it described KD as "unreliable".  Maybe "unreliable" at making free-throws in that game?  We'll probably never know fully what exactly that headline was getting at.  For Kevin Durant, he was a champ about it, didn't care, basically had the "I've got a game to play" mentality that rose above the chatter, and put up 36 points.

In terms of what reliability in an NBA player is, in general I thought about it as consistency.  Is the player consistently good or can we rely on them to perform well.  To measure where we could place KD, I took the deviation of minutes and points for the top 20 scorers in the NBA and graphed them against each other.  There are plenty of other metrics, just chose minutes and points.

As you can see, there are several players in the top 40% (top 8) whose "consistency" in minutes played and points is high (or low deviation from what they averaged for the season).  Dirk Nowitzki and Monta Ellis are the most consistent by this measurement.  But obviously this isn't a necessarily impressive stat unless we know how many points they are making or how many minutes they are playing.  Consistency or reliability isn't helpful for a team if a player is consistently bad, not that these two players are at all bad.  

Below is another graph showing the total number of minutes played and the total number of points made during the regular season.  Not totally surprisingly, KD is in another realm this season in both minutes and points compared to the other top 40%.  This is important because no one else in the top 40% of consistent players in minutes and points is in the top 40% of total minutes and points except Kevin Durant and Kevin Love.  

Kevin Durant is playing an incredible amount of time and scoring an incredible amount of points.  While doing that, he's in an elite group of "consistent" or "reliable" players.  He's not only in a top tier in terms of minutes and points reliability, but is being reliable in the most awesome way.  

Boston Marathon Winner

In a previous post I described the current level of competition between countries/continents in the marathon and specifically the Boston Marathon.  The dominance of Africa in the marathon was discussed at length and recent times for the winners of past Boston Marathon events were shown as the times descended and the winners were represented by African countries.  It was also mentioned that recently, the range of expectation has changed to where I thought it would allow an athlete representing a non-African country to win the race.

Monday this came true.

Meb Kflezighi (USA) won the Boston Marathon and set a personal record in the process (not bad for an almost 39 year old).  There are a couple of interesting things about his win in the Boston Marathon that I think allow for a shift in the way spectators and participants view this race.

Expectation
As can be seen in the graph below, spectators and runners alike had a mutual understanding that of all the countries represented the chances of a non-African country winning were slim from the sheer numbers of African runners that can run the kind of times needed to win.  Meb's win changes that expectation for participants who on their best day could run in the 2:07-2:10 range.

For spectators, it allows for a shift in understanding about the variance in races.  Seven of the runners in the elite field had run a marathon in under 2:05.  Relative to the potential of the field and past times, the race this year was not run remarkably fast at least for these athletes.  As can be seen below this time was very comparable (within 1 minute) of the last time this race was won by American Greg Meyer in 1983.

In 1983 Greg Meyer ran a race time that was faster than what could be "expected" to win the race at that time ("expected" that is within the confidence interval).  30 years later our expectations change again where the times of 30 years ago can win races.

I think variance in this regard is healthy for the interest in a sport.  It increases the interest of spectators because the possibilities are more wide-ranging and (arguably) the race is more entertaining.  It provides hope to all marathon runners who can run times within what could be "expected".  Meb's win also provides inspiration across age groups who may have seen running as a mid-20s to early 30s sport.  This next year's Boston Marathon will no doubt see a very competitive field like previous races.  The shifting expectations about this race will hopefully lead to increased participation from athletes and heightened interest by spectators.    

The Current Golden Era of the NBA

We are in the midst of what many are calling a "golden age" of the NBA.  Being in the midst of a time where attention to the sport has seemingly increased is difficult to quantify.  For most people who have had an interest in the NBA over a long period of time, the current state of the game just "feels" like a time unlike recent years.  Awareness of the calibre of game we are witnessing is important to more fully appreciating the games and the players we get to see perform.

In the last couple of years we have seen two giants in the game emerge as contenders (Durant/Lebron) that reminds many of the Bird/Magic years.  Friendship coupled with competition in the kind of way that keeps you glued to the screen when they play.  The most valuable player (MVP) distinction, I would argue is a reasonable way to see where the game is at in terms of the quality of play in the league.  The site basketball-reference provides an enormous amount of data on the sport and is a great place to begin looking at MVP as a metric for determining the "era" of current play in the NBA.

Below is a heatmap showing different statistics gotten from the website for players that were awarded the MVP in different years.  The colors show a distribution of how the players ranked compared to eachother based on these yearly stats (Red>Blue).  On the left side of the heat map is a dendrogram showing how players could be grouped based on these stats.

The stats are total games (Games), field goal % per game (FGoalPercen), free throw % (FTPercen), assists per game (Assists), rebounds per game (Rebounds), minutes per game (Minutes), average points per game (Pts), and player age (Age).  Next we take this same dendrogram and divide players into clusters using a method (kmeans) based on the above statistics.  The red lines outline the different clusters we get when creating 5 of them.  Again these groups are based on the similarity in these stats between players.

What results is a set of data where we can see how MVPs could be grouped based on the stats in the heatmap above.  Kevin Durant hasn't been awarded the MVP yet, but let's just assume he does, and his current stats don't change at all after these 81 regular season games (this is all on a per game basis).

Clearly, cluster/group 2 or what I will call the "Golden Era Group" is the largest.  Even though some players arguable shouldn't be in this group, it's mostly comprised of players that reflective NBA watchers can agree were apart of what many have called "golden age(s)" in the NBA.  Also interesting to note are the Bill Russell and Wilt Chamberlain clusters.  In the case of Wilt Chamberlain his rebound and shooting numbers were much higher than his peers, whereas Bill Russell is placed into his own group because of his free-throw % being in the 50%-60% range...or much lower than his MVP peers.

Here are other players in the "Golden Era Group" with their Points per game against the year.  Notice how comparable Durant is to other giants in the "Golden Era", and how amazing Jordan was compared to his MVP peers.

In general, we can see that recent years' MVP awards are grouped with Bird, Magic, and Jordan.  As a proxy for measuring each players' performance in the league, measuring the performance of MVPs seems to indicate that the current level of play of the best in the NBA could be associated with these by-gone eras of greatness.  In many ways knowing that the current level of play is comparable is intuitive without looking at the numbers, just by watching the game.  In support of feeling like it's a "golden age" of the NBA there are numbers to support it.

Time for the playoffs.....

Boston Marathon Winners and Challenging Africa

The marathon is dominated by African runners.  David Epstein in a relatively recent interview mentions about a specific tribe in Kenya called the Kalenjin, "There are 17 American men in history who have run under 2:10 in the marathon...there were 32 Kalenjin who did it in October of 2011". The times and number of African runners reaching those times times rarely achieved by their racing counterparts is impressive.  Below is a graph showing the top 50 times recorded by Association of International Marathons and Distance Races (AIMS) over the past few years.  

The Boston Marathon is perhaps the most sought after race for marathon distance runners.  At least in the US, qualifying for the Boston Marathon can be the pinnacle achievement for an avid runner's career.  As one would expect, this race draws runners from all over the world who seek the prestige and purse of winning the Boston Marathon.  Over time the winners of this race have changed, as arguably, the physiology (and arguably culture) of runners has become more of a factor since access to the race has become easier over time (for more on Kenyan physiology and culture as running determinants, see this Radiolab podcast).  Like in all marathon races, the times are getting lower and African runners have shown a clear dominance over the last several years.  In the graph below you can see the descent into Boston Marathon winning times that 30-40 years ago were unimaginable.  

 
Here are the same times and years broken out by continent instead of country.  Notice the break in dominance of winning this marathon from Europe/North America to Africa in the mid 1980s.  Prior to this time, the race enjoyed a larger amount of variety in countries/continents winning the race.

The grayish line intersecting these points is basically a confidence interval (95% confidence interval).  One could interpret any point within this grey area as a time that would not be a statistical outlier or a time that could be expected to win the Boston Marathon.  The interesting thing about this graph is how the gray area is now widening in the past few years.  This is partially because of the fastest marathon ever run is included in this graph (This was done by Geoffrey Mutai in 2011, which did not count as a world record formally because of the change in relief of the Boston Marathon).  Notwithstanding this time, we also see times more recently that have historically been run by North Americans, Australians, Asians, and Europeans.  Though it is clear that Africa demonstrates clear dominance in this marathon and others, the times that African participants have been running are not insurmountable from a historic perspective.

This "widening" of race time expectations I believe provides opportunities to continents and countries who have run races at this speed in the past.  The question now becomes how many runners in these continents/countries can currently run at these paces.  There are some.  Both Ryan Hall and Dathan Ritzenhein are US runners who have run marathons in 2:08, which would make them both very competitive with the recent winners of the Boston Marathon.

Stripping out the African countries we can see the times of other continents over the past several years.  In fact all of these times fit into the range of "expectation" (95% confidence interval) of the most recent races.

Running this fast a race must take into account multiple other factors such as weather, injury, etc.  However, based on the data of previous races, the times produced by these runners in the graph above would have been very competitive if not won previous recent years' marathons.  There may not be as many challengers in other continents, but those challenging African runners stand a chance.  More recently if a non-African runner had run the Boston Marathon in what would possibly have been their best race, they would have had a great chance at winning.  

Most Entertaining NBA Teams

In a previous post I highlighted the Thunder keeping more of their games closer (within 3 points) than anyone else in the NBA.  As the season has progressed, this has changed.  The Thunder are still in the top five of most close games as of 2/19/2014, but the Mavericks, Warriors, and Hawks have more.  If a game is close, by itself that doesn't say necessarily much about either team that was in the match-up or that their match-up is particularly good.  This isn't necessarily indicative of the ability of teams (more close games=better team or vice versa), but I think it does indicate entertainment value of the games or stress incurred by fans.  Below is a bar graph showing each team and how many games they have had that ended with each team being within 3 or less points.

The network below shows who the match-ups were with.  Again, the Pacers having only 3 games within 3 points isn't indicative of their current No. 2 rank at 41-13.

Here I'll propose a metric that attempts to capture the entertainment value of the games a team plays, with a few assumptions:  we are more entertained the more teams win and close games are fun to watch.  Below shows a graph with close games against total wins of a team.  The points are sized according to a "team entertainment" metric or (No. Close Games/Season Losses).  This communicates a team's ability to win as well as to provide entertaining games along the way (close games being entertaining).

The Thunder rank the highest on this metric with the Trail Blazers, Heat, Warriors, and Mavericks also ranking high.  Arguably, this makes the Thunder the most entertaining team to watch...at least this far in the season.

OKC Thunder: Too close for comfort?

The Thunder this season have already played some pretty exciting/anxiety-causing basketball.  Not just the excitement of watching new and old players return to the court, but the games seem to have been back-to-back nail-biting.  Peering around on the data from the good people at basketball reference, I wanted to see if this feeling actually was supported by data comparing this season so far to last season.  What better way to visualize it than with the rgexf package in R.

So this season the Thunder have had some close games.  So have many other teams in the West.  Most pundits can agree the West is stacked and there are a lot more close games to come.  But what is interesting is that so far the Thunder have had way more games than last season where the point differential between the two teams is 3 or less.  In fact as of 12/4/2013 they have the most in the NBA.

Here is a visualization of the match-ups.  2012 the Thunder had two by early December:  Pistons and Spurs. We have had two games with the Warriors within 3 already (as most may recall).  Also interesting to note where western teams are when comparing 2012 to 2013 so far.  In general it will be interesting to see how this year pans out with this season starting out this way.  OKC fans have been biting their nails this season....perhaps more than any other fans and definitely more than last season around this time.  So in general fans...the data supports the emotion :-)

*Few notes on the networks:  Thunder away games are in blue, Thunder home in black along with every other NBA game.  Warriors had two games against the Thunder in 2013 but only one edge represented in the network (sorry couldn't work that one out).  

2012 Match-ups within 3 

2013 Match-ups within 3