Sunday 25 September 2016

Data Viz Assignment 1


This article done by the Washington Post entitled "The Wizard Shooting Stars" is an interactive data visualization charged with helping Washington Wizard fans compare the type and success rate of the shot selection their team took in 2013.  

The article begins with showing us a rather stimulating graph that has 6,920 lines (1) heading towards a basketball hoop.  Each line illustrates where each shot was taken on the court by the Washington Wizards in 2013.  The use of these lines to correspond to each shot in this graph is both a strong way to show each piece of data/shot attempt in that it follows the same route aesthetically as a ball going towards a hoop as opposed to a chart that shows no similarity to a literal basketball shot.  These lines follow in line with the data visualization rule of simplicity in that no extra detail is put in to detract readers from the actual data.  What this chart lacks is being able to distinguish made field goals and attempts in this chart for there is no explanation as to what the varying colours of the lines mean.  We see a clutter of colour varying from white to orange and no way of distinguishing which is which.




Viewers are then able to further break down the shot selection into individual players' shots.  In doing so, using the same chart but now only showing the lines/shots that correspond to a specific player on the team (2).  This interactive option helps us break down the cluttered data we see in the first graph and see it broken down into 15 different players.




Once viewers have chosen a player, ((2) We picked John Wall in our case) we see a similar graph but now with just the shots John Wall has taken.  On top of the updated graph we saw before, we now have two extra graphs (4, 5) that give us a birds eye view of where the data/shots take place rather than just the length away from the hoop.  Specifically in chart (4), this is a very accurate way of showing exactly where the player is shooting from with each tiny red and white circle symbolising a shot. As well as little bagels to break down how the specific player was taking his shot ie. Jump shots, layups and/or dunks.  This is effective because we can definitely tell the difference between the 3 bagels in chart (4).  Chart (5) is helpful in comparing the specific player to the average player in the NBA and does a good job in showing the data's specificity; however, it brings in new information that isn't directly relevant to the team.  A good comparison but perhaps not one that needs to be there.




 The info-graph continues it's interactivity by selecting all shots (3) and by selecting the other options shots made (6) and shots missed (7)  helps us solve the mystery the different colours of white, orange and red.  As well as finding out what each line colour symbolises, the data visualization does a good job on further simplifying itself and un-cluttering the data.  It shows that it is using colour extremely well to highlight the differences in the data.  By clicking on either (6) or (7) readers change the original graph as well as the new (4) graph.





The static nature of graph (5) is not a failing per say, but probably would have been more effective if it was some data that changed with the different options like the two other graphs.




The truly impressive thing is how many ways they have made the data available to be viewed and while the colours seemed cluttered and complicated before, now very simplistic and easy to maneuver.  Overall the interactive part of the data visualization is very easy to use.

The article then goes into trying to visualize the trend of John Wall's shooting by trying to symbolize the writing "58 percent of Wall's shots came from the 15 to 29 feet range" (8) in the varying degrees of circles corresponding with where he's shooting from the floor.  This is a little too complicated in that the only way of connecting the information being portrayed in (8) by careful inspection.  As well as the failure of the cardinal rule of bubble graphs not needed, as the numbers are powerful enough statistics.  Or perhaps even if they put the numbers in the bubbles.  If they had highlighted (8) with a greyish background it would have been easier to connect.  

Lastly, all the labels of graphs are labeled in full words and not acronyms but here in (9) we see it used here.  For a basketball fan these acronyms would be easily discernible as FGM = "field goal made" and FGA = "field goals attempted" but may be too cryptic for the average sport fan.



URL: http://www.washingtonpost.com/wp-srv/special/sports/wizards-shooting-stars/
















9 comments:

  1. Your assignment does a very good job of explaining how the interactive graphic works. It seems very comprehensive, and you did a good job on breaking the complexities down.

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  2. Great job annotating your images, your critiques were very clear! I agree with your comments on chart 5. Overall upon first glance, these charts seem quite complex so your analysis definitely made these graphs easier to understand. Good job!

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  3. I love this visualization. Without reading a word it is quite clear that that it involves basketball. The fact that it is as pretty as it is makes you feel like it was fire works of magic...hinting at the team the graphic is about, the Wizards. However, like you noted it is impossible to tell that almost half of the shots were missed, perhaps showing the missed trajectory or a change of colour for the missed shot lines? Great work!

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  4. Good job on a very detailed analysis. It's a very complex infographic so it's good that you're very clear on explaining how it works. As mentioned in another comment, the annotations that you've put in throughout your analysis are very helpful.

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  5. You've explained this visualization very detailed and you can tell that you interacted with the visualization for quiet some time to understand and explain it. It's impressive how much information you can read out of this visualization.

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  6. Nice visualization. you were able to graphically explain a very piece of information . i was more interested in reading the images because they were explanatory enough for the reader to get the information you are trying to pass across. Excellent

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  7. your visualization provide a clear idea about how the author analyze Washington Wizard. also, you provide your opinion about what author could improve. with your visualization, i could understand author's idea more clearly.

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  8. A really cool visualization with a lot of information available in it. You explained all the factors in it very well.

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  9. Hi Max, your wrote a wonderful report. I know in basketball league that statistic analysis are very important for a team to evaluate their player. All the charts in your report and well made. I agree with your criticism, in my opinion that all the charts contains a little too much information in one chart. Separating one chart into two chart will improve the idea that author what to show in the charts. Great work!

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