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Reflection6-timconnors33-Tim-Connors #85

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24 changes: 24 additions & 0 deletions week1.md
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[Link to visualization](http://duelingdata.blogspot.com/2016/01/the-beatles.html)

Adam McCann's "An Analysis of the Beatles?" visualization displays songwriting data
about The Beatles discography, specifically the songwriting habits of each member.
It uses color scaling in combination with a scatterplot and a pie chart to link
together the success, time period, and writer of hit Beatles songs. I enjoy how
these three individual visualizations interact with each other. However, I think the
rainbow color scale is a bit difficult to interpret. I would have preferred it to be on
a scale using saturation or luminance instead.

The "Which Songwriter Has the Largest Vocabulary" section is fascinating to look at, but
a bit unintuitive. A song's position on the y-axis is determined by how many unique words
the song has, but the visualization does not define what a "unique" word is. The songs seem
to be organized on the x-axis (within each individual songwriter's chart) alphabetically.
This information is not very meaningful, and I think it would make more sense to
instead organize it by the release date of the song, which might reveal some more
interesting trends in the data.

Overall, the visualization is very interactive and effectively displays a lot of data.
It highlights specific graphics when mousing over them and displays text boxes with
further information. It also includes a search feature to find specific songs, which
makes the visualization a lot easier to navigate through. I would have liked to see
a bit more polish in displaying the data meaningfully, but McCann's visualization
does a very effective job at visualizing Beatles songwriting data.
9 changes: 9 additions & 0 deletions week2.md
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[Link to visualization](https://nba3d.peterbeshai.com/)

After Professor Harrison showed some of Peter Beshai's NBA visualizations in class, I was curious to delve further into them as an avid NBA fan. In particular, I found some of his 3D visualizations and decided to explore them. They center around player career totals in things like points, assists, and rebounds.

It is interesting how Beshai uses the added dimension to display further information about data points. He has tools for laying out the data by teams in columns and geographically. There is also a "step" format, where the cylinders are put together in a grid and sorted by value. All of the formats are fascinating to look at, but some are more informative than others. There is a cluster-type format that is very elegant but seems inferior to the grid format in comparing stats across players. There are also formats to divide players by teams in a grid, but I feel that the geographical layout is a lot more intuitive. It is much easier to find a team by looking for their home city than by searching through a grid sorted in alphabetical order.

Additionally, I feel that Beshai could have made some use of color instead of having all the cylinders be gray. Perhaps there could have been some toggle-able feature that would enable a color scale based on a player's height, seasons played, or time period in which they played.

Overall, I found it fascinating to explore a 3D visualization. It helps provide an extra sense of scale to the data, and really makes me appreciate the statistical outliers more.
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[Link to visualization](https://blog.ebemunk.com/a-visual-look-at-2-million-chess-games/)

I am fan of chess, and the turn-based, methodical nature of the game makes analytics very wide-spread in the community. The "Opening Tree" visualization featured in the above link is one I found particularly fascinating. It displays, in a pie chart-esque format, the most common five opening moves from a database of over two million chess games.

While pie charts in general are not very effective at displaying information meaningfully, this one has some added features which makes it a bit more intuitive and impressive. When mousing over a particular slice, it displays a tooltip with a variety of useful information, including the percentage of games featuring the previous move it was played in. It also plays the move on the board in the center, making the chart more interactive and aiding in visualization. Another nice feature is that it displays a lighter shade of color for moves played with the white pieces and a darker shade for moved played with the black pieces. I also found the splitting of the chart into different colors based on the first move to help in digesting the data.

I think what makes the visualization effective is that the creator does everything they can to minimize the weaknesses of pie charts with these additional features. In fact, I think a pie chart might have been used by necessity and not by choice. I cannot think of another chart or data structure that could display all this information at once. It has made me reconsider my previous position that using a pie chart leads to a bad visualization by default.
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[Link to visualization](https://whydocatsanddogs.com/)

"Why do cats & dogs ..?" visualizes the most commonly searched terms asking about the behavior of cats and dogs. There are two separate visualizations for dogs and cats, both following the same style. There is a variety of bubbles laid out, with the word after the prompt "Why do dogs/cats" or "Why does my dog/cat" in the center of each bubble. Subsequent words in the search branch off from that main word inside each bubble. The data is taken from Google Trends.

The visualization is extremely pleasing to look at. It has a great color palette and is fun to explore. The search bar feature allows you to convieniently search for terms like in a standard search engine. The tooltip that comes up when you mouse over a node offers a written view of it, and clicking on the node redirects you to a Google search with the inputted inquiry.

For exploratory purposes, the visualization is extremely effective. It would be interesting to see how the author could incorporate more expositive features, such as using color scales to furthur express how common a search term is. There are supplemental visualizations which do offer some more general data comparisons, but I would like to see how an integrated way of measuring the nodes against each other would look, beyond just the size of the bubbles.
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[Link to visualization](https://public.tableau.com/app/profile/brandon8775/viz/EPL202021MonthlyTeamsAnalysis_16109050407400/5GameFormGuide)

This graphic was made in Tableau and depicts the form of Premier League teams over the 2020-21 season though a line graph. It takes the average number of points for a club over their last five matches and uses that to plot out their trend of success over the course of the season. The graphic does a good job at depicting form for individual teams. It is easy to follow the lines and get an intuitive sense of how each club performed.

However, it is a bit harder to interpret when there are more than three or four clubs being charted at once. There are only so many colors that can be used to distinguish the lines from each other. Many teams have very similar main colors and color schemes, so there end up being some clubs that are represented by a color they are not known for. In addition, many of the clubs clunk together in the middle fo the graph, making it hard to discern any meaningful trends in the data overall.

There are some nice tools, like only displaying clubs who finished at a certain position in the table and displaying home and away form separately. Additionally, you can click on a club's graph or name in the legend to isolate their line, making it a lot easier to see. You can also filter through different dates in the season for a closer look at a particular period in the season. Overall, I think the visualization is a good attempt but could use some extra features for making the data clearer to see.
7 changes: 7 additions & 0 deletions week6.md
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[Link to visualization](https://www.reddit.com/r/dataisbeautiful/comments/h94umw/oc_most_frequent_nba_shot_locations/)

This visualization depicts a distribution of the most frequent shot locations in the NBA in select seasons from 1997 to 2020. There is a node overlayed on the background if that position is considered a "frequent" location. The data shows a clear trend away from mid-range shots and towards three-point shots. As analytics have played a bigger and bigger role in in the NBA, the three-pointer has been found to be a more efficient shot than the mid-range shot, and this graphic does a good job of displaying this.

I would have liked to see some use of color and a legend in the visualization. There is no indication as to what is considered a "frequent" shot, and since frequncy is being used as a binary value, it is import to define what it actually means. An alternate way of showing the data would be to have a heat map for each displayed season's shot chart, with a luminance scale displaying the frequency of a given location's shot. This would be defined by what percentage of the total shots taken that season were attempted from that position. I think this would paint a more complete picture of the data being displayed without making the visualization harder to understand.

The visualization is fun to explore, but it could be improved with some minor adjustments. You could even have a timeline feature that would display one chart and have it change gradually along with the season being displayed. This would allow you to fit more seasons into the visualization and make the trends in the data more intuitive to understand.