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Week 5: How Close They Came to Winning
09-24-2014, 07:31 PM
Post: #1
Week 5: How Close They Came to Winning
I took this visualization from the New York Times:

This visualization represents a data set that has one categorical attribute for some events that took place in the last winter Olympics in Sochi and one quantitative attribute representing the distance difference between the winner and other participants in a particular event. The distances are calculated from the average speed throughout an event.

The main point of this visualization is to illustrate the closeness of competition in the events by showing the difference in distance between the participants and the gold medal winner. The viewer can select different subcategories of the various events from the menu provided on the right side of the visualization, this selection will trigger an animated transition between data sets. The viewer can obtain more information about the eight participants who came first in a particular event by hovering over the corresponding bar in the chart plot, this is implemented by visually encoding selection using highlighting.

The golden color of the icon that represents the first place winner is used as an encoding channel to represent the player who has won the golden medal in that event and it is also used as a reference point since the first rank will always have a distance of zero.

Things that I like about the visualization:
1. On the left side of the visualization there are two buttons that can change the units by which the distance difference between the winner and other players is measured either in meters or feet, which is very useful as this is a worldwide event and not all people are familiar with one particular measuring unit.
2. As I click on one of the event’s button, the top left corner text and subtitle immediately changes to reflect what the event is now.
3. The use of highlighting and providing extra information when hovering over the bars is a good future that will reduce the complexity (ink ratio) of the visualization.
4. The bar lengths reflect the distance difference of the players from the winner and its use is consistent across all events.
5. Showing how much distance a one second really is in these events makes it seem like the gap is much larger than it really was.
6. When changing the type of event, the icon that represents the athletes reflects that event type and the sliding motion of the icons depicting skiing is especially attractive.

Things I did not like about the visualization:
1. Date and time of when that particular event took place is not included.
2. Tilted bars makes it hard to measure the difference between different players in the same event. However, without the tilt in the bars the icon’s sliding effect, which occurs when we click on a new event (i.e. we transition from one data set to another one), will be lost, and this doesn’t make the visualization interesting.
3. It always shows only the first eight participants.
4. The color of bars and background is similar in contrast. It would have been better if the bars were darker in color.
5. If I click on the event I am currently on, the chart gets refreshed, which is an unnecessary feature.
6. The data is prearranged on the difference in distance from the winner, and other choices of ordering are not provided (like arranging alphabetically by players’ names or country of origin). This is expected since the purpose of this visualization is to show the difference between the competitors and the winner, and thus other information is considered additional.
7. In some events, like the Speed skating Men’s 10,000 meters, the difference between First Place and the other participants is large enough that their icons don’t appear on the chart, and thus their information (name and country) doesn’t appear. An improvement to this point would be to remove the first ranked participant from the graph, then put this information in a separate textbox in the visualization. Consequently the other participants will appear on the chart.

Overall I think this visualization clearly presents the information it was designed to communicate to the readers by illustrating the ranking of the players according to their distance difference from the winner and event in a simple attractive visualization.
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09-24-2014, 11:47 PM
Post: #2
RE: Week 5: How Close They Came to Winning

1.I really liked the animation used I this visualization. The ease it offers for people to switch between different events and showcase the standing of top performers elegantly.

2.The diagonal background theme seems to go very well among the list of sports they are representing.

Critique points:

1.This visualizations suffers on instances when the players finish at very close distances. By directly looking at 2 distances one cannot directly identify by how much timing does the other participant wins. Although we can easily guess the performance based on his position as the graph is sorted and the designer has also given a provision to click on individual players to obtain more detailed information. I personally felt including country and lap timings in the graph itself instead of forcing people to scrolling on to a particular person would have been more helpful.

2.The diagonal representation although has a very good visual appeal. But the scale provided at the back hardly provides any significant contribution to the visualization as most of the people ca easily analyze the positions based on the distances provided in the graphs.
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09-25-2014, 02:54 PM
Post: #3
RE: Week 5: How Close They Came to Winning
I liked the visualization. The sliding effects, background color (makes it feel like snow), various character representations used to represent various events (which is very likely, additional information on how that kind of skiing is done), different distance metric representations, the Name, Country, Lap time, distance between the competitors provides almost all the necessary information needed at a general point and the highlighting with a dark color (when hover on it) makes it visually appealing & successfully provides the information needed.
1. On the top, it just says "Olympics" but the year and place is not provided (need to switch page to figure out the place and year)
2. Since the distances between the players are mentioned directly on the figure, the inclined y-axis doesn't contribute much.
3. Number of players participating in the individual event could have been specified (may be there are only 8 participants in each event)
4. The y-axis in each graph could have been different to better differentiate between close finishers.
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09-26-2014, 12:53 PM
Post: #4
RE: Week 5: How Close They Came to Winning
The visualization is visually appealing and the animation used here is interesting. The bar chart and distance between the players is efficient to express the order in which the players came to winning. However, the scale on the y-axis is limited to 70 feet as in most races the distance between the players is minimum. As in some cases such as Women's 5000m speed skating, the distance between the winner and other players is as large as more than 100 feet, the bar chart becomes roughly the same size. This makes it difficult to find the order of the players. Though the time each players took to finish is not explicitly mentioned in the graph, the time difference between two players is too small to represent this in a bar chart.
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09-26-2014, 03:27 PM
Post: #5
RE: Week 5: How Close They Came to Winning
This is one of the best visualizations I have come across. I could not possibly find any fault with the visualization at the first glance.

The aspects of the visualization which I liked the most are:
1. The colors of the background and the bars being close to the color white, makes it appear like snow
2. Tilted bar graph - This gives the skiing effect.
3. Icons - The icons change according to the event selected
4. Visualization in different metrics - feet and meters. As already indicated by Khalid, not everyone is comfortable with a particular measuring unit. It is good have the visualization in both the metrics.
5. Highlighting each player's data when the mouse is hovered over a player.

Certain features which could have been represented in a better way:
1. Icons - Different icons could have been used to distinguish men and women.
2. Fixed Scale - The maximum value of y-axis has been fixed to 70 ft over all the events' visualizations. In certain events where the distance between the winner and the other players is over 70 feet, only the winner is present in the visualization.
The visualization could have been developed in such a way that it picks up the data of first 8 players and the y-axis is scaled appropriately.

The visualization gives information only about the first eight players. Incorporating more details about other players would clutter the visualization and it would lose its aesthetic appeal. Apart from the minor changes suggested above, the visualization is at its best in representing the data.
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09-28-2014, 12:02 PM
Post: #6
RE: Week 5: How Close They Came to Winning
When i first see this visualization, i feel it is insteresting, i like this visualization, for the following reasons:
1, The color of each chart like snow so much.
2, I can change the measuring unit of distance by feet or meters, because not everyone is familiar with every measuring unit, for me, i'm not familiar with feet.
3, I can change different event on the right side.
4, It has the explaination of current event on the left side, above the measuring unit.

But, i donot think it is perfect for the following reasons:
1, The related data of the olympic is missing, i do not know which olympics games it is about.
2, I think this visualization just shows that, when the first player reach the destination, the distance of other players from their current position to the destination, but if the distance of two players is small, but they have different speed, it hard to say, which can reach the destination first.

But in general, i do think it is a good visualization.
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09-28-2014, 01:57 PM
Post: #7
RE: Week 5: How Close They Came to Winning
Overall I think this is a very interesting and good visualization. The way we can browse through different events is very intuitive. I also like the fact that the visualization is very detailed having multiple events and the usage of bar graphs gives it a nice skiing effect.
I liked that the icons used for every event was different from others which gives an idea of what the event was about. For the events Bobsled, Luge, Skeleton I did not know what they were about. But after seeing the icons I could recognize them. The Visualization gives a vivid picture of the different events by how many feet they came close to winning.
I agree to the critique that it would have been better if the context of the event like, details of when the event occurred was provided. It would have also been better if there was an option to change the scale of the y-axis. For example in cases like Women’s 5000m speed skating where the difference between the first place and second place is more than 70 ft (the y-axis measure) you cannot see the other players.
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09-28-2014, 10:06 PM
Post: #8
RE: Week 5: How Close They Came to Winning
The visualization is simple and good. The most visually appealing thing in the visualization is the sliding animation transition effect that appears when we select different events from the list. The other good thing is the participants are represented by icons which change according to sport from which one can know the type of sport. As said by Khalid this visualization lacks in many aspects. There is no date and time of the sport and adding them would have been better, the color of bars are in very light color and choosing dark color would have made it a better illustration.

In speed skating event in Men’s 5000m and 10000m and Women’s 5000m I can’t see any player except winner, changing y axis feet or meters interval range would have made them visible. Also, only 8 players are compared neglecting other players, they would have mentioned that they are comparing 8 players otherwise someone may misinterpret that only 8 players participated in the sport. The feet in the y axis can be represented with darker color as numbers are in very light color. The main positive in the visualization is we can chose any sport easily from the list in the right side and the change is very fast.
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09-28-2014, 11:53 PM (This post was last modified: 09-29-2014 12:10 AM by soumyasmruti.)
Post: #9
RE: Week 5: How Close They Came to Winning
First I would like to appreciate Khalid for a fantastic visualization, I completely agree with the post what he has critiqued.

The graph is completely appealing and perfect to some extent but the problem with the viz is that it should portray the actual data of wins in the bar provided either with numbers or as a percentage of total data.

The color of the chart should be more eye catching and appealing.

The critique's decision to improve the chart is perfect and really appreciated.
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09-29-2014, 07:51 AM
Post: #10
RE: Week 5: How Close They Came to Winning
I liked this visualization. The use of different markers for the different events prevents the user from forgetting which event they selected. The constant scaling makes sense because it represents a distance and the marks seem to be sized to fit the scaling. I also liked the use of animation to create a "freeze frame" effect as if we paused a replay of the races. This animation made the chart more appealing and the data encoding more intuitive.

I see a few drawbacks with this visualization. I think the time difference could have been displayed in the rollover text. Displaying just the times forces the user to subtract values that are shown one at a time. Adding the time difference helps give more information. Is the typical speed for racers in a given event 40 kmh or 80 kmh? Does 3 meters represent one second or one tenth of a second? I also think the chart should have scaled the axis to prevent marks from leaving the chart. The marks should also scale to keep the scale consistency mentioned before. Even without scaling the chart, rolling over the arrows should have shown the expanded data. For Men's 10,000 Meter Speed Skating I can't get information about anyone except first place. This lack of information makes the visualization less useful.
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