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Data Misrepresentation
2
What message they are trying to show
- They are trying to show that R.A. Dickey has gotten worse as a baseball pitcher.
- They are trying to show this by misrepresenting the data and making the 2012 results double the 2013 results.
- They may have a motive to portray the baseball pitcher as not an important part of the team or that his skills and performance are deteriorating.
Who benefits from this misrepresentation
- I don't think that anyone really benefits from the misrepresentation of the data. It doesn't really make sense as to why they wanted to show the data like that.
- If someone were to benefit from it, it would be the television station/ news broadcast that showed the message benefits from the misrepresentation. They may gain higher ratings or stir up controversy on their show/ channel.
Implications of the misrepresentation
Data Misrepresentation
The implications of this graph would give someone the wrong information. Looking at the data briefly and not in depth would make one believe that R.A. Dickey's baseball pitch has decreased significantly (approximately 50%). Without actually looking at the exact numbers on the graph, it does not give the viewer an accurate reflection of the speeds.
What the data actually shows
Summary
What the data is presenting
- This is an example of data misrepresentation for a Blue Jays pitcher R.A. Dickey. It shows the difference between his pitches in 2012 and 2013.
- The 2012 bar is double the size of the 2013 bar.
- In 2012 his average pitch was 77.3 MPH, and in 2013 his average pitch was 75.3 MPH.
- The data actually shows that the pitcher's speed has decreased by only 2 MPH between 2012 and 2013.
- The data shows that overall his skills and performance have not deteriorated much at all.
- After all, I am pretty sure it is not likely to pitch the same average speed two years in a row but he sure stayed very consistent.
References
Parikh, R. (2014, April 14). How to Lie with Data Visualization. Retrieved January 28, 2015, from http://data.heapanalytics.com/how-to-lie-with-data-visualization/
Conclusion
In summary, data can be misrepresented in anything we do. We have to take the initiative to look beyond the first impression to analyze what is really being represented by the data. I personally will be more aware of how easily I can be tricked with data that is incorrectly shown.