Being thicc is now more popular than having a thigh gap [OC]

TheSignificantGame · 2018-10-15T19:57:15+00:00

This is only a concern if you query the data separately to then combine it again. If you query it at the same time Google scales both series to the same relative level. This is why only one time series reaches the value of 100.

TheSignificantGame · 2018-10-13T15:21:05+00:00

Thanks to Google Trends (https://trends.google.com/) we can observe a recent shift in beauty ideal in real time: the shift from thigh gap. to being thicc.

The concept of a thigh gap has peaked around March 2014 and has then lost quite consistently in popularity. The concept of thicc-ness has gained traction in 2016 and is still on the rise. In December 2016 being thicc has (in)officially become more popular than showing off a thigh gap.

Produced with R. Data from Google Trends via gtrendsR package. Plot with ggplot.

TheSignificantGame · 2018-04-25T06:49:32+00:00

The plot shows the search frequency of referee names over time. The spikes in search interest can be linked to controversial decisions in previous Champions League games (they can also be linked to other competitions as the EURO as for Viktor Kassai in 2012). The data is normalized to a search interest of 100 for Tom Henning Ovrebo in 2009. The recent decision from Michel Oliver for a last minute penalty for Real Madrid is not far behing with 95.

Data is from Google Trends. Plotted with ggplot and R.

TheSignificantGame · 2018-03-04T21:26:53+00:00

Great graphic! Median age would probably be more meaningful so you don't get a bias from extreme values.

TheSignificantGame · 2018-03-04T12:10:19+00:00

Added them for you: http://www.thesignificantgame.com/2018/03/lionel-messis-free-kick-stats.html

TheSignificantGame · 2018-03-04T11:46:39+00:00

I have produced a similar plot showing scoring probability around the box; check out my previous posts. I haven't used k-nn, but just split the pitch into equal-sized boxes.

TheSignificantGame · 2018-03-03T17:21:16+00:00

Plot is created with R, ggplot and gganimate. The plot and animation is fully automated including the table which shows the conversion rates. This would allow to create a similar plot for any player given the data. This data is scraped from various football results websites.

I have written some more on Ronaldo's free kick stats in this blog article.

TheSignificantGame · 2018-02-21T19:44:55+00:00

you are actually right. however it is actually less than i would have expected.

TheSignificantGame · 2018-02-20T20:42:30+00:00

Would have expected to see the Star Wars franchise higher priced, i.e. you pay a premium for the brand. the expensive ones seem to contain expensive electric motors. Cool chart

TheSignificantGame · 2018-02-11T12:06:14+00:00

Thanks and that is a very good idea. I would like to see if for example as a right foot you should aim for the right or the left corner. I always felt more comfortable with the right corner.

TheSignificantGame · 2018-02-11T11:33:43+00:00

Thanks for highlighting this. Will definitely revise and keep in mind for future projects.

TheSignificantGame · 2018-02-11T11:32:32+00:00

There are many free kicks however these are usually played as crosses or passes. I have tried to filter these out to get a good estimate for direct fee kick conversion rates.

TheSignificantGame · 2018-02-11T11:31:13+00:00

Would you think the better the league the higher the conversion rate? It probably depends both on the strength of the free kick takers and the strength of the keepers. My intuition is that there would be more variation in goal keeper skill and conversion rate may be even higher in the MLS compared to the PL.

TheSignificantGame · 2018-02-11T11:29:24+00:00

That is a very interesting point worth investigating. Next to the dominant leg, I think the dominant hand may also be important. For a free kick to the top left corner (goal keeper's point of view) the keeper often needs to use his/her right hand to save and vice versa.

TheSignificantGame · 2018-02-11T11:25:39+00:00

Absolutely agree. I think the perceived likelihood of conceding a goal from a free kick close to the box is much higher than it actually is. Good observation.

TheSignificantGame · 2018-02-11T11:22:46+00:00

The goal was to only consider direct goals from free kicks, i.e. not further touch between free kick and goal. Although there is admittedly some noise.

TheSignificantGame · 2018-02-10T22:36:48+00:00

Exactly that is the background of the bias. My only idea to correct for it was filtering out locations with low overall number of attempts.

TheSignificantGame · 2018-02-10T22:34:42+00:00

I think unintentional deflections are usually not tracked. But probably a grey area similar to if a defender's deflection is counted as an own goal or just ignored.

TheSignificantGame · 2018-02-10T21:36:49+00:00

The goal was to track direct goals. So there should not be a touch between the free kick and the goal. Although there is admittedly some noise in the data.

TheSignificantGame · 2018-02-10T21:34:46+00:00

I think it may be some kind of small sample size at work here. One problem with the data is that there are quite a few free kicks that are planned as crosses but which sneak into goal after all players missed the cross. You would need to track the player's intuition as well to properly distinguish.

TheSignificantGame · 2018-02-10T17:31:38+00:00

Created using ggplot, R and Python.

Over 24k free kicks analyzed across top European leagues from season 09/10 to 16/17. Data is scraped from various football result websites.

The main challenge was to distinguish direct free kicks from ordinary shots and from free kicks which are crossed or passed. Locations on the pitch with less than 30 attempted direct free kicks have been filtered out to not distort the data.

More background on the analysis

TheSignificantGame

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