Posts Tagged ‘data’

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Mona Chalabi’s Data Art: Maths never looked so good even the dick graphs

April 10, 2022

Mona Chalabi is a British-Iraqi data journalist and illustrator based in London. She specialises in all things data.

An outstanding communicator her work proves that MATH can be artistic and ART can be data-based. She is an honorary fellow of the British Science Association.

Mona Chalabi Self Portrait on INSTAGRAM

WARNING: Mona Chalabi INSTAGRAM account is politically graphic and contains sexually explicit graphs. Yeah! Dick graphs etc. The Instagram links in this post connect with individual illustrations.

1. Mona Chalabi on Jeff Bezos’ Wealth. 

I found Mona Chalabi through her illustrated New York Times article (7 April, 2022) 9 WAYS TO IMAGINE JEFF BEZOS’ WEALTH. 

So Jeff Bezos personal wealth is $172 Billion (US$) Her Toblerone Block vs Mt Everest comparison was in this article.

NOTE: Median wealth is the mid-point wealth ie. 50% of Americans have more wealth. 50% of Americans have less wealth.

2. Mona Chalabi CAFFEINE DATA ON INSTAGRAM

Mona also includes relevant the data in her posts. eg.

 

3.  Mona Chalabi on OUR MOST COMMON FEARS on INSTAGRAM

4. Mona Chalabi on Earth’s Orbit on INSTAGRAM

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Tennis Math: Is player height a BIG advantage? USA units

February 13, 2021

Metric version here.

The Australian Open is on at the moment in Melbourne in LOCKDOWN. (We have 1 community-acquired case. Tennis players are in a bubble.)

Is height a BIG advantage in tennis?

Here are the heights of 3 top seeds in the Australian  Open. (See graph below)

No. 1 Novak Djokovic    6ft 1″  (73 in)

No. 6 Alexander Zverev  6ft 6’   (78 in)

No. 8 Diego Schwartzman  5ft 7’  (67 in)

1. Does serve speed increase with height?

That would be an advantage. Here is a Height vs Serve Speed chart for the Top 6 seeds in the tournament:

Find serve speed data here.

There is NO CLEAR HEIGHT ADVANTAGE for serve speed.

2. Does the serve return rate increase with height?

Here is Diego Schwartzman (L) when he defeated Alexander Zverev (R ) in the 2019 US Open.

What is Schwartzman’s Super Power?

Schwartzman, the shortest player on the circuit (see graph above), tops the service returns stats. Highest 2nd serve return rate. Third highest 1st serve return rate. See data below. So agility, speed, and reaction times are also important factors in becoming a tennis star.

Go Schwartzee! Check data here.

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Tennis Maths: Is player height a BIG advantage? Metric

February 13, 2021

USA units post here.

The Australian Open is on at the moment in Melbourne in LOCKDOWN. (We have 1 community-acquired case. Tennis players are in a bubble.)

Is height a BIG advantage in tennis?

Here are the heights of 3 top seeds in the Australian  Open. (See graph below)

No. 1 Novak Djokovic  188cm

No. 6 Alexander Zverev  198cm

No. 8 Diego Schwartzman  170cm

1. Does serve speed increase with height?

That would be an advantage. Here is a Height vs Serve Speed chart for the Top 6 seeds in the tournament:

Find serve speed data here.

There is NO CLEAR HEIGHT ADVANTAGE for serve speed.

2. Does the serve return rate increase with height?

Here is Diego Schwartzman (L) when he defeated Alexander Zverev (R ) in the 2019 US Open.

What is Schwartzman’s Super Power?

Schwartzman, the shortest player on the circuit (see graph above), tops the service returns stats. Highest 2nd serve return rate. Third highest 1st serve return rate. See data below. So agility, speed, and reaction times are also important factors in becoming a tennis star.

Go Schwartzee! Check data here.

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And the Oscar for Best Mathematical Performance Goes to …..

March 5, 2018

  ………………………………………..

And the Oscar for Best Mathematical Performance Goes to …..

Ben Zauzmer

Ben Zauzmer, a Harvard Applied Math graduate who has a 75 per cent success rate in predicting the winners of Oscar Awards every year, has correctly predicted 20 of 21 winners in 2018 Oscars, which is a success rate of 95%. 

How does he do it? He gathers thousands of data points on Oscar ceremonies over the past two decades – such as categories movies are nominated in, other award results, and aggregate critic scores – and he uses statistics to calculate how good a predictor each of those metrics is in each Oscar category. Then, he plugs in the numbers and that gives him the % chance that each film will win in each category according to  the Boston Globe.

Ben, who writes for The Hollywood Reporter, uses his mathematical model to produce  Bar Graphs like this:

This year the Best Picture was a close call, but Ben’s Mathematical Prediciton was correct.