What are the chances that two England teammates share a Birthday?

Cast your mind back to the summer of 2018… we saw the warmest ever weather in the UK, Brexit was not yet a complete and utter disaster, and seemingly against all the odds the England football team reached the semi-finals of the World Cup for the first time since 1990. No doubt the team had a huge celebration together afterwards – but it wouldn’t be the first time that two of them have celebrated an occasion at the same time. As well as playing together at the heart of England’s defence, Manchester City duo Kyle Walker and John Stones also share the same birthday! Stones was born on 28th May 1994, making him 24 years old; Walker was born on the same day in 1990, meaning that he is exactly four years older than his teammate. How strange! Or is it…?

John_Stones_2018-06-13_1 Kyle_Walker

On the face of it, it seems quite surprising that in an England squad of just 23 players, two of them happen to share a birthday. However, as we’re about to see, this isn’t a freakish coincidence – maths says that it’s quite likely! What we’re talking about here is commonly known as the birthday problem: if there are a group of people of a certain size, what is the likelihood that at least two of them have the same birthday?

Let’s start by saying that we have a group of N people, and assume that birthdays are equally likely on every day of the year. (There is some evidence to suggest that this isn’t the case for top athletes; some say that they tend to be born early in the school year, such as around September in England. This is because they are slightly older than the other children in the year, and so they have a slight head-start in their physical development. However, we don’t want to make things too complicated, so we’ll ignore that for now.)

The easiest way to think about the problem is to first try to work out what the probability is that none of the N people share a birthday. Suppose our N people walk into a room, that is empty at first, one at a time. When the first person walks in, it’s obvious that they don’t share a birthday with anyone else in the room, because there isn’t anyone else. Therefore, they have the maximum probability of not sharing a birthday with anyone else in the room, which is 1.

Now think of the second person who walks in. The only way that they could share a birthday with someone in the room is if it happens to be exactly the same day as the first person. That means there is a 1 in 365 chance that they do share a birthday, so there is a 364 in 365 chance that they don’t.

Suppose that the first two birthdays don’t match, and then the third person walks in. They now have 2 days that they can’t share a birthday with, so there are 363 possible choices out of 365. Because we assumed that the first two didn’t match, we multiply the probabilities, so now the chance that none of them share a birthday is (364/365) * (363/365).

We can repeat this process until we get to our final person, number N. For example, the fourth person has 3 birthdays that they cannot share, so we multiply by a chance of 362/365; the fifth person has 4 days to avoid, so we include a probability of 361/365… By the time the Nth person walks in, there are N-1 people already in the room, so there are N-1 days that their birthday cannot fall on. This leaves them with 365-(N-1) possibilities out of 365.

To work out the total probability, we multiply all of these terms together which gives the likelihood that none of the N people share a birthday as

1 * (364/365) * (363/365) * (362/365) * … * ((365-(N-1))/365).

You might be thinking that this still looks like quite a big probability that none of them share a birthday, because all of the terms are very close to 1. But, if we try some values of N in a calculator, then it tells a very different story. (The percentages are calculated by finding the probability from the equation above and multiplying by 100.)

When N = 10, we get an 88% chance that none of them share a birthday. However, this drops down to 59% when there are N = 20 people. When we get to N = 23, the number of players in the England squad, the probability reaches just under 50%. That means that, incredibly, the likelihood that at least two of the 23 people share a birthday is just bigger than 50%!

So, in a random group of 23 people, it’s more likely than not that two of them share a birthday! This seems very strange at first; surely you’d need more than 23 people for a shared birthday to be more likely than not?! This is why the problem is commonly known as the birthday paradox – it might be very hard to get your head around, but the maths doesn’t lie!

Perhaps, in order to convince ourselves, we should look at some real-life examples. This is where the World Cup squads come into play: each team is restricted to bringing 23 players to the tournament. (We’ve seen that number before…) If our calculations above are correct, then if we picked any one of the World Cup squads, there would be roughly a 50:50 chance that at least two of the squad members share a birthday, which means that out of all of the squads that went to Russia, we would expect about half of them to have a birthday match. Well, let’s take a look…

Of the 32 teams, which were divided into 8 groups of 4, the following teams have at least one pair of players who share a birthday:

Group A Russia
Group B Iran, Morocco, Portugal, Spain
Group C Australia, France, Peru
Group D Croatia, Nigeria
Group E Brazil, Costa Rica
Group F Germany, South Korea
Group G England
Group H Poland

So, not only is there at least one team in every group with a birthday match, but if we count the total, there are 16 squads with a shared birthday pair – exactly half of the teams! The experimental results have matched up with the mathematical theory to perfection. Hopefully that’s enough to convince you that our calculations were indeed sound!

A slightly different question that you might ask is as follows: if I am in a group with a certain number of people, what are the chances that at least one of them shares my birthday? Is it the same idea? What we have worked out above is the probability that any two people in the room share a birthday (or rather, we worked out the opposite, but we can find the right answer from our working). Note that the pair doesn’t necessarily include you; it’s a lot more likely that it’s some other pair in the group.

In order to work out the answer to this similar sounding question, we work the other way around again, by calculating the probability that none of the N people share my birthday. For each of the N people, there is only one birthday that they cannot have, and that is mine (14th November, in case you were wondering), which means there are 364 out of 365 possibilities for each person. We no longer care whether their birthdays match up; we only care if they match with mine. So each person has a 364/365 chance of not sharing my birthday; and the overall probability is just 364/365 * 364/365 * … * 364/365, N times, which we write as (364/365)N.

Once again, we can plug some values of N into a calculator: N = 10 gives a 97% chance that no-one else has my birthday. For N = 50 the probability is still very high: there is an 87% chance that none of these 50 people have the same birthday as me. N = 100 gives 76%; N = 200 gives 58%; you have to go all the way to N = 253 before the probability dips below 50%, and it becomes more likely than not that at least one person will celebrate their birthday with me.

Applying this idea to all 736 players (32 squads of 23 players) involved in the World Cup, we should expect around 3 of them to have been born on the same day as me – 14th November. And I am very happy to confirm that France’s Samuel Umtiti, Switzerland’s Roman Burki, and Belgium’s Thomas Vermaelen all have what is undoubtedly the best birthday of the year… Two similar problems with two very different solutions!

Thomas_Vermaelen_2018 Samuel_Umtiti_2018AUT_vs._SUI_2015-11-17_(250)

You can check which footballers share a birthday with you at www.famousbirthdays.com/date/monthDD-soccerplayer.html, where you enter the month in words and the day in numbers (no preceding zero required).

Kai Laddiman 

Carnival of Mathematics 167

Next month (March 2019) I will be hosting the ‘Carnival of Mathematics’ – a monthly blogging round up hosted by a different blog each month and organised by the Aperiodical.

The Carnival of Mathematics accepts any mathematics-related blog posts, YouTube videos or other online content posted during the previous month (February 2019): explanations of serious mathematics, puzzles, writing about mathematics education, mathematical anecdotes, refutations of bad mathematics, applications, reviews, etc. Sufficiently mathematized portions of other disciplines are also acceptable. Links to the previous monthly posts and a FAQ section can be found on the Aperiodical website here.

The deadline to submit your posts will be the 1st March 2019.

Click here to submit an idea!

This incarnation will be the 167th Carnival of Mathematics so here are some fun facts about the number 167…

  • 167 is the only prime number that cannot be expressed as the sum of 7 or fewer cube numbers.
  • 167 is the number of tennis titles won by Martina Navratilova – an all-time record for men or women.
  • 167P/CINEOS is a periodic comet in our solar system.
  • M167 Vulcan is a towed short-range air defence gun.
  • 167 is the London bus route from Ilford to Loughton.

The previous Carnival can be found at Math with Bad Drawings hosted by Ben.

My favourite Carnival is number 146 which featured Tom Rocks Maths for the first time!

Climate Change will increase Turbulence on Flights

We’ve seen many recent extreme weather events – from mudslides in Columbia to flooding in Australia – which scientists say are a consequence of climate change; but it’s not just the weather that is affected. The Earth’s atmosphere is made up of several layers of air which all flow around each other in patterns known as jet streams and an increase in temperature will cause these to speed up. This is bad news for air passengers, including the 1 million people currently airborne at this very instant, because an increase in the speed of the jet streams will cause more turbulence making flying less comfortable and potentially more dangerous. I spoke to atmospheric scientist Paul Williams…

  • Climate change will cause a 59% increase in light turbulence, 94% increase in moderate turbulence, and 140%  increase in severe turbulence.
  • Turbulence is measured on a scale from 1 to 7 where 1 means light turbulence, 3 means moderate, 5 means severe, and 7 means extreme.
  • Light turbulence is a slight strain against the seat belt, moderate turbulence causes unsecured objects to become dislodged and makes walking around difficult, and severe turbulence results in anything that isn’t strapped down being catapulted around the cabin.
  • Turbulence is caused by wind shear – the higher you go up into the atmosphere the windier it gets – and instabilities within these layers of shear generate turbulence.
  • As the atmosphere is heated, the temperature increase causes the jet streams to move faster, creating more wind shear and thus more turbulence.
  • The researchers hope that results such as this will encourage us to think more carefully about our carbon footprint as there are likely many effects of Climate Change that we do not know about.

You can listen to the full interview for the Naked Scientists here.

Size matters when it comes to speed

How fast should an animal be able to move? And why are the biggest animals, which pack more muscle, not the fastest? That’s what Yale scientist Walter Jetz was wondering, so he and his colleagues looked at hundreds of animal species and have come up with a new theory that successfully puts a speed limit on most species…

  • There is a theoretical maximum speed that is expected to increase with body size,  however, in order to actually get to any speed you need to first accelerate, and larger animals take much longer to do so – much like a truck accelerating to 60mph compared to a motorbike or car.
  • Large bodied animals simply do not have sufficient energy to reach their theoretical maximum speed.
  • The general distribution is a ‘hump-shape’ as shown in the plots below. Maximum speed increases with size until we reach a critical mass beyond which the maximum speed reached starts to decrease.

screen shot 2019-01-24 at 10.59.30

  • Data for over 450 species were included in the study, across land, air and water.
  • The study provides insight into evolutionary trade-offs for different species as they evolve to increase their chances of survival.

You can listen to the full interview with the Naked Scientists here.

Image copyright Dawn Key

 

Why do Bees Build Hexagons? Honeycomb Conjecture explained by Thomas Hales

Mathematician Thomas Hales explains the Honeycomb Conjecture in the context of bees. Hales proved that the hexagon tiling (hexagonal honeycomb) is the most efficient way to maximise area whilst minimising perimeter.

Produced by Tom Rocks Maths intern Joe Double, with assistance from Tom Crawford. Thanks to the Oxford University Society East Kent Branch for funding the placement and to the Isaac Newton Institute for arranging the interview.

Would Alien (Non-Euclidean) Geometry Break Our Brains?

The author H. P. Lovecraft often described his fictional alien worlds as having ‘Non-Euclidean Geometry’, but what exactly is this? And would it really break our brains?

 

Produced by Tom Rocks Maths intern Joe Double, with assistance from Tom Crawford. Thanks to the Oxford University Society East Kent Branch for funding the placement.

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