One in Eight (thousand)

One in eight thousand

A warm Sunday morning in May. I’m up (too) early and heading off to my local football ground. Its cup-final day for the local youth leagues and I shall be officiating in a couple of games, first as an assistant referee, then I shall swap with the man in the middle who will be my “Lino” as I referee the game.

I arrive early, park up and, as I step out of my car, another vehicle pulls up and a young man, also clad in black, hops out – my fellow referee. There is a glimmer of recognition as we shake hands and I begin to quiz him about his background so I can work out where I have encountered him before. It turns out he’s on the books of a local League 2 side and I’ve ref-ed some of their U16 trials games and will have seen him on the pitch there.

We chat some more as we wait for kick off. He’s currently in Year 11 at a local school, is quietly confident about his forthcoming GCSEs and is even more excited to tell me that he has been signed on as a “Sixth Form Scholar” at the aforementioned League 2 club.  No mean achievement, and he is right to be proud.

I quiz him further (I’m a nosey old git!) He’ll be going to the sixth form college in the town of his club – he admits he probably wouldn’t have chosen that college if it weren’t for his football commitments, but it is a condition of signing on as a “scholar.”

He also tells me that none of the current “lower sixth scholars” at the club have been signed on for the “upper sixth.” They have all been “let go” – they will, of course, continue their studies at the local college but ties with the football club have been severed.

Another school, another Year 11, another Football Club (but also in League Two.) I found myself chatting to this student, asking what he would be doing next year. Again, it was with some pride that he told me he would be joining   …… FC as one of their football scholars.

We chatted away, I was interested to see what this meant in practice. Like the young ref mentioned above, this lad will spend several days each week training with the club, and several days each week at the local college.  Asking him what he would study he told me that all football scholars at the club followed the same academic course.

And that set a few alarm bells ringing. These boys are mortgaging their future on the possibility, and, as we will look at below, a slim possibility, of “making it” as a professional footballer. The football scholars will have different skills and strengths on the pitch, and it will be the same in the classroom. To shoe-horn them all onto the same academic course, to deny them the choice of studying the subjects that they want to study is doing them a disservice. But with the lure of of a glimmer of a pro-contract in two years time, heads are easily turned.

If they make it, does it really matter that they’ve missed out on A Level Maths, or English or Geography or whatever? No, it doesn’t. But most won’t make the grade.

The Independent recently published an article promoting a book: “No Hunger in Paradise: The Players. The Journey. The Dream” by Michael Calvin, who documents the sometimes seedy story from schoolboy to professional footballer, a story littered with shattered dreams, shady agents and broken promises.  In it, he offers the staggering stat that only 180 out of the 1.5 million boys who play organised youth football in England will go on to play in the Premier League. That’s a success rate of 0.012%

I thought I’d do some of my own sums, and they back up the authors claims.  I’ve made many generalisations, but I think I’m in the right ball park.  Here are my calculations.

20 Teams in the Premier League each with, say, a playing staff of 30, so that’s 600 players playing in the Premier League.

Assume a career length of 15 years, so each year one fifteenth of the players will retire. To replace these we need 1/15 x 600 new players each season, or 40 new footballers each season, 40 youngsters to take the place of those who have reached the end of their playing days.

There are 48 counties in England, so its not enough to simply be the best player in your county, you’ve got to be better than that. And that’s before we even factor in overseas players.

Another ballpark figure, let us say that half the players in the Premier League are English, half not. So we probably only need 20 new English lads each year to join the elite ranks of Premier League footballers. To succeed, statistically, you’ve got to be – at least – the best player in your county and the neighbouring county.

But of course, you don’t have to play in the Premier League to enjoy a successful, and financially rewarding, career in football.  Those plying their trade in the Championship, League One and League Two will enjoy a good living and lifestyle. And as you drift further down the football pyramid their is still scope to make a significant additional income from playing the game. (Alas, my playing days were so far down in the roots of “grass roots” football, I was paying to play, a long way off being paid to play!)

I don’t want to deny anyone their dreams and I wish those two young men I mentioned above the very best of luck – it would cheer me enormously to cheer them on a professional football pitch – but I can’t help but worry that, because of choices that are being made for them by their clubs, doors are being closed to them, when education should be about opening as many doors as possible.

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Let the numbers do the talking

Lawro – Mark Lawrenson – former player, football pundit and expert took a bit of a battering today, with a website castigating his weekly predictions of upcoming football fixtures:

The table based on Mark Lawrenson’s predictions is actually ridiculous

Reading the article, it goes on to rubbish Mr Lawrenson’s weekley predictions for Premier League fixtures. Joe.co.uk has turned his predictions into a league table and the Give Me Sport article goes on to say

we’ve discovered how the Premier League table would look going into the final game week if all of Lawrenson’s predictions had come true.

To say it’s ridiculous would be an understatement.

Firstly, his former club Liverpool sit top on 89 points having not lost a single game. Not biased at all…

Also, Lawrenson clearly still rates Leicester with them sitting seventh with 71 points! After the Foxes, there’s then a bizarre 22-point gap to Everton in eighth.

Despite Sunderland being rock-bottom with 24 points in real-life, Lawrenson’s predictions has them in the dizzy heights of 14th on 34 points.

Later on the article describes Lawrenson’s predictions as

embarrassing

Harsh words, so do the numbers back up such criticism?

In a word, no.

Using Spearman’s Rank Correlation Coefficient we actually discover that Mark Lawrenson has done rather well.

Spearman’s Rank is a great tool to compare how closely two different variables compare. It is perfect for a case like this where we want to compare a predicted league rank with the actual league rank.

It is based on the difference between the square of the two different rankings.

For example, Lawrenson predictions would have Bournemouth in 13th place, in reality they are in 10th place, so the difference is 3, the square of the difference is 9 (we square the values so we don’t have to worry about negative numbers).  This is the formula used to calculate Spearman’s Rank Correlation Coefficient:

Spearman's rank correlation coefficient

Formula for Spearman’s Rank Correlation Coefficient

where d is the difference and n is the number of pairs of data (in this case 20 as there are 20 teams we’ve compared.)

The value will always be between -1 and 1, where 1 tells us there is perfect agreement in ranking.

So how did he do?

Pretty well – he ended up with a coefficient of 0.890 (to 3 decimal places) which suggests that there is a strong positive correlation between his predictions and the actual league positions. In other words, the boy’s done good.

So ignore the sensationalist headline of the Give Me Sport piece and let the numbers do the talking. Mark Lawrenson has been proven, mathematically, to be a pretty good pundit and predictor.

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When Refereeing and Maths Collide

PenaltyD

Regular followers of my blog know that I like my sport and some of you are aware that I am a qualified football referee and regularly ply my (alternate) trade as the “man in black.”

With my referee’s hat on, I am an active member of a forum that discusses the finer points of the offside law and whether or not any particular offence as seen on Match of the Day warranted a red or yellow card. All part of the learning process, everyday is a school day, and all that.

Today someone posted a problem that was a little left field for the average referee discussion – what is the area of the “D” on the edge of the penalty area.

What a great question!

Lets fill you in with a few dimensions. From the goal line to the edge of the penalty area is 18 yards. The penalty spot is 12 yards from the goal line.  When a penalty is being taken all players (other than the penalty taker and the goal keeper) must be both outside the penalty area and at least 10 yards from the penalty spot. Hence the “D” on the edge of the penalty area – it’s arc marks the points that are 10 yards from the penalty spot.

Its not a simple question, but one that should be within the grasp of of a “good” GCSE student. It’s a problem involves trigonometry and Pythagoras, sectors and segments; the area of a triangle and fractions …

I got the answer to be 44.73 square yards (to two decimal places), do you?

If you’re not sure how to approach the problem you can see my back of a yellow card working below.

penaltyAreaworkingoutMidsize

 

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The Magic Washing Machine

2016 will be remembered for many things, Brexit, the election of Trump, a surfeit of celebrity deaths, but it was last week, 7th February 2017, that a true giant passed away, although you might have missed the news of his passing.

Hans Rosling was a statistician, an educator, a communicator. He used facts to explain and enlighten. Four years ago, I wrote a short blog embedding one of his videos, where he illustrated the change in family size and life expectancy over the last fifty years. He really was a remarkable man with the ability to communicate some difficult and challenging ideas with ease and clarity.

Please, take the time to watch the video above – it’ll only take ten minutes of your time and, even if you learn nothing (but you will) you will enjoy the show.

In these times of alternative facts, mis-speak and other Orwellian horrors, I hope that Hans’ legacy will be a willingness to use facts and statistics to inform and shape understanding.

Hans Rosling, 1948 – 2017. The world is a better place for your passage through it.

(A quick youTube search for Hans Rosling produces a wealth of results. If you’ve watched the video above and would like to see some more, below are a few links you may enjoy)

Where are the Syrian Refugees?  Although made in 2015, so the numbers may have changed, this short video is quite sobering and shows that we, in Europe, have probably got it wrong.

200 Countries, 200 Years    Hans examines how the wealth and health of 200 countries has changed since 1810

Channel 4 News Interview  Interviewed on Channel 4 News. Worth a watch.

Posted in Handling Data, Large Data Sets | Leave a comment

Large Data Sets

image

Large data sets – not the most inspiring of titles, but one which we teachers of A Level maths will become increasingly familiar over the next few weeks and months.

A Level maths is changing, but two plus two remains four, most of the content that is in the current A Level syllabus is in the new syllabus, to be taught from September ’17. It’s place in the syllabus may have changed – i.e. a topic that currently appears in Core 3 may now find itself in AS maths and be taught in the lower sixth/first year of A Level, but there is nothing that will be too unfamiliar to today’s teacher or today’s student.

Except for large data sets.

Candidates are to be familiar with one or more specific large data sets, to use technology to explore the data set(s) and associated contexts, to interpret real data presented in summary or graphical form, and to use data to investigate questions arising in real contexts.

… and that is new.

So its time to start thinking about large data sets, what they are, how we will teach with them, how they will impact on the exams…

The boards have, helpfully, published the large data sets that they will be using, and I’ve put copies of them here:

Further down this page you can see some sample questions from the boards that relate to the large data sets.

So what is to be expected of the student in the exam?

I must add a caveat that I am crystal ball gazing, and this is just my own view and not that of any board, but it seems that students won’t have to have whizzy excel skills to manipulate the data in the exam. They will be examined on this part of the syllabus like all others: in questions on paper in an exam hall, no computerisation of the exam.  They could succeed on these elements of the exam with no knowledge of how to use Excel (or any other package) to manipulate data. But, as I will begin to explore below, manipulating the data with Excel (or similar) in lessons or homework will help them develop a good knowledge of the structure and content of the data, and this is important.

Have a look at this sample question from OCR, in particular part ii):

OCR Large Data Set Sample Question

OCR Large Data Set Sample Question

To effectively answer this you need to know that the data set contains different regions with different geographical characteristics, and the differences between, say Unitary Authority and a Metropolitan Borough and how the provision of public transport within different areas varies.  (If you don’t believe me, have read of the mark scheme below.)

I must confess, I am a little uneasy with this – are we examining mathematical skills or geographical knowledge?

 

OCR data set sample question answer

OCR data set sample question answer

After years of staff shortages meaning Geography teachers have ended up teaching maths  it now seems that we maths teachers are going to have to do a bit of Geography teaching!

However, we must do the best for our students and so we must familirise them with the nature, structure and content of the data sets. To do this I will look at how I can incorporate them into teaching as many aspects of the syllabus as possible. For example, I will get students to calculate the Standard Deviation of all, or a sample of, the data set – and not just using the single Excel formula.  But that’s a post (or several) for another day.

This post was hopefully a brief introduction to Large Data Sets. Download the files for yourself, have a play and a think about how you might use them.  As ever, I’d be delighted to hear your suggestions. And have a look at another couple of sample questions designed to examine the students familiarity with, and ability to explore and manipulate, large data sets.

EdExcel sample Large Data Set question

EdExcel sample Large Data Set question

 

EdExcel large data set sample question answer

EdExcel large data set sample question answer

 

MEI Large Data set sample question

MEI Large Data set sample question

MEI Large Data Set sample question answer

MEI Large Data Set sample question answer

Posted in Exam tips, Handling Data, Large Data Sets | Tagged | 3 Responses