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More or Less: Behind the Stats

Why is it so hard to predict the outcome of competitions like the Premier League?

More or Less: Behind the Stats

BBC

Business, Mathematics, Science, News Commentary, News

4.63.5K Ratings

🗓️ 12 August 2023

⏱️ 10 minutes

🧾️ Download transcript

Summary

Football competitions are kicking off all around Europe in the coming days and weeks, including the world’s most watched division: The English Premier League. We might make our predictions on who we think is going to win a sporting competition but what factors are we considering? In this programme we look at some of the most popular variables that are taken into account when making sporting predictions and why even these have drawbacks. From upcoming football leagues to the Olympic Games, Head Analyst from Nielsen Gracenote, Simon Gleave tells us what are some of the most difficult sports to predict and why. Presenter: Paul Connolly Producer: Natasha Fernandes Editor: Richard Vadon Production Co-ordinators: Debbie Richford and Janet Staples Sound Engineer: Graham Puddifoot Image: Premier League Trophy, Credit: Carl Recine/Reuters

Transcript

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0:00.0

The captain, you know, he went on the radio and he's like,

0:04.3

we just want to make sure everyone knows he has a perfect champion on the plane.

0:08.3

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0:11.9

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0:15.4

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0:18.5

Hello and many thanks for downloading the more or less podcast.

0:31.2

Think of us like fitness coaches for numbers and venues and in everyday life.

0:36.2

We put them through a battery of tests, make them sweat buckets, stand with the help of experts.

0:41.1

We decided there was good, well, as accuracy as people say they are.

0:45.7

Football, the women's world cup is in full swing and leagues around Europe and elsewhere in the

0:50.8

world are just beginning to kick off, including the most watched division of all, the English

0:57.3

Premier League.

1:07.9

So, my question is this, why is it so hard to predict the outcome of competitions like the

1:16.5

Premier League? So, my name is Simon Gleeve. I am the head of analysis for Nielsen's Grace

1:22.5

Note, which covers all sorts of entertainment data, including sports. Simon is the very best

1:28.4

person to help me tackle this. Sorry. So, let's start with the kinds of data analysts need

1:35.9

in order to set about predicting the winners, the losers and the also rants.

1:39.8

Okay, so we have a model for football. It's called the Euro Club Index.

1:45.2

It's a relatively simple way of looking at things because it uses results over a number of years.

1:50.3

You would get more points on our ranking system if you beat one of the top sides and if you

1:55.2

beat one of the bottom sides when you're expected to. And that produces a ranking.

2:00.6

From that ranking, Simon is able to simulate how a Premier League season might be expected to

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