Posts Tagged ‘graphs’

Parkrun 131

Wednesday, October 25th, 2017 | Sport

Last Saturday I completed my 131st Parkrun. I was feeling good so was determined to make it a PB (personal best) day. Of course, intentions don’t always match up to how you feel on the morning.

As it happens, the wind as with me. Not literally, there was a headwind on the back straight. But I pushed, and having only set my current PB back in August, managed to set a lower one of:


I’m pretty pleased with that, especially with the Abbey Dash looming large. Here is an updated graph of my Parkrun history:

You’ll notice that with the trend line, I should be world champion sometime next week. I’m pretty sure that’s how it works.

What kind of food does Leeds eat?

Wednesday, December 23rd, 2015 | Food

Following on from my previous post looking at statistics we can pull out from the Leeds Restaurant Guide dataset, I wanted to look at how the restaurant scene has changed since we first published the guide.

Here it is:


In this graph I have plotted each cuisine type against the number of restaurants. This is shown for the 1st edition (2013), 3rd edition (2014) and 5th edition (2015). As we learned in the last post, the number of restaurants has risen, so in general we would expect most categories to have grown between each addition. I have not included pub grub as the size of it makes the rest of the data difficult to see.

For the most part, this holds true. Some cuisines have grown faster than others though. We have seen a rise in restaurants serving American, British, International (those that serve food from all over the world with no real speciality) and steak.

In other areas we have seen a decline though. Buffet, French, Indian and seafood have all seen a decline. Persian has too, but this was always a small market. The biggest change is possibly Chinese restaurants. In the first edition we had seven Chinese restaurants, now we have only four.

In terms of the most popular cuisines, Italian remains king. When we first wrote the guide we even considered splitting Italian into two categories, one for general Italian and one for restaurants that specifically did pizza. Latin is also very popular thanks to the growth of tapas bars. It used to be equally as popular as Indian, but Indian has since fallen away.

We can draw the most popular cuisines in a table. I have omitted hotels and casinos, and international, because these do not really tell us anything about people’s tastes.

Position 2013 2015
1 Italian Italian
2 Latin Latin
3 Indian British
4 British American
5 American Indian

It is a pretty consistent story. The only change is that Indian has dropped off from a joint-second spot in 2013 to now being 5th, behind British and American. Much of the growth in these categories is down to meat places such as burgers and BBQ, so it could be people are looking towards more meat-heavily dishes in recent years. Or it could also just be random chance. The sample size is not that big after all.

Leeds restaurants in numbers

Tuesday, December 22nd, 2015 | Food

Earlier this month I launched the 5th edition of the Leeds Restaurant Guide. Now, with five editions behind us and several years of data, I decided it would be interesting to see what we could mine from that information.

Number of restaurants

You might expect the number of restaurants in Leeds to be going up. It is, but only slightly.chart_restaurant_count

This graph shows the total number of restaurants. Over the past two and a half years the number of restaurants has increased 10%. These are not the same restaurants though. It is a case of them opening faster than they are closing.


This graph shows the number of new restaurants opening and old restaurants closing between each edition. Restaurants have consistently opened while closures have been more sporadic. It is worth noting though that the release of each edition of the guide has not been equally spaced, even though it is shown this way on the graph, so that distorts the picture somewhat.

How we rate

Most restaurants are likely to be middle-of-the-road, with some not so good restaurants, some very good restaurants, and a few poor and excellent restaurants at either ends. So what happens when you plot frequency against rating?


Ah, just what we wanted: a beautiful bell curve! Two is a little low for a perfect curve, but normal distributions are often imperfect in the real world. This suggests to me that our ratings are consistent with what you would expect from restaurants running in the free market.

That only shows data from restaurants that are still open. What about restaurants that have closed?


What we would expect to see here is a little less clear. Perhaps that 1-rating is the highest as poor restaurants should close the most. But given there are some many 3-rating restaurants, this might not be the case, and you may have to adjust it for frequency to see such a result. As it is we have another bell curve.

There is a clear asymmetry in the graph though. Far more 1-rating restaurants close than 5-rating restaurants, and far more 2-rating restaurants close than 4-rating restaurants, indicating that our ratings are broadly consistent with where the market chooses to spend, or not spend, it’s money.

What type of food is the best?

What cuisine produces the highest standards? Can you provide any correlation between type of food and how good a restaurant is?


This graph shows each cuisine type and the average rating it receives. No category can maintain an average rating lower than 2 or higher than 4 because no range of restaurants can be that consistent.

I was not surprised to see Thai so high up. Steak houses are also typically on the higher price range, so score well (though we do factor in price to an extent when awarding ratings). Chinese scoring to high is mostly a result of the less nice Chinese restaurants closing down.

The number in brackets after each cuisine indicates the number of restaurants in that category. So the ratings for Persian, German and seafood are pretty meaningless because it is based on a single restaurant.

What useful information we can draw from this is less clear. Just because the average restaurant scores well or poorly does not mean that all restaurants will. There are bad Thai restaurants for example (actually, there aren’t, but there used to be one) and good Indians (lots of them!). However, if you were to avoid eating at new hotels, casinos, fast food and pubs based on it being unlikely to be a good meal, few people would fault you for that.