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Premier League Preview Introduction

By Mike Goodman | July 30, 2018 | Main

Welcome to the StatsBomb Premier League season preview. Over the next two weeks we’ll be previewing every team, breaking down the good and the bad, but sadly no longer the Charlie Adam. This post is your one stop shopping for all of those previews. You can scroll on down and find links to each piece as they post. If you’re familiar with our work, that should be just about all you need. Check back daily, we’ll be posting a couple of teams per day more or less. If you’re discovering us for the first time, here’s a little FAQ to get you started.


What exactly is StatsBomb?

We do cool things with football data. We collect it, poke it, slice and dice it, and learn stuff from it. The company does all sorts of consulting. You probably don’t care about that, but if you do here’s how to get in touch. The website also does cool stuff with football data. We use our data to do analysis that is intelligent and fun, and hopefully makes interested readers smarter about the game.


Oh, so you’re nerds. Do you even watch the games?

Yeah. We watch the games. Everybody here loves the game. That’s why we do this. We just also happen to like numbers and using numbers to help learn about the game.


But if you watch the games, why do you even need numbers?

An excellent question! There are a handful of different ways that numbers are useful even to people who obsessively watch the sport. First, no matter how diligent a watcher you are, even if you’re one of the lucky few who get paid to watch the sport for a living, there’s more football going on across the globe than you have hours in a day. Stats are not a replacement for watching, but they can be a guide. You’re definitely not watching the vast majority of League One matches, but if your team gets linked to a precocious talent plying their trade there, these stats can give you a road map for what to expect if you decide to tune in.

Second, stats are an extremely effective tool for detecting small changes across large numbers of games. It’s hard to watch a couple of different strikers play five games each and then come away and know for sure which one took five shots per 90 minutes of play* and which took three. The former would be the most prolific shooter in the Premier League, the latter barely in the top twenty.

*Most stats you’ll see here are recorded on a per 90 minute basis instead of a per game basis, that way starters who play the lion’s share of the minutes and substitutes who get short runouts get examined on even footing. Knowing that a player shoots more, or scores more, or does anything else more isn’t useful if it turns out the only reason they do it more is that they play more minutes.

Third, numbers are an extremely effective way to describe what’s going on over large periods of time. Using statistics to describe a team or a player is a way of supporting (but not proving) and argument. Most football analysis is done at the minute and specific level, analyzing unique and individual moments. Watching a fullback get spun inside out by a winger is a good way to determine what went wrong in the leadup to a goal. Statistics are the tool that helps to figure out whether that fullback gets roasted too often week in and week out.

In the end statistics, and their slightly fancier cousin analytics, are a tool to be used. Coaches have video now to help them prepare for games in ways they wouldn’t have been able to before. Television viewers have instant replay, and tactical cameras, and super slow motion all of which provides more information than a generation ago. Statistics work similarly, providing more information than ever before, another tool in the arsenal for players, managers, supporters and anybody else interested in the ins and outs of how football works.


Okay but what about that Disraeli thing about lies, damn lies, and statistics?

Well, first of all, it’s not actually a Disraeli quote. Mark Twain attributed it to him, but nobody really knows where it came from. It’s also not wrong. Be wary of people using statistics to provide firm answers. While analytics and stats can answer some basic questions, what they’re best used for is helping to ask better questions. They can, for example, move the conversation from “Is Raheem Sterling bad because he can’t shoot?” to how is Raheem Sterling so good, even though it seems like he can’t shoot.

Analytics has all sorts of tools to help do that. Whether it’s normalizing things to per 90 minutes, or using expected goals to try and figure out exactly how good or bad various shots are, using numbers smartly helps to offer a better baseline for discussions about the game that fans are having all the time.


You wore me down, and I clicked on that last link. It talked a lot about expected goals. It’s the same thing annoying people on twitter talk about all the time. Why should I care about expected goals?

An official sounding definition of expected goals would be something like, the average number of goals that a player or team would be expected to score given the set of shots that they took. Here’s a whole bunch of details on how the metric works, but the long and short of it is that expected goals is a better predictor of future goals than actual goals. Because scoring is so difficult, and so much stuff can go right or wrong on a given shot, or in a given game, or even a given month, taking a step back and looking at the quality of chances created and conceded is a more reliable indicator of a team’s ability than the actual amount of goals teams have scored.

Frequently, the stat is also used as a shorthand way to describe a single game. It’s useful knowing a team’s expected goal total in a single game, and can help describe a game with more context than other numbers, but single game expected goals aren’t particularly predictive. Asking the question, “do the expected goal numbers of these two teams accurately reflect the game I just watched?” is a useful exercise for analysis. Saying, “This team had 3 goals, but only 1.2 xG so they were undeserving winners” isn’t. Like I said before, analytics is about asking better questions more than it is about providing definitive answers.


So all you people do is nerd out about expected goals and I’m supposed to read 20 previews about that?

Nope. We’ve got lots more. It’s useful having a metric that helps more accurately depict which teams are better than they might seem and which teams are worse, but it’s much more interesting diving into the why and how of it all. That’s what these previews will be about. Using data (and lots of other stuff), these previews will be breaking down not only how good each Premier League team might or might not be, but more importantly what they actually do that makes them tick. Which players do which things in which areas of the pitch and how often. Who needs to up their game and how. Which managers will have the biggest impacts, and which ones might be past their primes. All of the kinds of questions that football fans are interested in, StatsBomb is interested in too. We just use data to help figure out the best way to approach them.


Shockingly, you’ve persuaded me, a fake person on the internet asking you questions on the website you run, to go ahead and read all the previews on your site.

Excellent. Enjoy! And be sure to check back every day.


The Previews:

July 30 -- Manchester City

July 31 -- Tottenham Hotspur

July 31 -- Leicester City

August 1 -- Manchester United

August 1 -- Liverpool

August 2 -- Wolverhampton Wanderers

August 3 -- Watford

August 5 -- Southampton

August 5 -- Cardiff City

August 6 -- Bournemouth

August 6 -- West Ham

August 7 -- Everton

August 7 -- Arsenal

August 8 -- Newcastle

August 8 -- Brighton

August 9 -- Chelsea

August 9 -- Burnley

August 10 -- Crystal Palace

August 10 -- Huddersfield Town

August 10 -- Fulham


Header image courtesy of the Associated Press

Article by Mike Goodman