What to Expect When You're Expecting Goals

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What to Expect When You're Expecting Goals

Expected goals (xG), love ‘em, or hate ‘em, are increasingly being accepted across the soccer world, with misguided notable exceptions. While there are multiple xG models in the soccer analytics world, the concept basically boils down to quantifying the likelihood of a shot being scored based upon where and how the shot was taken. xG quantifies what you may understand intuitively, a shot taken close to goal is more likely to be scored than a shot taken 30 yards away. There are many ways to misinterpret expected goals, one of the most common is that xG tells you exactly how many goals a team will score in a game. Obviously, this cannot be the case, as the sum of xG values of shots in a game is rarely a round number. A team cannot score 1.62 goals in a game, but it can score 1 or 2. xG gives the most likely outcome for goals scored in a game. But since goals come in discrete units of 1, and no more than 1 goal can be scored per shot, calculating the probability of goals scored in a game gets a bit complex. The number and quantity of shots that go into a team’s overall xG for a game matter, it’s not just the sum of xG.

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Updated Game-by-game Expected Goals

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Updated Game-by-game Expected Goals

Starting today, if you go to our xG by Game page (also listed at the bottom of this post), you'll notice that there are now two expected goals totals for each team. That's because we have multiple xG models, and they give different results. Crazy, we know. One is called the team expected goals model, and the other the player expected goals model. There are only two simple differences between these models, but they are significant. 

  1. Penalty kicks are worth less in the team model. 
  2. Sequential shots get their value diminished in the team model.
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Kaka, Higuain, and the Effect of the Aging Playmaker

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Kaka, Higuain, and the Effect of the Aging Playmaker

Yesterday, Kaka announced he would not be returning to Orlando City in 2018. Though unfortunate, the move makes perfect sense. Kaka will be 36 for most of next season, and he’ll end 2017 having played the fewest minutes in his MLS career. His production is down markedly on a per-90 basis:

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Expected Goal Chains: The Link between Passing Sequences and Shots

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Expected Goal Chains: The Link between Passing Sequences and Shots

For those who are not familiar with Expected Goal Chains (xGC), the metric looks at all passing sequences that lead to a shot and credits each player involved with the xG. Instead of just looking at expected goals and expected assists, which primarily benefits strikers and attacking midfielders, xG Chains is beneficial to every player involved in a sequence. Most importantly xGC credits those defensive or two-way players who are integral to a play’s build-up but don’t necessarily serve that final key pass. To calculate xGC, I assembled every pass, shot, foul, and defensive action so far in MLS and assigned a unique ID to each passing sequence. When a sequence ended in a shot, each player is attributed with the xG from that shot. StatsBomb defines it very succinctly, so the below steps are stolen directly from them: 

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Kevin Molino is still very good

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Kevin Molino is still very good

Two years ago I first composed a list of my top under-appreciated wide midfielders. Guys like Mike Grella, Kekuta Manneh, Patrick Nyarko, Lamar Neagle, Lloyd Sam and Sebastian Le Toux painted the top of my list. Again, no, I’ve never done work for DC United.

When sifting through some old USL numbers, which long ago went extinct due to the merger between USL and MLS, I came away enamored with Kevin Molino. He sat at the top of my list of wide midfielders and I ended up getting him for a steal in our fantasy draft that year.

It seems Molino is the type of player that in a lot of ways floats under the radar of many fans in Major League Soccer. This may be partially due to a wrecked ACL during an exhibition game in May of 2015 which ended his first season in MLS prematurely. The lost season forfeited most of the “possibly interesting” stock that was seeded him coming into the league when he had blown out the scoring and assist records in USL.

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The 22 Stats that Explain the MLS Season so far

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The 22 Stats that Explain the MLS Season so far

We’re a bit more than a month into the 2017 season. While that’s way too early to say anything definitive, it’s probably enough time to get a feel for where teams stand. Here are 22 stats (one per team), that explain something of each team’s season so far.

Eastern Conference

Columbus: $642,500 - combined guaranteed compensation due Ola Kamara and Justin Meram (as of September 2016’s salary release) 

For the money (equal to roughly one Nocerino), Kamara and Meram are the best attacking partnership in the league. Meram has looked good both out wide and in the middle, which bodes well for the Crew as Federico Higuain hits the wrong side of the age curve. And Ola Kamara has picked up exactly where he left off last year, with 3 goals in his first six games. 

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Is Minnesota Really The Worst Defensive Team In MLS History?

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Is Minnesota Really The Worst Defensive Team In MLS History?

Let me say, first and foremost, I have a fondness for the underdog or down and out. My first true love, the Seattle Mariners, have the longest tenured playoff drought in Major League Baseball. They've missed out on 15 straight seasons of postseason play much due to their own ineptitude.

So I don’t write this to demean what is happening in MLS to Minnesota, as the expansion club is taking body shots both on and off the field with the tremendously rough start they’ve faced over the last month.

Let me say, first and foremost, I have a fondness for the underdog or down and out. My first true love, the Seattle Mariners, have the longest tenured playoff drought in Major League Baseball. They've missed out on 15 straight seasons of postseason play much due to their own ineptitude.

So I don’t write this to demean what is happening in MLS to Minnesota, as the expansion club is taking body shots both on and off the field with the tremendously rough start they’ve faced over the last month.

After their third loss in four games, all with opponents posting five or more goals, most pundits are ready to declare the Loons on the path to having the worst MLS season of all-time. These types of narratives aren’t really anything new for the start of any particular sports season. Especially when they’re so blatant and obvious.

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Expected Goals and Atlanta United

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Expected Goals and Atlanta United

I spent a bit of time on twitter Monday evening trying to explain why expected goals don't hate Atlanta, and why all those goals over the weekend by Josef Martinez and company weren't as easy to score as you might think. I'm also not saying they were poor attempts, either.

Here is a quick look at each of Martinez's goals on Sunday.

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A Deep Dive Into Shot Location and Placement

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A Deep Dive Into Shot Location and Placement

The 2017 MLS season began with a bang over the weekend! During this time, I had a look in the archive room on shots taken (2011-2016) and thought it would be a nice time to examine shot placement in MLS. This analysis will use some of the ideas from Colin Trainor’s article from Statsbomb a couple of years ago (using one season data from Europe’s Top five leagues (2012/13), while also building upon his piece and examining shot locations and placement in further detail.

At the start of Colin’s piece, he straight out stated that one thing has to be reiterated time and time again: “you can never just take the first metric at face value as further analysis can be undertaken, and inevitably this second level of analysis can provide insights that are missed at the higher end of data review”. Now that is not to say that my piece will be anything better, that was actually Colin’s second analysis on the topic (the first you can access when you read his post above). I will try and build upon his analysis by using MLS shot data to look at more ‘specific zones’ in greater detail and how these end up in placements/areas (in the goal). Before I do that, let’s look at the placement conversion rates in MLS.

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Goodbye expected goals, hello expected points!

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An expected goals model frames quite well what everyone knows to be true: that not all goals are created equal. Goals are created from tap-ins and bicycle kicks and all the shots in between, and expected goals allow analysts and fans to quantify the likelihood a shot will go in the net, and therefore the value of a shot. The next logical step in the discussion of goals is to analyze their value. Goals aren't created equal but neither are goals equal. The goal that makes a two goal lead a three goal league does not nearly have the value as that stoppage time goal that captures all three points.

To measure the value of a goal I looked at game states in MLS from 2011 to 2015 and built a series of functions that estimate the expected points for home and away teams given the score of the game and the minute being played. Each of the functions fit tightly with the actual data and had an R squared greater than 85%. The expected points functions look like this for games with a difference of two or less goals.

Pretty graphs after the jump

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