Priors and Penalties: Finding the best penalty takers with Bayes

/
Priors and Penalties: Finding the best penalty takers with Bayes

It’s MLS playoff time, and that means the possibility of penalties and shootouts. Rodrigo Schlegel led Orlando to victory after Pedro Gallese was sent off. Zack Steffen earned a move to Manchester City, in large part, due to leading Columbus to two penalty shootout victories. Portland named a bar in Providence Park after the famous double post shot in the marathon 2015 shootout against Sporting KC. Seattle won their first MLS Cup without registering a shot on goal by winning a shootout. Landon Donovan blasted his penalty over the bar to hand RSL the 2009 MLS Cup. Penalties and shootouts are remembered in ways that few other plays are.

Read More

Where Goals Come From: How Do You Train The Killer Pass?

/
Where Goals Come From: How Do You Train The Killer Pass?

Introduction

In our last article, Jamon introduced the concepts of the “killer final balls” that lead to the best goal conversion rates - those types of key passes that are the most efficient and dangerous. More specifically, the two most “deadly” types of passes in this sub-category are through-balls and cutbacks. As is always the case with this series, our purpose is to create a framework which is actionable and applicable to teams- theory has very little meaning for us.

Read More

How Sophia Smith Turns Good Passes Into Great Ones

/
How Sophia Smith Turns Good Passes Into Great Ones

Ask any data analyst about the utility of statistics and you’ll get any number of answers, from the ability to measure shot quality to the potential to build all-in-one impact measurements, such as American Soccer Analysis’ goals added, or g+ for short.

However, the most fundamental utility of data is not found in the answers it provides, but in how it empowers us to ask questions.

For example, while pouring through ASA’s numbers on Sophia Smith in anticipation of a piece on her and the Portland Thorns, I noticed something curious. Despite dominating the NWSL in xG, she came off looking far more mediocre in Receiving g+, ranking only 26th among her peers (xG and g+ data accurate as of October 30th).

Read More

Where Goals Come From: Playing the Killer Final Ball

/
Where Goals Come From: Playing the Killer Final Ball

This is the third article of Season Two and tenth overall article in a series of articles and videos in the Where Goals Come From project from Jamon Moore and Carl Carpenter.

In our last article, we discussed how Expected Goals (xG) helps us evaluate the quality of the shots that a team takes and concedes. That evaluation can happen with individual shots, with a full game, several games (looking at trends), and over the course of a season. As we know from Season One of this series, the type of shot a player gives us a good indication of the typical Goal Conversion Rate (GCR), starting with the very effective shots from Through balls and Cutbacks. We have twelve types of shots in total in our framework, including penalty shots, across five different categories.

Read More

How much would it cost to buy the Lamar Hunt US Open Cup?

/
How much would it cost to buy the Lamar Hunt US Open Cup?

Let’s pretend that we could buy the Lamar Hunt U.S. Open Cup from the US Soccer Federation. It would not cost all that much, and in a sports universe where owners usually play the villains, we could be the heroes. A new owner would give life to a neglected tournament and return open soccer to the American consciousness.

Read More

We're Gonna Celebrate and Have a Good Time

/
We're Gonna Celebrate and Have a Good Time

Tracking data is the next frontier in soccer analytics. Now that we have access to the location of every player on the field updated 25 times per second, we can measure things like off-ball runs and defensive positioning that were mostly invisible when using only event data. Tracking data holds the potential to unlock the game’s secrets. This article is not going to solve soccer. It is going to solve something arguably even more important: who the best teammates are based on their goal celebrations.

When Metrica released their first few games of anonymized tracking data over a year ago, I first dreamed of using tracking data to revolutionize how we look at goal celebrations. However, with just a handful of games available, and unknown players and teams, this dream would have to wait. But now, thanks to Major League Soccer and Second Spectrum, American Soccer Analysis has access to tracking data from the last couple of years of MLS play and the dream of measuring player celebrations can now be realized.

Read More

Where Goals Come From: Using past goals to create future goals

/
Where Goals Come From: Using past goals to create future goals

The outline for this article is going to be:

  • If you’ve heard about or looked at xG in the past but either 1) didn't see its utility or 2) didn't know how to make it useful, we want to help with these scenarios in this article and upcoming articles.

  • xG is always improving, so regardless of what you saw or read about a few years ago, it is much better now at evaluating individual shots because of better and more data.

  • Not all xG values from various sources are equal because there is not equal access to the data points and data volume, and because data providers, clubs, and analysts have varying ideas on how to value shots and optimize their models.

  • There are other stats and metrics that are not talked about as much as xG but can also be very useful in addition to or along with xG. Some may be better suited to your audience.

  • xG helps us answer the quality question about a shot, and we'll be talking about improving shot quality utilizing xG and other tools throughout this season. Without xG, shot quality becomes highly subjective and experiential.

Read More

We Have A New Win Probability Model

/
We Have A New Win Probability Model

Across recent weeks, we’ve set out to improve the performance of our in-game win probability model, while:

  1. starting to take each team’s strength into account, based on its performance in prior games; and

  2. introducing more fluctuation between goal-scoring events, to better reflect teams’ chance creation throughout the game.

In this article, we’ll cover our methods for accomplishing those goals, how we plan to use this new and improved model, and how deconstructing that model can teach us more about the conditions under which goal-scoring events occur.

Read More

Where Goals Come From: What It Takes For Teams To Be Elite

/
Where Goals Come From: What It Takes For Teams To Be Elite

This is the first article of Season Two and ninth overall article in a series of articles and videos in the Where Goals Come From project from Jamon Moore and Carl Carpenter.

Season Two Introduction

In the eight articles of Season One of the Where Goals Come From project we demonstrated how “progressive pass” goals make up 40% of the goals scored in professional soccer.

Read More

Valuing goalkeepers with goals added

/
Valuing goalkeepers with goals added

We have updated our goals added (g+) methodology to produce g+ components for goal keepers. You can find these new metrics on the Goals Added window in the app under the Goalkeepers tab (MLS, NWSL). Up to this point, we had not published g+ metrics for goal keepers. We recognize that goalkeepers perform many unique tasks on the field, and the first version of our expected possession value models and g+ framework missed a lot of those. Below I’ll explain the specific keeper g+ components and what they try to measure, share a few examples, and then wrap up with some nitty gritty details.

Read More