Welcome to Lowered Expectations, week 29 edition! Each week, we go about posting chalkboards and GIFs of the weekend’s best open-play shot attempts which did not quite live up to expectations (and rarely do we update this paragraph). We look at each one and not only evaluate the results, but also the process leading to them.
Read MoreWelcome to Setting the Table. Each week we take some time to focus on the best chance creators in MLS from the last weekend. If you want to see the best chances that were wasted check out Lowered Expectations. Here we focus on chances that ended with the ball in the back of the net.
Read MoreRecently we introduced Expected Possession Goals (xPG) as an experimental metric. In our latest article, we introduced four uses of xPG. Like any good experiment, things are subject to change as you take input. To help xPG be a little easier to consume, we’re updating some of the terms to be more understandable:
Chance xPG (formerly called Positive xPG) is the total value of a possession based on weighted values gained from actions such as ball-winning actions, passes, dribbles and shots. It is assigned to all the players involved in a possession. A player or team with high Chance xPG is getting the ball into higher opportunity areas for a shot. Chance xPG is expressed in a positive value.
Shot xPG (formerly called Successful xPG) is the total Chance xPG earned by possessions ending in a shot. Players and teams with a high Shot xPG relative to Chance xPG are ones which are good at turning chances into shots. Easy, right?
Read MoreLast week in Part One of this series, we looked at the overall player value rating and it’s underlying method, top players, its validity, and year-to-year consistency. In this part, we’ll turn to the categories of events make up the overall rating, and examine what can be gleaned from these subcategories.
Player Value Subcategories
The main goal of the player value metric is quantifying a player’s overall contribution to a team winning. But recognizing players help teams in different ways; I decided to track where the “value” was coming from. This led me to break down the overall player value into eight subcategories; (1) shot value, (2) turnovers (defense actions), (3) shot blocks (defense actions), (4) pass value, (5) turnover or loss-of-possession value, (6) movement value, (7) F-up value (conceding PKs and red cards), and (8) goalkeeper value. In addition, I have found it useful to create a sub-index of actions associated with “playmakers”; which is called the Create Index and consists of the Pass Value, Turnover/LOP Value and Movement Value added together.
Read MoreCoaching the New York Red Bulls must be a dream for most managers in North America's soccer circle, but Chris Armas also has had one of the toughest tasks in MLS. A mid-season takeover is never easy, let alone the takeover of a contender from the legendary Jesse Marsch. The Red Bulls organization may have boasted that they focus on the same pressing style starting from the academy, but everyone has their own unique ideas they want to implement. Armas is treading a fine line: he is introducing new elements while also keeping what was working for Marsch. The Red Bulls are still playing a similar style of soccer, so it appears Armas has been making quantitative, rather than qualitative, changes. Deciphering those changes will require some analytics techniques.
I first look at how New York has fared under the two managers using different variants of Expected Possession Goal (xPG). I recommend you read that full article, but in short it’s a score that measures the risks a team bears vs the rewards it creates. In short, Negative xPG measures the risks a team bears, while Mistake xPG measures the amount of turnovers a team commits from those risks.
Read MoreThe MLS playoff drama is peaking with all but a half dozen teams dreaming of postseason glory. All the teams have played their tactical cards by now and the chess matches from here on out should be very entertaining. It’s therefore high time to look at a model whose goal is to examine the very chess moves that teams are making and look for insights. The Proactivity Score (Pscore), an attempt to numerically represent a teams basic tactical approach, has been updated through August 27th and there are some interesting new trends. Here’s a chart of where teams stand:
Read MoreStarting yesterday, you will find playoff seeding probabilities in our web app. We show the probability that each team finishes in each playoff seeding position in its conference, as well as the Supporters’ Shield probabilities for all teams.
What is this based on? Well, it’s a two-part process. First, we built a model capable of predicting the probabilities of future game outcomes based on team performance to date. Then we set up a simulation to randomly determine outcomes for all the remaining games this season, with probabilities derived from that predictive model. For each of 1,000 simulated seasons, we tallied each team’s final points, wins, and goals scored and allowed, and seeded the teams in each conference. Then we figured out what proportion of those 1,000 seasons each team finished in each place.
Read MoreSporting KC survived at home against Orlando on Saturday, winning 1-0 on a Felipe Gutierrez strike. They were missing three potential starters (Daniel Salloi and Diego Rubio on int’l duty, Ike Opara on yellow card suspension) and consistently lacked ideas in the final third. But Orlando didn’t generate anything, so SKC walked away with three points.
Under James O’Conner, OCSC have bunkered. Their defensive line is deep, the midfield is tight to the backline and they often sit 11 in the defensive half. At some point, they’ll have to figure out how to do more than that.
They struggled to possess the ball against Sporting, who are a notoriously difficult team to possess the ball against. That doesn’t make abomination this any better from Orlando:
Read MoreFor years I’ve been interested in how players contribute to team results. I’ve sought a measure of player contributions to a win that covered all aspects of a game. While many valuable and informative soccer metrics have been created, common stats are not entirely on point with this issue.
For example, xG stats apply only to scoring attempts, and perhaps goalkeepers. Adding xAssists and key passes broadens the scope of included players. But the contribution of defensive oriented players would not be expected to show up on these metrics. And offensive-oriented players would still rely on teammates to threaten the net before their effort can be measured.
The xGChain metric is useful for identifying players that participate in the most productive attacks, and includes players that play further away from the goal. But this metric does not include non-offensive actions. And each players’ contribution is given equal weight, whether it’s the initial square pass to a CB in the defensive half, or delivering a cross into the penalty area. Experienced analysts consider the dashboard of key performance indicators and piece together insights from the elements. But I’m looking to consolidate all game elements with a common perspective.
Read MoreWayne Rooney has conquered Major League Soccer. It is time to forget all of that clearly misguided “young South American talent Atlanta-y whatever” and get back to what really works and that’s bringing in over 30 stars from European leagues. I chuckle heartily at all of you “it’s not his age, it’s how many minutes he’s played - the man can barely run” types who seem somewhat surprised to watch the captain and leading goalscorer of England and Manchester United perform at a high level in Major League Soccer. I mean he’s turned Luciano Acosta into Ronaldo (either one, take your pick - I don’t care), and vanquished the mighty outliers of Atlanta. All hyperbole aside, DC United are looking good and that’s something worth smiling about.
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