Christopher Wondolowski should be an American sports icon. He should be beloved and admired. If he is hated by anyone, it should be by MLS fans in the same way Indianapolis Colts fans “hate” Tom Brady. He is the underdog of underdogs – the working class man who beats the talented elite at their own game. At 36, he keeps breaking scoring records in MLS, including setting the all-time big one a few weeks ago with a four-goal match. He is on the precipice of being the first player to score 10+ goals in 10 straight MLS seasons. His time and opportunity with the US Men’s National Team should have been longer than it was – but for many fans, there would be no cry for Wondolowski’s return to the national team. No matter how many goals he scored or how often his league form was more impressive than the strikers getting the call, his national team legacy was cemented. Outside of a few San Jose Earthquakes fans and pundits, there are no calls for “Wondo” to be on the team by the American soccer public because of one infamous situation that occurred on July 1, 2014.Read More
Narrative: Ambition Rankings
If there is one day on the MLS calendar that I dread with a clarity and purity often seen only in very expensive diamonds (let’s call them “diamonds of ambition”), it’s Grant Wahl’s annual musings on which MLS teams have proven their ambition the most. For those unaware, every year our nation’s preeminent soccer scribe sends out a questionnaire to every MLS team asking them to flex their financial bonafides and then ranks them according to how expensive their DPs are, whether or not they get good crowds, and that “it” factor that you can’t explain but Grant knows it when he sees it. Unsurprisingly, Atlanta tops this year’s list and Colorado pulls up the rear, but the middle is just gluttonously full of incisive takes. “We’ve invested 10 million dollars in our academy says one team”, “oh yeah well WE expanded our stadium so suck it” says another. “Tell me more” says Grant Wahl, and we’re left with a bunch of people squabbling over whether Jan Gregus or Pedro Santos is a more ambitious signing.Read More
I recently created a decent set of MLS possession data while working on another project, and I was curious if the patterns of the famous Reep analysis would hold for MLS. Thus, I attempted to replicate his result, and perhaps offer a couple new perspectives to the data.
I was first introduced to the legacy of Charles Reep while reading The Numbers Game (by Chris Anderson & David Sally). Reep was an early advocate for applying statistics to soccer, and was famous for tracking game events by hand over many seasons. According to his data, most goals were scored from possessions with three passes or fewer. And this was taken as empirical justification to play directly; minimizing the touches with longer passes in order to improve results.
Although Reep’s status as a pioneer in the sport is secure, many still debate the results and interpretation. Some critiques assert the underlying data was misinterpreted. Highlighting a simple majority of goals may not be the best analysis when most possessions had three or fewer passes anyway. Others suggest the structure of the analysis confuses correlation with causation; leading to misapplication of the results. In short, one can’t tell if the results were caused by the number of passes, or whether some other factors have causal roles. As I attempt to recreate the analysis; it’s worth stating the same criticisms and critiques apply to this replication effort as well.Read More
We recently updated the app with a few more bells and whistles so that you can make more noise. I explained the xGoal model changes earlier(link), so here I’ll highlight the app’s key updates. We’ve made the app accessible publicly, we’ve integrated compensation into the data tabs, and we’ve improved sidebar filter options.Read More
With our most recent app update, you might notice that some numbers in the xGoals tables have changed for past years where it wouldn’t normally make sense to see changes. As an example, Josef Martinez had 29.2 xG in 2018, but updated app shows 28.7 (-1.7%). No, this is not an Atlanta effect, though I can understand why you might support such an effect. Gyasi Zardes lost 0.5 xG as well (-2.4%), and no one dislikes Columbus.
We have updated our xGoal models with the 2018 season’s data, and that is the culprit of all the discrepancies since the last version of the app. I have already cited the largest two discrepancies by magnitude, so this isn’t some major overhaul of the model. In fact, only 2018’s xG values have been materially adjusted.* The new model estimated 35.6 fewer xGoals in 2018 than it did before, equivalent to a 2.8% drop.Read More
Michael Bradley and Wil Trapp share several obvious qualities. They are both captains for club and country. They are both smooth passing defensive midfielders, and they both possess excellent heads of hair. Another similarity is that they rarely shoot or score goals, each collecting only one goal over the last three seasons. Coincidentally, both of those goals are what we could enthusiastically describe as "wonder-goals." Bradley's long-distance chip for the US national team in a World Cup qualifier against Mexico at the Azteca (a goal not remembered as fondly as it deserves due to the rest of qualifying) and Trapp for the Crew to win a match in stoppage time against Orlando City this past summer. However, one difference between these two players was how each responded to the confidence boost that came after scoring a once-in-a-career goal.Read More
We all know that some teams play a certain style, Red Bulls play with high pressure and direct attacks, Vancouver crosses the ball, Columbus possesses the ball from the back. Although we know these things intuitively, we can use analytical methods to group teams as well. Doing so seems unnecessary when we have all these descriptors like press-resistance, overload, trequartista-shadow striker hybrid, gegenthrowins, mobile regista, releasing, Colorado Countercounter gambits...etc (we actually don’t know what some of these terms mean and may have made some up, but the real ones are popular so just google them yourself). Those terms are nice, but no qualitative descriptor can tell us how the styles of New York City and Columbus differ from each other. We need to measure, compare, and model two teams’ playing styles and efficiencies. If we are able to do these things we may be in a position to answer what style really is.Read More
A few weeks ago, we introduced Expected Possession Goals (xPG) GameFlow, a visualization of the momentum of a soccer match from kickoff to final whistle of each game. xPG GameFlow uses the accumulation of Chance xPG to measure the strength of an opportunity for a team to get a shot. The higher the Chance xPG differential between the teams, the longer the bar for that minute for the team with the higher amount. Quite often goals are scored when the momentum bars on the xPG GameFlow chart are at their longest.
Many people have asked us, “what is the difference between xPG and xG?” or “how does xPG translate to xG or to goals?” To aid xPG GameFlow in answering questions such as “which team had the better chances?” and “when should a team have scored?”, we introduced a couple improvements after the first week of tweeting MLS game charts on @GameFlowxPG. I wanted to provide more context for these improvements and dive deeper into them.Read More
By Matthias Kullowatz (@mattyanselmo)
When we produced the game-by-game expected goals results last week, we were surprised to see that Seattle had outpaced Portland 4.0 to 1.7. That didn't feel right, but it didn't take long before we noticed that Seattle recorded five shots inside the six-yard box leading up to its first goal. Those shots added up to more than 2.0 expected goals, despite the fact that soccer's rules limit scoring to one goal at a time.Read More
You know how you go to some sports websites and you can sort and filter their data, and there are lots of options and it looks cool and stuff? Well starting today, we’re rolling out interactive versions of our stats that also look cool. You can find the link up at the top under "xG Interactive Tables." This first iteration focuses on shot stats and expected goals, and it gives you guys more ways to filter and explore the data.Read More