Offseason Outlook: Minnesota United

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Offseason Outlook: Minnesota United

Minnesota ended the season with 53 points and fourth place in the Western Conference. This represented the team’s best finish in MLS, and first playoff appearance since a 2015 NASL encounter against Ottawa. 

The team’s improvement was largely due to the dramatic improvement of the defense; conceding 28 fewer goals than 2018 (accounting for most of the +31 improvement in goal differential). And it’s worth noting the team evolved over the course of the season. Team captain Francisco Calvo was traded after seven games; midfielder Romario Ibarra was sent on a loan (his request as I understand it). And an influx of new faces over the summer meant the team entering the playoffs looked different than the team that started the season. At the start (first seven games), Minnesota conceded over 2 goals per game and scored over 2.4 goals per game. Since then, Minnesota conceded about 1 goal per game, and scored 1.4 goals per game. In short, the team transitioned from a high-risk, high-reward approach to a defense-first mindset. 

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Reep Revisited

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Reep Revisited

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.

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Going to WAR for Points Above Replacement in Soccer

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Going to WAR for Points Above Replacement in Soccer

Baseball popularized the use of the Wins Above Replacement (WAR) statistic; representing a player’s estimated contribution to a team’s win tally above what a generic replacement would contribute. In this sense, it’s a roster management tool to support a keep/replace decision. However, WAR stats are often used by others for general performance comparisons. But soccer (or football if you like) does not have widespread use of a WAR-like statistic.

In soccer, performance indices are typically confidential and proprietary, making it difficult to verify their validity. Teams and analysts, understandably so, do not want to give away their competitive advantage. And those that are shared publicly, do not usually describe values in terms of team performance, or comparisons to replacements.

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Player Value Recap 2018: Refining a System for Ranking MLS Players

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Player Value Recap 2018: Refining a System for Ranking MLS Players

Creating an all-encompassing player value metric is an ongoing process, with more data adding more insight and texture to its meaning. But the challenges are worthwhile. The ability to compare players from different positions on equal footing, like PER for the NBA or WAR for MLB, allows one to test assumptions for what makes a team successful, how players fit together, and where resources might best be spent. If you haven’t already, read my pieces from last year (here are parts one and two). But this is an update on my progress to creating a metric to describe how game actions affect game outcomes, based on the context of team possessions.

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Building A System for Assessing Player Value, Part 2

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Building A System for Assessing Player Value, Part 2

Last 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.

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Building a System for Assessing Player Value

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Building a System for Assessing Player Value

For 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.

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