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. Read More
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. Read More
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. Read More
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. Read More