Football is a very simple game – we once thought so…
Data is so normalized in modern games that the soccer conversations you overhear on buses, trains, gyms, pubs, etc. are now peppered with baffling statistics. And X (formerly Twitter) is like a tactical candy store, with expert accounts digging deep into the detailed aspects of the beautiful game.
At the World Cup, this number is expected to increase by 100 times as everyone tries to be the smartest soccer fan in their WhatsApp group.
But don’t worry. Mirror Football has your back.
We’ve teamed up with AI platform Machine Football to create a series of England-focused tactical articles and a glossary of buzzwords to help them make an impressive run in Austria’s early morning 0-0 win over Jordan.
What is “machine football” and how does it work?
This is a new AI-driven platform that makes in-depth analysis easy to understand for the average armchair football analyst. This helps give you an edge in prediction leagues, fantasy football teams, betting accumulators, and “in the know” credentials.
Founded by Chelsea and Blackburn Rovers legend Graham Le Saux, Machine Football is essentially a supercomputer that maps 320,000 elite players and over 2,000 clubs across multiple layers of soccer data mapping, providing insight into team styles, player relationships and individual effectiveness on the pitch.
They focus on measuring the quality of action, ranking every shot, dribble, pass and tackle, and taking a close look at the areas of the pitch where players are likely to do their best work.
This is a completely new way to explore beautiful games and predict outcomes. Over the next few days, we’ll be using it to gain a deeper level of insight ahead of England’s bid for World Cup glory this summer.
First, let’s break down some of these difficult terms…
Glossary: “What are you talking about?!”
Tired of hearing about “xG”? Here are the next soccer data terms used for gatecrashing armchair analysis
1. Cohesion
The relationships between the different players, how they work together and the quality of their interactions. Machine Football quantifies what everyone once considered intangibles into easy-to-read scores. This is especially useful in international soccer, where training time is limited and players need to adapt quickly.
Scores reflect how well a team’s players interact with each other, based on how often passes between certain players have a positive outcome. PSG’s treble-winning season saw them reach the 70-point range, the highest among Europe’s elite.
(Image: Machine Football)
Machine Football rates each player’s “cohesiveness” with their teammates on a scale of 100 – Note (Image: Machine Football)
This chart ranks partnerships across England’s XI on a scale of 100 for ‘cohesiveness’, or how well they work together.
Former Rangers and Southampton manager Russell Martin has explained how modern coaches use cohesion metrics to select teams.
“I think cohesiveness is very underrated,” Martin said.
“So you get a score based on relationships, how productive certain people are playing together or next to each other, how many points certain players get when they play together in a team, all that at your disposal.
“I’ve always asked for that. It’s important to understand, because sometimes your eyes and your biases can lie. So, as I think as a head coach, it’s important to have all the data and evidence that can be gathered.”
2. Expected Threats (xT)
This is simpler than you might think. Essentially, it’s a model for evaluating the value of passes, rather than just counting them. Similar to the Machine Football Creativity Index, which is a 100-point index, xT measures how much a player’s actions in any area of the pitch increase the team’s chances of scoring.
3. Creativity
Reflects a player’s ability to create high-value passes, especially those that lead to shots and goals. Cole Palmer and Trent Alexander-Arnold both rank in the top 1%. It is used to measure both individual players and teams.
The bigger it is (Image: Machine Football)
(Image: Machine Football)
These graphics show the best areas of Cole Palmer’s creativity.
(Image: Machine Football)
4. Post shot xG
Post Shot xG, also known as xGOT, measures where the shot actually travels after the ball is struck, providing deeper insight into the performance of strikers and goalkeepers.
While xG only measures when the shot is taken, xG after the shot tells you more about the quality of the shot.
6. Retrieving dangerous balls
Specific measures for recovering the ball that are directly linked to attacking opportunities. Nuno’s Forest improved this from 2.56 to 3.82 per 90 under Cooper, helping to transform their transition play.
7. Expected points per game (xPPG)
Machine Football uses xPPG predictions to map seasonal trends in a team’s expected finishing position and can be used to measure long-term progress or predict future results. This is great for evaluating how your team’s performance at the start of the season will play out by the business end.
8. Passes per defensive action (PPDA)
Measure the pressing force. Reflects the number of passes a defending team allows to an opponent before attempting to recover the ball. A lower PPDA indicates more aggressive pressing.
A higher PPDA indicates a team can sit deeper and spend more time on the ball. The low PPDA of Enrique’s PSG reflected their suffocating press.
9. Evaluating actions using expected probabilities (VAEP)
It is essentially a decision evaluation. This shows the areas of the pitch where players and teams are most at risk. It helps guide you on what actions to take based on the probability of success.
10. Finish score
Measures a striker’s ability to take chances. Gokeres remains in the top 10% of Arsenal’s results despite a decline in his overall performance.
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Glossary: Team Play Style
(Image: Machine Football)
1. Attack Engine A high-intensity style focused on creating frequent clear chances. Teams press aggressively, pushing their bodies forward and trying to win the ball inside the opponent’s half. Full-backs push high and act as playmakers. The trade-off is that it becomes more vulnerable to counterattack. Recent Champions League winners Man City, Real Madrid and PSG all played this way.
2. Possession Builder A controlled but less sharp (calculated) style that focuses on ball possession and maintaining shape. The team builds patiently, prioritizing structure and aiming to limit the opponent’s clear chances. Fullbacks tend to focus on defense.
3. Vertical Transition A direct fast-break style that relies on deeper, quick vertical forward play, often emerging from a compact low-block structure. They are less developed and complete fewer passes than possession builders, but they are more direct in transition. Aston Villa adopted this style during their mid-season comeback.
4. Passive Controller A risk-averse style that prioritizes conservative ball possession and team stability over attacking dynamism. Teams accept that their opponents have the ball and focus on staying compact. They are effective against attacking teams, but have a hard time breaking down deep defenses, especially at home. A current example is Everton, managed by David Moyes.
5. Low Block Counter A defense-oriented style that emphasizes structure, shape, and set pieces. Teams sit deep, absorb pressure and try to take advantage of transitions. Currently playing for Tottenham, managed by Thomas Frank.
6. High Octane Presser Aggressive front foot style characterized by relentless pressing and direct play. Teams aim to win the ball high up the pitch and with pure intensity. He played an active role at Tottenham under manager Postecoglou and at Wrexham in the early days of the National League.
7. Aggressive Disruptor A combative style that focuses on stealing the ball through physical duels and disrupting the opponent’s rhythm. High Flyers Millwall (Championship) and Hearts (Scottish Premier League) are both playing this way this season.
8. Boiler Room relinquish control of ball possession and instead focus on maintaining a deep defensive shape to minimize opponents’ chances.
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