Data metrics and development always feel or look innovative. About 40% of the time, they are. It is very satisfying to create something that completely suits your needs, but one question always remains: how necessary is it to do so? My reason for it is that some metrics already exist, and it isn’t always necessary for your analysis to create everything yourself. It’s very time-consuming, and that’s not something we analysts often have.

To me, there is one clear exception: you don’t have the right resources. Often, we can look at event data and create our metrics, indexes and models from there. However, sometimes we want to look at off-ball data and we need tracking data to generate positional data and off-ball runs, for example. I’m quite fortunate to work with clubs that have access to that data, but what if you don’t have that? You want to use the event data to be creative and give an indication of what is close to tracking data.

This might sound familiar to you, reading this or the concept from it. We do love pressing data, but not all providers have that, so we have a metric that measures pressing intensity: Passes Per Defensive Action (PPDA). It calculates the number of passes allowed by a team’s opponent before the pressing team makes a defensive action (like a tackle, interception, or foul) in the opponent’s defensive two-thirds of the pitch. A low PPDA means aggressive pressing, and a lower PPDA means a more passive pressing approach.

In this article, I want to calculate a way to get third-man runs data with event data. After that, I want to evaluate how sound this method is and how reliable the results are.

Contents

1. Why this research?
2. Data collection
3. Methodology
4. Calculation
5. Analysis
6. Checks and evaluation

Why this research

This research is twofold, actually. My main aim is to see if I can find a creative way to capture third man runs with event data. I think it will give us some off-ball data via an out-of-the-box thinking pattern. Then we can use this in terms of off-ball scouting, analysis of players and teams, and of course, we can go even further and make models of that that predict a third-man run.

The second reason is that I feel that I can do better with evaluating the things I make. Check myself, check the validity of the models and the representation of the data. If I build this more into my public work, it will lead to a better understanding of the data engineering process, which is often frustrating and takes a long time to get right.

Data collection

The data collection is almost the same as in all other projects. The data I’m using is even data from Opta/Statsperform, which means it contains the XY-values of every on-ball data as collected by this specific provider.

The data has been collected on Saturday, 12th of April 2025. The data concentrates on one league specifically, and that is the Argentinian League 2025, as I’m branching out to South American football content only on this platform.

What I also will aim to do is to create a few new metrics, which will then be my own metrics. These are my designs based on Opta/Statsperform data and will be available on my GitHub page.

Methodology

Third man runs are one of football’s most elegant attacking patterns — subtle, intelligent, and often decisive. At their core, these movements involve three players: the first (Player A) plays a pass into a teammate (Player B), who quickly connects with a third player (Player C) arriving from a different area. It’s C who truly benefits, receiving the ball in space created by the initial pass-and-move. This movement structure mirrors the concept of triadic closure from network theory, where three connected nodes form a triangle — a configuration known to create stability and flow in complex systems. In football, this triangle is a strategic weapon: it preserves possession while simultaneously generating overloads and disorganizing defensive shapes.

To detect these patterns in event data, we treat each pass as a directed edge in a dynamic graph, with time providing the sequence. The detection algorithm follows a constrained path: A→B followed by B→C, where A, B, and C are distinct teammates. The off-ball movement from A’s pass to C’s reception is measured using Euclidean distance — a direct application of spatial geometry to quantify run intensity. But there’s more at play than just movement. From an information-theoretic perspective, third man runs are low-probability, high-reward decisions. Using Shannon entropy, we can frame each player’s passing options as a probability distribution: the higher the entropy, the more unpredictable and creative the decision. Third man runs often emerge in moments of lower entropy, where players bypass obvious choices in favor of coordinated, rehearsed sequences.

Over time, these passing sequences can be modeled as a Markov chain, where each player’s action (state) depends only on the previous action in the chain. While simple possession patterns often result in high state recurrence (e.g., passing back and forth), third man runs introduce state transitions that break the chain’s memoryless monotony. This injects volatility and forward momentum into the system — qualities typically associated with higher goal probability. By combining network topology, geometric analysis, and probabilistic modeling, we build not just a detector but a lens into one of football’s most intelligent tactical tools. And with a value-scoring mechanism grounded in normalization and vector calculus, we begin to quantify what coaches have always known: the most dangerous player is often the one who never touched the ball until the moment it mattered.

Calculations

Let’s walk through how we identify and evaluate a third-man run using real match data. Imagine Player A, a midfielder, passes the ball from position (37.5, 20.2) on the pitch. Player B receives it and quickly lays it off to Player C, who arrives in space and receives the ball at (71.8, 40.3). By checking the event timestamps, we know this sequence happened over 35 seconds — enough time for Player C to make a significant off-ball movement. The key here is that Player A never passed directly to Player C; the move relies on coordination and timing, a perfect example of a third man run.

We start by calculating the Euclidean distance between A’s pass location and C’s reception point, which comes out to about 39.75 meters. Dividing that by the time difference gives us a run speed of 1.14 meters per second. We also look at how much progress the ball made toward the goal, called the vertical gain, which in this case is around 0.33 (when normalized to a standard 105-meter pitch). Each of these factors — distance, vertical gain, and speed — is normalized and plugged into a weighted scoring formula. For this example, the resulting value score is 0.468, indicating a moderately valuable third man run.

This process helps quantify the kind of off-ball intelligence that often goes unnoticed. Instead of just knowing who passed to whom, we begin to understand how space is created and exploited. With just a few lines of code and some well-defined logic, we turn tactical nuance into measurable insight — bridging the gap between data and the game’s deeper layers.

Analysis

By running the Python code, I get the results of the third-man runs and every player involved. I also calculated a score that gives value to the third man run as to how useful it is. The Excel file that came out of it looks like this:

First, let us have a look at the teams with the most third-man runs during these games we have collected:

As we can conclude from the bar graph above, Argentinos Juniors, Estudiantes and Boca Juniors have the most third-man runs. Having said that, Newell’s Old Boys, Belgrano and Deportivo Riestra have the fewest third man runs.

Of course, these are just volume numbers. Let’s compare them to the average value:

What we see is quite interesting. The majority of the teams have an average value between 0,3 and 0,5 per third-man runs. The more they participate in third man runs, the more the value is even. As you can see, the teams with the fewest have more positive and negative anomalies.

Finally, I want to find the players who are player C, as they are the ones doing the third-man run. I also want to see their value score on average, so we can see how dangerous their runs are.

As we can see, there is a similar pattern with how the value is assigned. More third man runs also means a more similar value to their peers. This is very interesting.

We can see the best players in terms of the value they give with their runs. I have selected only players with at least 5 runs to make it more representative. It’s also important to stress that the league hasn’t been concluded and the data will be different when the season is over.

Now this is a way how we create the metric and analyse it, but how valid is this data actually?

Checks and evaluation

Now I’m going to check whether this is all worth the calculation or that I need to make drastic changes

First, I will look at the feature. The composite score is based on the following: distance (50%), Vertical gain toward the goal (30%) and speed (20%). This seems like a reasonable way to use weights, but I could increase the vertical gain for the values.

Second, looking at the output, I need to ask myself the following questions:

• Are top-scoring actions meaningful? (e.g. fast break runs, line-breaking passes)
• Are short, back-passes scoring low?
• Are there any nonsensical values like 0 distance or negative speed?

Following that, looking at the distribution validation:

The distribution of normalized value scores for third-man runs appears healthy overall, showing a positive skew with most scores clustered between 0.2 and 0.5. This shape aligns with expectations in football, where high-value tactical patterns — like truly effective third-man runs — should be relatively rare. The presence of a single peak at 1.0 suggests a standout moment, potentially representing a high-speed, long-distance run that created significant forward momentum. Importantly, the model avoids overinflating value, as there’s no unnatural cluster near the top end of the scale, which supports its reliability in distinguishing impactful actions from routine ones.

However, the tight cluster around the 0.2–0.3 range may point to a limited scoring spread, potentially reducing how well the metric separates moderately good actions from low-value ones. This could be a result of either low variability in the input features (like speed or vertical gain) or an overemphasis on distance within the weighted formula. If the score distribution stays compressed, it may make ranking or comparative use of the metric less insightful. Adjusting the weighting or introducing non-linear scaling to amplify score separation could help refine the index for better tactical and scouting utility.

If you liked reading this, you can always follow me on X and BlueSky.

Football tactics is what originally swayed me in the direction of football analysis. I’m a sucker for patterns and causal relationship, sos when I was introduced to football tactics, I was completely emerged and sucked into the world of tactical analysis.

A few years later, I ran into a quite common problem: data vs. eyes. I have mostly focused on data lately, and that’s what I work with. In other words, right now, I work as a data engineer at a professional football club, where I don’t really focus on any aspect of video. The problem arises that for tactics, we mostly use our eyes because the data can’t tell us why something happens at a specific time on the pitch.

This got me thinking. Which sport has decision-making and tactical approaches, yet can be analysed with data? My eyes — pun intended — turned towards chess and the openings/defences you got there. In this article, I aim to translate, convert and surpass chess strategies and make them actionable for analysis.

This article will describe a few chess strategies and how we can look into them with data and create playing styles/tactics from them. The methodology will be a vital part of this analysis.

Contents

1. Introduction: Why compare chess and football?
2. Data representation and resources
3. Chess strategies
4. Defining the framework: converting chess into football tactics
5. Methodology
6. Analysis
7. Challenges and difficulties
8. Final thoughts

Introduction: Why compare chess and football?

First of all, I love comparisons with other disciplines. It gives us an idea of where we can improve from other sports and/or influence other sports. Until now, I have focused mostly on basketball and ice hockey, but there are other sports I want to have a look at: rugby, American football and baseball.

Truth be told, do I know a lot about chess? Probably not. I’m a fairly decent chess player, and I know the basic theory of strategies in chess, but you won’t find an ELO rating to be proud of you in my house. But that’s beside the point. The point is that I love chess for its tactics and strategies. It’s so prevalent in our language that we often call close-knit games: a game of chess.

My brain wanted to make a comparison between chess tactics and chess moves that can be seen in the light of certain actions and patterns in football. For that specific little research and to get the tactics, I am going to look at chess moves and relate them to passing in football. This will become slightly clearer in the rest of the article.

Data representation and sources

This remains a blog or website where I love to showcase data and what you can do with it. This article is no different.

The data I’m using for this is data gained from Opta/StatsPerform and focuses on the event data of the English Premier League 2024–2025. The data was collected on March 1st 2025.

We focus on all players and teams in the league, but for our chain events and scores, we focus on players that have played over 450 minutes or 5 games in the regular season. In that way we can make the data and, subsequently the results, more representative to work with.

Chess strategies

There are 11 strategies that I have found in chess that I found were suitable for analysis. Our analysis focuses on passing styles based on the chess strategies.

Caro-Kann Defense (Counterattacking, Defensive)

• Moves: 1. e4 c6 2. d4 d5
• A solid and resilient defense against 1. e4. Black delays piece development in favor of a strong pawn structure. Often leads to closed, positional battles where Black seeks long-term counterplay.

Scotch Game (Attacking)

• Moves: 1. e4 e5 2. Nf3 Nc6 3. d4. White aggressively challenges the center early. Leads to open positions with fast piece development and tactical opportunities. White often aims for a kingside attack or central dominance.

Nimzo-Indian Defense (Counterattacking, Positional)

• Moves: 1. d4 Nf6 2. c4 e6 3. Nc3 Bb4. Black immediately pins White’s knight on c3, controlling the center indirectly. Focuses on long-term strategic play rather than immediate counterattacks. Offers deep positional ideas, such as doubled pawns and bishop pair imbalances.

Sicilian Defense (Counterattacking, Attacking)

• Moves: 1. e4 c5 Black avoids symmetrical pawn structures, leading to sharp, double-edged play. Common aggressive variations: Najdorf, Dragon, Sveshnikov, Scheveningen. White often plays Open Sicilian (2. Nf3 followed by d4) to create attacking chances.

King’s Indian Defense (Counterattacking)

• Moves: 1. d4 Nf6 2. c4 g6 3. Nc3 Bg7. Black allows White to occupy the center with pawns, then strikes back with …e5 or …c5. Leads to sharp middle games where Black attacks on the kingside and White on the queenside.

Ruy-Lopez (Attacking, Positional)

• Moves: 1. e4 e5 2. Nf3 Nc6 3. Bb5. White applies early pressure on Black’s knight, planning long-term positional gains. Leads to rich, strategic play with both attacking and defensive options. Popular variations: Closed Ruy-Lopez, Open Ruy-Lopez, Berlin Defense.

Queen’s Gambit (Attacking, Positional)

• Moves: 1. d4 d5 2. c4. White offers a pawn to gain strong central control and initiative. If Black accepts (Queen’s Gambit Accepted), White gains rapid development. If Black declines (Queen’s Gambit Declined), a long-term strategic battle ensues.

French Defense (Defensive, Counterattacking)

• Moves: 1. e4 e6. Black invites White to control the center but plans to challenge it with …d5. Often leads to closed, slow-paced games where maneuvering is key. White may attack on the kingside, while Black plays for counterplay in the center or queenside.

Alekhine Defense (Counterattacking)

• Moves: 1. e4 Nf6. Black provokes White into overextending in the center, planning a counterattack. Leads to unbalanced positions with both positional and tactical play. It can transpose into hypermodern setups, where Black undermines White’s center.

Grünfeld Defense (Counterattacking, Positional)

• Moves: 1. d4 Nf6 2. c4 g6 3. Nc3 d5. Black allows White to build a strong center, then attacks it with pieces rather than pawns. Leads to open, sharp positions where Black seeks dynamic counterplay.

Pirc Defense (Defensive, Counterattacking)

• Moves: 1. e4 d6 2. d4 Nf6 3. Nc3 g6. Black fianchettos the dark-square bishop, delaying direct confrontation in the center. Leads to flexible, maneuvering play, often followed by a counterattack. White can opt for aggressive setups like the Austrian Attack.

Defining the framework: converting chess into football tactics

A Caro-Kann Defense in chess is built on strong defensive structure and gradual counterplay, mirroring a possession-oriented style in football, where teams maintain the ball, carefully build their attacks, and avoid risky passes. On the other hand, the Scotch Game, an aggressive opening that prioritises rapid piece development and early control, aligns with high-tempo vertical passing. Teams using this style move the ball forward quickly, looking to exploit spaces between the lines and catch opponents off guard.

Some openings in chess focus on inviting pressure to counterattack, a principle widely used in football. The Sicilian Defense allows White to attack first, only for Black to strike back with powerful counterplay. This is akin to teams that play deep and absorb pressure before launching devastating transitions. Similarly, the King’s Indian Defense concedes space early before unleashing an aggressive kingside attack, where the team defended deep and then launched precise, rapid counterattacks.

Certain chess openings focus on compact positional play and indirect control, mirroring football teams that overload key areas of the pitch without necessarily dominating possession. The Nimzo-Indian Defense, for instance, does not immediately fight for central space but instead restricts the opponent’s development, where tight defensive structure and midfield control dictate the game. Likewise, the French Defense prioritizes a solid defensive structure and controlled build-up, where possession is carefully circulated before breaking forward.

Teams that thrive on wide play and overlapping full-backs resemble chess openings that emphasize control of the board’s edges. The Grünfeld Defense, which allows an opponent to take central space before striking from the flanks. In contrast, teams that bait opponents into pressing only to bypass them with quick passes follow the logic of the Alekhine Defense, which provokes aggressive moves from White and counters efficiently.

The flexibility of the Pirc Defense, an opening that adapts to an opponent’s approach before deciding on a course of action, can be likened to teams that switch between possession play and direct football depending on the game situation. The adaptability of this approach makes it unpredictable and difficult to counter.

Methodology

So now we have the strategies from chess that we are going to use for the analysis, and we know how they resemble similar football tactics already. The next step is to look at existing data and see how we can derive the appropriate metrics to design something new in football.

From the event data we need a few things to start the calculation:

• Timestamps
• x, y
• playerId and playerName
• team
• typeId
• outcome
• KeyPass
• endX, endY
• passlength

First, individual match data is processed to extract essential passing attributes such as pass coordinates, pass lengths, and event types. For each pass, metrics like forward progression, lateral movement, and entries into the final third are computed, forming the building blocks of the tactical analysis.

Once these basic measures are derived, seven distinct metrics are calculated from the passing events: progressive passes, risk-taking passes, lateral switches, final third entries, passes made under pressure, high-value key passes, and crosses into the box. Each metric captures a specific aspect of passing behavior, reflecting how aggressively or defensively a team approaches building an attack.

For every tactical archetype — each modeled after a corresponding chess opening — a unique set of weights is assigned to these metrics. The overall strategy score for a team in a given match is then computed by multiplying each metric by its respective weight and summing the results. This weighted sum provides a single numerical value that encapsulates the team’s tendency towards a particular passing style.

Strategy Score=w1​(progressive)+w2​(risk-taking)+w3​(lateral switch)+w4​(final third)+w5​(under pressure)+w6​(high-value key)+w7​(crosses)

You can find the Python code here.

Analysis

Now, with that data we can do a lot of things. We can for example look at percentile ranks and see what the intent is of a team regarding a specific style:

We can see how well Liverpool performs in the strategies coming from chess, and that leads to a shot. This shows us that the French Defence and the Caro-Kann are two strategies wherein Liverpool scores the best in, in relation to the rest of the league.

The next thing we can do is to see how well Liverpool in this case does when we compare two different metrics/strategies.

In this scatterplot we look at two different strategies. The aim is to look at how well Liverpool scores in both metrics, but also to look at the correlation between the two metrics.

Liverpool perform in the high average for metrics, while Nottingham Forest and Manchester City are outliers for these two strategies — meaning that they create many shot above average from these two strategies.

Anothre way of visualising how well teams are creating shots from certain tactical styles adopted from chess, is a beeswarm plot.

In the visual above you can see the z-scores per strategy and where Liverpool is placed in the quantity of getting shots. As you can see they score around the mean or slight above/under the mean with small standard deviations. What’s important here is they don’t seem to be outliers in both the positive or negative way.

Challenges

• Chess moves are clearly categorised, but football actions depend on multiple moving elements and real-time decisions.
• Assigning numerical values to football tactics is complex because the same play can have different outcomes.
• In football, opponents react dynamically, unlike in chess where the opponent’s possible responses are limited.
• A single football tactic can have multiple different outcomes based on execution and opponent adaptation.
• There is no single equivalent to chess move evaluation in football, as every play depends on multiple contextual factors.

Final thoughts

Chess and football might seem worlds apart — one is rigid and turn-based, the other a chaotic dance of movement and reaction. Chess moves have clear evaluations, while football tactics shift with every pass, press, and positioning change. Concepts like forks and gambits exist in spirit but lack the structured predictability that chess offers. And while chess follows a finite game tree, football is a web of endless possibilities, shaped by human intuition and external forces.

Bridging this gap means bringing structure to football’s fluidity. Value-based models like Expected Possession Value (EPV), VAEP, and Expected Threat (xT) can quantify decisions much like a chess engine evaluates moves. Reinforcement learning and tactical decision trees add another layer, helping teams optimize play in real-time. Football will never be as predictable as chess, but with the right models, it can become more strategic, measurable, and refined — a game of decisions, not just moments.