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In 2011, the author witnessed Barcelona defeat Manchester United 3-1 in the Champions League final. He was moved to tears by the beauty of Barcelona’s football under Pep Guardiola, which he saw as representing the pinnacle of the evolution of the sport.
In 2017, the author saw software that integrated match video footage with detailed data analytics. This software represented a fusion of football’s visual and numerical elements.
When the author wrote a previous book eight years earlier, he had claimed football was becoming “a game of numbers.” But at that time, the data did not correctly correlate with and represent the game.
In 2017, the author realized the data revolution was genuinely happening in football. More data was being collected, and computer scientists had developed better techniques for making sense of it and integrating it with video footage of matches.
The author sees this as the beginning of a new adventure in football analysis, where numbers and human expertise will be combined to gain new insights into the sport. He is excited by the possibilities this opens up.
Stefan Reinartz, a young Bundesliga player, met the author to show him inconsistent player ratings from different media outlets for the exact matches.
Reinartz needed to be more satisfied with these ratings’ subjective and arbitrary nature, which could vary wildly for a player’s performance in the same game.
The author explained how ratings are produced - often quickly under deadline pressure, relying on a few moments or the “wisdom of crowds” in the press box. Personal agendas and lack of expertise also play a role.
Reinartz accepted evaluation was part of professional football but wanted it to be objective and fair. He saw inaccurate media ratings as an injustice.
The author acknowledged the media’s flaws. Reinartz made it his mission to analyze football more objectively and fairly through statistics and data.
The incident highlights issues with subjective opinions in football analysis, and Reinartz’s pioneering use of data to counter this with greater objectivity.
Jörg Schmadtke has had great success as a sporting director at various Bundesliga clubs, overachieving despite not winning major trophies. He excels at scouting and acquiring talented strikers.
Scouting has the problem of cognitive biases, where judgments can be systematically skewed. Schmadtke tried to avoid confirmation bias by not telling his scouts which players he was interested in, so they would watch games with an open mind.
Different scouts have biases towards specific player attributes they favor or overlook. External factors like weather can also influence judgments.
Cognitive biases are unavoidable in scouting. The key is being aware of them and trying to counteract them by getting multiple objective perspectives. Schmadtke widened the focus of his scouts to help avoid biases.
Many cognitive biases can cloud judgment in scouting and evaluating players. Minimizing biases takes effort but is essential to make good decisions. Schmadtke provides an example of a sporting director trying to implement best practices.
Cognitive biases like the clustering illusion and hindsight bias are common in football analysis and perceptions. People see patterns where there may be coincidence, and believe after the fact they knew what would happen.
Narrative bias leads people to latch onto convenient storylines about teams, coaches, and players rather than look at circumstances more objectively. Pep Guardiola is seen as a genius while other coaches who tinker are criticized, mainly because of team resources.
Availability heuristic causes people to focus on memorable singular moments like missed penalties rather than look at the complete statistical picture. This shapes perceptions of players unfairly.
Compared to high-scoring sports like basketball, football’s low scores mean isolated events take on inflated importance in analysis. There needs to be more focus on statistics and patterns.
Cognitive biases affect essential decisions in football, like transfers, tactics, and lineup choices. More objective data analysis is needed as an antidote to snap judgements based on bias.
Overall, psychological factors lead to faulty, narrative-driven analysis in football rather than objective assessment. Being aware of biases is essential to counteract them.
Managerial decisions in football are often made based on cognitive biases rather than careful analysis. Teams frequently fall victim to outcome bias, judging decisions solely on results rather than the intentions and logic behind them.
There is often a disconnect between team performances and match results, as luck plays a significant role in football outcomes. This was evidenced by Jurgen Klopp’s Borussia Dortmund struggling in 2014-15 despite underlying solid statistics.
Based on historical shot location data, expected goals (xG) analysis can quantify the quality of chances created and allowed. This showed Dortmund was unlucky, with far better underlying numbers than their lowly league position suggested.
XG accounts for shot locations and contexts like counters or dead balls. It calculates the probability of each shot becoming a goal based on historical conversion rates. Summing a team’s xG gives a metric of their performance.
For Dortmund in 2014-15, xG showed they should have scored many more goals and conceded fewer. Their expected points total would have placed them fourth, not 17th, highlighting how they were let down by random chance.
Shot location is a significant factor in scoring probability, with central shots much more likely to result in goals. Complex mathematical models can calculate expected goals (xG) based on shot location and other factors.
However, top players like Ronaldo, Messi, and Kane only marginally outperform league averages in finishing ability. Their primary talent is taking lots of shots from high xG areas.
xG models are helpful but need to be more definitive, as they need to account for game situations and timing of chances. xG plots over time illustrate this.
In significant tournaments, xG often aligns with perceptions of which team deserved to win. But randomness and refereeing decisions also play a role, so xG doesn’t reveal a “true” result.
Overall, xG provides valuable insight into matches by quantifying the quality of chances created and finished. But football remains complex and unpredictable.
In football, low-scoring matches are standard, so individual goals have colossal importance. This contrasts with higher-scoring sports like basketball and rugby, where there are often over 100 points per game.
The low scores in football mean performance and results are less aligned than in other sports. This helps explain why footballers are more superstitious, trying to influence outcomes through rituals and lucky charms.
There is an unofficial taboo on blaming bad luck openly in football. Coaches may privately acknowledge luck’s role, but publicly, they focus on performance to avoid excuses.
The common saying “the table doesn’t lie” is wrong - luck plays a big part, so the final league table often misrepresents teams’ trustworthy quality. Refereeing mistakes and uneven goal distributions mean some groups get more points than they deserve.
The 2011-12 Newcastle team is an example - they finished 5th but with a poor goal difference and many narrow wins, suggesting they were lucky. The role of luck is football’s “elephant in the room” - influential but not acknowledged.
Goal difference and points totals can be misleading indicators of a team’s performance. Newcastle United had very different results in back-to-back seasons despite similar underlying stats like expected goals and shots on target.
Small differences in luck can have significant impacts. A few more draws turning into wins or losses can mean a swing of 6+ points—examples of teams like Arsenal and Man City should have won titles based on expected goals.
Statistical outliers tend to regress to the mean over time. Borussia Dortmund is cited as an example - their poor first half of 2014-15 was mostly bad luck, and their performance improved in the second half.
Liverpool analyzed Dortmund’s underlying stats when considering hiring Klopp as manager. The analytics suggested the poor results were due to luck, not systematic issues with Klopp’s management.
Firing managers is often a reactionary move based on poor results influenced by luck. Expected goals provide a better way to evaluate if a manager is genuinely underperforming. An example is a MLS club sticking with their manager despite poor results after seeing the team’s expected goals metrics.
In summary, expected goals provide valuable insight that raw results often lack. They help separate luck from genuine performance issues when evaluating teams and managers.
Matthew Benham is a professional gambler who has built a sophisticated “betting factory” to calculate probabilities and betting odds for football matches.
His company, Smartodds, employs mathematicians, statisticians, and IT specialists to develop mathematical models for predicting match outcomes.
They watch hundreds of matches closely to log goalscoring chances in granular categories like “Oooh!” for big misses. This data feeds into their models.
Benham sees many widely-held beliefs in football, like bogey teams or judging forms based on previous results, as “noise” rather than proper “signal.”
His models focus on identifying meaningful predictive information like injuries, travel distance to away games, etc.
Benham and his syndicate can make money through high-volume betting by being more intelligent than bookmakers in calculating probabilities for more than half of matches.
Benham’s probability-based approach predated and foreshadowed the rise of expected goals and analytics in football.
Matthew Benham owns the Brentford FC football club, which uses data analytics to gain an edge. He founded a company called Smartodds that provides statistical models to gambling syndicates.
Another data analytics company in football is Starlizard, founded by Tony Bloom. Bloom also owns Brighton & Hove Albion FC. Starlizard and Smartodds were based around the same time in the mid-2000s.
Benham and Bloom have become wealthy through their data analytics companies. They have invested money into the football clubs they supported since childhood.
Benham tries to avoid cognitive biases and translate his gambling knowledge into football data models. In a way, he is attempting to adapt the Moneyball concept to football.
Chris Anderson read Moneyball in 2009 and wondered where football’s Billy Beane was. He started analyzing football data as a hobby. He eventually met with Fulham FC and did some analytics projects for them.

In summary, Benham and Anderson represent attempts to bring an analytical, data-driven approach to football inspired by Moneyball. Both stumbled into it via other paths but are now trying to gain an edge for their clubs through advanced statistics and models.

Chris Anderson, an American academic interested in sports analytics, teamed up with a colleague to write a book exploring the use of data in football/soccer. They aimed to gain credibility and contacts to eventually implement a “Moneyball” analytics approach at a football club.
The book, The Numbers Game was successful. But their research found clubs needed to utilize analytics meaningfully.
Anderson realized they’d need to buy a club and implement analytics from the top down. They nearly bought some Championship clubs, but deals fell through.
An American investor was interested in buying a Premier League club with Anderson’s analytics approach. Anderson met with potential investors, but no deal materialized before he left to gain experience as Managing Director of lower league Coventry City.
At Coventry, Anderson was overwhelmed learning the day-to-day realities of running a club versus his analytics dreams. After over a year, problems arose, and Anderson left, seeming to give up on his vision.

Here are the key points from the passage:

Rasmus Ankersen transformed provincial Danish Club FC Midtjylland into a football “laboratory” for mathematical models and algorithms with the help of Matthew Benham.
Ankersen and Benham introduced many innovative and unorthodox ideas at the Club, like using analytics to make decisions and having a neurobiologist on staff.
Captain Kristian Bach Bak was initially skeptical of the new methods but came around as the team succeeded.
During games, the coach would receive texts with analytics and data from Benham’s company in London that provided objective feedback on the team’s performance.
The analytical approach freed the coach from relying on luck or vague impressions of how the team was playing.
Midtjylland were heading towards their first-ever league championship, showing the success of Ankersen and Benham’s modern, analytical approach.
Midtjylland coach Riddersholm embraced the data-driven approach of owner Matthew Benham, believing it brought objectivity and helped him coach better. He evaluated players based on stats like Expected Goals.
Midtjylland punched above their weight, winning league titles despite having a budget half the size of rivals in the capital. Benham’s analysts provided recruitment targets from obscure leagues.
The Club values innovation, as shown by their football academy in Nigeria. This fit with Benham’s philosophy of taking an unconventional approach, like athlete Dick Fosbury’s backward high jump technique.
Benham disliked the Moneyball label, feeling it oversimplified a complex process and ignored the human element. He saw innovation as key, not just data.
The story echoes David vs. Goliath - underdogs can beat richer rivals by being clever and taking an asymmetric approach, not just spending more. This was Benham’s vision for Midtjylland and Brentford.
There is a strong correlation between a club’s wage spending and its success on the pitch over the long term. The more a club spends on wages relative to others, the higher it finishes in the league table.
In the short term, this correlation needs to be clarified. Factors besides wages, such as coaching and luck, play a significant role in a single season.
Transfer spending is a weaker indicator of success than wage spending. Clubs that spend big on transfers don’t necessarily achieve better league positions.
A club’s financial power and transfer strategy depends on its position in the football hierarchy. Superclubs buy star players at their peak, while smaller clubs develop young talent.
Leagues like the Eredivisie have declined and become ‘sellers’ rather than buyers of talent. The financial inequality between clubs distorts competition, though outliers like Leicester show shocks are possible.
To compete with richer rivals, clubs must either increase revenue, find more innovative strategies and efficiencies, or revolutionize their approach to the game. There are many ways to gain an edge besides just spending more.
Thomas Tuchel was an unconventional football manager who lectured the Rulebreaker Society in 2012 about how he broke with traditional approaches as the manager of Mainz.
Tuchel needed to stick to a fixed playing system like most teams at Mainz. Instead, he had his squad tactically mirror the opponent’s formation each match to compensate for inferior players.
Tuchel had his team practice attacking through the middle instead of predictably from the wings like most teams. He also used reserve/youth players to simulate upcoming opponents in training.
Tuchel and his staff created detailed match plans analyzing opponents, going further than most teams. This surprised some who thought all managers had game plans already.
Tuchel’s innovative approaches were enabled by Mainz’s general manager, Christian Heidel, who broke tradition by hiring inexperienced managers like Tuchel and Jürgen Klopp based on potential rather than experience.

Thomas Tuchel and Pep Guardiola brought innovative approaches to football tactics and formations when they became managers. Tuchel had great success at Mainz by constantly changing personnel and shapes to keep the opposition guessing. Meanwhile, Guardiola’s Barcelona team pioneered aggressive pressing and relentless possession play. Guardiola moved Messi to ‘false 9’ with tremendous success, while other players like Busquets took on hybrid roles that defied conventional positions.

Both managers thought deeply about creating numerical superiority and occupying space dynamically. They focused less on rigid formations and more on principles and patterns of play adapted to specific situations. Although sometimes accused of over-intellectualizing the game, their granular understanding of positioning and space opened up new possibilities in football tactics. Small details like exact foot positioning and 40cm differences could be significant. The key was guiding players’ decision-making to exploit weaknesses in the opposition, not choreographing the team’s moves. Their innovations influenced many other top managers and changed football tactics significantly.

Football tactics have historically overlooked set pieces like corners, free-kicks, and throw-ins. However, they account for a significant proportion of goals scored. Some teams, like Stoke City under Tony Pulis, have specialized in exploiting set-pieces. Pulis focused on long throw-ins and other dead-ball situations to help a section with less talent compete. Other notable examples are Midtjylland, Atletico Madrid, and Iceland. The perception is that relying on set pieces is an unsophisticated approach, but top teams like Bayern Munich and Juventus also excel at them. Coaches are dedicating more time to practicing set-pieces, which paid off at the 2018 World Cup, where a record 28% of goals came from dead-ball situations. Set-pieces represent a significant opportunity for teams to gain an edge if they can execute them well.

Video analysis has revolutionized football coaching by allowing managers to study opponents and review matches in detail. In the 1990s, Ottmar Hitzfeld’s Borussia Dortmund struggled to scout upcoming European opponents. Michael Henke solved this by working with a handball video analyst to create scouting videos.
Sports Analytics, founded by Henke and Markus Schulz, secured rights to film Bundesliga games with additional cameras to capture tactics. This allowed coaches to review matches on laptops while traveling. Technology progressed from VCRs to CD-ROMs to online video.
No other invention has influenced football much in the last 25 years. Video enables coaches to learn from others, analyze games, visually instruct players, and scout opponents. Players can see what to expect before matches.
Michael Henke became Bayern Munich’s chief analyst under Jürgen Klinsmann. He and Michael Niemeyer pioneered halftime video analysis by editing and sending clips to the dressing room. This exemplified how video tech gave elite clubs an advantage.
Data and analytics have since taken analysis even further. New mathematical models can find hidden patterns and provide deeper insights. Video laid the foundation for data analysis to transform how the game is studied.
Niemeyer studied sports science and art before finding his passion in football analysis. He believes the managers you work with are more important than your background.
At Bayern Munich, Niemeyer learned from top managers like Van Gaal, Heynckes, Guardiola, and Ancelotti. He was especially inspired by Guardiola’s meticulous video analysis and tactical discussions.
Video analysis has become integral for top managers to study games and develop tactics. It has changed the job of a football manager.
The ubiquity of video footage has made football more tactical. Websites dedicated to tactical analysis have sprung up, showing fans are interested in the tactical side of the game.
There is a question of how data analytics can help represent and understand tactics and events on the pitch.
A meeting of football analysts in Boston discussed concerns that data analytics has yet to transform football as much as anticipated a decade ago. They question if football will ever fully embrace quantitative outsiders.

Here is a summary of the key points about data analytics in soccer/football:

Data analytics in soccer has grown enormously since the first MIT Sloan Sports Analytics Conference in 2006, though soccer analytics remains a niche compared to sports like baseball and basketball.
Early tracking technology like Prozone enabled new metrics and insights, leading pioneering managers like Sam Allardyce to develop data-driven tactics and strategies.
Access to comprehensive game data since 2005 has transformed analysis and perceptions of the game, with measurable impacts like increased passing accuracy.

However, data analytics in soccer faces issues like the non-uniformity of data between providers, inaccuracies in tracking and definitions, and the challenge that metrics sometimes correlate differently with winning.

The stunning 7-1 German win over Brazil in the 2014 World Cup semifinal demonstrated the limits of soccer data analytics, as by the stats, Brazil was superior in nearly every category.
Questions remain about how useful current soccer data analytics are for predicting and determining success on the pitch. More work is likely needed to develop meaningful metrics and interpret the numbers in the proper context.
Hans-Dieter Clemens, a top analyst for the German national team, expressed disillusionment with football data analysis. He felt most data just described what happened in a match rather than providing meaningful insights.
Data on physical performance, like running distance, is helpful for fitness coaching. However, data on tactics and skills needed more benefits.
Despite this, coaches like Jürgen Klöw cited stats like fewer fouls and quicker passing as evidence of the national team’s progress. The view of data’s usefulness has evolved.
Clemens felt valid data should provide non-obvious insights, not just describe past events. Data analysis was set to get more complicated.
Analyst Colin Trainor studied why Borussia Dortmund struggled in 2014-15. He used new metrics like “After Shot Expected Goals” to assess shooting quality.
His analysis found Dortmund’s finishing had worsened, especially after losing Lewandowski. New strikers like Immobile underperformed expected goals.
Trainor concluded data shows players have consistent tendencies in their positioning and awareness. Dortmund failed to replace Lewandowski’s substantial shot volume and quality.
Dortmund assistant coach Peter Krawietz said losing Lewandowski was significant as their game was tailored to him. Replacing him with a different style striker in Immobile could have worked better.
Dortmund’s pressing intensity, measured by a new PPDA metric (passes per defensive action), dropped significantly from the previous season. They allowed opponents to play almost one more pass before pressing.
Multiple factors contributed - less time leading in games, World Cup winners needing to be more motivated, and injuries to vital pressing players like Reus. Lewandowski’s replacement, Immobile, pressed far less.
Dortmund conceded many more goals than expected based on shot quality. Keepers Weidenfeller and Langerak underperformed the desired goals conceded.
The episode shows how new advanced metrics like PPDA can reveal tactical problems not visible from watching games. Appropriately used by coaches, they could lead to tactical adjustments and improved performance.
Pep Guardiola relies more on videos than data and numbers for tactical insights. He is a “visionary” focused on finding spaces and creating numerical superiority.
New metrics like “Expected Assists” (xA) and “Expected Goals Chain” (xGC) provide additional ways to evaluate players beyond just goals and assists. They can quantify the contributions of players in deeper positions.
Lucien Favre is an intriguing manager who has overperformed expected goal models at multiple clubs. His teams consistently scored more goals and conceded fewer than expected. Analysts have been fascinated trying to understand why his teams exceed expectations.
Metrics like xA, xGC, and xGA for goalkeepers provide additional ways to evaluate player performance beyond raw numbers. They account for factors like shot quality and location.
While new metrics shouldn’t be over-interpreted, they can provide additional context and prompt evaluation of playing styles when appropriately reviewed. The aim is to quantify hard-to-measure contributions.
American Michael Caley and Indian Ashwin Raman tried to quantify Lucien Favre’s playing style to understand his success.
Favre’s Gladbach allowed opponents to pass freely but clamped down in the box. They moved the ball slowly into broad areas, then back inside to create chances.
Favre’s team had high possession and passes but few shots at Nice. However, their photos faced high pressure but with few defenders, leading to goals.
Opponents faced the opposite - shots from good positions but with high pressure and defenders, reducing scoring.
Favre forces his team to take good shots and opponents to take bad ones. Advanced stats are working to capture his approach better.
The “Packing” statistic tried to measure players taken out of the game but was mocked as overly complex. Stats must balance detail and meaning to offer real insight.
Ultimately, stats offer glimpses into the deeper layers of the game, but human judgment is still required to interpret and apply them meaningfully.
Bayer Leverkusen players Reinartz and Hegeler sought to develop new metrics to quantify players’ performances better. They focused on quantifying the number of opponents bypassed with passes.
They founded Impact to collect this “Packing” data. It provides new insights into players like Mesut Özil, showing he is excellent at managing passes between the lines and bypassing defenders.
Packing data correlates well with team success. At Euro 2016, teams that bypassed more players generally won more games. The top teams in Bundesliga 2016-17 had the most bypassed players.
Packing quantifies defensive performance too. It measures defenders’ interceptions and how many teammates were out of the game before a turnover. This indicates defensive organization.
The new data changes perspectives on games, shining light on killer passes and risky losses of possession. As managers use this data, it may alter player behaviors and further improve passing rates.
Packing is a new metric that quantifies players’ ability to bypass opponents with passes. It provides new insight into team and player performance.
Other new metrics, like Opta’s sequences, measure passages of play to identify which team dominates possession. This can reveal differences in playing styles.
A fundamental issue is measuring players who do little statistically but contribute through game intelligence and positional play, like Paolo Maldini.
Controlling space is seen as a critical ability at the highest level. The best players like Messi and Ronaldo avoid duels by finding space.
New data and metrics aim to quantify abilities like space control. This can provide insight into defending against such players and teach those skills.
Overall, new data and metrics further analysis and provide new perspectives, quantifying previously hard-to-measure abilities. But challenges remain in holistically capturing all aspects of the game.
The term “Raumkontrolle” (controlling space) was coined by Daniel Memmert, a German sports scientist who studied game data from the Bundesliga.
His research found that winning teams were better at controlling space, especially in critical areas of the pitch. They used passing to bypass opponents and faced fewer opponents on average.
Memmert used Voronoi diagrams to visually represent space control on the pitch by dividing it into regions belonging to each player.
Another study by Daniel Link at TU Munich developed a ” Dangerousity ” metric to quantify attacking threats. It considers factors like player position, control, pressure, and density.
Dangerously measures the probability of scoring at any moment and can chart the ebb and flow of danger over time during a match.
These metrics can help analyze player performance, scout talent, and evaluate tactics. However, coaches must still use their judgment and philosophy to interpret the data.
Advanced analysis like this shows how data and video are being combined to break down the game at a more granular level.

Here are the key points from the passage:

Lars Link developed a new metric called “Dangerous” to quantify risk and danger in football. Barcelona was impressed and started using his algorithm.
Advanced analytics like Dangerousity are becoming more common in football. Soon every action on the pitch may have a mathematical value. This could fundamentally change how the game is analyzed.
Data is being used more in scouting and visual analysis. Computers can search footage for specific situations defined by data parameters. This automation allows analysts to focus on more complex insights from the data.
Like star players, top football scouts like Sven Mislintat are gaining recognition. Mislintat helped build successful squads at Dortmund and Arsenal through shrewd scouting.
Other “super scouts” like Luis Campos at Monaco and Monchi at Sevilla have unearthed talents for considerable profits in player sales. Their transfer policies increased squad value significantly.
Mislintat’s scouting was crucial for Dortmund’s success. He found hidden gems like Shinji Kagawa that increased squad value efficiently. Dortmund had the best transfer policy in Germany between 2012 and 2017 regarding value creation.

In summary, advanced data analytics like Dangerousity transform football analysis, while scouting is elevated by data-driven models and impressive work by top scouts like Mislintat. Data and scouting are crucial strategic areas in modern football.

Mislintat presented a data analysis project called Matchmetrics to Dortmund’s bosses. It was designed to evaluate players by dividing the pitch into 100 squares and assigning values to actions in each area.
Initially, it impressed, but then it picked Lasse Sobiech, a mediocre player, as one of the best defenders in the Bundesliga. This was because it needed to account for Sobiech’s number of defensive actions compared to his team.
Sobiech’s team, Fürth, defended very deep, allowing him to clear many crosses and boost his stats. But Bayern defenders would have far fewer defensive actions so they needed to be evaluated differently.
Without adjusting for team context, the model was flawed. Dortmund’s bosses lost interest in funding it after the Sobiech issue. Mislintat couldn’t find other investors either.
The presentation failed, and they needed to determine how to improve the model to evaluate players across different leagues and levels.
Professional football clubs are constantly pitched innovations and technologies by startups, like training equipment, software, etc., but club officials often need more expertise to evaluate these offerings properly.
Wealthy clubs feel pressure to buy the latest gadgets and hire specialists, even if the value is questionable. Many expensive acquisitions later get forgotten.
Mislintat realized the importance of visually appealing data after his Matchmetrics startup was initially rejected. Once redesigned, clubs became interested.
Matchmetrics uses algorithms and data from providers to rate players. It allows setting filters like age, position, and stats like take-ons and stability.
The software helps identify overlooked talents and avoid costly transfer mistakes. It rated Renato Sanches as poor defensively, which Bayern may have noticed with this data.
Mislintat combined using the software’s data with traditional scouting. Different scouts with various backgrounds assessed prospects earmarked by the data.
The blend of data analysis and human scouting judgment was crucial for Dortmund’s successful recruitment under Mislintat. Data alone shouldn’t drive decisions, but ignoring data entirely is also flawed.
Sven Mislintat, former head scout at Borussia Dortmund, used data and analytics to identify transfer targets and relied on subjective human judgment. An example is Julian Weigl - the data didn’t capture his scanning ability before receiving the ball; only a scout’s observations could detect that.
Arsenal acquired data company StatDNA in 2012 to improve their scouting and avoid expensive transfer mistakes. StatDNA provided very detailed match data coded by workers in Laos.
StatDNA data provided new metrics like ‘Pass Value’ and ‘Defensive Errors’ to quantify players’ contributions. This helped Arsenal identify undervalued talents like Per Mertesacker, who needed to stand out in traditional stats.
Arsenal spent $2 million per year on analytics and was an early adopter of using data in scouting. But Mislintat felt some defenders needed to be more valued by focusing only on data. Human judgement was still crucial in scouting based on his experience.
Arsenal were one of the earliest adopters of data analytics in player recruitment under manager Arsene Wenger. He brought in analysts like StatDNA founder Jaeson Rosenfeld and later Huss Fahmy.
Other Premier League clubs like Liverpool and Manchester City also invested heavily in data analytics. Owners like Fenway Sports Group and City Football Group were driving the push.
Key analytics hires for Arsenal included Raul Sanllehi, Sven Mislintat, and Huss Fahmy. Mislintat, in particular, helped identify talent through data.
Analytics is now widely used in the Premier League for recruitment. Other leagues like MLB also influenced some club owners to push analytics.
Players like Pascal Gross were identified partly through their substantial underlying analytics numbers, even if they weren’t big names. Data is influencing more clubs’ transfer decisions.
There is still a balance between analytics and traditional scouting. However, data is increasingly used to support and guide recruitment and transfers.
Brighton & Hove Albion made smart, inexpensive transfer deals that allowed them to allocate more money to wages, following a data-driven approach by owner Tony Bloom.
Pascal Groß bought for only £2.7m from a relegated Bundesliga club, was an immense bargain and instrumental in Brighton’s first Premier League season.
A ‘football exchange rate’ between leagues allows clubs to find bargains by signing players from slightly weaker companies where they are undervalued.
Data analytics firm Impact developed a Packing model that quantifies a player’s contribution to scoring and defending. It consolidates various performance metrics into one number for each position.
This allows for easy comparison of players and identification of optimal profiles to fit tactical systems. It is a unique data approach that gives scouts, and sporting directors informed metrics to guide transfer decisions.
Pogba and Kanté have complementary skills that make up an ideal central midfield pairing for France and show how data can reveal players’ strengths.
Data averages can be misleading as players’ performances vary. Impect’s software accounts for consistency issues, as shown by the example of Philipp Max.
Impact is expanding its data coverage to more leagues to provide clubs with detailed analytics. Reinartz believes data will enable the simulation of squad options and precisely evaluate players’ strengths/weaknesses.
The concept of ‘plus-minus’ from basketball tracks the net points difference when a player is on the court and can identify their unseen contributions.
Jörg Seidel applied this concept to soccer through his Goalimpact algorithm, calculating players’ net positive effect on their team’s goals scored/conceded.
The model rates players worldwide, accounting for league strength and age factors. It avoids biases of visual assessment and uncovers hidden talents through objective data.

Here are the key points from the passage:

The passage describes Hoffenheim’s finely choreographed counterattack goal against RB Leipzig in the 2016-17 Bundesliga season.
Hoffenheim’s coach, Julian Nagelsmann, had trained his players in specific attacking patterns like the ‘zig-zag-opening.’
The goal exemplified the highly structured and fast-paced play Nagelsmann implemented at Hoffenheim.
At 29, Nagelsmann was considered an innovative young coach who led Hoffenheim to their best-ever Bundesliga finish.
The passage illustrates Nagelsmann’s tactical acumen and how he drilled his team to think and act quickly in orchestrated attacking moves.
It shows how top coaches like Nagelsmann train players to accelerate decision-making and execute play patterns quickly.

In summary, the passage highlights how modern coaching is focused on cognitive training to develop faster and more structured thinking on the pitch.

Julian Nagelsmann, Hoffenheim’s manager, has implemented a structured and detailed training program based on 31 principles. The drills and exercises are designed to teach and ingrain specific tactical concepts through repetition.
Nagelsmann focuses on forcing opponents into mistakes rather than winning the ball through tackling. He wants his team to intercept passes and then quickly counterattack against an unbalanced defense.
Training is tailored each day of the week towards different objectives. Tuesdays focus on tactical education, Wednesdays on preparing for the next opponent, and Thursdays have video analysis and a full XI vs. XI practice match.
Nagelsmann is very hands-on, constantly providing feedback and making adjustments. He uses video screens at training to quickly illustrate points.
The team works on improving players’ speed of thinking and decision making. They use brain training apps and virtual reality technology. The goal is for players to reach a point of intuitive, automatic reactions during matches.
Nagelsmann enjoys creating new drills and continually seeks to improve his methods. His structured, focused approach represents a deliberate way of teaching the game.
The Footbonaut is a high-tech training cage invented by Christian Güttler that tests and improves players’ ball control, situational awareness, and decision-making. Hoffenheim has one and uses it for all their teams.
The Helix tests and trains players’ visual memory and ability to track multiple objects in motion. It was initially developed for video conferences but adapted for football training.
Hoffenheim uses scientific studies and tests like the Vienna testing system to evaluate players’ cognitive abilities and reaction times. Faster reactions correlate with better on-field performance.
Virtual reality and gamification are emerging technologies that Arsenal and other clubs explore to simulate game situations and make training fun and engaging.
Footballers’ personalities and characters are crucial besides physical and cognitive abilities. FC Midtjylland uses a color-coded personality profiling system, and shares results publicly to improve self-awareness and team interactions. The captain found it helped his performance and leadership.
Understanding positive and negative traits of one’s personality type can help players play to their strengths while managing their weaknesses. Diverse personality types are essential for an effective team.
21st Club is a consulting firm that aims to help football clubs succeed through intelligence and strategic decision-making rather than just spending money.
They believe in taking a long-term, analytical approach to things like talent scouting, the age structure of squads, player wages, etc.
Blake Wooster and Rasmus Ankersen founded the company. Wooster has a background in sports science and data analysis.
Omar Chaudhuri, who studied data analysis and previously wrote a blog debunking football myths, is their Head of Football Operations.
They published a strategy guide called “Changing the Conversation” with 80 short chapters on strategic topics for football clubs.
The aim is to shift the football conversation away from short-term thinking and towards more calculated, long-term strategies for success.
They use data and analytics extensively but also emphasize principles of decision-making and strategy rather than just “big data.”
The company believes success can be achieved through intelligence and strategic planning rather than outspending rivals.
Chaudhuri, formerly of Prozone, now works for 21st Club, developing new metrics to evaluate player performance, including his Expected Goals model.
In 2018, he suggested Dutch player Lorenzo Ebecilio to Red Star Belgrade after data showed he was doing very well in Cyprus. Red Star signed him, and he later played in the Champions League.
Chaudhuri tries to answer fundamental questions like quantifying a player’s value to the team. Their model showed Messi/Ronaldo would add 15 points to a relegation candidate.
They found losing star players only costs about 2-4 points over a season, so big money on individuals may not be wise.
21st Club uses contract management software to simulate scenarios and coach evaluations to find tactical fits. They look for strategic advantages like Dinamo Zagreb’s youth development.
Many good ideas are born from the crisis as creativity excels when means are exhausted. Successful strategies often fail to comply when complacency sets in.
Owners are reluctant to fully embrace “Moneyball” and hand clubs to data academics as they fear embarrassment if it fails. New ideas need time to go mainstream.
In 2016, Michael Lewis published The Undoing Project, about psychologists Daniel Kahneman and Amos Tversky. It discussed how poor experts are at making predictions.
Kahneman’s research showed simple formulas can outperform expert predictions. Lewis wrote that experts have a “hunger for certainty” even when it is impossible.
Football players and coaches are used to certainty and avoiding doubt. But as managers and officials, doubt and careful consideration are helpful.
At Midtjylland, Rasmus Ankersen used expected goals to defend, keeping coach Jess Thorup despite poor results. This led to debate and acceptance of desired goals in Denmark.
At Brentford, Ankersen and others used statistical models and strategic recruiting to compete despite limited resources. But they still made mistakes like hiring the wrong manager.
Brentford lost vital staff members to bigger clubs but was promoted internally. Tragedy struck when head of football operations Robert Rowan died suddenly at 28. He was hugely important for player scouting and an enthusiastic presence.
Brentford and FC Midtjylland are small clubs that have tried using data and analytics to gain an edge, with mixed results.
Both clubs have benefited from some lucky transfer deals that could have been more data-driven.
Players like Tim Sparv of Midtjylland question how much Impact the data analytics have had versus other incremental improvements.
However, new technologies like “Ghosting” show the potential of using data to simulate player movements and optimize positioning.
Football may use more real-time simulation and predictive analytics during matches, similar to how Formula 1 teams use simulations and data analysis.
But football has more variables than racing with its machine sensors - it may take more work to get robust predictive models for a match’s chaotic, fluid dynamics.

Overall, data analytics in football holds promise but remains a work in progress, with luck and human intuition still playing a significant role alongside technological innovations. Smaller clubs are leading the way in experimenting with new approaches.

Coaches increasingly use technology like video analysis and data metrics during matches to gain tactical insights and advise on adjustments. In the future, they can run simulations to see the potential Impact of changes.
Clubs set up dedicated analytics departments but keep the research secretive to gain a competitive advantage. Barcelona is a notable exception, openly sharing knowledge through its Innovation Hub.
The data revolution is progressing rapidly at big clubs, while smaller clubs still need help using the insights. There is a growing divide.
In US sports like baseball, analytics is now universal, though the ‘Moneyball’ story is often oversimplified. Success requires more than just data.
Many companies now provide data and analysis services to clubs. The market is competitive and evolving quickly through mergers, acquisitions, and new startups.
The essence of ‘Moneyball’ is using critical thinking and evidence to challenge traditional perspectives and narratives, not just using data. Success requires a combination of factors.
The author is fascinated by the digitalization of football as it provides new ways to understand the game, though narrative ‘stories’ still dominate over more complex explanations. There is a constant quest for further insights.

Other key points in the summary:

Football needs more headstrong characters and outsiders with imagination to bring new ideas, not just those with money.
Data and analytics provide a new, often better story of the game but will not replace human coaches and decision makers.
Players like Toni Kroos have an artistry in their passing and movement that data cannot fully capture.
The scope needed is to use data as a tool while still appreciating the poetry and wonder of the game.
Outsiders often bring the most innovative ideas to football.
Data provides insights, but human creativity, empathy, and artistry are still essential.

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Football Hackers - Christoph Biermann