Literary Insights

The author grew up in a middle-class town on Long Island where soccer was not a popular sport. Had he been born in another country, he likely could have played professional soccer given his skills and abilities.
In America, basketball and football players are easy to identify due to height and size requirements. But almost anyone can physically become a professional soccer player, yet the talent development system uses the same filters.
The author’s father, despite no soccer experience, realized there were flaws in the American youth soccer system. He knew the author wouldn’t succeed just by being bigger and faster than other kids.
The author’s father took him to train with Ron Alber, a Dutch soccer trainer, even though the author resisted because he was comfortable being the best player on his teams. The father knew the training would challenge the author in needed ways.
The summary highlights how the American soccer system overlooks talented smaller kids in favor of those who mature faster, and how the author’s unconventional father helped put him on a better development path.
As a kid, the author joined an elite club soccer team that focused on skill development and passing drills rather than just winning games. This was seen as controversial by some parents.
The author believes this decision set him on a better developmental path as a player, aligning him with the style of play favored by coaches like Pep Guardiola rather than the conservative approach of someone like José Mourinho.
The author feels he didn’t reach his full potential in college soccer due to limitations of the system, including restrictive NCAA rules, results-focused coaching, and lack of innovation.
He suggests the American youth and college soccer system overly prioritizes winning over player development, hindering the growth of talented players. The author reflects on the tensions between aesthetics and results that have shaped soccer tactics throughout history.
In the end, while he cherished the bonds formed through college soccer, the author feels the experience didn’t help him improve as a player as much as it could have due to the systemic focus on results over development.
In the early years of the World Cup, the tournament was dominated by teams from South America and Europe. Teams from other regions rarely qualified or advanced past the group stage.
Africa in particular struggled, with only one African team (Tunisia) winning a World Cup match prior to 1982.
Coming into the 1982 World Cup, Germany was seen as an unbeatable powerhouse, having won two titles and reached the semifinals five of the previous seven tournaments. Their efficient, systematic style was contrasted with the beautiful but fragile approach of Brazil.
The Germans were extremely confident heading into their opening match against Algeria, with players and their manager making arrogant comments about defeating the African side easily. This set up a shocking upset when Algeria went on to win the match 2-1 over the heavily favored Germans.
The upset highlighted the flaws in relying too much on reputation and conventional wisdom in soccer, rather than objectively analyzing the strengths of opponents. It marked the start of African teams being taken more seriously on the global stage.
In the 1982 World Cup, West Germany and Austria played for a result that would see both teams advance at the expense of Algeria, who had already exceeded expectations. The match was highly uncompetitive and ended 1-0 to West Germany. This became known as the “Disgrace of Gijón”.
Algeria filed protests against the result but FIFA dismissed them. However, FIFA changed the tournament structure after this to prevent similar collusion in the future.
The anger was misdirected at the German and Austrian teams rather than the tournament structure and incentives of the sport itself.
Luke Bornn worked on using movement data to analyze structural health, first of helicopters at Los Alamos National Lab, then of bridges. He studied climate systems and movement data.
Israeli scientists Gal Oz and Miky Tamir created SportVU, an automated player tracking system using cameras, originally for soccer then acquired by the NBA. This provided new detailed movement data.
Bornn acquired tracking data and brought his movement analysis expertise to sports. He joined the NBA’s Toronto Raptors in an analytics role.
Cameron Bornn was an academic focused on analyzing crop data, but became interested in sports analytics when he got access to NBA player tracking data in 2012. He shifted his research to papers on basketball analytics and eventually left academia for sports industry jobs.
Soccer has proven very difficult for analytics compared to other major sports like baseball, basketball, and football. Those sports have more discrete events like downs, possessions, innings etc. that make analysis easier. Soccer is free-flowing with 22 players, making it hard to measure and model.
Teams are trying to optimize “win probability” through analytics - figuring out which events contribute to winning and creating more of them. This revolution in probabilistic thinking has impacted strategy in sports like basketball and football.
But soccer lacks structures like time outs, downs, innings to enable clear in-game decision making. The continuous play makes it hard to measure and model. There are fewer chances to optimize strategy.
Bornn took a job with AS Roma soccer club to bring analytics to soccer. But with its free-flowing nature, soccer has proven a very difficult sport for analytics compared to baseball, basketball etc. Modeling and measuring player contributions is a major challenge.
Luca Bornn was hired as chief analytics officer by Italian soccer club AS Roma in 2016, despite having little soccer experience. He had limited impact, as the manager Luciano Spalletti was not interested in analytics.
Bornn focused on improving the video analysis given to Spalletti by providing more objective data on opponents’ tendencies over many games, rather than just recent games.
Soccer lags behind other sports in adoption of advanced analytics. Prozone developed player tracking technology in the 1990s, but it has not been widely embraced.
Player tracking provides useful data on physical outputs like distance covered, but soccer analytics remains focused on basic stats rather than strategic decision making.
Bornn left Roma in 2017 and now co-owns French club Toulouse. He also founded a sports analytics company Zelus with the ex-Dodgers analytics director.
The soccer world has been slow to adopt advanced analytics compared to other sports like baseball and basketball. There is significant potential for growth in this area.
Prozone offered visual tracking data of player movements in the 1990s, giving teams insights into physical performance like distance run and sprints. However, it lacked ball tracking, which was key context.
Event data like passes, shots, and tackles became the focus for companies like Opta instead, as it was cheaper and could be collected for all matches. Today, over 5 million event and qualifier datapoints are collected per Premier League season.
However, Cruyff and others note that most important is what happens off the ball - player movements and positioning. The event data doesn’t capture that.
Early tracking data had issues - it wasn’t detailed enough, wasn’t available for all matches, and teams didn’t know how to analyze it. So the cheaper event data became the focus instead.
There is still a need today for more detailed tracking data and advanced analysis to better understand the full context of the game.
Soccer suffers from a lack of useful data. Teams only get tracking data for their own league’s matches, which limits scouting and analysis. Most tracking data just provides trivial facts rather than meaningful insights.
Goals are rare in soccer compared to other sports. This makes it hard to connect events on the pitch to goals scored. Coaches develop subjective theories on scoring based on their own biased observations over years. Different coaches can have wildly divergent tactics based on which biased patterns they perceive.
Soccer managers get outsized credit and blame compared to coaches in other sports. They are treated like philosophers and celebrities. This compounds the problem of subjective biases driving tactics, as managers are reluctant to challenge their own belief systems.
Overall, the lack of data combined with the distorting impact of rare goals means soccer tactics may be dominated by coaches’ observational biases rather than objective evidence. More and better data is needed to bring evidence-based decision making to the sport.
Eduardo Galeano, in his 1995 book Soccer in Sun and Shadow, warned that soccer was becoming too focused on tactics and statistics at the expense of creativity and freedom.
Recent studies have found that soccer managers don’t have as big an impact on team performance as commonly believed. Teams often perform similarly whether they have a long-term manager or an interim manager.
Sam Bornn, a data scientist and part owner of French club Toulouse FC, is skeptical of overvaluing managers and complex tactics. He believes in focusing on what works to win games.
At Toulouse, Bornn implemented some basic analytically-driven strategies like signing younger players and being more aggressive in playing style. This led to Toulouse having one of the best offenses in their league.
Despite analytics, soccer outcomes can come down to randomness, much like baseball playoffs. Bornn recognizes the limitations of data and the unpredictability of soccer results, especially in small samples like playoffs.
The author discusses how he was taught in Catholic school that Jesus did not have any biological brothers or sisters. However, some scholars like Mikael Haxby argue that references to “the brother of Jesus” in early Christian texts suggest Jesus did have a brother named James.
Haxby wrote his dissertation on a Gnostic text called the First Apocalypse of James, an imagined dialogue between Jesus and James before their martyrdoms. The text shows James overcoming his fear of death with Jesus’ help. Haxby argues the text re-centers James as a key early Christian figure.
The author suggests that non-canonical texts like the First Apocalypse can provide fuller understandings of early Christianity, challenging orthodox narratives. We can’t know the full truth but incorporating diverse perspectives paints a richer picture.
This segues to the story of Michael Caley, a baseball analyst who challenged the orthodox narratives in baseball analysis by incorporating new data and perspectives, only to meet resistance from the establishment. Just as with early Christianity, bringing in new voices and information can clarify long-held stories in sports that turn out to be wrong.
In the early 2000s, Michael Lewis’s book Moneyball sparked a debate between baseball traditionalists like Joe Morgan and new statistically-minded analysts like Mike Schur. Schur criticized Morgan’s conventional wisdom through his blog Fire Joe Morgan.
Around 2010, Mikael Haxby, writing under the pseudonym Michael Caley, decided to take a similar data-driven approach to analyzing soccer. He wanted to break down goals into component parts to understand what creates them, like linear weights does for baseball.
Caley started writing for the Tottenham Hotspur fansite Cartilage Free Captain. Tottenham had a reputation for inconsistent “Spursy” performances despite talent, making them an ideal case study.
With little existing soccer stats, Caley collected his own shot and shots-on-target data for teams. He wanted to assess things like how good a defense was based on shots allowed.
Coming from baseball fandom, Caley brought a fresh statistical perspective unlike lifelong soccer fans. He aimed to reveal new insights into soccer strategy and player impact.
Baseball analytics provided a framework for thinking about value production in soccer, but the sports are quite different - baseball has segmented matchups while soccer is continuous and chaotic.
Hockey was the first sport to have a major analytical breakthrough with “Corsi” - a measure of shot attempts vs shots allowed that predicts future performance. This revealed that the best teams/players take the most shots.
Soccer analysts applied a similar “total shots ratio” (TSR) stat and found it strongly predicted performance. But it treated all shots equally rather than accounting for shot quality.
Further analysis by Pugsley found shots on target better predicted match winners than just total shots, raising questions about shot quality vs quantity for top teams.
Caley dug into new Opta data on shot locations but had to self-teach coding and statistics to interpret it. He found evidence that shot location matters for goal likelihood. Teams try to create different shot types with different value.
Gordon Strachan, a former soccer player and manager, was angry after being told by his doctor that he could no longer eat some of his favorite foods.
Strachan did not like being told he was wrong, especially by outsiders. He was set to meet with Paul Power, an early soccer analytics pioneer, who would challenge some of his beliefs.
Power became interested in soccer analytics after reading Moneyball and research on complex systems theory. He worked as a video analyst intern at Sunderland AFC.
Power tried to show basic Opta data on shots and passes to managers Martin O’Neill and Paolo Di Canio, but they were not interested as the data lacked context.
After seeing the limitations of the data at Sunderland, Power started a blog to explore his own ideas on analytics in soccer. His blog gained a following and led to consulting work.
Power was set to meet with Strachan, who did not appreciate analytical challenges to his beliefs, to discuss new approaches to analyzing the game.
Gavin Power, a soccer analytics expert, got his start by analyzing player tracking data and writing his master’s thesis on valuing the space players create with their runs. He impressed the data company Prozone and was hired full-time.
Power had to convince skeptical old-school managers like Scotland’s Gordon Strachan that his analytics were worthwhile. He did so by showing them Roy Hodgson’s rant about shots on target not capturing the full picture, and linking it to the concept of expected goals (xG).
xG quantifies the likelihood of a shot resulting in a goal based on factors like location and defensive pressure. Though not created by any one person, Michael Caley coined the “xG” abbreviation and helped popularize the concept.
xG reflects the randomness of soccer results on a per-game basis. Teams can outperform or underperform their xG in a given match. Over time, xG is a better predictor of future performance than past goals scored.
The concept resonates with experienced soccer minds, even if the terminology is new. xG captures the distinction between match results and underlying performance that managers have talked about for decades.
Expected goals (xG) analysis by Michael Caley showed that Cristiano Ronaldo was not actually in decline despite a slow start to the 2017-18 season. His xG showed he was getting good chances and was due for more goals.
Ronaldo ended up recovering to score 26 league goals that season, vindicating the xG analysis. This demonstrated the flaws in narrative-based analysis about “confidence” and “finishing ability.”
In reality, most top players convert chances at a similar rate. The key skill is getting into good positions to take high quality shots, not “finishing ability.” Even all-time greats like Messi only marginally exceed xG.
There is randomness and luck inherent in converting chances. Going through hot/cold stretches is normal due to the low conversion rates on chances. Players can appear better or worse than they are due to randomness.
xG provides a more objective way to judge quality over narratives prone to bias. It shows that goal scoring slumps are often just bad luck rather than decline in ability. This can prevent teams from making misguided personnel decisions based on short-term results.
In the mid-late 1800s, baseball and soccer were both emerging as popular sports in the US and England respectively.
Baseball was faster-paced and more exciting than cricket, leading to its popularity in the US. Soccer became the national sport in England, though early versions were very brutal and chaotic, focused on kicking and tackling rather than passing.
Many early soccer and baseball teams were affiliated with churches or elite schools and the sports were seen as activities for the upper classes.
However, the working classes soon got involved too as the sports required minimal equipment. This led to class tensions as working class teams would often beat upper class teams.
In both sports, there was a lack of organized structure or agreed-upon rules at first. Baseball had the National Association of Base Ball Players and soccer had the Football Association, but teams operated quite independently.
This resulted in issues like teams arguing over rules before matches, players switching teams frequently for better pay, inefficient scheduling, and uncertainty over championships.
To address these problems, leaders in both sports tried to form governing bodies with standardized rules and organized leagues and schedules. This professionalization led to the formation of MLB and the English Football League.
However, there was resistance to giving too much power to centralized authorities, with tensions over control and money. This resulted in breakaway leagues starting up when disputes arose.
In the late 19th century, baseball in America and soccer in England went through a transition from amateurism to professionalism. This was driven by working-class players who were skilled but couldn’t afford to play for free.
Teams started surreptitiously paying players by giving them jobs, but eventually professional leagues were formally established - baseball’s National League in 1876 and England’s Football League in 1888.
The National League adopted a closed, monopolistic structure with no promotion or relegation. The Football League had promotion/relegation and existed within England’s FA structure.
These contrasting approaches reflect the different cultures at the time. America was embracing ruthless competition while England was more traditional.
The sports economist Stefan Szymanski studied English soccer finances in the 1990s to understand business competition. The simple structure of soccer clubs made them ideal case studies.
Szymanski’s research revealed how soccer clubs succeeded financially, showing the importance of on-field performance and TV money in the modern game.
In 1991, economist Stefan Szymanski published a pioneering paper finding that player wages were the main predictor of team success in soccer. Higher spending led to more success on the field.
This contrasted with the prevailing view at the time that success was random or dependent on tactics/coaching. Szymanski’s paper set him on a path to becoming a leading expert on the economics of soccer.
In 1992, the top English clubs broke away to form the Premier League. This allowed them to negotiate their own lucrative TV deals and commercialize the league.
The Premier League adopted an American-style model focused on revenue generation. But it maintained promotion/relegation which added drama and high stakes.
The financial incentives of the Premier League concentrated talent at the top clubs. The gap between the haves and have-nots grew over time.
However, despite having more resources, top clubs have not necessarily become more ruthless in pursuing competitive edges. The market has not weeded out inefficient clubs as some theories predicted.
Simon Kuper and Stefan Szymanski found a strong correlation between wages and performance in English soccer in the 1990s. This suggested clubs were not innovating beyond simply spending more on players.
Szymanski has recently re-examined the data and found the correlation still stands - 90% of variation in performance is explained by wages. He sees this as evidence that soccer clubs are still not innovating.
The author disagrees - he sees the continuing wage-performance correlation as proof that no one is really innovating in new ways. The only innovation is having more money to spend on players and coaches.
As an example, Manchester United were struggling in the 1980s but revived under manager Alex Ferguson. They focused on smart squad building and youth players rather than big spending. This allowed them to overtake rivals like Liverpool.
However, today the biggest clubs like Manchester United have caught onto these same innovations, while also spending huge amounts. So money remains key - the rich get richer as they copy innovations but also outspend.
The wage-performance link endures because no one is trying new ideas - they just copy and spend. True innovation would break this link, but it hasn’t happened.
Manchester United was one of the most successful English soccer clubs in the 1950s and 1960s under manager Matt Busby. After a plane crash killed several players in 1958, the team recovered to win the European Cup 10 years later.
Despite mediocrity from the 1970s-1990s, Manchester United maintained a huge global fanbase. Alex Ferguson led the team to great success from the 1990s-2013.
In 2005, the American Glazer family bought Manchester United in a leveraged takeover, saddling the club with debt. But the team continued winning for several more years under Ferguson.
After Ferguson retired in 2013, banker Ed Woodward took over football operations. Despite spending a lot of money, the team declined under Woodward’s leadership, unable to replicate past success.
Woodward focused on commercial deals rather than on-field performance. His lack of football knowledge and reluctance to hire a proper director of football led to poor player recruitment and team building.
Billy Beane, the baseball executive of “Moneyball” fame, inspired a new generation of sports executives who relied on data and analytics rather than intuition and tradition. However, this revolution has been slower to take hold in European soccer.
Many top jobs in English soccer are still held by former players who rose up through the system rather than outsiders with new ideas. There is a cultural skepticism and fear of analytics and computers among traditional soccer figures.
The threat of relegation in European soccer encourages risk-aversion, as the financial costs of dropping a division are massive. Teams often cling to conservative traditions like hiring old-school managers rather than innovating.
Structural factors in soccer also make it harder for analytics to be rewarded, as low-scoring games mean luck plays a bigger role. A forward-thinking team could still get unlucky and be relegated.
Some argue soccer culture values beauty and drama over ruthless optimization. Americans may be more inclined to see sports as a puzzle to solve efficiently.

In summary, European soccer has been slow to adopt analytics and outside perspectives due to cultural inertia, financial disincentives for risk, and structural factors - though this may be gradually changing with a new generation.

In the 1980s, soccer was exploding in popularity across the world, with successful teams and players emerging from diverse places like Argentina, Italy, and Africa. There was a sense that any country could compete at the highest level.
However, over the past 30 years, there has been a “re-unleveling” where the richest leagues in Western Europe - England, Germany, Spain, Italy, France - have pulled away from the rest of the world. Now the power center of soccer is concentrated in Western Europe.
In the 1980s, Richard Pollard, a trained mathematician and statistician, was doing sophisticated statistical analysis of soccer in Fiji, showing with data that short passing from defense was ineffective and goals came from getting the ball quickly into the attacking third to shoot more often.
This annoyed Fiji’s national team coach Rudi Gutendorf, a famous German coach who advocated short passing. But Pollard’s ideas were based on objective data analysis, not subjective opinions.
The analysis showed reaching the attacking third and shooting more often led to more goals. This framework focused on moving the ball quickly forward to create shooting chances, rather than excessive passing in defense.
Though ignored at the time, Pollard’s ideas presaged the data-driven analysis that now dominates soccer tactics and strategy. His evidence-based approach was ahead of its time.
Soccer and war have long been connected in England, with the English style of play often likened to wartime strategies. Thorold Charles Reep was an accountant in the Royal Air Force who became obsessed with analyzing soccer statistically after attending lectures on tactics by an Arsenal captain in the 1930s.
During World War II, the Royal Air Force switched from precision bombings of military targets to widespread “area” bombings aimed at destroying civilian morale. Reep later applied a similar philosophy to soccer, believing teams should just get the ball forward quickly rather than build up attacks.
On March 19, 1950 (or possibly March 18), Reep recorded every action during a soccer match to compile statistical data. He concluded that most goals came from possessions of 3 passes or fewer. Based on this, Reep became a zealot for direct “long-ball” play. However, he failed to realize that while most goals came from short possessions, you were more likely to score from longer possessions.
In the 1960s, Reep’s statistical match analysis attracted the interest of a young Richard Pollard, who thought the quantitative approach could work. Reep invited Pollard to visit and discuss his ideas, helping spark Pollard’s later development of quantified analysis in soccer.

Here is a summary of the key points about the trip to meet the man who wore the miner’s hat:

Charles Reep was an accountant and amateur soccer analyst who recorded detailed match statistics and advocated for a direct, vertical style of play.
Graham Pollard, a soccer enthusiast, visited Reep in his home and was impressed by his statistical analysis of the game. They became friends and corresponded for years.
Pollard noticed the soccer style of Watford FC changing under new manager Graham Taylor, becoming more direct like Reep advocated.
Pollard arranged for Reep and Taylor to connect. Taylor implemented Reep’s ideas at Watford with success, rising through the divisions.
Charles Hughes of the English FA also met with Reep and Taylor and was influenced by their direct style philosophy, incorporating it into national team coaching.
However, the direct style was later blamed as England struggled in tournaments under Taylor’s management. The meeting between Reep, Taylor and Hughes marked the peak of their influence.
Charles Reep was an accountant who became obsessed with analyzing soccer matches in the 1950s and 60s. He believed short passing moves were ineffective and that teams should play more direct “long ball” style.
Reep’s ideas gained traction in English soccer, influencing manager Charles Hughes who created coaching manuals for the English FA advocating direct play.
England manager Graham Taylor adopted these tactics for the national team in the 1990s, but they failed to qualify for the 1994 World Cup after losing to Norway. Norway’s manager Egil Olsen had met with Reep and utilized data analysis, but was more flexible in his approach.
Reep’s legacy was tarnished as his rigid philosophy was blamed for holding back the English game. Books like Inverting the Pyramid criticized Reep’s flawed statistical methods.
However, Richard Pollard has defended Reep, arguing his findings were misinterpreted. Pollard claims successful long passing sequences were more likely to produce goals, but short sequences were often building up to longer ones.
The debate continues over Reep’s impact and whether his data analysis was fundamentally flawed. But his desire to bring stats to soccer was pioneering, even if the execution was questionable.

Here is a summary of the key points regarding getting the ball into the opponent’s third of the field:

Liverpool manager Jürgen Klopp employs an aggressive “gegenpressing” system where his attackers try to win the ball back immediately after losing it. This often allows them to regain possession close to the opponent’s goal.
Reep advocated similar tactics, with the goalkeeper distributing the ball quickly into the opponent’s penalty area whenever possible.
Pep Guardiola has adopted this at Manchester City by signing goalkeeper Ederson, who can kick the ball very far downfield accurately. This pins back opponents and allows City to essentially have 11 vs 10 outfield players.
In analyses of matches in Fiji, Pollard tracked statistics like the number of attacks reaching shooting range and originating in shooting range. This provided insight into each team’s ability to advance into dangerous areas.
Reep’s analyses with Benjamin found 50% of goals come from moves starting in the attacking quarter of the pitch. Getting the ball into the opponent’s third frequently was key.

So in summary, Reep, Pollard, and modern coaches like Klopp and Guardiola have focused on quickly getting the ball into the opponent’s third of the field as a key tactical aim. Their analyses and strategies centered on increasing possession in dangerous areas near the goal through long balls, pressing, and goalkeeper distribution.

Charles Reep was an early pioneer in soccer analytics, attempting to measure and analyze aspects of the sport in the 1950s and 60s using basic tallies of events like passes and shots. His work influenced coaches like Graham Taylor to adopt a long-ball style of play.
Reep’s analysis had flaws, overemphasizing long passes and direct play. As analytics has advanced, it is clear Reep did not “have it all figured out” as he claimed. Soccer strategy and tactics remain complex.
Reep faced skepticism and criticism from traditional soccer figures who wanted to preserve the “mystique” of the sport. Despite limitations, his work was groundbreaking in trying to bring data analysis to soccer.
Richard Pollard built on Reep’s efforts, developing more advanced metrics like expected goals in the 1970s-90s. He analyzed games for free or unconventional compensation like art, showing his passion for the field.
The history illustrates the difficulties pioneers in soccer analytics faced in getting acceptance and applying new approaches to a traditional sport. Pollard’s line about coaches being “paranoid” at analytics sums it up.
Science involves creating hypotheses and then rigorously testing them through experiments and data analysis. The scientific method requires controlling for variables and repeating tests to prove or disprove a hypothesis.
Soccer presents challenges for scientific analysis due to the many variables involved in a match. Teams, players, referees, field conditions, weather, and other factors make it difficult to control an experiment.
The Danish club FC Midtjylland was formed in 1999 from a merger of two struggling clubs, Ikast FS and Herning Fremad. The new club was an immediate success.
However, unsustainable economics caught up with Midtjylland and by 2014 they were on the verge of bankruptcy.
Rasmus Ankersen, a former Midtjylland player, became obsessed with talent development. As a youth coach he helped produce top talents.
Ankersen wrote books on talent development, catching the attention of Matthew Benham, a sports betting entrepreneur. Benham bought Midtjylland and brought Ankersen on to apply scientific ideas to improve the club.
Matthew Benham became convinced that the statistical modeling he used for sports betting could be applied to running a soccer club. He bought his boyhood club Brentford in England, but it would be a long journey to reach the top level.
His associate Rasmus Ankersen suggested taking over FC Midtjylland in Denmark instead, as it competed in Europe already, had no historic baggage, and was desperate for new owners.
In 2014, Benham became majority owner of Midtjylland. The club won its first ever league title in 2015 under his analytics-driven approach. They have since established themselves as a top club in Denmark.
Midtjylland focuses on metrics like expected goals rather than the league table to judge performance. They give the manager less power and judge him on improving underlying metrics he can control.
They have used data analysis to identify undervalued players. But they found data alone wasn’t enough - they still need scouting to evaluate players fully.
Their goal is to become a top 50 European club through boosting their player budget via better performance, transfer fees, and commercial revenue. If successful, it will validate their analytics approach.
Midtjylland faced challenges implementing data and analytics due to their small budget compared to top clubs. As a small club, the cost of analytics can be 10% or more of their player budget, whereas top clubs only spend 0.5%.
For small clubs, the return on investment from analytics is unclear, so it’s hard to justify spending a lot when salaries are low.
However, Midtjylland was able to overcome cultural resistance to analytics because they were a new club desperate to avoid bankruptcy. There was more openness to new ideas.
But soccer has more complexity and randomness than other sports, so the models have limitations. Midtjylland tries to focus on what they can control.
The coach Allegri reveals most coaches think tactics and schemes are “bull” - soccer is an art and you just need to put the great players in position to succeed.
However, on set pieces the game becomes more scientific since offenses can plan and defenses must stay back. There is an opportunity there for analytics.
Roberto Martínez coached Wigan Athletic to success in the Premier League using a possession-based style despite having one of the cheapest rosters. This approach eventually failed due to limitations in personnel - the attackers couldn’t convert the possession into goals and the defenders conceded too easily on the counterattack.
Martínez was open to using data analytics to fine-tune his tactics, except for set pieces which he didn’t focus on much like many other managers.
Set pieces present a major opportunity - investing in set piece routines could generate 15-25 extra goals per season, equivalent to spending £80 million on a top striker. But most teams only practice them briefly.
Tony Pulis’ teams like Stoke City and West Brom were very successful at scoring from set pieces despite playing unattractive long-ball soccer. Pulis was brilliant at choreographing set piece routines.
Many thought Pulis’ style and set piece success were inextricable - his big target men weren’t good on the ball but could execute routines, and he supposedly didn’t have time to practice both set pieces and open play.
However, FC Midtjylland disproved this - they focused heavily on set pieces under Matthew Benham’s analytics approach and won the Danish league in 2014-15, scoring 25 set piece goals. When they publically revealed their set piece success, other Danish teams copied them and set piece scoring rose league-wide.
In 2014-15, Danish club Midtjylland scored a league-high 25 goals from set pieces, helping them win the title. Their success showed the value of practicing set pieces rather than just focusing on possession play.
In 2017-18, Midtjylland won again while scoring 25 set piece goals. More teams also scored high set piece goal totals, showing Midtjylland’s success wasn’t a fluke.
This was evidence against the idea that set piece practice hurts possession play, as total goals per game also increased over this period.
Midtjylland brought in specialists to improve set pieces, like a kicking coach and a throw-in coach. The throw-in coach later joined Liverpool and may have helped their title win.
International soccer is less innovative than club soccer, but managers have started using set pieces more given their limited preparation time.
Set pieces provide higher conversion rates than open play, yet many clubs still undervalue practicing them. In-swinging corners to the near post tend to be most effective.
Ted Knutson grew up in De Motte, Indiana, a small, predominantly white Midwestern town with little soccer culture.
He later ended up working in Paris, which has a huge soccer culture and produces a disproportionate number of world-class players for the French national team.
Historically in European soccer, talent and resources have not necessarily concentrated in the biggest cities like they often do in American sports.
But in recent decades, Paris has emerged as the epicenter of French soccer, producing far more top players for the national team than any other city.
Knutson’s improbable journey from soccer backwater to soccer hotbed mirrors the data-driven revolution in the sport, as he went from outsider to influential analyst.
His work helped bring sophisticated analytics to player recruitment and evaluation, challenging traditional scouting methods.
Though initially met with skepticism, Knutson’s analytics have gained increasing acceptance and transformed how some clubs operate.
His career arc shows how soccer analytics has gone from obscure to integral, led by pioneers from unlikely backgrounds who saw untapped potential.
European soccer has no draft system, distributes TV revenue unequally, and only limits spending relative to overall revenue. This might be expected to advantage the richest cities like London and Paris, yet smaller industrial cities like Liverpool, Manchester, and Dortmund have historically been more successful.
These smaller city teams gained identity and popularity during the rise of professional soccer in industrial England and Europe. Many are still benefiting from this early advantage over larger cities.
In the 2000s, mega-rich owners started acquiring teams in big cities to try to buy success, like PSG in Paris. Backed by Qatar’s sovereign wealth fund, PSG shattered transfer records for Neymar and Mbappé but still couldn’t win the Champions League.
In 2018, PSG hired Ted Knutson, an American soccer analyst, to try to gain an edge with data and analytics. Knutson had an unorthodox path, from rural Indiana to academic scholarships but also depression and aimlessness in early adulthood.
Through video games, the 1998 World Cup, and working night shifts, Knutson unexpectedly got into soccer. He built a career in stats and data analysis, eventually becoming a pioneer in soccer analytics. PSG hoped he could help transform their fortune in Europe.
Knutson started playing Magic: The Gathering, a fantasy card game, and managed a website for it. The game helped train skills useful for poker, sports betting, and gaming.
He joined a sports betting syndicate, exploiting bad math by bookies to make money betting on spreads, money lines, and totals in the NFL and NBA.
The 2006 Unlawful Internet Gambling Enforcement Act made it illegal to transfer winnings from offshore sportsbooks, so he took a job at a sportsbook in Curacao.
Asked to set lines for soccer, he initially lost a lot of money to sharps in Asia. He then focused on not losing money, learning the market, and creating new betting options to attract bettors.
Building models and hiring smart people, he was able to better set lines and win bets against competitors who mispriced things.
Though not revealing inner workings of soccer itself, the job gave insight into the outsized impact of individual players on match outcomes.

The blog post contrasts the value of star player Neymar to a team compared to the value of lesser-known player Ron Vlaar. While Neymar is clearly a skilled and valuable player, Vlaar’s importance to Aston Villa was revealed through statistical analysis of betting odds. When Vlaar was out with injury, Villa struggled greatly, showing the outsized impact he had despite not being a big name.

The post then explains how the author, Ted Knutson, transitioned from writing about Magic: The Gathering to soccer analytics. After being diagnosed with cancer in 2012, he had more free time and started seriously applying principles of Moneyball to soccer. On his blog Mixed Nuts, he broke down concepts like why crossing and headers are generally inefficient.

Knutson realized he had a talent for high-level soccer analysis and foresaw the rise of data in the sport. In 2013, he launched the site StatsBomb as a hub for the disparate soccer analytics community, aiming to organize and grow the field. The summary demonstrates Knutson’s pioneering role in soccer analytics, transitioning from card games to upending conventional wisdom in soccer strategy.

Here are the key points from the passage:

Ted Knutson started the soccer analytics website StatsBomb in 2013 as a place to share innovative statistical analysis of the sport. It was an immediate success.
In 2014, Knutson worked with Cesc Fàbregas’ representatives to produce statistics showing his value, helping Fàbregas secure a transfer to Chelsea. This demonstrated the potential for analytics to benefit players.
More players started approaching Knutson for similar statistical analyses to bolster their contract negotiations. Analytics became a tool for players to quantify their value.
In 2015, Knutson was hired by Brentford FC but faced resistance from traditional soccer culture. After 2 years, he returned to StatsBomb full-time, finding it easier to innovate from outside clubs.
StatsBomb has continued publishing analytical articles on soccer, with Knutson’s requirement that the site stays active and archives remain public. This allows the information to benefit the broader soccer community rather than just one club.

In summary, StatsBomb demonstrated the potential for soccer analytics to empower players and provide public insights, despite initial resistance from the traditional soccer establishment. Knutson has pushed the field forward while preserving public access to analytical knowledge.

Knutson turned StatsBomb into a consultancy, while keeping the blog to market StatsBomb’s approach. The consultancy allowed him to reach curious people at clubs worldwide, giving them advice on transfers and coaching hires.
Coaches like Tuchel, Schmidt, and Marsch were interested in Knutson’s work on creating high-probability chances. StatsBomb helped clubs with analysis, though some just wanted Numbers to make their signings look good.
Knutson developed a theory for how to play: focus on set pieces, press high up the pitch, run a lot. This creates chances and keeps action away from your goal.
StatsBomb spent time figuring out how to communicate ideas to clubs. They tailored advice to coaches’ needs and helped them succeed quickly.
The logical end for a consultant is being so successful you are no longer needed as your ideas spread. Knutson knew StatsBomb had to keep evolving.
In 2018 StatsBomb became a data company, aiming to create new expected goals models that captured action in the moment rather than just aggregates. This could better mimic reality on a per-shot basis.
Expected goals (xG) models attempt to quantify the quality of chances in soccer, but they have limitations. A basic xG model may not account for important contextual factors like whether the shooter is facing pressure from defenders.
To compensate, some models use subjective “Big Chance” tags to boost the xG values for high-quality chances. However, this introduces human judgment and reduces the precision of the models.
StatsBomb developed a new xG model that uses computer vision to account for pressure on the shooter, defensive structure, and goalkeeper positioning. This makes the model more predictive without using subjective tags.
The new model also better evaluates goalkeeper performance by comparing their save percentage to an expected save percentage based on shot location and velocity. For example, it showed that record-signing Kepa Arrizabalaga was below average despite mainstream praise.
Incorporating previously overlooked factors like pressure and expected save percentage sheds more light on player performance. The difference between good and average is often invisible to the eye, but quantifiable in the data. Advanced analytics provide new ways to see the game.
Baseball has a long history of data collection and public availability of stats, going back to 1876. Soccer lacks this historical record, with limited data prior to the 2000s from companies like Opta.
The public availability of baseball data allowed amateurs like Bill James to make discoveries and advance analytics. Soccer’s data has been more proprietary and expensive.
Wider data availability opens up analytics to more people from diverse backgrounds. So far this hasn’t happened much in baseball or basketball, but the context is different in soccer.
Publicly available soccer data is expanding through sites like FBref, thanks to deals with companies like StatsBomb. This could lead to new discoveries and make the field more accessible.
Baseball-Reference founder Sean Forman expanded into soccer stats with FBref to tap into global interest in the sport. More public data creates more opportunities for amateurs to contribute to analytics.

Here is a summary of the key points in the passage:

American sports are designed for easy entertainment, with rules that make spectacular athletic feats readily apparent even to casual viewers.
However, each sport also has a “secret code” of subtle indicators that allow true devotees to demonstrate their deeper knowledge and appreciation.
Soccer lacks these obvious displays of athletic excellence. A casual viewer may struggle to appreciate the nuances that make one player or team better than another.
This puts soccer at a disadvantage as a spectator sport in America compared to sports like basketball where elite athleticism is plainly visible.
Soccer analytics and new tracking technology aim to identify and quantify elements of the game that are not easily discerned by the naked eye.
By developing new metrics and visualizations, analysts hope to unlock soccer’s hidden code and make the game as understandable and entertaining to casual American fans as other major sports.
However, these efforts are still in their early stages. Significant challenges remain in capturing the fluid, dynamic essence of the game through data.

In summary, soccer lacks the overt displays of athletic brilliance that make other American sports naturally entertaining. Analytics seek to decode the game’s hidden sophistication to make it more accessible and popular.

Sergio Busquets was a key player for Barcelona and Spain, enabling their success, but didn’t receive the same plaudits as stars like Messi and Xavi. His coaches and teammates praised his intelligence, positioning, and passing ability.
Busquets went from being an unheralded youth prospect to becoming a crucial part of Pep Guardiola’s all-conquering Barcelona team. He replaced Yaya Touré in 2008 and Barcelona’s success took off from there.
Omar Chaudhuri studied economics but wanted to work in sports media. He started a blog challenging poor punditry and using data to analyze football. This led to jobs at data companies like Prozone and 21st Group.
21st Group aims to find edges by researching what factors affect winning, such as playing young players, margin of victory, and more. They look at the wider picture rather than just granular stats.
There are questions over how to quantify Busquets’ impact and the value of different roles like creative midfielders versus holders like him. Statistics struggle to capture the nuance of his positional play.
21st Group is a sports consultancy that uses data analysis to help professional teams improve their decision-making. They started in soccer but have expanded to other sports like golf.
They have worked with major clubs and organizations like the European Ryder Cup golf team. Their analysis has been credited with helping teams make better strategic choices.
The head of 21st Group argues most soccer clubs acknowledge data analysis now but don’t fully empower the analysts. Coaches tend to rely more on subjective opinions rather than data.
He believes soccer clubs could improve their performance by hiring more non-traditional thinkers from diverse backgrounds rather than relying on traditional soccer people.
When evaluating things like player transfers, he advocates for simple analysis like looking at minutes played rather than complex modeling. This captures basic insights like the fact that record signings often become bench players.
He argues soccer should value goal scorers more than other positions, like baseball does with catchers, because scoring is so precise and low-scoring compared to defense and midfield play. But this goes against the tendency to value central midfielders most.
There is a theory that midfielders make the best managers because they have awareness of the whole field. Pep Guardiola, a highly successful manager, was a midfielder.
However, some argue midfielders play too far from goal to be most valuable. Players tend to move backwards on the field as they age or join better clubs.
Stats and data led FC Midtjylland to sign midfielder Tim Sparv. He became captain and helped them win their first Danish title.
Sparv was initially skeptical of stats but became a convert at Midtjylland. He appreciated how they used expected goals data to track team performance objectively.
The story explores the debate around the value of midfielders versus attackers. It suggests stats and data can reveal midfield contributions not captured by goals and assists. Sparv is an example of a midfielder identified by analytics as hugely valuable.
At Danish club Midtjylland, statistics and data were incorporated into messaging for players, but not in an overwhelming way. The data informed guidance given to players, such as advising them not to shoot from certain areas of the field.
Tim Sparv, a Finnish midfielder playing for Midtjylland, noted how stats made him think more about shot selection and passing to teammates in better positions. He cares more about the team’s results than his individual stats.
Stefan Reinartz, a German midfielder, took on the challenge of creating a soccer statistic that correlated with winning after a professor claimed it couldn’t be done.
Reinartz and a teammate measured opponents “packed” or bypassed per action to quantify line-breaking passes. This stat, which they called “packing,” showed a moderate correlation with winning.
When focused just on defenders bypassed, the correlation was even stronger. The team with more defenders bypassed won 79% of matches with a winner.
Having developed this as a current player, Reinartz was uniquely positioned to get people in soccer to listen to his statistical ideas.
Simon Reinartz was a German midfielder who created a new soccer statistic called “packing,” which measures how many opposing players a player takes out of the game with their passes.
He brought the idea to his coach at Bayer Leverkusen, Roger Schmidt, who was interested in statistics. Leverkusen became the first client of Impect, the company Reinartz started to sell the packing data.
Packing data appeals to clubs for its simplicity - complete a pass beyond a defender and you get credit. Impect has expanded packing to other areas like dribbling.
There is a debate about the value of defensive statistics like tackles and interceptions for midfielders. N’Golo Kanté became renowned for his high tackle and interception numbers, which some argue helped Leicester City and Chelsea win titles.
But players like Xabi Alonso argue tackling just shows you were poorly positioned and should not be seen as a skill. Reinartz agrees positioning and stability are more important than tackles for holding midfielders.
However, Reinartz and analytics experts like Michael Caley believe interceptions have value as they remove opponents from the play, which Impect’s data shows boosts a team’s chances of winning.

Here are the key points from the passage:

Karun Singh grew up in India as a cricket fan but eventually became hooked on soccer after watching Arsenal play beautiful passing soccer under Arsene Wenger.
After moving to the US, Singh studied computer science and worked in machine learning and computer vision. In his free time, he started analyzing soccer data.
He created a new type of pass map visualization that highlighted the most dangerous passes between players in a game. This got some attention and led people to ask how he determined which passes were most dangerous.
Singh then spent 5 months developing a model called Expected Threat to quantify the danger of each pass. He announced it in a detailed blog post in early 2019.
The Expected Threat model took off in the soccer analytics community, with Singh getting invited to speak at conferences and getting coverage from analysts like Tom Worville.
The key innovation was finding a way to quantify the danger or value of passes to capture important moments, not just passes that directly lead to shots. This provides more context compared to just tallying up passes or looking at aggregate pass maps.
Karun Singh developed Expected Threat (xT) to quantify the value of different areas of the soccer pitch and reward players for progressing the ball into more dangerous areas.
xT divides the pitch into 150 squares and assigns each one a value based on historical data of possessions leading to goals. Higher xT values are near the goal.
Players get credit for increasing xT through passes, carries, etc. This helps identify threatening actions and undervalued contributions.
However, xT and similar expected possession value (EPV) models struggle to quantify the value of midfielders like Sergio Busquets who facilitate play off the ball.
The models tend to overvalue actions in the penalty area, as getting the ball there is clearly valuable. But actions in midfield have less obvious impact on scoring chances.
Singh developed xT to advance soccer analytics, not for a practical team application. The limitations show the difficulty of quantifying a complex, fluid game like soccer.
The author discusses a 2018 paper by Bornn and Fernández that used GPS tracking data to measure space creation and occupation in soccer. The paper found Lionel Messi was unmatched at occupying valuable space, while Sergio Busquets stood out for his ability to both occupy space and create space for teammates.
However, the author questions whether midfield play really matters that much compared to other positions. He suggests midfielders may be like running backs in football - there is a difference in skill, but success comes more from the surrounding cast and system.
Current models don’t fully capture the defensive contributions of midfielders. Measuring defensive impact remains an elusive goal in sports analytics.
Midfielders also make decisions aimed at eventually winning the game, not just immediately increasing the chance of scoring. Their ability to control tempo and know when to push forward versus sit back is difficult to quantify.
The author concludes the true value of great midfielders like Busquets may lie in this innate feel for the flow of the game, and the ability to make decisions that pay off over the long run of a season, even if not evident in a single game’s stats. Quantifying this remains an unsolved challenge.

Here’s a summary of the key points:

Jesse Marsch was an American soccer player who enjoyed great success early in his MLS career, winning titles with DC United and Chicago Fire. In the late 1990s, a friend asked him if statistical analysis could be applied to soccer. Marsch dismissed the idea, saying the sport was too free-flowing and random.
In contrast, RB Leipzig has used data and analytics to great success. In 2009, Red Bull purchased a small German club and rebranded it as RB Leipzig. Through data-driven player recruitment and coaching, Leipzig rocketed up the leagues. They drew criticism for circumventing Germany’s fan ownership model, but won plaudits for their on-field success.
Leipzig’s rise was led by Ralf Rangnick, hired by Red Bull to oversee football operations. Rangnick implemented a clear tactical identity based on high-pressing and vertical passing. This gave Leipzig a defined style of play and aligned their football operations.
The success of RB Leipzig shows how having an identity, implemented through data and analytics, allows clubs to build sustainable success. Marsch failed to foresee this, while Leipzig have proven it can work in practice.

Here is a summary of the key points about Ralf Rangnick’s influence and Jesse Marsch’s rise:

Rangnick made waves in German soccer by advocating for a flat back four defense rather than the sweeper system popular at the time. This allowed for more efficient use of space.
He also pioneered a system of intense pressing and counterattacking, aimed at winning the ball high up the field and immediately creating scoring chances. This efficient, proactive style became known as “Rangnickian.”
Rangnick implemented these tactics successfully at Hoffenheim and RB Leipzig, helping both small clubs rise rapidly. His style fit Red Bull’s goals of winning with young, energetic players.
The Red Bull clubs follow a clear playing philosophy regardless of the manager. This provides continuity and helps identify transfer targets.
Jesse Marsch realized his leadership abilities and interest in coaching while a student-athlete at Princeton.
After a playing career in MLS, Marsch took coaching courses and became an assistant with the US men’s national team.
He joined the Red Bull organization in 2015, managing New York Red Bulls and RB Salzburg. This prepared him to take over at RB Leipzig in 2021.
Jesse Marsch played MLS soccer and began coaching youth teams and assisting at the college level while still a player. His former coach Bob Bradley brought him on as an assistant for the US Men’s National Team in 2010.
With the USMNT, Marsch traveled Europe scouting players and teams. He saw top teams like Barcelona dominating through possession, but felt the game was shifting more toward transition, counter-pressing, and intensity.
Marsch’s first head coaching job with Montreal Impact in MLS didn’t go well, as he didn’t have the right players for his desired aggressive, transition-focused style.
After traveling and volunteering as a college assistant, Marsch interviewed for the New York Red Bulls job in 2014. He impressed Ralf Rangnick with his ideas on pressing, transitions, and vertical play.
Rangnick mentored Marsch on detailed pressing and counter-pressing tactics. In New York, Marsch implemented Rangnick’s principles and found success.
Marsch later became Rangnick’s assistant at RB Leipzig. When Rangnick stepped in as manager, their pressing style saw great improvement before Marsch took over as head coach.

Here is a summary of the key points about Jesse Marsch’s managerial philosophy and approach:

Marsch’s high-pressing, vertical attacking style was heavily influenced by coaches Bob Bradley and Ralf Rangnick. It fits his aggressive personality.
At Red Bull Salzburg, his teams dominated Austrian leagues but were tested against top European clubs in Champions League. They played exciting, attack-minded soccer and launched careers of players like Haaland.
Marsch embraces analytics and data, using it for training, tactics, analysis more than most coaches. At Leipzig he tweaked tactics to be more effective on transitions after early struggles.
Set pieces are a big emphasis, drawing inspiration from basketball coach friend. His Salzburg team scored 29 more goals than conceded on set pieces.
Overall, Marsch firmly believes in high-risk, aggressive attacking soccer as the optimal style. He has proven it can succeed and produce exciting, fluid play against elite European competition.
A smart soccer club would integrate data and analytics into all personnel and strategic decisions, using a common currency to assess decisions.
It would understand the inherent randomness of soccer results, judging itself on predictive metrics rather than past results. It would make decisions based on those metrics even when they contradict recent results.
For player assessment, it would look at expected goals and assists rather than raw goals and assists, exploiting any discrepancies between surface stats and underlying metrics in the transfer market.
The club would have a defined playing identity to increase its chances of scoring more goals than the opposition. This identity would focus on keeping the ball near the opponent’s goal, quick transitions into the penalty area, and risk-taking.
It would constantly practice set pieces and develop new routines.
Due to the uncertainty in the midfield, it would devote a higher percentage of resources to defenders and attackers rather than midfielders.
The club would use data to maximize the potential of its players, tailoring training and tactics to their strengths.
It would embrace fresh ideas from other sports and countries, being open-minded to new approaches. The emphasis would be on evidence over tradition.
Fulham FC struggled after being purchased by Shad Khan in 2013, nearly getting relegated to the third tier of English soccer.
In 2016, Khan’s son Tony took over as director of football and embraced an analytics-driven approach, leading to promotion to the Premier League in 2018.
However, Fulham’s style of play that worked in the lower Championship division failed in the Premier League, resulting in immediate relegation back down after the 2018-19 season.
Khan admitted his mistake in not adjusting Fulham’s tactics and approach for the Premier League. Back in the Championship in 2019-20, they earned promotion again by playing a more conservative, reactive style.
But in 2020-21, Fulham struggled to score goals in the Premier League and were relegated again after one season, despite improving defensively.
Fulham still lacks a clear tactical identity, having cycled through multiple managers with differing styles. But their underlying process has improved, even if they are still searching for the right balance to succeed in the Premier League.
The story illustrates how nepotism provided Fulham with a data-focused director in Tony Khan, but also the challenges of finding consistency and the right formula when bouncing between divisions.
Liverpool defender Jamie Carragher harshly criticized Fulham’s chaos and drama under American owner Shahid Khan, suggesting Khan should keep quiet and let the club operate properly.
Khan defended Fulham’s “yo-yo club” status, saying it was better than finishing 20th in the Championship when he took over. He claims data backs up his decisions.
Arsenal and Barcelona built success through proactive, aesthetic styles of play and individual skill. But both have declined after hiring brilliant managers (Wenger and Guardiola) and not fully empowering them.
Sarah Rudd went from software developer to winning a soccer analytics competition to leading Arsenal’s in-house analytics team StatDNA. But Arsenal has declined since buying StatDNA in 2012.
Analytics in soccer has two paths - things we still don’t know, and things we know but struggle to apply due to inertia and established practices. Arsenal shows the challenges of integrating analytics.
Molly Rudd worked as an analytics executive at Arsenal FC, applying advanced analytics to better understand and improve soccer strategy and player recruitment.
Rudd found that some long-held assumptions about dividing the soccer field into thirds made sense based on data analysis of where teams change strategy. Other ideas, like the value of set pieces, were underappreciated by coaches until recently.
Rudd believes soccer clubs are still only at a “3 or 4” on a scale of 1-10 in adopting analytics, versus baseball which is at a “10”. Managers have too much power and control to allow analytics to fully transform tactics and team selection.
Arsenal went through coaching changes after Arsene Wenger’s long tenure, which made it challenging for the analytics team to establish a consistent approach. Different coaches have different styles and don’t always have a clear profile of what they want in players.
The analytics team has had input but not full control over Arsenal’s player transfers. Rudd has since left Arsenal and wants to work across different leagues and clubs with varying budgets and ambitions.
Barcelona was dominated by Johan Cruyff’s philosophy focused on beautiful, flowing soccer with players interchanging positions. But other influences have shifted the club away from that pure Cruyff style recently.

FC Barcelona has long had a distinct style and identity rooted in the club’s Catalan ideals. This was solidified under Johan Cruyff’s leadership in the 1990s. The style emphasizes possession, ball movement, and comfort on the ball. Barcelona’s devotion to this local philosophy made the club appealing globally.

Pep Guardiola continued Cruyff’s ideals when managing Barcelona in the 2000s and 2010s, leading the club to unprecedented success. Luis Enrique won the treble in 2015 with a star-studded attack, though his pragmatic tactics were seen by some as a betrayal of Cruyff’s style.

Recent financial mismanagement and debts over €1 billion forced the club to let Lionel Messi leave in 2021, despite him being critical to their modern success. This exemplified the club’s fall from grace.

Javier Fernández joined Barcelona’s analytics department in 2014 for his PhD thesis. He learned their philosophy and worked to quantify aspects like ball progression. He co-authored papers with Luke Bornn applying advanced analytics like expected possession value to soccer. This work demonstrated Barcelona’s distinct style and allowed individual player assessment.

Overall, Barcelona’s identity was shaped by Cruyff and Guardiola’s ideals. Though recent failures show cracks in their philosophy, analytics advances like those by Fernández help maintain their stylistic distinction. Their finances may determine if they can restore their success.

Fulham was an early analytics adopter in soccer under Mohammed Al Fayed in the early 2000s. They used analytics to gain promotions to the Premier League twice, but struggled to stay there.
Arsenal was also an early analytics user under Arsene Wenger. But they failed to fully embrace analytics and faded from their early 2000s peak, losing ground to data-driven teams like Liverpool.
Barcelona embraced possession-focused analytics under Pep Guardiola, dominating with a unique style. But they misinterpreted the lessons, overly focusing on possession at the expense of pressing and chances created. This led to decline as their golden generation declined.
Liverpool hired Moneyball-inspired executives like Damien Comolli. After early struggles, Michael Edwards and Ian Graham built an analytically advanced recruitment program. This helped turn Liverpool into one of Europe’s elite despite limited resources compared to rivals.
The story illustrates how soccer analytics has evolved from its early days. Teams need to avoid missteps like Barcelona and Arsenal in order to fully benefit. Liverpool provides a model of how to successfully incorporate analytics.
After finishing 6th in 2016, Liverpool signed three attackers - Firmino, Mane, and Salah - for less than $150 million combined. At their peak, their estimated combined value was over $580 million. They scored 279 goals and 121 assists for Liverpool through 2022.
Liverpool targeted undervalued players like Firmino and Mane who had underperformed their expected goals (xG) in prior seasons. Salah had great xG and expected assists (xA) numbers but was undervalued after struggling at Chelsea.
Liverpool hired manager Jurgen Klopp from Borussia Dortmund in 2015 after he mutually parted ways there. Despite Dortmund’s poor 2014-15 season, Liverpool saw the underlying expected goal numbers indicated Klopp’s team should have finished 2nd.
Klopp established a strong relationship with Liverpool’s data department, unlike at Dortmund. He trusted their analysis, including recommending Salah over his preferred target.
After the front three excelled, Liverpool sold Coutinho to Barcelona for €135m. They reinvested to address defensive weaknesses, breaking records for van Dijk and Alisson.
Liverpool capitalized on other edges like set pieces and acquiring players from relegated teams. They ignored midfield, focusing creativity through fullbacks.
Success still required luck - Alexander-Arnold blossoming from their youth system, van Dijk and Salah becoming arguably the world’s best at their positions.
Liverpool’s on-field success drove commercial revenue growth, rising club valuations, and enabled further squad investment.
The book explores how data and analytics are transforming soccer. It looks at the rise of expected goals (xG) and other advanced stats, and how they are changing how teams play, scout, and make decisions.
The author traces the history of analytics in soccer, from Charles Reep’s primitive tallying in the 1950s to Opta’s development of detailed event data in the 2000s. Teams like Liverpool now employ large analytics departments.
Analytics have helped teams better understand the value of positions, players, and strategies. For example, set pieces and crosses into the box are found to be efficient ways to score.
However, soccer has been slower than other sports (like baseball) to adopt analytics, due to cultural resistance and the complexity of the game. There is still much unknown.
The book considers how analytics could continue to change soccer tactics and the game itself. While it has become more efficient in some ways, the beauty and unpredictability of the sport remains. The search for better data and models continues.
The book draws on original interviews as well as past reporting and other sources.
It covers the history of soccer analytics over the past 50+ years.
Key figures interviewed include managers, analysts, and executives.
The narrative looks at how analytics have evolved in soccer and their impact.
Sources range from books and articles to podcasts and blogs.
The reporting provides new insights into the use of data in soccer.
The bibliography lists many of the sources consulted for the book.

Here is a summary of the key points from the chapters and sources you provided:

Chapter 6

Allegri is an old-school tactician who doesn’t use data or technology, yet he led Juventus to 6 Serie A titles from 2010-2019. His success demonstrates experience and intuition still have value in soccer.
Clubs like Brentford, Midtjylland, and others have embraced analytics to gain a competitive edge, using data to inform scouting, training, tactics and other decisions. This analytical approach has helped smaller clubs punch above their weight.
Set pieces provide opportunities for goals, but most clubs don’t practice them effectively. Analyzing set piece data can help teams improve their scoring on free kicks, corners etc.

Chapter 7

Many elite athletes and coaches are skeptical of sports analytics, believing gut instinct and experience are more important. But data and analytics have proven value in sports like baseball, basketball, and soccer.
Studying the birthplaces of players reveals trends about which nations develop the most talent. The data shows France produces the most World Cup talent per capita.
Looking at the stats of steals in basketball, assists in soccer, or card values in Magic The Gathering provides new insights about what actions are most valuable in each sport.

Chapter 8

Analytics like packing, expected threat, and quantifying space creation provide new ways to measure effective passing, attacking threat, and tactical dynamics in soccer.
Spatial tracking data allows analysts to study patterns and systems of play in new depth, revolutionizing analysis of tactics and player roles.
Knowing a player’s position isn’t enough; analysis needs to consider their specific role and responsibilities within the team’s system.

Chapter 9

Ralf Rangnick is a pioneer of counter-pressing tactics and soccer analytics who influenced coaches like Klopp. His work at Hoffenheim, Schalke, Leipzig advanced the use of data in the sport.
RB Leipzig rose to success through Red Bull’s investment in analytics, data-driven scouting and signing undervalued players. They disrupted German soccer’s status quo.

Chapter 10

Advanced metrics like expected possession value use machine learning algorithms to quantify the value of different actions on the pitch beyond just goals and assists.
Liverpool has blended analytics, coaching and strategy under Klopp to achieve success. Their analysts collaborate closely with coaching staff.

Here are the key points about haudhuri, Omar:

Omar Haudhuri is a sports analytics expert who has worked for various major clubs and organizations like Arsenal FC, Chelsea FC, Toronto FC, and OptaPro. He helped develop analytical systems at these clubs to guide data-driven decision making.
At Arsenal, he worked on analytics under Arsene Wenger and helped develop models for things like expected goals. At Chelsea, he led efforts to build their analytics department.
He co-founded 21st Club, a football consultancy, with Rasmus Ankersen. The company advises clubs on using data analytics to gain an edge.
He created the Chaudhuri metric for evaluating midfielders. It considers factors like passes into dangerous areas, progressive passes, and ball recoveries.
He writes the blog “5 Added Minutes” focused on statistical analysis in football. He aims to make analytics concepts more accessible to general fans.
Jonathan Clegg co-authored the book “The Club” with Haudhuri’s business partner Rasmus Ankersen. It examined football management ideas like coaching mentality and leadership.
Haudhuri advocates for the use of analytics and data to drive better decision making at football clubs regarding things like transfers, tactics, salaries. He has pushed the field forward.

Here is a summary of key points about the Seattle Sounders:

The Seattle Sounders are an American professional soccer club based in Seattle, Washington. They play in Major League Soccer (MLS).
Founded in 2007, the Sounders were the 15th team to enter MLS. They have quickly become one of the most successful MLS franchises, winning 2 MLS Cups, 4 U.S. Open Cups, and 1 Supporters’ Shield.
The Sounders have set multiple MLS attendance records and have a passionate fanbase. Their home stadium is Lumen Field, which they share with the Seattle Seahawks NFL team.
Notable players for the Sounders include Clint Dempsey, Obafemi Martins, Nicolás Lodeiro, Stefan Frei, and Jordan Morris. Longtime coach Sigi Schmid helped build the Sounders into a powerhouse before his death in 2018.
Current coach Brian Schmetzer took over in 2016 and has led the Sounders to continued success. GM Garth Lagerwey has also been instrumental in player acquisitions and team strategy.
Ownership group includes comedian Drew Carey and NFL Seahawks owner Paul Allen. The Sounders place an emphasis on community engagement and have strong ties to the Pacific Northwest.
Overall, the Seattle Sounders have become one of MLS’s model franchises in a short period of time, finding success on the field and resonating with their passionate supporter culture.

#book-summary

Net Gains - O'Hanlon, Ryan;