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Abstract the Efficacy of Counter-Pressing As An ABSTRACT THE EFFICACY OF COUNTER-PRESSING AS AN OFFENSIVE-DEFENSIVE PHILOSOPHY By James Warwick Analytics in soccer have been revolutionized over the last two decades. In particular, models that can measure success using thousands of data points have emerged. Counter-pressing as a philosophy has been linked to success. Despite this, there is disagreement in the analytics community over the best method of measuring the effectiveness of counter-pressing in soccer. This study will examine whether counter- pressing is effective as an offensive-defensive philosophy. We will also explore how video analysis can be used to measure the efficacy of counter-pressing. Video analysis was used to interpret the performance of Miami University’s women’s soccer team during the 2018 Fall MAC season. It was found that teams who pressed efficiently and higher-centrally were more likely to have a successful game outcome. This paper presents how counter-pressing is an effective offensive-defensive philosophy through the successful use of video analysis. THE EFFICACY OF COUNTER-PRESSING AS AN OFFENSIVE-DEFENSIVE PHILOSOPHY Thesis Submitted to the Faculty of Miami University in partial fulfillment of the requirements for the degree of Master of Science by James Warwick Miami University Oxford, Ohio 2019 Advisor: Adam S. Beissel Reader: Brody J. Ruihley Reader: Christopher Henderson ©2019 James Alan Warwick This Thesis titled THE EFFICACY OF COUNTER-PRESSING AS AN OFFENSIVE-DEFENSIVE PHILOSOPHY by James Warwick has been approved for publication by The School of Education, Health and Society and Department of Kinesiology and Health ____________________________________________________ Dr. Adam S. Beissel ______________________________________________________ Dr. Brody J. Ruihley _______________________________________________________ Dr. Christopher Henderson Table of Contents Table of Contents Column1 List of Tables IV List of Figures V Introduction 1 Literature Review 2 Soccer Analytics 2 The Phenomenon of counter-pressing 4 Counter-pressing and success 5 Methodology 6 Results 8 Counter-pressing is an Indicator of Success 8 Discipline is Crucial 9 Counter-pressing across 90 minutes decreases 10 Discussion 12 References 15 Figures 19 Tables 29 Glossary of Terms 36 iii List of Tables Table 1: Total counter-pressing actions of Miami University women’s soccer team and opponents Table 2: Counter-pressing efficiency of Miami University women’s soccer team and opponents Table 3: Presses leading to shots for Miami University women’s soccer team and opponents Table 4: Counter-pressing zones and percentage of presses for Miami University women’s soccer team Table 5: Counter-pressing averages per 15 minutes for Miami University women’s soccer team Table 6: Counter-pressing action averages across final six games for Miami University women’s soccer team Table 7: High-central counter-pressing across entire MAC season for Miami University women’s soccer team iv List of Figures Figure 1: Counter-pressing action symbols Figure 2: Individual/team counter-pressing template Figure 3: Example individual counter-pressing map Figure 4: Example team counter-pressing map Figure 5: High, mid, and low counter-pressing zones Figure 6: Right, central, and left counter-pressing zones Figure 7: 4-2-3-1 formation example Figure 8: Miami University counter-pressing heatmap vs Kent State Figure 9: Miami University counter-pressing heatmap vs Northern Illinois Figure 10: Example of the FWD to CDM gap v Introduction In contemporary society, big data is all around us. This year 2.5 quintillion bytes of data were created every day (Marr, 2018). All aspects of life are quantified to make decisions: predicting the stock markets, healthcare industries, supply chain management, etc. This big data transformation has seemingly reduced human beings, their actions, and wider society to a quantifiable cadre of statistics, metrics, and algorithms. Computers use machine learning to calculate millions of data points. These machines can quantify decisions and simulate all results to systematically determine the correct outcome (Mills, 2019). This analytical revolution is taking over sport as well. Data and analytics are changing the way the sports are contested and administered in a range of ways by: identifying labor market inefficiencies, determining win optimizing in-game strategies, improving the physiological development and health of athletes, and maximizing profitability through ticket sales, sponsorship, and media reach. The sports industry will only increase its use of analytics as technology for data collection improves. An NFL game can often yield over a million data points using modern analytical technology (Ricky, 2019). In Baseball, the popularized term Moneyball describes the analytics revolution and subsequent success of the Oakland A’s who used data to scout and analyze players (Kleinschmidt, 2019). The Houston Rockets implemented ‘Moreyball’, a tactic based on analytics to produce points more efficiently (Lozada, 2016). Individual teams such as Houston Rockets and Oakland A’s are prime examples of how analytics are successful in sport. Analytics are often used to inform practice, determine player selection, regulate player workloads, and create tactics. Indeed, you would be hard pressed to find a single North American sport franchise not using data analytics in at least some aspect of their operations. Despite such transformations, the sports analytics revolution has been slowly adopted in the sport of soccer. Although the sport analytics revolution has come later than in other sports (Kidd, 2018) - some have argued this was due to the low scoring in each game (Brooks et al, 2016) - analytics today in soccer are commonplace, with numerous big data companies such as OPTA providing data feedback to teams live. Analytics have provided success to those who embrace it at all levels of soccer. For example, analysts and their data feedback at Lincoln City FC was accredited for their English FA Cup success in 2016/17 (Brown, 2017). On the other end of the soccer scale, elite level clubs such as Liverpool FC are also heavily investing in analytics. Liverpool FC recently hired astrophysicist Ian Graham as the team’s Director of Research. Through use of analytics, Graham built a model to track the performance of over 100,000 players worldwide to help Liverpool’s scouting process (Schoenfeld, 2019). For Liverpool, this model helped determine their tactics in the famous 18/19 Champions League semi-final against Barcelona. Analytics in soccer have advanced from basic statistics such as passing percentages and shooting efficiency to advanced predictive models such as xG, which tracks the probability of scoring from a certain position on the field based upon a database of previous shots taken (Goff, 2018). Analytic companies and soccer clubs are building large datasets to create models that interpret the performance attributes of players. These models allow professional soccer clubs to quantify performance attributes such as counter-pressing effectiveness. Among the most important aspects of soccer analytics is the emergence counter- pressing. Counter-pressing is an offensive-defensive philosophy which involves players attempting to win possession from the opposing team as high up the field as possible (Whitehouse, 2014). The philosophy emerged through Rinus Michels and the Dutch team Ajax 1 during the 1960’s. In recent years, the counter-pressing philosophy has been used successfully by world-class soccer teams such as Liverpool, Manchester City, and Bayern Munich. Even in the 2018 World Cup, teams that do not counter-press have been decimated by those who can (Morales, 2018). Perhaps the coach most famous for counter-pressing in today’s game is Jurgen Klopp at Liverpool and Borussia Dortmund. In a 2016 interview, Klopp stated that ‘no playmaker in the world can be as good as a good counter-pressing situation’ (Nalton, 2016). The counter-pressing philosophy emerged in response to the extremely successful philosophy of Tiki- Taka, a tactic popularized by Barcelona (Whitehouse, 2014). Tiki-taka is a possession-based philosophy, where the team dominate possession of the ball for the majority of the game. In comparison, counter-pressing is used to combat a possession-based team. The best example of counter-pressing against Tiki-Taka was the 2013 Champions League semi-final where Barcelona faced a strong counter-pressing Bayern Munich team. In the semi-final, Bayern set-up to play reactively and counter-press against Barcelona’s high levels of possession with great success, winning 7-0 across two legs (Wilson, 2014). With the recent Champions League success for Liverpool FC and domestic success for Manchester City FC, it is clear that counter-pressing is revolutionizing the soccer world. Analytics in soccer are often based upon subjective knowledge of an attribute that is then quantified into large datasets. This means that measuring attributes such as counter-pressing efficiency can be difficult. Despite this, counter-pressing efficiency and success has been measured by large data companies and numerous studies. One of the most notable measures of counter-pressing is the PPDA (passes allowed per defensive action) model. Using this model, analysts can measure counter-pressing efficacy by calculating the total amount of opponent passes successfully completed per defensive action, across 3/5 of the field (Trainor, 2014). However, a major flaw of PPDA is that for teams who dominate possession in
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