Three Reasons Why Chelsea Won the 16/17 EPL Season

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In the 82nd minute of their 36th game of the EPL season, Michy Batshuayi’s goal secured Chelsea’s fifth English Premier League title. Even though Chelsea won the league with relative ease, it wasn’t all roses from the start. After the first six games, Chelsea had tallied only 10 points. Things came to a head when Chelsea was easily beaten 3-0 by red-hot Arsenal in late September. After that loss, Conte went from a back 4 to a back 3 that served him well at Juventus, and the results improved immediately with Chelsea reeling off 13 straight wins to put them on firm course to win the league.

With the new tools STATS have developed using machine learning, we give three reasons on how Chelsea won the league.

Reason No. 1: Chelsea were incredibly effective in converting chances

Although Chelsea scored the most goals this season, they only ranked fifth in the league in terms of chances created (see Figure 1). To estimate the number of chances created, we use the expected goals (xG) measure, which estimates the likelihood that the average league player will score a goal based on the situation (i.e., ball position, game-context etc. – see [1] for more details). 

Expected-Goals

Figure 1: Plot showing how many goals each team could have expected to score given the situation (Chelsea rank 5th with approximately 60 goals expected).

However, what highlights their offensive effectiveness in this year’s EPL is their xG plus-minus (xGpm), which is +22.4, meaning that Chelsea scored +22 more goals this season than expected[1].  To put this +22.4 measure in context, when we compare Chelsea with other teams this year, we see that they are executing their chances in a much more clinical fashion (see Figure 2). Tottenham are the next-closest team in terms of plus-minus with a +15.4 (although with two games remaining the Spurs were only +7.6 – meaning the last two games where the scored 13 goals somewhat inflated this statistic), followed by Liverpool (+4.8), Bournemouth (+4.6) and Burnley (+3.1). Southamption, on the other hand, were quite the opposite, missing more than 16 goals that the average team would have converted.

Figure2-Chelsea-Season-Review

Figure 2: Ranking the teams on their goals-expected goals in the 16-17 EPL. Chelsea have a +22.4, seven more than Spurs.

From a historical perspective in terms of how this team compares in xGpm across the last six seasons in which we have calculated this statistic, we see that this Chelsea team are ranked third, with only Liverpool and Manchester City in the prolific 13-14 season being more effective (see Table 1).

Table 1: Ranked list of the most effective offensive teams across the last 6 seasons.

Rank Season Team xGpm
1 16-17 Chelsea +22.4
2 13-14 Liverpool +22.0
3 13-14 Manchester City +19.1
4 16-17 Tottenham +15.4
5 13-14 Arsenal +13.4

Needless to say, scoring 22 goals more than expected goes a long way to securing a title. However, as we will see in the next section, their defense played a massive role as well.

Reason No. 2: Defensively, Chelsea did not give up many chances

Similarly to what we did in the previous section, we can use the expected goals measure to analyze how effective a team’s defense is. Although Chelsea ranked fifth in creating chances, they are first defensively (see Figure 3).

Figure3-Chelseas-Season-Review

Figure 3: Expected goals against measure which estimates how many goals a team should have conceded based on game situation. Chelsea gave up the fewest chances.

In terms of goals conceded, it is clear that Tottenham were far superior in terms of defense (26 vs 33). But when we look at the expected save (xS) measure, which estimates the likelihood that a shot will end up as a goal based on the player’s position and shot location, we can see that Hugo Lloris saved more than 10 goals that the “average league goalkeeper” would not have. Chelsea’s goalkeeper performance this season, on the other hand, was -2.  Figure 4 shows how the goalkeepers fared based on goals conceded minus the expected save value.

Figure4-Chelsea-Season-Review

Figure 4: Comparing goalkeeping performance this year based on Saves vs Expected Saves

Reason No. 3: Chelsea went to a back 3 to provide more defensive stability

In the previous two sections, we showed quantitatively how Chelsea fared both offensively and defensively in terms of goal-scoring chances. But as noted earlier, after six games and a poor run of form, Antonio Conte changed from a back 4 to a back 3 – a move that’s been hailed as a key decision in turning things around. In this section, we show how the change in formation changed their style of play.

To do this analysis, we compared the performances of Chelsea for the first six games (until the Arsenal vs Chelsea match on Sept. 24) to the performances after. A summary of some key performance metrics are shown in Table 2. From this table, it can be seen that although Chelsea averaged more shots with a back 4 (16.8 vs 14.1 per game), they actually averaged more goals with a back 3 (2.2 vs 1.7). Defensively, they conceded the same amount of shots, but with a back 3 they conceded far fewer goals per match (0.7 vs 1.5). In terms of possession, with a back 3 they actually gave up around 4% possession per game, which indicates a change in playing style.

Table 2: Comparing offensive and defensive metrics when Chelsea had a Back-4 and Back-3.

Measure Offensive Defensive
  Back 4 Back 3 Back 4 Back 3
Shots per game 16.8 14.1 8.5 8.6
Goals per game 1.7 2.2 1.5 0.7
xG per game 1.6 1.5 0.9 0.7
Possession per game 57.3% 53.3% 42.7% 46.7%

Using a new metric developed at STATS, we can break up all continuous play possession into a series of “style” states, which automatically assigns a portion of a game into one of these distinct game phases. These style names are quite self-descriptive (i.e., direct-play, counter-attack, maintenance, build-up, sustained-threat, fast-tempo, crossing, high-press – but for more details see [2]).

In Figure 5, we compare Chelsea’s playing style between when they played with a back 3 and a back 4. From viewing this plot, it can be seen that when Chelsea played with a back 3 they used a lot more direct-play and their use of maintenance, build-up and sustained threat reduced. With a back 3, they also utilized less crosses. In terms of goal-scoring efficiency, this makes sense as it has been shown previously that the most effective way of scoring is via direct-play [3].

Figure5-Chelsea-Season-Review

Figure 5: Chart comparing Chelsea’s playing style with a back-3 (blue) compared to back-4.

In Figure 6, we show the defensive playing style of Chelsea (i.e., how opposition teams tend to attack when they have possession of the ball).  What is interesting to note is that we have the opposite occurring, with Chelsea having less direct-play and more maintenance and crossing against them. As having a back 3 is thought to give a team more “defensive stability,” it also correlates with Chelsea conceding fewer good chances.

Figure6-Chelsea-Season-Review

Figure 6: The defensive playing style of Chelsea (i.e,. when teams have the possession of the ball against Chelsea).

Summary:

Using new analysis tools developed at STATS, we have been able to objectively measure Chelsea’s title run using expected goals, expected saves and playing styles measures.

[1] In our analysis, we have classified own goals down to luck, so in determining the “expected goals plus-minus” (xGpm) we exclude own goals from the goals values (i.e., xGpm = (Goals – Own goals) – xG.

“Conte vs Mourinho:” Comparing the Chelsea Playing Styles of Champions

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Much has been made of the impact and difference of Chelsea’s playing style since the arrival of Antonio Conte. Seeing that most of the squad that won the title in 2014-15 is on the current 2016-17 squad, this leads us to the question: “what is the difference between the two?” Specifically, how is the style between the two teams different?

With the new tools STATS has developed using machine learning, we can see where the teams are similar and where they are different. In this article, we run through a playing style “checklist,” which enables us to have a better understanding of how they have achieved success in these respective seasons. Overall, it will give us a sense whether there is a distinct change in playing style under Conte, whether they are the same – or somewhere in between.

Comparison No. 1: Creating and Executing Chances

The first thing to compare is how many chances each team created, and how effective they were in executing those chances (see Table 1 for summary). In the 14-15 season, Chelsea created around 15.1 shots per game, which is slightly above what they are achieving this year (14.6) but not significant.

What is staggering though, is how effective they have been in converting chances this year. The expected goals (xG) measure is a tool we can use which estimates the likelihood that the average league player will score a goal based on the situation (i.e., ball position, game-context etc. – see [1] for more details).  This year, across 35 games, Chelsea have scored 75 goals but have an expected goal value of 54. Given that one of these goals were from two own-goals, their xG plus-minus (xGpm) is +19, meaning that Chelsea have scored +19 more goals this season than expected[1]. This compares to the 14-15 season, when Chelsea had a xGpm of +8.5 and scored 73 goals but were expected to score 63.5 (one of those was an own goal as well).

Table 1: Comparing the chance creating and execution between the 14-15 and 16-17 Chelsea squads.

Season Games Shots/(shots per game) Goals/ (goals per game) Own goals Avg chance xG xGpm
14-15 38 573 (15.1) 73 (1.9) 1 11.1% 63.5 (1.7) 8.5
16-17 35 510 (14.6) 75 (2.1) 2 10.5% 54.0 (1.5) 19.0

In terms of explaining why Chelsea are more effective this season compared to the 14-15 version, let’s look at the individual contribution of the top scorers for the respective seasons (see Table 2). In the 14-15 season, Diego Costa was the leading goal scorer, netting 20 goals from an expected value of 14.8 – meaning he was +5.2 better than the average striker that year. The next-leading scorer was Edin Hazard, who scored 14 goals, with a plus-minus of +1.2.

Fast forward to this season, where Costa has been the leading scorer with 20 goals so far. However, his plus-minus has only been +2.4. Hazard has also been a leading light, scoring 15 but with a plus-minus of +4.5. Probably the key difference between this year and other years, has been the contribution of the other players – not in terms of the number of goals they have scored but by how efficient they have been. Pedro and Willian have been excellent – and in terms of their effectiveness in front of goal, they have been quite clinical with plus-minuses of +2.6 and +4.6, respectively. Even Marcos Alonso has been effective from his customary left-wing-back position, chiming in with six goals, with a plus-minus of +2.5.

A thought of Jose Mourinho teams in the past has been their reliance on individual brilliance instead of focusing on offensive team play. This table suggests that Conte has been able to extract more from other key offensive players – not just Costa and Hazard. This begs the question: Do Chelsea create chances in a different manner from Mourniho’s 14-15 team, or are they just better finishing the chances?

Table 2: Table comparing goals-scorers of the 14-15 season and the 16-17.

2014-2015   2016-2017
Player Goals xG G-xG Player Goals xG G-xG
COSTA 20 14.8 5.2 COSTA 20 17.6 2.4
HAZARD 14 12.8 1.2 HAZARD 15 10.5 4.5
REMY 7 3.7 3.3 PEDRO 8 5.4 2.6
OSCAR 6 5.5 0.5 WILLIAN 7 2.4 4.6
TERRY 5 4.1 0.9 ALONSO 6 3.5 2.5
IVANOVIC 4 4.1 -0.1 CAHILL 6 2.8 3.2
DROGBA 4 2.4 1.6 FABREGAS 4 2.4 1.6
IVANOVIC 4 4.1 -0.1 MOSES 3 2.9 0.1

Comparison No. 2: Is there any difference in how they created chances?

Even though the 14-15 and 16-17 teams created approximately the same amount of chances per match, there could be a difference in how they created the chances. Recently, we have created a dictionary of scoring methods which are described below: Build-Up/Normal, Counter-Attack, Direct-Play, Corner Kick, Free-Kick, From-Free-Kick, Cross, From Cross, Throw-In and Penalties. In Figure 3, we show how many chances were created for each type of shot.

Jose-vs-Mourinho-Figure1

Figure 1: The creation of chances between the 14-15 and 16-17 seasons.

In Figure 3, we can see a number of things:

  • In the 14-15 season, 50.1% of the chances created were in the build-up style, compared to this year, which is 37.7%.
  • This season, Chelsea are creating more chances from direct-play, free-kicks and crosses.
  • There is no difference in terms of chances created for counter-attacks, corner-kicks and penalties.

As Chelsea are +19 in terms of expected goals plus-minus, does this change where the goals are coming from? In Figure 4, we show the comparisons of how they’re scoring. The key points are that Chelsea are getting fewer goals from build-up and corner kicks and more from the counter-attack and direct-play in the 16-17 season.

Jose-vs-Mourinho-Figure2

Figure 2: Where are the goals coming from? Comparison of goals between the 14-15 and 16-17 squads.

Comparison No. 3: Conceding Chances

Now that we have compared the offensive performance between the 14-15 and 16-17 squads, we can do the same on the defensive side. Table 3 gives a summary of both teams. The first thing to notice is that the current squad gives up far fewer shots per game (8.6 vs 11.2). When we look at the expected goal plus-minus, we can see that Chelsea were -4.9, meaning that they should have conceded nearly five more goals then they have (see next comparison on goalkeeping to see a reason why this was the case). This season, their plus-minus is 1, meaning that they have conceded one more goal than what we would expect them to have.

Table 3: Comparing how opposition teams created and executed chances between the 14-15 and 16-17 squads.

Season Games Shots/(shots per game) Goals/ (goals per game) Own goals Avg xG xG xGpm
14-15 38 424 (11.2) 32 (0.8) 1 8.5% 35.9 -4.9
16-17 35 301 (8.6) 29 (0.8) 1 9.1% 27.0 1.0

A strong cue into describing the defensive discrepancy between the two seasons relates to goalkeeping. Using the expected save (xS) value, which estimates the likelihood that a goalkeeper should have saved a shot based on the game situation (i.e., player position, ball position and game-phase). In Table 4, we can see that Thibaut Courtois was more effective in the 14-15 season with a expected-save plus minus of +4.3 compared to this year’s -0.5. This means that he saved four goals more than the league-average keeper would have saved in the 14-15 season, compared to -0.5 goals this year – just below the league average.

Table 4: Comparing goalkeeping performance between the 14-15 and 16-17 squads.

Season Games Saves xSaves Saves-xSaves
14-15 38 91 (2.4) 86.7 4.3
16-17 35 72 (2.0) 72.5 -0.5

Comparison No. 4: Playing Styles

Using a new metric, which we have developed at STATS, we can break up all continuous play possession into a series of “style” states, which automatically assigns a portion of a game into one of these distinct game phases. These style names are quite self-descriptive (i.e., direct-play, counter-attack, maintenance, build-up, sustained-threat, fast-tempo, crossing, high-press – but for more details see [2]).

In Figure 3, we compare Chelsea’s playing style. Generally, they have a similar possession percentage, and a similar amount of direct play. But there is a substantial relative increase in counter attack, from 14.2% to 24.5% compared to the league average. To give this increase some context, Leicester last season showed a +26.2% in counter attack, just slightly above Chelsea this year (and Leicester were considered very counterattack-heavy). Interestingly, Leicester paired that with an above average direct play, whereas Chelsea are well below average in this respect.

In terms of the possession-based styles (i.e., maintenance, build up and sustained threat), while they have a similar amount of possession, they seem to be less offensive when they have long possessions than they were in 14-15. In terms of the other style categories, there was a big increase in fast-tempo this season, which means they had more possessions where they circulate the ball quickly in the opposition’s half. In terms of crossing, this season they were on par with the league average in terms of crossing, whereas before they were quite a bit below the league average (+0.6% this season, -24.1% in 14-15). In terms of high pressing, they do less than in 14-15.

Jose-vs-Mourinho-Figure3

Figure 3: Chart comparing Chelsea’s playing style in 14-15 vs 16-17.

Summary: Chelsea Composite Squad: (Formation 3-4-3)

Based on our analysis and advanced metrics, we have put together a composite starting 11. As the 3-4-3 this year has been more effective defensively, we have used this formation.

Goalkeeper: Thibaut Courtois (14-15)

Back 3: Cesar Azipuleta, David Luis and Gary Cahill (c) (all from the 16-17 squad)

Left Wing-Back: Marcos Alonso (16-17) – due to his goal-scoring feats and as well as the defensive exploits in this 16-17 title winning squad).

Right Wing-Back: Pedro (16-17) – obviously being played out of position and Victor Moses would feel hard done by, but there were some other performances from the 14-15 squad that were impossible to not include.

Holding Midfielders: N’golo Kante, (16-17) and Nemanja Matic (14-15)this year’s PFA player picks himself, but Matic’s imperious form in 14-15 was monumental in helping the Blues win the title.

Forward Three: Cesc Fabregas (14-15), Diego Costa (14-15) and Edin Hazard (16-17) – Fabregas was immense in the 14-15 season (and has played some very important cameos this season) and deserves a spot in the starting 11 (although he played out of position). Diego Costa has been a colossus in both seasons, scoring 20 goals in each – we choose the 14-15 season version as he was slightly more efficient in terms of xG. Similarly, both versions of Edin Hazard would be first name of the team-sheet, but again, due to his xG efficiency this season, the 16-17 version gets the starting birth.

Manager: Antonio Conte – both the 14-15 and 16-17 squads were similar in a lot of their attributes. But in terms of sheer efficiency in both offensive and defensive departments, Conte gets the nod by revamping the squad and by changing formations – which ultimately altered the fortunes of this Chelsea squad.

[1] In our analysis, we have classed own-goals down to luck, so in determining the “expected goals plus-minus” (xGpm) we exclude own goals from the goals values (i.e., xGpm = (Goals – Own goals) – xG.

Bringing Design to Performance Analysis

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STATS became the leader in sports intelligence by being the pioneer of live sports data. We strive to continue to innovate in the sports industry and our latest effort has focused on the design of our new products. STATS has engaged with our customers and partners in Design Thinking during the creation of our new products to gather instant feedback.

Design Thinking is a methodology used by designers to solve complex problems and find desirable solutions. The design mindset for our products is solution focused and action oriented towards creating a preferred future state. Design Thinking draws upon the logic, imagination, intuition, and systemic reasoning of many users, to explore possibilities of what could be—and to create desired outcomes that will benefit the customer.

For our new football product, launching this summer, we have conducted in-depth interviews with 10 clubs to understand team needs and validate the functionality of the product. We believe that co-creation with customers is key in design thinking – as our partners meet product managers, sales, marketing and design, we are able to journey map pain points and co-create possible ideas and solutions.

Ryan Nasipak, STATS Product Designer, has implemented the Design Thinking process at STATS for our Team Performance products. The iterative method of Design Thinking enables the product teams to accelerate the design process in combination with understanding real use cases from the user’s perspective.

“Design Thinking lets us see how managers and coaches would actually use our product for their match analysis, versus theorizing how they would,” said Nasipak. “Our goal is to develop a product that not only solves problems that all teams face, but also unlocks a new reality for their day to day work. Having our users involved early in that process helps us address their most critical needs and test that our products are properly addressing them. This not only creates a better user experience but happier customers.”

How Playing Style Impacts the Physical Demands of Football

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Off-the-ball movement during a football match is one of the most vital tactics for coaches. On average, during a game, a footballer spends 97% of their time without the ball. However, measuring the quality and value off-the-ball movement brings to the team has proved difficult.

In this webinar, we will use STATS Playing Style methodology to measure how different playing styles impacts the workload required of a player to perform on offense and defense.

STATS segments the game by style of play to understand how we can assess the tactical workload and finds new ways to use these insights, which allow sport scientists and coaches to develop evidenced-based sessions and create a holistic tactical periodization program for teams and players.

Goal Expectancy: How to Use the Most Advanced Metric in Football

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Join us for a webinar hosted by Dr. Patrick Lucey, STATS Director of Data Science, as he provides us with a new perspective on how to accurately calculate an Expected Goal value, while also looking at exciting new ways to use this metric.

Managers, coaching staff and journalists are constantly looking for better ways to measure and predict team and player performance. With Expected Goal value at the forefront of football analytics. Dr. Lucey will highlight the application of Expected Goals For(xGF), Expected Goals Against(xGA) and Expected Save(xS), while also providing us with a look into Leicester City’s championship winning season by analyzing expected goal value.

STATS Playing Styles – An Introduction

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Football is such a diverse sport with numerous tactics, unique scenarios and systems of playing. In order to capture the different ways a team plays, STATS have developed a framework that captures the playing style of a team throughout a match.

The framework has moved away from the traditional accumulations of single events to provide an impression of the way a team plays. STATS Playing Styles is a multivariate approach, therefore taking into consideration numerous events and factors that determine a team’s style of playing. This provides a vast amount of insights compared with an accumulation of events, from the amount an individual player contributes to a particular style of play to the playing styles teams use to create shooting opportunities.

The following are the eight Playing Styles:

  • Maintenance
  • Build Up
  • Sustained Threat
  • Fast Tempo
  • Direct Play
  • Counter Attack
  • Crossing
  • High Press

Each of the eight Playing Styles are calculated independently and, as a result, several styles can occur simultaneously within the same team possession. The strength of a Playing Style is represented by a membership value between 0% and 100% that is assigned to every team possession, based on the individual definitions and calculations. An exception to this is crossing, which can only be assigned a value of either 0% or 100% due to the crossing definition.

In this first article, four possession-based Playing Styles are introduced. In a second article, the remaining Playing Styles will be discussed.

Maintenance

This is the first of three styles that are calculated in a very similar way. Maintenance captures possessions in which a team looks to maintain and secure possession of the ball within the defensive area of the pitch (see image below).

playing-styles_web

The time spent in possession directly relates to the Maintenance membership value. As a result, the more time a team has possession in the defensive area the higher the maintenance value.

Build Up

Build Up also captures long and controlled ball possessions – but is aimed at periods of play where a team is looking for opportunities to attack. The calculation is similar to Maintenance, with the differences being the zone on the pitch and the time thresholds. The Build Up area is between the halfway line and the opposition’s penalty area (see image below) and works on the same principle that the longer the team has the ball in the highlighted area, the higher the Build Up value.

playing-styles_web_bu

Sustained Threat

The Sustained Threat playing style is, again, similar to Maintenance and Build Up. However, here the focus lies on possessions in the attacking third of the pitch. The time spent in possession must be more than six seconds to be a Sustained Threat possession, which linearly increases to 100% membership.

playing-styles_web_sust

Fast Tempo

The objective of the Fast Tempo Playing Style is to capture when the team is moving the ball quickly to increase the tempo and speed of the game. Fast Tempo looks at sequences of consecutive individual ‘fast possessions’. An individual fast possession must occur in the opposition’s half and can be achieved when the player releases the ball to a teammate in less than two seconds or when the player dribbles at a high tempo.

playing-styles_web_ft

Ranking Premier League Teams

Teams are compared to the league average, which therefore allows a direct comparison between each club. Interestingly, in the Premier League, Hull City are 31% higher for Maintenance than the league average, making them the number one ranked side in the division. However, when it comes to Build Up and Sustained Threat, Hull City’s position is reversed as they drop down to into the bottom three in both styles, indicating that Hull are failing to transition possession from the defensive area into the opposition’s half.

playing-styles_web_hull

On the other hand, Arsenal, who are renowned for their possession-based style, dominate possession in the Build Up and Sustained Threat areas. For Fast Tempo Arsenal are 146% above the Premier League average. Highlighting that when Arsenal have possession in the opposition’s half, the team aims to move the ball quickly.

playing-styles_web_ars

Players Contributions to a Playing Style

Not only can a team’s playing style be measured, players that contribute to the playing style of each team can be identified. The graph below shows Sustained Threat and Build Up involvements with offensive ball movement points plus (oBMP+) as the bubble size (we will introduce oBMP+ at a later date).

Unsurprisingly, the graph is dominated by players at the clubs who are top in the Sustained Threat and Build Up Playing Styles (above). However, STATS Playing Styles effectively highlights the players who are key in particular styles for their clubs, providing an alternative objective measure to the number of passes, penalty area entries or assists.

The three standout performers in both Build Up and Sustained Threat so far this season are Mesut Ozil, Eden Hazard and David Silva. However, this also begins to highlight players who contribute more towards the Build Up play, such as Santi Cazorla, Ilkay Gundogan and Paul Pogba, as well as players with more involvement in the Sustained Threat area of the field, such as Willian, Heung-Min Son and Raheem Sterling.

playing-styles_web_susvbu

STATS Playing Styles can, therefore, be an incredibly useful tool, objectively detailing anything from how a team tends to play in various situations to which players tend to contribute the most in each area. How a team’s style of play changes over time can also be examined, allowing users to assess what a manager has done in an attempt to improve his side.

For more on STATS Playing Styles, you can view our webinar on the subject here.

By: Marc Flynn

The £150 Million Game: How to Succeed in the Premier League

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Promotion to the Premier League can be worth as much as £150 million to teams that ascend from the Championship, but how can clubs best prepare themselves to become a stable presence in the top flight of English football?

Applying advanced analytical models such as playing styles analysis, expected goals and ball movement points, the Prozone Performance.LAB is able to showcase the strategic tendencies that have seen promoted teams thrive in the Premier League.

Delivering unique insights into Premier League performance, Prozone Data Scientist Marc Flynn will highlight innovative applications of analytics in football and show how data can support an enhanced understanding of what it takes to succeed at the highest levels of the sport.

Champions League Final 2016

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Milan’s San Siro will host two teams from the Spanish capital in the 2015/16 UEFA Champions League. The competition’s most successful club, Real Madrid, will compete in its 14th final, aiming to win its 11th European Cup, while City rivals Atlético have reached a third final with the aim of winning Europe’s premier club competition for the first time.

Analyzing the United States Men’s National Team at the Copa América

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The United States will host the Copa América Centenario in what is predicted to be the biggest men’s soccer tournament in the country since the 1994 World Cup. The 1994 World Cup led to the formation of Major League Soccer, and interest in the sport in the US has grown steadily ever since, with the 2014 Men’s and 2015 Women’s World Cups setting records for television ratings in North America. Can this summer’s Copa América have a similar impact?

Team Cohesion: Quantifying Unity in Elite Sport

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Join us for a webinar where we quantify team cohesion through the development of the Teamwork Index (TWI) metric.

Cohesion, or the amount of unity a team has, is regularly identified as being a key factor in the success of failure of teams in elite sport, and yet it is something that few attempt to quantify. Defining cohesion as the level of interrelationship between team members, Prozone uses TWI to measure cohesion and determine the ways in which it influences performance levels.