In modern football, data analysis has revolutionized how coaches and match analysts prepare their teams and plan matches. Kama.Sport, with its combination of **advanced software** and expertise in **data science** and **artificial intelligence**, stands as a leader in this revolution, offering analytical tools that go beyond simple visual observation. Within the **all-in-one platform of Kama**, the match report is one of the flagship tools, allowing for an unprecedented level of detail and depth in examining every aspect of a match. Its innovative approach, concrete results, and outstanding analytical capabilities have enabled Kama to become the **[Innovation Partner of Lega Serie A](https://www.legaseriea.it/it/media/serie-a/inizia-l-era-data-driven-6k3090l)** and **[Technical Provider of FIGC](https://blog.kama.sport/kama-sport-and-figc-together-for-a-data-driven-future)**, achieving remarkable successes such as SSC Napoli's Serie A championship victory in 2022/23 as the Official Technical Provider and [Bologna FC](https://blog.kama.sport/bologna-fc-1909-and-kama-sport)'s qualification for the Champions League in the 2023/24 season.

## The Kama Match Report
The Kama.Sport match report is a detailed and intuitive document that provides a comprehensive view of the match, divided into sections covering various key aspects of the game. This advanced tool is designed to give coaches and match analysts the crucial information they need to make informed decisions and improve team performance.
The true strength of the match report lies in its ability to delve into every detail of the game thanks to the vast amount of data that the **system integrator of Kama.Sport** processes through sophisticated algorithms. This tool is **unique** in the world of football, allowing for an unparalleled level of analysis and precision, transforming raw data into easily understandable and usable strategic insights.

### Timeline of Events
The timeline provides an overview of the key events of the match, such as goals, yellow cards, substitutions, and other relevant moments, allowing for a clear and immediate understanding of how the match unfolded minute by minute.
Notable are the **Momentum** and **Cumulative** graphs:
- **Match momentum**: Displays the momentum of the match minute by minute, highlighting the periods of dominance and the critical phases.
- **Cumulative xT**: Compares the overall volume of play generated by the two teams throughout the whole game.

### Possession Phase
This section analyzes the ball possession of the teams, including details on goal kicks, offensive build-up, and types of attack. Data on total ball possession, percentage of possession in the offensive half and the offensive third, as well as Field Tilt, which measures pressure exerted on the field, are provided.
- **Goal Kicks**: Total number of goal kicks, with percentages of offensive development.
- **Offensive Build-up**: Analysis of offensive build-up, including the percentage of offensive developments and finishes.
- **Ball Possession**: Percentages of total ball possession and in the offensive areas of the field.
- **Attacks**: Number of attacks by both teams based on the field area and type.
- **Entries into the Opponent's Area**: Number and type of entries into the opponent's area, such as from dribbling, short pass, cross, etc.
- **Expected Threats (xT)**: Value that quantifies the goal opportunities generated by different actions of the game.
<hr>

##### Euro 2024 Final Spain 2 - 1 England: Build up Analysis
![Spagna_Inghilterra_EN.webp](https://blog-staging.s3.eu-central-1.wasabisys.com/Spagna_Inghilterra_EN_b3724d09b9.webp)
_A notable image representing an extract from the **Spain - England final** of the 2024 European Football Championships.
Regarding the build up theme, it is evident how Spain's **full-field pressing** is clear from the high percentage of build up involving the England goalkeeper and the **high number of low build up**. This underscores England's difficulty in moving out of their defensive zone with the ball at their feet.
Analyzing **ball possession** and **Field Tilt**, it is clear that **Spain dominated the match**. The Spanish team maintained control of the game in all areas of the field, demonstrating **superiority in both possession and territorial control**._
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### Contested Phase
This section examines the contested phases of possession, providing data on recovered balls, lost balls, duels won, and successful tackles. Offensive and defensive recoveries, the percentage of recoveries over balls played conceded, and positive transitions are analyzed.
- **Recovered Balls**: Number of recovered balls in offensive and defensive phases.
- **Lost Balls**: Number of lost balls in offensive and defensive phases.
- **Duels**: Percentage of duels won, including aerial duels and tackles attempted and succeeded.
- **Recoveries from Lost Balls**: Total number of lost balls by both teams and percentage of total and offensive lost balls, as well as PPDA, acronym for Passes (allowed) Per Defensive Action, a metric measuring the effectiveness of pressure exerted on the offensive half.
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##### Euro 2024 Round of 16 Switzerland 2 - 0 Italy: Pressure Analysis
![Svizzera_Italia_EN.webp](https://blog-staging.s3.eu-central-1.wasabisys.com/Svizzera_Italia_EN_aa427c8396.webp)
_The image represents an extract from the **Switzerland - Italy** match of the 2024 European Football Championships. The **heatmap** clearly shows the **pressure exerted by Switzerland on Italy**, indicating a **higher recovery center of gravity** for Switzerland compared to Italy._
_The **data on recoveries from lost balls**, both in absolute and percentage terms, further **emphasize** the **importance of aggression**, particularly the counter-pressing after losing the ball, as a **key element** for Switzerland's victory._
<hr>

### Player Analysis
This section analyzes in-depth the possession phase, the offensive phase, and the duels of individual players, presenting the data in tabular format. Each table includes detailed information on minutes played, number of passes, crosses, goals, and assists. Additionally, recovered balls in offensive and defensive phases, aerial duels won, and fouls committed or suffered are highlighted.

## Conclusion
The **Kama.Sport match report** is an indispensable tool for anyone who wants to bring their team to the highest level of performance. Thanks to its **detailed structure** and the **depth of the analyses provided**, **coaches** and **match analysts** can gain a **comprehensive understanding of the game**, transforming data into concrete actions to continuously improve team performance. With **Kama**, innovation and expertise are at the service of football.

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Discover how the Kama Match Report is becoming increasingly indispensable for technical staff who want to delve deep into football match analysis.

Kama Match Report: the match analysis with a fine-tooth comb.

As football continues to grow and evolve technologically, Kama stays one step ahead to support football professionals in elevating their game to new heights. To highlight our innovative efforts, we recently unveiled one of our newest solutions, Kama Team Labels. Powered by advanced statistical models and machine learning, Kama Team Labels summarize the most impactful aspects of each team's game, allowing for the instantaneous and intuitive analysis of a team's statistics.

The outputs are segmented into 3 unique sections (Strengths, Weaknesses, Style of Play) that reflect the overall performance of a team while highlighting their strong points, shortcomings and overall game style. The use of Kama Team Labels allows users to quickly navigate the complex football ecosystem and facilitate a comprehensive, rapid, and intuitive analysis of a team. Despite all of its perks, the feature was limited to teams and served more as a general overview rather than an extensive report. 

To ensure that users receive the most accurate and detailed information about a football team’s capabilities, we are now excited to present the newest addition to the Kama platform - Kama Player Labels. An extension of the Team Labels, Kama player labels zoom in on a player’s strengths and weaknesses, making it more intuitive for users to analyze the true value a player brings on the pitch.  

Kama Player Labels are categorized into 3 unique sections: Game Features, Strengths and Weaknesses. 

![Kama Player Labels: Strengths and Weknesses Scale.](https://blog-production.s3.eu-central-1.wasabisys.com/Whats_App_Image_2024_03_07_at_15_28_01_28a0596afb.jpeg)

**1. Game Features**

Through Machine Learning algorithms, players are categorized based on specific playing characteristics for their respective roles. The goal is to provide a precise and objective description of the player's on-field abilities, based on their contribution to the team, evaluated across a substantial group of game statistics. Each group is displayed with a relative percentage, indicating how well the player's characteristics align with the specified archetype.

For the specific role, the algorithm identifies the following archetypes:

- _Offensive phase enhancer:_ Striker who makes 1vs1 skills his specialty, able to dribble past defenders and turn the action from defensive to offensive through effective carries.
- _Target offensive player:_ Offensive reference who is not necessarily a striker, physically strong and who plays for the team.
- _Central scorer:_ Striker who prefers patrolling the central area of the attack, seeking to finish the play.
- _Breakthrough offensive player:_ Player who positions himself in the offensive zone but makes breakingthrough plays his specialty.
- _Between the lines target:_ Positional offensive player, dominant on the high ball, with great physical presence. Tends to position himself as a reference in the central zone.
- _Reliable offensive player:_ Offensive player who does not excel or flaw in any precise characteristic of the game, a balanced player.
- _Versatile offensive player:_ Multipurpose offensive player, able to cover multiple roles reliably, physically strong and dangerous when carrying. Not always effective in scoring.
- _Offensive chance creator:_ Clever offensive player whose priority is to create through dangerous passes and crosses, rather than finishing.
- _Dynamic scorer:_ Forward who likes to roam across the entire offensive front, dangerous with the ball at his feet, and a threat in the goal area.

**2. Strengths**

Strength points are divided into offensive and defensive, then evaluated on three distinct levels, identified as follows:

- Top in the best 5 European competitions: indicates that the player is in the top 5th percentile among all players in teams belonging to the top 5 European leagues.
- Top in the league: indicates that the player is in the top 5th percentile among players in their respective league.
- Strong in the league: indicates that the player is between the 85th and 95th percentile among players in their respective league.
If the player is both Top in the best 5 competitions and Top in their own league, only the European level will be shown, implying the one related to their own league.

We consider the Premier League, Serie A, Bundesliga, Ligue 1 and LaLiga to be the Top 5 European leagues. It is also important to mention that all labels are calculated by analyzing data from the last 1000 minutes played.

**3. Weaknesses**

Weakness points are divided into offensive and defensive, then evaluated on three distinct levels, identified as follows:

- Worse in the best 5 European competitions: indicates that the player is in the worst 5th percentile among all players in teams belonging to the top 5 European leagues.
- Worse in the league: indicates that the player is in the worst 5th percentile among players in their respective league.
- Weak in the league: indicates that the player is between the 5th and 15th percentile among players in their respective league.

Same as the strength labels, If the player is both Top in the best 5 competitions and Top in their own league, only the European level will be shown, implying the one related to their own league.

![Lewis Ferguson Player Labels.](https://blog-production.s3.eu-central-1.wasabisys.com/Whats_App_Image_2024_03_07_at_15_28_01_1_e2acab27b6.jpeg)




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Find out how Kama’s newest feature enhances the analytical process and provides you with a comprehensive depiction of a player’s qualities at the click of a button. 

What are Kama Player Labels and Why Are They The Future of Player Analysis 

Among avid football enthusiasts, it is a common phenomenon for every supporter to showcase and assert their coaching prowess, especially in the aftermath of a disappointing match. Fans spend countless hours engaging in passionate discussions, dissecting player performances, critiquing tactical decisions, and delving into controversial game incidents. Although most opinions are constructed purely based on a team’s weekend performance, this passion and controversy contribute to football’s immense popularity while at the same time making it so difficult to analyze objectively.

Given the great number of playing styles and game tactics, we are constantly seeing new, creative ways of approaching the game. Beyond the spirited "pub debates", football experts are now interested in objective facts about their players and opponents to make informed, tailored and timely decisions and elevate their chances of success. This is where data analysis comes into play.

The main objective of football analytics is to comprehensively and objectively evaluate player performance through a meticulous examination of match event data. This process not only highlights the perceptible highs and lows of outstanding and lackluster performances but also offers nuanced insights into players who may not garner a lot of attention due to their specific roles or assignments on the field.

![Expected Threat Statistics for the Serie A. ](https://blog-production.s3.eu-central-1.wasabisys.com/221_a70f415dd3.jpg)

However, navigating the football analytics landscape presents challenges that cannot easily be overlooked. Some of the main challenges are:

**1)Offensive Bias:** As the primary objective of football is to score more goals than your opponents, event data is predominantly skewed towards offensive actions. This results in a noticeable imbalance between offensive and defensive statistics.

**2)Player Roles and Statistics:** Offensive players are often considered "volume" players, accumulating statistics through frequent involvement in the play. In contrast, defenders are deemed "situational" players, requiring readiness for infrequent but crucial tackles and duels. This discrepancy not only limits the availability of statistics for defenders but also challenges the notion that "more" necessarily equates to "better."

**3)Opponent Quality Bias:** Evaluations based on a single game are susceptible to bias stemming from the caliber of the opposing team. Statistics that appear commendable against top-tier teams may lose significance against lower-ranked opponents.

**4)Normalization Challenges:** While normalizing statistics "per 90 minutes" aids in achieving a more comprehensive representation of data, it introduces potential outliers for players with brief playing durations. Robust checks become imperative to mitigate distortions arising from such anomalies.

In light of these considerations, we have chosen to sidestep the intricate evaluations of individual matches, and instead concentrate on comprehensive assessments spanning entire tournaments. The rationale is that aggregating data across multiple matches helps mitigate the impact of various issues inherent in single-game analyses, providing a more holistic perspective on player performance.

## Kama Score

In the preliminary stages of formulating our comprehensive performance index (Kama Index), it is important to delve into the detailed nature of the data, which is ranked based on various magnitudes:

- Low-ranged data, exemplified by metrics such as aerial duels won.
- Medium-ranged data, represented by factors like lost balls.
- High-ranged data, such as successful passes.
- Fixed interval data, encompassing every percentage statistic.

Given our intent to construct the index through a synthesis of key statistical evaluations, a crucial prerequisite is to normalize these diverse statistics to a uniform scale, ideally ranging between 0 and 10. This normalization facilitates a seamless integration of scores, making it easier to compute the final index score. In essence, this approach can be conceptualized as a translation: scores change more rapidly where observations are densely concentrated, and the rate of change diminishes in regions where observations are less dense.

## Kama Index

In the context of our statistical analysis, each type of data now corresponds to objective scores. Negative occurrences, such as fouls, possessions lost, and missed chances, have been inverted to align with a standardized scale. The subsequent task involves the judicious selection of scores most fitting for various player profiles.

To recognize the distinct roles and characteristics of each footballer, we categorize players into seven distinct roles:

1. Goalkeeper
2. Full back
3. Center back
4. Midfielder (including defensive midfielders)
5. Wide midfielder (including those in a mid-5 formation)
6. Winger and attacking midfielder (players operating around the opponent's area)
7. Forward

While we collect an extensive pool of over 300 statistics, we also ensure to curate a meaningful subset. Initial attempts with a mere 10 statistics revealed an undesirable flattening of results and a loss of valuable information. Consequently, for each outfield role, we judiciously select between 6 and 8 statistics, with goalkeepers requiring only 4 key statistics due to their comparatively limited dataset.

![Kama Index explanation](https://blog-production.s3.eu-central-1.wasabisys.com/Index_Info_3781b876b9.png)

To derive the KamaIndex, we use a very straightforward approach: a scalar product between scores and a vector of weights, where each element represents the importance weight of the corresponding statistic. Normalization is achieved by dividing the result by the sum of weights. This process, being a convex combination of values within the [0-10] range, ensures that the KamaIndex also falls within this specified range [0-10].

In the future, we are contemplating an extension that focuses on evaluating performances based only on the last 1500 minutes played. This feature would allow for the interpretation of recent form throughout an entire season for players who surpass this threshold. Conversely, players falling below this minimum threshold (due to hierarchy or injury reasons) have their data sourced from the previous season, albeit at the cost of reduced result reliability.

## Kama Index for Players

To showcase what the Kama Index looks like, we have selected and ranked a few Serie A players based on their performance so far this season. 

![Wojciech Szczęsny Kama Index analysis. ](https://blog-production.s3.eu-central-1.wasabisys.com/Szczesny_2689a44657.png)

We first take a look at goalkeepers as they are the ones whose evaluation is most likely to be misleading and misread. Goalkeepers playing for teams with weaker defenses may showcase seemingly impressive statistics, while those in teams with robust defenses may appear relatively inactive. Notably, Juventus’ goalkeeper, Wojciech Szczęsny, has emerged as one of the standout performers of the season. Despite a lower-than-average saves per game metric, attributable to the team's solid defensive organization, Szczesny holds the league's best save percentage while also shining in the building-up phase of the game, where he shines with his impressive passing abilities. Szczesny has a Kama Index of 8.28 so far - one of the leading rankings among goalies in the Serie A. 

![Lucas Martinez Quarta Kama Index analysis.  ](https://blog-production.s3.eu-central-1.wasabisys.com/Quarta_74bb562811.png)

Transitioning to the center-back role, Fiorentina's defensive stalwart, Lucas Martinez Quarta, consistently garners recognition as a top-class performer in the league. Although he doesn’t shine with any superior physical attributes, he manages to excel as a consistent and versatile defender who displays stellar playmaking from deep positions and makes his presence known on the offensive end with three goals so far this season. He rakes in an impressive 7.97 Kama Index. 

![Hakan Çalhanoğlu Kama Index analysis. ](https://blog-production.s3.eu-central-1.wasabisys.com/Chala_59f6419110.png)

In the midfield, the zone characterized by the most diverse parameters, Hakan Çalhanoğlu emerges as the epitome of excellence, achieving the highest KamaIndex in Serie A with an impressive 9.7. Calhanoglu's multifaceted skillset encompasses defensive intensity, superb playmaking abilities, a potent long-range shooting ability, and an impeccable record as a penalty kicker.

![Matteo Politano Kama Index analysis. ](https://blog-production.s3.eu-central-1.wasabisys.com/Politano_64266740ec.png)

And finally, turning our attention to strikers, we spotlight Matteo Politano, who has played a pivotal role in maintaining Napoli's resilience amidst a challenging season following their last title-winning season. While Osimhen and Kvaratshkelia are struggling to reach the heights achieved during the 22/23 campaign, Politano has been outstanding during the first half of the 23/24 season. Leading the standings for expected threats, his proficiency lies in his dynamic dribbling abilities, strategic movement and his ability to create space for his cannon of a left foot. All of these qualities contribute to his impressive 8.69 Kama rating.  

## Kama Index for Teams

While our focus has primarily been on evaluating individual players, it's essential to highlight that Kama Index is equally suitable for assessing team performance, with some minor distinctions. In order to have a complete, holistic picture of team's capabilities, the Kama Team Index incorporates three different indexes instead of the one applied for players. 

1. Offensive Phase Index
2. Defensive Phase Index
3. Transition Phase Index

Similar to the player index, each of these three indices incorporates a unique set of parameters that embody the essence of the respective phases. In some instances, certain parameters that are unique to a given team are also considered in the evaluation. 

![Roma Kama Index breakdown](https://blog-production.s3.eu-central-1.wasabisys.com/241_33da30d7dd.png)

While elite teams are naturally expected to excell across all three indices, the division into offensive, defensive, and transition phases also reveals a team’s playing philosophy and approach to the game. The Kama Index showcases the strengths and weaknesses of teams while providing valuable insights into their distinctive strategies. 


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Introducing the revised and improved Kama Index, the best way of evaluating a player's performance and understanding their true value. 

Kama Index: Understanding a Player's True Value

The concept of "expected threat" is a relatively recent addition to football data analysis and it has taken the world of analytics by storm. It's primarily used to gauge the offensive goal threat of in-game actions. This measurement considers factors like player positioning, distance from the goal, and the likelihood of a shot being taken. It provides a precise indicator of how much danger a team poses to its opponents.

The calculation of expected threat relies on statistical models developed from extensive data on game events. This data helps assign a threat score to an action, signifying the probability that the action will result in a goal. Expected threat is a valuable tool for coaches, analysts, and managers, as it enables them to assess the effectiveness of their game strategies by analyzing past offensive actions and predicting the success of future ones. Furthermore, the analysis can be utilized to evaluate your own team’s performance, as well opposing players and teams, supporting decision makers in their preparation operations.

## More than just Expected Threats

Delving deeper into the concept of expected threat, we can break it down into several sub-metrics or derived parameters. In KAMA, we have generated four additional metrics to offer a more refined analysis:

-**Expected Threats (xT)**: This is the generic statistic encompassing all of a team's offensive plays.

-**Opponent Expected Threats**: Similar to the previous statistic, but from the opponent's perspective. 

-**Expected Threats from Carries**: This statistic focuses solely on carrying actions, helping identify players and teams that pose a threat by dribbling.

-**Expected Threats from Passes**: This metric considers the dangers generated by passing.

-**Goal / Expected Threats**: This parameter measures the conversion rate of threats into goals by the team. A higher rate indicates a team's proficiency in capitalizing on offensive plays.

-**Goal conceded / Opponent's Expected Threat**: This rate reveals how well a team defends against opposing threats.

## Match analysis use case

To fully demonstrate the value of the expected threat metrics, we are going to look into a practical example. Imagine you're a match analyst preparing your team to face Roma in the upcoming league day. You can use the parameters mentioned above to gain insights into the opponent's threat generation.

Let's start with the parameters that give a wider perspective on the team, using the Serie A team ranking for the 2023-2024 season as a benchmark:

### 1. Standart statistics and conversion rates

**Expected Threats (xT)**

Roma ranks fourth in the league, with an average xT per game of 2.6 points.

![Expected Threat rankings for the Serie A season 2023/2024.](https://blog-production.s3.eu-central-1.wasabisys.com/Expected_Threat_1_3b98e49aca.png)

**Goal / Expected Threats**

Roma leads the competition with a conversion rate of 0.9, showing their efficiency in turning threats into goals, especially with star players Dybala and Lukaku.

![Goal/Expected Threat rankings for the Serie A season 2023/2024.](https://blog-production.s3.eu-central-1.wasabisys.com/Expected_Threat_2_33533481cc.png)

**Opponent Expected Threats**

Roma is among the best in Serie A, conceding only 1.7 xT on average, making it difficult for opponents to create scoring opportunities.

![Opponent's Expected Threat rankings for the Serie A season 2023/2024.](https://blog-production.s3.eu-central-1.wasabisys.com/Expected_Threat_3_f8a444ab28.png)

**Goal conceded / Opponent's Expected Threat**

It is very interesting to note that while Roma excels offensively, they face challenges defensively, as they concede more goals compared to the effort they put into preventing threats.

![Goal conceded / Opponent's Expected Threat rankings for the Serie A season 2023/2024.](https://blog-production.s3.eu-central-1.wasabisys.com/Expected_Threat_4_5c628faad9.png)

### 2. Expected Threats from passess and carries

**Expected Threats - from Passes**

Despite their high xT ranking, Roma is only sixth in Serie A when it comes to generating threats through passing.

![Expected Threat from passes and crosses rankings for the Serie A season 2023/2024.](https://blog-production.s3.eu-central-1.wasabisys.com/Expected_Threat_5_119d45ee5f.png)

This is also confirmed by the statistics for players: the player who on average carries the most xT from passing on the team is Zalewski, who is only 102° in Europe for this statistic.

![Player rankings for Expected Threat from passing for season 2023/2024.](https://blog-production.s3.eu-central-1.wasabisys.com/Expected_Threat_6_194f96c8e1.png)

**Expected Threats - from Carries**

Roma excels in carrying the ball, ranking fourth in the league. Spinazzola is the third-best player in Europe for creating threats through carries, just behind Doku and Dembèlè, who are specialists in this area.

![Expected Threat from carries rankings for the Serie A season 2023/2024.](https://blog-production.s3.eu-central-1.wasabisys.com/Expected_Threat_7_3fe17498df.png)

Other players who who rank high on expected threat from carries are world-class dribblers such as Vinicius Junior from Real Madrid and Kaoru Mitoma who rank 4th and 5th respectively in the overall rankings.

![Top 5 player rankings in expected threat from carries for season 2023/2024.](https://blog-production.s3.eu-central-1.wasabisys.com/Expected_threat_8_d86eabd254.png)

By considering the insights we've gained from the dangers generated by passes and carries, we can form a clearer picture of Roma's attacking style. They appear to be a formidable force in open spaces, leveraging the exceptional carrying abilities of players like Lukaku and Spinazzola, particularly at high speeds. Conversely, they might face more challenges when up against tight defenses, where the ability to find the key pass becomes essential.

## About Kama.Sport

Kama.Sport is a cutting-edge software and data science company specializing in sports data, digital transformation, web services, and the 4.0 industry. Kama provides high-tech solutions that enhance the outcomes of both tactical and strategic decisions, support a team’s competitive development, and aid in their digital transition.

Kama’s all-in-one platform integrates the world’s most advanced sports data and technology to help coaches, teams, leagues, and federations structure their work and operate more efficiently. Kama’s tailored solutions allow teams to simultaneously analyze both proprietary and third-party inputs, streamlining operations and providing football teams with an edge in a highly competitive industry. 

Learn more about Kama [here](https://kama.sport/en/home/).



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With the advancements in data analytics in football, experts can now dissect tha game to the smallest of data parameters. Learn what the "expected threat" statistic is and discover why it is held in such high regard in modern football. 

What is the Expected Threat Metric in Football and Why it Matters

In recent times, the use of analytics has been a prevalent practice in the world of sports with several goals: Its utilization can lead to improved team performance, optimization of athlete fitness potential, and broadening one’s understanding of the dynamics of the game. 

In football, extracting the full value of data and analyses is extremely complex, mainly due to the vast number of outcome possibilities and the intrinsically volatile nature of the sport itself. To get a better understanding of the comparative complexity of football, let’s compare it to another popular sport, basketball, where analytics have been consistently utilized for decades. 10 players compete on a 15m x 28m court, with a time limit of 24 seconds per play. On the other hand, football is simultaneously played by 22 players on a 105m x 68m, without any time constraints in terms of offensive/defensive transitions. This disparity is reflected in the sheer number and complexity of the statistics that can be extracted. And as comprehensive as they may be, football statistics are often difficult to interpret and there is always the risk of misinterpreting the data at hand.

At Kama, we fully understand the complex nature of this issue all too well. Therefore, we have made it our purpose to not only provide [all the tools necessary](https://blog.kama.sport/the-kama-platform-the-power-of-performance-intelligence) to effectively conduct football analysis but to guide and support users by helping them take advantage of the most advanced techniques and provide them with actionable inputs that can instantly be applied and converted into practical actions.

![The Kama all-in-one platform. ](https://blog-production.s3.eu-central-1.wasabisys.com/Our_P_Latform_7f7c33394e.png)

## What are Kama “Team Labels”?

Within the KAMA platform, we have introduced a new feature called Team Labels. Team Labels summarize the most impactful aspects of each team's game, allowing for the [instantaneous and intuitive analysis](https://blog.kama.sport/reimagine-the-game-kama-game-sense) of a team's statistics. 

This game-changing functionality is made possible through the use of Kama’s unique game situations (Kama Game Sense), which creates and visualizes measurable statistics that are then evaluated for each team in comparison to its opponents.

The structure consists of three sections:

1: **'Style of play'**, showcases the special characteristics of the tactics adopted by a team.

2: **'Strengths'**, demonstrate the game situations in which a team excels over others.

3: **'Weaknesses'**, identify a team's deficiency or 'Achilles' heel compared to their competitors.

The 'Strengths' of a team are distributed over three different levels of value ('Top,' 'Very strong,' and 'Strong'), giving the possibility to identify a hierarchy of excellence. Similarly, 'Weaknesses’ are exemplified on a three-level hierarchy of deficiencies, ranging from “Weak” to “Worst”. This evaluation is done for both teams competing within the same championship as well as over larger groups of competition. This is done to identify individual primary strengths and weaknesses, as well as portray true standouts or underdogs. 

![Kama Team Labels visual](https://blog-production.s3.eu-central-1.wasabisys.com/Team_Labels_1_0fb2140913.png)

## How to use Kama Team Labels? ‘AS Roma’ use-case.

To showcase the potential of the tool and practical application, let's jump into the role of a match analyst whose team will face Roma the next day. 

The role of a match analyst is to communicate to the entire staff the various aspects of the game in which they can exploit the weaknesses of their opponents and leverage their strengths to emerge victorious from the bout. The main difficulty in such situations is not knowing where to start the analysis, which statistics to focus on, and ensuring that the conclusions made are actually descriptive of the opponents' approach to the game. 
This is where Kama Team Labels come into play.

By looking at AS Roma’s overview, our new feature can clearly identify the team's strengths and weaknesses based on their statistical output throughout the season. 

Let's start with playing style, the category that describes a team’s approach to the game. According to our tool, Roma is a team that knows how to be patient in the offensive phase and tries to control the game as much as possible. Moreover, the team is very proficient in the ball-retrieval phase, turning defense into offense through quick counterattacking play. Lastly, the labels also highlight that once they recover possession, Roma is good at keeping it, making it difficult for opponents to dictate the tempo of the game.

The strengths consistently align with the expected style of play and shed light on another aspect of Mourinho's teams: their proficiency in set pieces (both offensive and defensive, which makes Roma one of Europe's top teams in this regard, as evidenced by their performance. Turning to weaknesses, a key concern is the team's lack of aggressiveness during the opponent's build-up. This translates to fewer disruptive actions and a decreased likelihood of encountering challenges in maintaining a solid build-up. Furthermore, Kama's Team Labels also showcase that Roma has a preference for building their attacks through the centerfield, indicating that their flanks are usually less dangerous during the offensive phase. 

The aforementioned analysis underscores how the new Team Labels tool facilitates a comprehensive, rapid, and intuitive analysis of a team. This tool’s use isn't limited to match analysts; it's equally beneficial for coaches, scouts, or anyone interested in delving deep into the performance intricacies of football teams. Work more efficiently and master football strategy through the power of Kama Team Labels. 

![Kama Team Labels analysis for AS Roma](https://blog-production.s3.eu-central-1.wasabisys.com/Team_Labels_2_50dd6adc08.png)

## About Kama.Sport

Kama.Sport is a cutting-edge software and data science company specializing in sports data, digital transformation, web services, and the 4.0 industry. Kama provides high-tech solutions that enhance the outcomes of both tactical and strategic decisions, support a team’s competitive development, and aid in their digital transition.

Kama’s all-in-one platform integrates the world’s most advanced sports data and technology to help coaches, teams, leagues, and federations structure their work and operate more efficiently. Kama’s tailored solutions allow teams to simultaneously analyze both proprietary and third-party inputs, streamlining operations and providing football teams with an edge in a highly competitive industry. 

Learn more about Kama [here](https://kama.sport/en/home/).



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Learn how Kama's newest feature can provide you with in-depth performance insights and help you in master football startegy.

Mastering Football Strategy: Introducing Kama Team Labels

Football is often referred to as the beautiful game, and rightfully so. Its intricate strategies, lightning-fast plays, and moments of sheer brilliance can captivate fans worldwide. But beyond the surface spectacle lies a world of data, statistics, and insights that add a new layer of depth to the sport. This is where match analysis comes into play. 

Match analysis is the art and science of breaking down every aspect of a match to uncover patterns, trends, and insights that might not be immediately apparent to the naked eye. Using a combination of data and video analysis, match analysts are capable of extracting valuable information that can then be transformed into insights and contribute to the overall performance and success of a team. 

At Kama, we understand the importance of data and video analysis in modern day football, which is why we are constantly striving to find innovative ways to support sport professionals to extract the absolute maximum value from their resources. Our strive to revolutionize the way match analysts observe has culminated in the creation of the world's fastest, most comprehensive match analysis tool, Kama Game Sense. 

![Kama Game Sense is part of Kama’s Video module.](https://blog-staging.s3.eu-central-1.wasabisys.com/Video_Module_visual_a12ef2dd7e.png)

## What is Kama Game Sense?

Kama Game Sense is a proprietary feature which is an instrumental part of the [Kama Platform](https://blog.kama.sport/the-kama-platform-the-power-of-performance-intelligence). It is specifically designed to enhance the video analysis experience by automating the video generation process, providing more extensive parameter options and visualizing intricate game scenarios based on specific inputs. In essence, Kama Game Sense excels at identifying and classifying concatenated successions of events during a football match. It  breaks down complex game situations, making them more digestible, actionable and overall easier to extrapolate value from.

Kama Game Sense main contributions come in terms of elevated video analysis and match analysis performance. 

Whereas you would need to watch an entire match and manually segment parts based on the actions in the video, our technology allows users to access segmented video clips of matches showcasing intricate game scenarios based on customizable inputs; these scenarios are automatically grouped based on similar features. For example, let's say that you would like to see all instances over the last 10 games where your team regains possession in their own three-quarters of the field and subsequently attempts a shot on goal. WIth Kama Game Sense, you simply need to choose what game scenario you would like to see and designate a time frame. The result is an instantaneous, automated visualization of all situations that match the search criteria. 

![Kama Game Sense automatically segments videos based on your inputs ](https://blog-staging.s3.eu-central-1.wasabisys.com/Screenshot_2023_09_15_105607_9ffea709e0.png)

Furthermore, Kama Game Sense’s capabilities go even further.  Instead of focusing solely on single events, it delves into successions of events or complete ball possessions. For instance, you can compare how many times a team successfully took a shot on goal following a long throw from the goalkeeper or a defensive build-up. Consequently, it allows you to magnify complex game situations to discover patterns, trends or simply evaluate the effectiveness of your strategic decisions.

## How does it work?

So, how does Kama Game Sense actually work? For end users, finding your desired game events is as easy as selecting a team you want to analyze, inputting the specific scenario you are looking for and choosing a time span, the software does the rest. To truly understand its functionality however, this is a short overview of the methodology used when creating a game scenario. 

- Kama Game Sense starts with basic event data from a football match.
- From the succession of events, it identifies actions that signify the beginning and end of a ball possession.
- Rigorous checks are performed on the events within a ball possession to ensure that possession can be accurately attributed to a specific team.
- Each possession is meticulously evaluated on various parameters. Amongst others, these include the position of individual events, total possession duration, types of actions occurring before, during, and after possession, player roles, and the pitch macro-areas covered during actions.
- Lastly, a set of attributes and qualifiers are associated with individual ball possessions. This classification allows us to extract precise moments in the game to create video clips, focusing on the most intriguing events.

## A quick tutorial

1. Select the team you would like to analyze or, alternatively, you can also select to examine the opponents of a given team. 

![Game Scenario Real 1.png](https://blog-staging.s3.eu-central-1.wasabisys.com/Game_Scenario_Real_1_eed42436cd.png)

2. Choose the game scenarios that you would like to observe by selecting from the extensive list of options. 

![Game Scenario 2.png](https://blog-staging.s3.eu-central-1.wasabisys.com/Game_Scenario_2_6ea054566c.png)

3. After your input, Kama Game Sense will automatically generate all the games in which the specific game scenario has occured. Select which games you would like to analyze.

![Game Scenarios 3.png](https://blog-staging.s3.eu-central-1.wasabisys.com/Game_Scenarios_3_9d9050df45.png)

4. Depending on how many different game scenarios you want to observe, the system will segment it based on relevancy. You can also see the different camera angles, number of events, and number of players involved in the scenarios. 

![Gae Scenarios 4.png](https://blog-staging.s3.eu-central-1.wasabisys.com/Gae_Scenarios_4_05abe983dd.png)

5. You can dynamically customize your selection to ensure that it caters to your exact search criteria.  

![Game Scenarios 5.png](https://blog-staging.s3.eu-central-1.wasabisys.com/Game_Scenarios_5_52e60b65f6.png)

6. Following the customization stage, you can analyze the video segments and build your project.

![Game Scenarios 6.png](https://blog-staging.s3.eu-central-1.wasabisys.com/Game_Scenarios_6_3a76f5fed9.png)

7. Each project can then easily be shared among members of staff, players, and other collaborators.

![Game Scenario 7.png](https://blog-staging.s3.eu-central-1.wasabisys.com/Game_Scenario_7_d89dc90bd0.png)

Kama Game Sense is not just a tool; it's a game-changer for football analysis. It empowers coaches, analysts, and enthusiasts to dissect the beautiful game in unprecedented detail. Whether you're seeking to improve your team's performance or simply want to enjoy a deeper understanding of football, Kama Game Sense is your key to unlocking the game's secrets.


## About Kama.Sport

Kama.Sport is a cutting-edge software and data science company specializing in sports data, digital transformation, web services, and the 4.0 industry. Kama provides high-tech solutions that enhance the outcomes of both tactical and strategic decisions, support a team’s competitive development, and aid in their digital transition.

Kama’s all-in-one platform integrates the world’s most advanced sports data and technology to help coaches, teams, leagues and federations structure their work and operate more efficiently. Kama’s tailored solutions allow teams to simultaneously analyze both proprietary and third-party inputs, streamlining operations and providing football teams with an edge in a highly competitive industry. 

Learn more about Kama [here](https://kama.sport/en/home/).



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Introducing Kama Game Sense, Kama's revolutionary video analysis tool that completely reshapes the way you approach the game of football.