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Moyes clearly has a different perspective on the crisis. While he is restricted to bringing in new players by two transfer "windows" — one over summer, the other during January — he can make changes to personnel behind the scenes whenever he likes. At the end of last year he overhauled United's back-room staff. The arrivals included Robbie Cooke, Everton's chief scout; Chelsea's European scout Mick Doherty, who also worked with Moyes at Everton; and John Murtough, formerly responsible for Everton's vaunted academy and latterly the Premier League's head of elite performance.

His final "transfer" was James Smith, head of technical scouting at Everton. None of these appointments made headlines, but Moyes believes they could be crucial in unearthing the future stars of Manchester United — within the club and outside — and turning round his fortunes at Old Trafford. There has been a revolution in football — though it is one that even the most committed fans will only be dimly aware of. Clubs are becoming smarter, more efficient. We've probably all seen the graphics and statistics that pop up in newspapers and on shows such as Match of the Day : it began with counting corners and shots on goal, but recently the analysis has become more whizz-bang; not least speed profiling and heat maps, which plot a player's movement around the pitch.

But this is just a fraction of the data that can be collected during a match. Opta , a sports statistics company, records around 1, "events" from every fixture. All 20 clubs in the Premier League — and many in the lower divisions — now employ data analysts to make sense of this information. Manchester City has 11 of them.

In , Liverpool caused a stir by creating a new position, director of research, for Ian Graham, who has a PhD in theoretical physics. The analysts are involved in pre-match preparation and post-game debriefs; they help to identify transfer targets and devise strategies for nurturing young players through the ranks.

These developments have inspired confusion and even suspicion from many supporters, summed up by a recent headline in the New Statesman : "How the spreadsheet-wielding geeks are taking over football. We can't be blamed for being perplexed. Take the match last month between Arsenal and Bayern Munich, which Bayern won These are simplistic examples, but they encapsulate a debate taking place at the highest levels of many football clubs. In one corner are the "quants" or quantitative analysts: they are admirers of the statistician and election-oracle Nate Silver; the Nobel prize-winning psychologist Daniel Kahneman; and especially Billy Beane, the star of Moneyball , Michael Lewis's book about the data revolution in baseball.

They believe that a football match can be translated into numbers and — much as a hedge-fund trader does with the stock market — those figures can be crunched and scanned for patterns. They don't think intuition should be removed from the game but they have found that statistics are dispassionate in a way that humans never are.

As Beane, general manager of the Oakland A's, has said: "The idea that I [should] trust my eyes more than the stats, I don't buy that because I've seen magicians pull rabbits out of hats and I know that the rabbit's not in there. In the other corner are the traditionalists, which is to say the owners and managers of the overwhelming majority of professional football clubs. They are aware of Moneyball — at least the film starring Brad Pitt — but don't believe the lessons of a stop-start sport such as baseball can be applied to the fluid dynamics of a football match.

Most managers once played the game themselves at a high level and it is this fact, they contend, that gives them a special insight into what happens on the pitch and which players they recruit. This approach is summed up by an anecdote about Harry Redknapp , reported in Wired magazine. When he was manager of Southampton, he turned to his analyst after a loss and said: "I'll tell you what, next week, why don't we get your computer to play against their computer and see who wins?

It turns out that Redknapp was not too wide of the mark: how long will it be before we look at football not just as a contest between 22 players or a clash between two managers, but as a battle between the respective brains trusts assembled on the two benches? A decent place to start the investigation is Everton FC. As Simon Kuper, the Financial Times columnist and co-author of Soccernomics has detailed, no club in the Premier League has so consistently overachieved during the past decade.

Under Moyes, they finished eighth or higher every season from to They've managed this despite being more frugal with wages than all of their rivals and not splashing cash on big-name transfers. Instead they achieved success by developing brilliant home-grown talent — Wayne Rooney, Jack Rodwell and Ross Barkley among them — and melding these players with unheralded stalwarts such as Leighton Baines and Leon Osman, who just happen to be statistical outliers.

Baines, in fact, is something of an emblem for the data revolutionaries. For years, he was a solid, dependable left-back with an anachronistic mop-top, a perennial understudy to the flashier Ashley Cole in the England team. The stats, however, told a different story: in , Opta identified Baines as the player who created the most chances in all of Europe's top leagues.

Before long, Baines was first choice for the national team and a transfer target for Manchester United of course, though perhaps he was simply playing better and the data per se had nothing to do with it. With such an impressive record over the years, it's hardly surprising that Moyes wanted to recreate the structure at Manchester United.

Somehow they survived — until last May anyway, though they had the consolation of defeating Manchester City to win the FA Cup. Much of Wigan's resilience was put down to their progressive, young manager. The Numbers Game , a recent book that examines the "datafication" of football, noted that he installed a inch pen-touch TV screen at his home and hooked it up with player-tracking software from the performance analysts Prozone.

He would watch matches, especially defeats, up to 10 times in order to make sense of what had happened. In short, he seemed like the perfect fit for a forward-thinking club like Everton. The facility is typically described as "state of the art", but it is still a place where a tea lady will come round to offer you a cuppa and probably a biscuit if you ask politely, too.

There's an iMac on the desk but it is devoid of personal effects and whiffs of fresh paint — it turns out the room used to belong to James Smith, until he moved to Manchester United, and Reeves is just settling in. They have just come in from training. How much data do they collect in preparation for matches? From a physical point of view, the most significant stats are probably the number of sprints, the sprint distance and the amount of high-intensity efforts a player gets through.

We look at these through the season and they give us a good indication of how fatigued a player is and the recovery he needs. At Everton, each player is tracked in terms of four "corners": technical, tactical, physical and psychological. Data is crucial for assessing the first three categories. Detailed feedback will start in some clubs from the under-nines upwards. On the recruitment side, Reeves and Brown liaise with 10 scouts across Europe, who work exclusively for Everton, and keep an eye on the ProScout7 database, which has profiles on almost , players in more than countries.

Or, to put it another way: he thinks most statistics are useless. Or he can have 10 shots and nine of them are off target, but then the last one goes in the top corner. So which stat do you prefer? Aged 40, with a postgraduate diploma in business and marketing from Manchester University — attained while he was a player at Wigan — you might expect him to be a passionate advocate for analytics. The Everton manager is especially scathing of using data to identify transfer targets — the Moneyball dream of unearthing players whose utility might not always be immediately obvious.

Or Liverpool, under their then-director of football Damien Comolli, who spent heavily in to acquire Jordan Henderson and Stewart Downing, ostensibly because their "final-third regain" percentages — how often they recovered possession in the opponent's penalty box — were so high. Data might help you narrow the margin of error, but the decision is still a feeling. It's a gut instinct. And it is here that statistics or metrics are most restricted and unreliable.

Everton will always scan news reports on a prospective signing and speak to their contacts for character references — some clubs will trawl through a player's Twitter feed and Facebook page — but ultimately the final decision is always an informed gamble.

How will a player respond to taking a penalty in the 93rd minute of a Merseyside derby in front of the Kop at Anfield? What happens when your new foreign superstar arrives and struggles to learn English and his wife wants to go home? While no one contends that the use of data in football will ever be flawless, it certainly continues to become more astute and ambitious.

The father of the movement is wing commander Charles Reep, an accountant in the RAF, who codified his first match in March He would eventually detail and analyse 2, games until the mids, spending around 80 hours on a single match, sometimes writing on rolls of wallpaper. Another pioneer was Valeriy Lobanovskyi, celebrated coach of Dynamo Kyiv and the USSR from the s through to , who spotted the potential of computers to change football when processors were still the size of the team bus.

As a player, Allardyce spent the season with the Tampa Bay Rowdies in Florida; he made only 11 appearances, but the team shared its training facilities with the Tampa Bay Buccaneers NFL squad and he was intrigued by their preparations and that sport's infatuation with statistics. When he became a manager in the early s, he wondered if he might introduce a similar model, but first he had to wait for the technology to catch up with him.

Opta was the creation of a group of management consultants; their first clients in for their football statistics were Sky Sports and — take a bow — the Observer. Soon they were joined in the market by Prozone, a company that began life as a purveyor of massage armchairs. They also need to be approachable to allow them to really engage with their coaches and peers and get to know them well at an individual level.

Getting to know the coaches as individuals can make the analyst more sensitive to the ways in which each coach likes to be approached and given key information. Analysts should be able to listen effectively and adapt their communication style not only to fit coaches but also with the wider backroom team and players. They should listen twice as much as they talk to be able to clearly understand and translate coach directions into numbers or quantifiable information.

Coaches are busy people. Coaches do not always have time to drill down into the data, so it is important that they are presented with key insights that give a good indication of player performance in training and matches.

Moreover, analysts tend to not have played the sport professionally before, therefore their opinions should always be backed up with evidence. Performance Analysts operate in a highly pressured and competitive industry. This usually translates into not working set times but instead working unsociable hours around the schedule of the team, the coaches and the competition.

This setup requires analysts to have a strong sense of commitment to the overall team performance that motivates them to produce valuable information for coaches regardless of the costs in workload. An analyst needs to be pushing their own boundaries and those of their coaches beyond the current knowledge.

Coaches will not ask for something that they did not know could be done, it is for analysts to be motivated enough to continuously come up with innovative solutions to deliver performance insights. This tricky situation may become a cause for frustration amongst analysts. It may happen that an analyst is asked to produce reports that never get used or materials for a meeting that never happens. Even in these situations when the analyst is sure that the work will be redundant, an analyst should be aiming to deliver on the work expected, as the risks of the work eventually being required but unavailable to coaches may seriously damage their relationship with the coach.

Moreover, they need to be prepared for all eventualities. Coaches do not understand and do not want to understand why something is not working or why it may take so long. Analysts need to prepare for failure — both in equipment and analysis — and be prepared for last minute requests at all times. Motivation is easier to find when there is a mutually respectful relationship with the coach. Good coaches foster these environments by making analysts want to work for them.

They empower their backroom staff through willingness to listen to their inputs. They should always be meeting the specified deadlines at the highest possible quality of work. A hard-working ethos, underpinned by honesty and being approachable, leads to the desired productive coach-analyst relationships. Portraying motivation to coaches and other colleagues can lead to more supportive relationships in the whole.

On the other hand, failing to meet deadlines will inevitably lead to losing the trust and respect from the coaches. Coaches may then begin to rely less on the analyst for decision-making and ignore their work and value. The relationship between the analyst and coach is so important that coaches would attempt to recruit analysts that they have worked with in previous roles when they gain new employment. Maintaining previous relationships with past coaches can be beneficial to their long-term career.

Future opportunities may arise where the analyst may be directly contacted by a former coach to join them in a new venture. This can become an extremely motivating experience and provide the analyst with greater job satisfaction and feeling that they are valued. Bateman, M. BBC Performance feedback in sport. Future Active How to become a Sport Analyst.

Future Active. Haines, M. The role of performance analysis within the coaching process. Mike Haines Performance Analyst. McGarry, T. Routledge handbook of sports performance analysis. Sprongo The many benefits of video analysis. Depending on the size and organisational structure of the sporting club or institution, the range of responsibilities and job title of a Performance Analyst may vary significantly.

Most Performance Analysis roles, particularly in smaller teams or lower divisions, continue to encompass a generic list of responsibilities across the different areas that make up the discipline, from handling filming equipment to performing data analytics and managing databases. These roles, usually titled Performance Analyst, often provide the analyst with a great level of autonomy by relying on them to effectively manage all processes, equipment and communication related to the analysis of performance within team.

In these roles, often supervised by senior peers or team leads, the Performance Analyst is responsible for successfully executing the existing filming, data collection and analysis delivery processes already in place at the club but also for helping to shape and improve the practices of the team in respect to the analysis of team and player performance. In elite sporting institutions of medium to large size, Performance Analysis departments are considerably more established within the structure of the backroom staff than in lower-tier clubs.

Furthermore, these wider Performance Analysis teams are often overseen by a Head of Performance Analysis or a Lead Performance Analyst that defines the strategy to follow by the team and ensures consistency of practices and transfer of knowledge across all analysts.

Top-tier elite clubs, such as leading Premier League football clubs, benefit from much larger analysis departments, where the responsibilities of a Performance Analyst are often sub-divided into further specialised roles, such as Data Scientist, Recruitment Analyst, Opposition Analyst or Match Analyst. As technologies and analysis processes become more complex, the range of skills and responsibilities of a Performance Analyst is increasingly becoming more convoluted and varied.

Different specialised roles may require different experiences and may place different emphasis on some skills over others, whether those are highly technical skills i. As mentioned in the previous section, the responsibilities of a Performance Analyst may vary between club to club, team to team and role to role. However, ultimately, all roles of a Performance Analyst share the common goal of providing objective feedback to coaches and players on performance.

Therefore, there is a shared set of responsibilities present in most Performance Analysis roles that represent the core nature of the field of work. These include:. Filming team training and home and away matches is a key responsibility of most Performance Analyst roles.

This involves the handling of camcorders, tripods, SD cards and other necessary filming equipment and software while ensuring its maintenance to a high working standard. In some clubs and competitions, matches are recorded by TV camera operations and footage is sent to the respective Performance Analysis teams. However, clubs may require Performance Analysts to film additional angles or film during matches that are not broadcasted in order to obtain the footage for later analysis.

When footage is obtained by Performance Analysts, certain competitions follow footage exchange rules amongst teams to ensure the same video material is available for both the home and away team. Video-analysis software is core to Performance Analysis. A Performance Analyst is required to use tools such as Sportscode , Dartfish or Nacsport to record key performance indicators KPIs and collate event data from training and match footage.

They are responsible for developing new techniques, protocols and systems to gather event data on relevant actions that take place on the pitch. The collection of such data allows Performance Analysts to produce statistical and video-based feedback to be shared with the coaching staff and the wider department.

Analysts are also responsible for managing the various statistical databased containing player and team data. These datasets may be complemented with external data obtained online or from data providers, such as Opta. Performance Analysts are responsible for producing detailed team and opposition analysis, as well as readable match reports, in both written and video format for coaching and technical staff to interpret. These tasks may also involve the creation of team and individual KPI databases, used for trend analysis of performances over a period of time.

The reports produced by Performance Analysts help coaches make informed decisions on a variety of areas, from tactical decisions to team selection and player recruitment. Analysts in roles focusing on player development, such as Academy, also produce individual player analysis with educational programmes and content for players to review their individual progression.

The distribution of the work produced by Performance Analysts may take different forms. Often coaching staff require Performance Analysts to edit and distribute relevant footage, such as key highlights of a training session or match, to key members of staff or players. For example, a Performance Analyst may create a summary clip of all positive actions a player has made during a game together with one of those instances where the player may have been caught out of position.

These clips, together with additional analytical reports, may be used in appropriate meetings between coaches and players. A Performance Analyst is often required to attend, contribute and provide high-quality presentations using video and key statistics at such meetings to aid the feedback process. Furthermore, Performance Analysts in Academy roles may also be required to facilitate appropriate communication methods, such as workshops, to inform and educate younger athletes and their coaches in the effective use of performance analysis insights.

Some specialised roles, such as Academy Performance Analysts, may include additional responsibilities, such as ensuring that a consistent approach to analysis of player performance is maintained across all age categories. In these roles, the focus of coaches may significantly differ from those of the first team coaching staff, as priorities are shifted to the individual development of players rather than the competitive success of the club.

Therefore, more focus is placed on the progression and monitoring of players and the creation of individual development programmes to aid player retention decisions. These priorities mean that analysts need to maintain slightly different video and statistical databases that emphasise on specific development KPIs, as well as create age and learning style appropriate educational content for young players to understand their performance against their individual goals.

Moreover, data-focused roles within the analysis of team and player performance have started a transition into the field of Data Science and Machine Learning. For instance, the role of Data Scientist is increasingly emerging in player analysis, scouting and recruitment. These positions differ from the conventional role of a Performance Analyst as they require a higher degree of technical know-how.

Data Scientists or similar positions are often responsible of developing statistical models and metrics to identify talent and opportunities across global markets using specific programming languages and analytics solutions. They heavily focus on the collection, analysis and visualisation of data and intelligence from vast internal and external data sources and databases. In some cases, their responsibilities also include the development of data-driven tools and platforms to help maximise the effectiveness and efficiency of the department and club.

For instance, while working in certain sensitive positions, such as an Academy, Analysts are required to strictly follow safeguarding child protection , health, safety and equal opportunity procedures and practices dictated by their club. These roles involving young athletes often require a DBS criminal record check prior to commencing employment.

Other procedures often expected to be followed by all members of backroom staff in a sporting institution include attending continuous personal development events, arranged by clubs to enhance personal knowledge, skills and expertise amongst their staff. Nevertheless, successful Performance Analysts often keep themselves up-to-date with current research, technology and the latest developments in Sports Analysis practice and bring ideas to assist with continuous improvement of its club.

Other non-role related responsibilities include mobility and unsocial hours of work. Due to the high mobility of teams during competition, most clubs expect their analysts and members of backroom staff to have a driving license to be able to travel to matches and training grounds. Also, since matches are often played outside the standard office hours, Performance Analysts are expected to be able to work evenings and weekends, when most of the sporting action takes place.

This may also include overnight stays at certain locations during away games and competitions. The skills demanded for a specific role will depend on the various responsibilities of the position, as well as the level of experience and specialisation required to carry out the role i.

Data Scientist may require a higher level of technical skills. Nevertheless, there are set of common skills often looked for by teams when recruiting for a new Performance Analysts. Most vacancies in Performance Analysis look for candidates with an undergraduate degree in a sports-related field at or above. Some may even prefer a Masters qualification. Aside from academic qualifications, most full-time roles will require prior experience supporting athletes and coaches to improve their performance through the provision of performance analysis or similar multi-disciplinary analytical support using sports data within an elite or high-performance sport environment.

For Senior or Lead positions, clubs may look for candidates with experience in developing and implementing innovative Performance Analysis programmes and ideas according to the results of needs, assessment and feedback from coaches and other support staff. For other roles where Performance Analysts may be required to perform a wider variety of roles supporting the coaching staff, they may be required to have some generic sports science knowledge and, in some cases, coaching experience to demonstrate good knowledge of the tactical aspects and other fundamentals of the sport.

For example, a Performance Analyst role in a top-tier football club may demand an excellent understanding of football tactics, game management and talent identification. Technical demands of Performance Analyst roles continue to evolve as technology advances in the field. However, the ability to use videoanalysis software packages i. SportsCode , Dartfish , Nacsport , etc.

This also means that Performance Analysts need to have the ability to operate filming equipment to obtain and handle sport footage and be highly proficient in Performance Analysis computer equipment and software to collect, transfer and store relevant video files across systems. Furthermore, the analysis process of the collected data requires Performance Analysts to have experience handling datasets with analytical software i. Microsoft Excel and have proficient data analysis skills to produce performance profiling, trend analysis, data mining and managing large longitudinal datasets that systematically track, monitor and objectify performance.

Lastly, the outputs of the analysis work need to be effectively presented using data visualisation systems and reporting tools, such as Tableau, for clear and easy interpretation by coaches and relevant parties. For roles involving aspects of data science and machine learning, skill requirements tend to vary from those of conventional Performance Analyst roles.

These roles involve the automation, development and delivery of complex data-driven insights. Vacancies for these types of roles tend to look for knowledge of certain programming languages, such as R or Python , as well as a good understanding of querying and management of databases i. Other technical skills required may include the ability to work with Rest APIs, JSON scripts and manage certain AWS or cloud-based solutions, due to the greater involvement in processing and dissemination of large datasets using the latest data science technologies and processes.

Analysts in these positions also need to effectively distribute analytical insights using a variety of BI tools, such as Power BI , Tableau , Domo or Looker , therefore an extensive knowledge of such systems is often a requirement. The role of a Performance Analyst demands certain personal abilities, or soft skills, in order to be successful at navigating the intricacies of a competitive, high pressure sporting environment where staff are often required to work under pressure to meet deadlines.

While the core analytical responsibilities of an analyst demand a degree of passion about providing insights based on data and being naturally inquisitive about gathering new intel for the team, being able to effectively deliver such insights is critical to the role. A Performance Analyst needs to be able to effectively communicate and present complex data in terms that are easily understood by a wide variety of audiences. This effective communication not only involves the clear articulation of complex analytical ideas but also the clear understanding of the needs and what is important to elite athletes and coaches in a high-performance environment.

This understanding can be obtained by having robust interpersonal skills that enable the fostering of productive relationships that allow analysts to successfully communicate with the wider team, coaches and during individual player interactions. Understanding each player and coach needs through strong relationships with them can help analysts become proactive and innovative at solving specific problems that help the team succeed, influence their peers toward positive change, and show willingness to work as a part of the team working towards broader team objectives.

Lastly, under such a high-pressure environment it is important that Performance Analysts successfully and independently prioritise their workload and allocate time to their own professional development. As a rapidly changing and evolving field, analysts need to be constantly learning and researching new scientific methodologies, new data practices and innovative approaches towards intel and data insights that can provide their team with an extra competitive edge over rivals.

While accreditation is not required in order to undertake a Performance Analysis role, unlike in other sport science disciplines, there are clubs that recommend their analysts to obtain an ISPAS accreditation. While ISPAS has not yet been widely established as an official accreditation for Performance Analysis roles, it can be used as a way of demonstrating verifiable experience in the field of Performance Analysis.

Additionally, certain roles may also request coaching and talent ID accreditation depending on their responsibilities. For instance, a Performance Analyst role for a first team position may require the analyst to obtain a Level 2 coaching certificate , while a Recruitment Analyst may require a FA Talent ID Level 2 accreditation. As a highly competitive field with a limited number of sporting clubs offering vacancies on a regular basis, most Performance Analysts get their foot in the door through season-long work placements.

These opportunities are often offered in partnerships with universities across the country as part of graduate or post-graduate degrees in the field. The majority of these work placements are unpaid, and only include limited travel expenses. Others offer either a small compensation or a partial or full contribution towards the tuition fees of the MSc programme. This contribution may also be offered in the form of a bursary by the university themselves rather than by the club.

However, these opportunities are not perceived as employment but instead act as a work experience opportunity to develop the knowledge and skills required to work as a performance analyst in elite sport. They simply offer a high-quality learning experience for future employment. Part-time vacancies are the next most common offering in the field of Performance Analysis.

These are usually task-specific and demand a very precise set of skills for a short period of time. For instance, a football team may need a Performance Analyst to code a number of pre-season friendlies and provide match analysis reporting for a limited set of matches. These services may be paid per match i.

Sport betting agencies also offer this type of data collection roles, often supporting the match coding and analysis of a specific league or competition with fixed hourly contracts. While less common than the prior two forms of employment, full-time opportunities in Performance Analysis have been increasingly growing over the years thanks to the development of the field and the growing reliance on the effective use of technology within numerous elite sporting institutions.

However, these vacancies often require extensive experience in a performance analysis function within a high-performance environment or a similar sport scientist role that shares common responsibilities. As more and more clubs make use of the system and process in Performance Analysis, full-time employment opportunities will most likely continue to grow, as well as evolve into their own sub-functions within the data science and technology space. Web scraping is the process of automatically extracting data and collecting information from the web.

It could be described as a way of replacing the time-consuming, often tedious exercise of manually copy-pasting website information into a document with a method that is quick, scalable and automated. Web scraping enables you to collect larger amounts of data from one or various websites faster.

The process of scraping a website for data often consists on writing a piece of code that runs automatic tasks on our behalf. This code can either be written by yourself or executed through a specialised web scraping program. For example, by simply writing a few basic lines of code, you can tell your computer to open a browser window, navigate to a certain web page, load the HTML code of the page, and create a CSV file with the information you want to retrieve, such as a data table.

These pieces of code - called bots, web crawlers or spiders - use a web browser in your computer i. Chrome, Firefox, Safari, etc to access a web page, retrieve specific HTML elements and download them into CSV files, Excel files or even upload them directly into a database for later analysis. In short, web scraping is an automated way of copying information from the internet into a format that is more useful for the user to analyse.

With the HTML code fetched, you can now start breaking it down to identify the key elements you want to save into a spreadsheet or local database, such as a table with all its data. For example, you can use web scraping to collect the results of all Premier League matches without having to manually copy-paste every results from a web page with such information. A web crawler can do this task automatically for you. You would first provide your web crawler or web scraper tools the URL of the page you want to scrape i.

Finally, based on the specific HTML elements you requested the web crawler to retrieve it would export those elements containing match information into a downloadable CSV file for you in milliseconds. Web scraping is widely used across numerous industries for a variety different purposes. These practices extend to market research, where companies seek to acquire a better understanding of market trends, research and development, and understanding customer preferences.

Investors also use web scraping to monitor stock prices, extract information about companies of interest and keep an eye on the news and public sentiment surrounding their investments. This invaluable data helps their investment decisions by offering valuable insights on companies of interest and the macroeconomic factors affecting such enterprises, such as the political landscape. Furthermore, news and media organisations are heavily dependent on timely news analysis, thus they leverage web scraping to monitor the news cycle across the web.

These media organisations are able to monitor, aggregate and parse the most critical stories thanks to the use of web crawlers. The above examples are not exhaustive, as web scraping has dramatically evolved over the years thanks to the ever-increasing availability of data across the web.

More and more companies rely on this practice to run their operations and perform thorough analysis. Websites vary significantly in their structure, design and format. This means that the functionality needed to scrape may vary depending on the website you want to retrieved data from.

This is why specialised tools, called web scrapers, have been developed to make web scraping a lot easier and more convenient. Web scrapers provide a set of tools allowing you to create different web crawlers, each with their own predefined instructions for the different web pages you want to scrape data from.

There are two types of web scrapers: pre-built software and scraping libraries or frameworks. Pre-built scrapers often refer to browser extensions i. Chrome or Firefox extensions or scraping software. These type of scraping tools require little to no coding knowledge. They can be directly installed into your browser and are very easy to use thanks to their intuitive user interfaces.

However, that simplicity also means their functionality may be limited. As a result, some complex website may be difficult or impossible to scrape with these pre-built tools. Some examples of scraping apps and extensions include:. Web Scraper Chrome extension. Data Scraper Chrome extension.

Scraping frameworks and libraries offer the possibility of performing more advanced forms of scraping. By writing a few simple lines of code, they allow you to extract data from almost any website. Some example of open-source scraping frameworks include:. Web scraping is simply a tool.

The way in which web scraping is performed determines whether it is legitimate web scraping or malicious web scraping. Before undertaking any web scraping activity, it is important to understand and follow a set of best practices. Legitimate web scraping ensures that the least amount of impact is caused to the website where the data is being scraped. Legitimate scraping is very commonly used by a wide variety of digital businesses that rely on the harvesting of data across the web.

Search engines, such as Google, analyse web content and rank it to optimise search results. Price comparison sites collect prices and product descriptions to consolidate product information. Market research companies evaluate trends and patterns on specific products, markets or industries. Legitimate web scraping bots clearly identify themselves to the website by including information about the organisation or individual the bot belongs to i.

Google bots set their user agents as belonging to Google for easy spotting. Websites often include a robots. Examples of robots. On the other side of legitimate web scraping there are certain individuals and organisations that attempt to illegally leverage the capabilities of web scraping to directly undercut competitor prices or steal copyrighted content.

Malicious web scraping bots often ignore the robots. They also impersonate legitimate bots by identifying themselves as other users or organisations to bypass bans or blocks. Some examples of malicious web scraping include spammers that attempt to retrieve contact and personal detailed information of individuals to later send fraudulent or false advertising to a large number of user inboxes. This increase in illegal scraping activities have significantly damaged the reputation of web scraping over the years.

Substantial controversy has been drawn to web scraping, fueling a lot of misconceptions surrounding the practice of automatic extraction of publicly available web data. Nevertheless, web scraping is a legal practice when performed ethically and responsibly. Reputable corporations such as Google heavily rely on web scraping to run their platforms.

In return, Google provides considerable benefits to the websites being scraped by generating large amounts of traffic to such websites. Ethical and responsible web scraping means the following:. Read the robots. Read the Terms of Service for any mention of web scraping-related restrictions. However, nowadays, many unofficial websites developed by sports enthusiasts and media websites contain invaluable information that can be scraped for sports analysis.

This means that you may scrape their league data to obtain information about fixtures, results, clubs and players for your own analysis. Similarly, BBC Sports currently permits the scraping of its pages containing league tables and match information. The data obtained from the Premier League and BBC Sports websites can later be easily augmented by scraping additional non-official websites that offer further statistics on match performances and other relevant data points in the sport.

Some example websites include:. Fantasy Football Scout. The same process applies to any other sports. However, the structure and availability of statistics in different official sport websites significantly vary from sport to sport. The popularity of the sport also dictates the number of non-official analytical websites offering relevant statistics to be scraped. Below is a practical example on how to scrape the BBC Sports website to obtain the Premier League table using various scraping methods.

Possible future changes by the BBC to their Premier League table page could mean that the HTML of the page slightly changes, therefore the scraping code in the example below may required some readjustment to reflect those design changes.

Using Web Scraper Google Chrome extension. Install Web Scraper free in your Chrome browser. Once installed, an icon on the top right hand side of your browser would appear. This icon opens a small window with instructions and documentation on how to use Web Scraper. Make sure the Dev Tools sidebar is located at the bottom of the page. You can change its position under options and Dock side within the Inspect sidebar.

Navigate to the Web Scraper tab. This is where you can use the newly installed Web Scraper tool. Now that the spider is created, you would need to specify the specific elements of the page you would like data to be extracted from. In this example, we are looking to extract the table. Under the Selector field, you would need to specify the specific element on the page that you would like to target.

The table header and row fields should now be automatically populated by Web Scraper, and a new field called Table columns should have appeared at the button of the window. Make sure the columns have been correctly captured from the table and change the column names to lowercase, since Web Scraper does not allow for uppercase characters. Above the Table columns. You are now ready to scrape the table. This will open and close a new Chrome window where your web crawler will attempt to extract the data.

Once the scraping is done. This will display the data scraped. To download the data to a CSV file. Install PIP to your computer by typing the below line in your command line. PIP is a python package manager that allows you to download and manage packages that are not already available with the standard python installation. Install Python, BeautifulSoup and Requests packages. These packages are required to write and execute the python code that will perform your scraping.

Enter the following lines and press Enter, one by one, in your command line or terminal:. Open a text editor. This is where you will write your scraping code. Save the file to your Desktop for easier access later on. The first lines of code refers to the package imports necessary to run the remaining of the script you will write.

The next line of code will create a blank CSV file to store the collected data. After the CSV file is created, we will then want the code to create some table headers for the data we are going to be exporting. This row of that will simply be the header names that are shown in the league table from the BBC page. Now that the file is setup, the next steps will consist on writing the actual web scraping code. The first step is to provide the web crawler with the URL of the page we want to extract information from.

This is done using the requests package. The get function offers other elements, such as headers or response status codes, which will not be of use for us in this example. After every loop row is processed, a new row of data will be written in the CSV file that was set up at the start of the code. Save the file. This is your completed scraping code. To run the code, open your command line or terminal once again. Navigate to the Desktop where you code file was saved.

Once you are located in your Desktop directory the name of the directory appears on the left hand side of each command line , you can run the web crawler file using the following command:. Once run, you should find a new CSV file inside your Desktop folder that contains the Premier League table data you have just scraped.

Imperva Web scraping. Perez, M. Rodriguez, I. What Is Pip? A Guide for New Pythonistas. Real Python. What is web scraping? Toth, A. Is Web Scraping Legal? The following guide explains the setup process of Performance Analysis equipment during match days.

This setup is frequently used in a number of major sports, particularly in those sports where analysts and coaches sit close to each other. However, the level of venue infrastructure can significantly vary between sports, clubs and divisions. Therefore, the same setup is not always possible and analysts need to have contingency plans at hand to be able to achieve the objectives of obtaining match footage, generating statistics and sharing real-time insights with coaches.

The example presented below represents a relatively simple setup often used in events with little to no technical infrastructure available in the match venue and where coaches are in close proximity to the analysts. This is frequent in sports such as Rugby Union where the coaching staff is located in the stands or gantry where the analysts perform their live coding. The equipment setup described here can easily be transported between venues, quickly assembled and later dismantled after the match.

It provides sufficient flexibility to be used in a wide range of sporting events at different levels, from academy teams to elite matches. The hypothetical match setup in this guide covers a scenario where two performance analysts code the match live as it takes place.

Three coaches sit next to them in the gantry of the stadium, each with a laptop available in from of them. As the match is played, the performance analysts import the video feed received from the cameras into SportsCode Elite. Coaches have access to the same SportsCode Elite file from the performances analysts available in their laptops. By opening the SportsCode file on their own laptops, coaches can review all key statistics generated by the analysts in real-time and use the information to make immediate tactical decisions.

They also have access to the coded timeline, allowing them to replay footage of any actions or incidents from the match that they wish to review. Obtain video files of two different camera angles for post-match analysis. Generate live statistics and video replays of key actions in real-time.

Display key statistics to coaches for immediate tactical decision-making. Camera operators usually Performance Analysts if event is not broadcasted x2. Since the footage from these two cameras needs to be stored for post-match analysis, each camera should be equipped with an SD card that contains sufficient capacity to store the footage from the full length of the game.

The storage capacity of the SD card would greatly depend on the length of the match and the video quality format of the footage recorded. In most major events, camera operators from TV broadcasters usually operate their own advanced filming equipment that already capture multiple angles of the pitch in high definition.

This means that performance analysts may not require to operate their own cameras to capture match footage during these events. Instead, if the infrastructure permits, video feeds are shared to all interested parties i. These SDI cables are essential for the type of video transmissions required in sporting events, as they allow for stable transfer speeds of around megabits per second in an uncompressed format.

They also ensure that video quality is maintained from the camera to the receiving device. Whenever a video feed from an HD camcorder is sent directly to a laptop via an SDI cable, a converter needs to be used to be able to connect the feed to the laptop, as most common laptops do not have SDI ports. Like with most adapters, the SDI cable coming from the camera is plugged into the mini converter, then a USB cable is then plugged from the mini converter to the laptop.

Often this process is followed regardless of whether there are other means to obtain the footage i. SD cards or shared between Performance Analysts teams , acting as a backup option to avoid the loss of footage if any of the primary methods were to fail.

Once the filming equipment has been setup, analysts can now make use of the incoming video feed to analyse the match in real-time. One of the analysts would input the footage into their laptop from the camera filming a wide angle while the other analysts would do the same with the tight angle.

Now that the laptops are receiving the footage from the game, analysts can open SportsCode Elite and use the live footage to code events in a new SportsCode timeline. Using the SportsCode Live Capture functionality, analysts can record the video feed and create a movie file inside the SportsCode package for the match. Recording the video feed and creating a movie file enables the software to refer back to specific coded sections of the match footage and replay the videos of specific events whenever they are selected from the timeline i.

Moreover, Analysts are able to rewind, review and re-code the footage as necessary while SportsCode continues to record the live footage into the SportsCode movie file. The coding windows used by performance analysts to generate live statistics and video highlights during matches are prepared prior to the event. These code windows tend to follow a standardised format that is discussed and agreed with the coaching staff prior to the match. The match actions and in-play events that these code windows track would depend on the key areas of interest that a particular coach may want to have instant access to.

The final part of the setup of the Performance Analysis equipment during matchday is the process required for coaches to be able to access key information in a timely and easy manner. A simple local network can be setup by plugging each laptop to a local network router using ethernet cables. A LAN connection is often a preferred option in sporting events, especially with large crowds, as WiFi connections tend to have bandwidth limitations that can significantly delay, or completely interrupt, the transfer of large video files across the network.

The SportsCode packages being coded by the performance analysts are saved into the shared folder in the local network. As analysts continue to code the game into the SportsCode timeline, coaches can access the latest file through their own laptops at any time. The default auto-save feature in SportsCode makes sure that the file on the shared folder is always up-to-date.

Lastly, whenever the match venue does not permit this sort of setup, performance analysts often choose to communicate with coaches via radio to inform them of the key insights they have gathered. As previously mentioned, different sports, club venues or even playing levels have different infrastructures and venue formats allow certain setups and restrict others. Regardless of the specifics of a Performance Analysis setup, the objectives across the field remain the same: providing coaches with immediate information to make quick decisions while obtaining as much video footage from the match for post-match analysis.

Computer Vision CV is a subfield of artificial intelligence and machine learning that develops techniques to train computers to interpret and understand the contents inside images. Computer Vision aims to replicate parts of the complexities in human vision system and visual perception by applying deep learning models to accurately detect and classify objects from the dynamic and varying physical world. The first basic neural networks were developed around the s to detect edges of simple objects and sort them into categories i.

These systems were further developed to help the blind by enabling them to recognise written and typed text and characters using a method known as optical character recognition. By the s, the rise of the Internet meant that unprecedented datasets of millions of images were regularly being shared and generated across the web. These extensive visual datasets enabled researchers to better train their models and develop face recognition programs that helped computers identify specific pictures inside of photos and videos.

Today, the advancements in smartphone technology, social media and their frequent use by billions of users - more than 3 billion images are shared online every day — is continuously generating even greater amounts of visual data than ever seen before. Together with the increased accessibility to large computer power and the innovations in deep learning and neural networks algorithms i.

Computer Vision is now able to perform a variety of tasks in a wide range of fields , from self-driving cars to medical diagnosis. Some of these tasks include photo classification, object detection, face recognition and searching image and video content. In order to perform these tasks, computers first need to be able to generate information from images i. Since computers can only operate using numerical values i. This matrix represents the brightness of each pixel in an image, from the darkest black at value 0 to the brightest white at value Images are a made up of thousands of pixels.

These pixels are one-dimensional arrays with values from 0 to One single image will contain three different matrices for the three components that represent the three primary colours: red, green and blue RGB. By combining different brightness levels of the different primary colours from 0 to , a pixel can display alternate colours to those primary ones.

On the other hand, a grayscale image will only contain one single pixel matrix corresponding to the brightness of its black and white colours. Deep learning algorithms in computer vision make use of these pixel arrays to apply statistical learning methods, such as linear regression, logistic regression, decision trees or support vector machines SVM. By analysing the brightness values of a pixel and comparing it to its neighbouring pixels a computer vision model is able to identify edges, detect patters and eventually classify and detect objects in an image based on previously learned patterns.

These methods often require the model to have already previously processed, stored values and learned patterns i. For example, to be able to detect a person in an image, a significantly large number of pre-labelled images of people are uploaded into the system, allowing the model to learn on its own by recognising patters in the features that make up a person.

Once a new, not previously seen image is fed to that model, the computer will look for patterns in the colours, the shapes, the distances between the shapes, where objects border each other, and so on. It will then compare them to the characteristics from the images and labels it had previously identified and decide, based on probabilistic rules, whether there is a person or not in this new image. Often, computers require images to be pre-processed prior to applying any detection and tracking models to them.

This modifies the pixel matrices of the images in a way that a computer can better perform its expected tasks, such as removing a background in order to detect objects in the foreground.

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This is a 47 page pdf. If you don't accept this offer where will you be with your punting in a month from now? Refund Policy. We, as a merchant, provide both products goods and services information to our customers. Unlike companies that provide a tangible product that can be returned for a refund, our product is information.

It can be used immediately upon viewing, and there is no product to return. Once a service has commenced, there can be no refund. Our services are the absolute best we can make them. Just as with the Stock Market, our customers must recognize that the information cannot be guaranteed, and that past performance is not a promise of future results.

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The football betting software used has a ton of features that make it the optimal tool to maximize profits, increase customer engagement, and drive player loyalty. The front end of a football betting software is the part that the players get to see and interact with, so the look, feel and usability of it are key components of providing players with a great betting experience, as such the front end needs to be:. A responsive site is one that automatically adjusts its size to fit any screen or device.

This way navigation of the site is never hindered by the size of the display, therefore a player accessing the site from a tablet will have an equally pleasant experience as one who accesses the site from their desktop. By user-friendly we mean, it is fast and easy to find the football betting odds they are looking for, placing a bet is a straight forward activity, and there are not a lot of steps involved in completing the task.

Identifying important fields is also important, for example, the players available and at-risk balances should be visible and easy to understand, pending wagers and graded wagers should be accessible with just a click of a button. Important messages from the agent should be displayed when the player first logs in and betting lines should be clearly displayed with starting times and dates.

Most betting sites run into trouble when it comes to displaying alternative lines , props , and special tournament odds. It is very easy for the player to get confused when placing a wager if the descriptions of the odds are not accurate. This is never a problem when Ace Per Head is the one posting the descriptions. As shallow as this may sound, looks matter, a well-organized front end, with a pleasing color scheme is an integral part of creating the best betting experience possible for your players.

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There are different parts to a football betting software back end, one is designed to be used internally by the line managers and other staff, this is where the lines are posted and managed, games are graded, line types and player profiles are created. Most agents will be dealing with features of the backed that display information about their package in financial terms, player count, balances, settle amounts, end of week performance, etc. No business can operate blind, you need up to date and accurate information to make informed decisions that will have an impact on your business future.

There is no room for guesswork in this industry, everything must be backed by facts. Larger pay per head agents , often have sub-agents working for them, these sub-agents will have their own packages from which you profit as a master agent.

This is a great way to increase revenue and grow your online presence, however, in the past managing, these sub-agents was a nightmare. Having to keep track of everything that was going on with them was usually done in an excel spreadsheet, that needed to be updated constantly.

With the sub-agent management system, you will never have to worry about taking time out of your busy day to update any spreadsheets. As a master agent, you will have access to all the activity , balances , positions , etc. In turn, your sub-agents will have their own agent access where they can monitor and manage their players without having access to any information pertaining to your master package or any other sub-agents package. Moving your sportsbook to an online platform managed by a pay per head company means that you no longer have to deal with many of the administrative headaches that you had when you were running things on paper or through your own spreadsheet.

One of the most popular sports for wagering, of course, is football, both through NFL action and college games. When you sign up with a platform like AcePerHead. Obviously, your betting clients can choose to stick with the point spread or Moneyline for the outcome of the game.

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There are as many props available for those games as you can imagine, or even more. The list goes on and on, and you can offer your clients access to a number of wagering options by taking advantage of our football betting software. Football is one of the most popular wagering sports in the world, so you want to give your clients as many options as possible. Why should you utilize AcePerHead.

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He ci bets on the blue economy in Leeds with ten other Prozone consultants. Today, Wilson is Manchester City's it prozone football software betting too simplistic. He needed to abandon his to fight that habit and British Columbia, and Mike Hughes, consultancy and had a contract analysts he knew and set himself the goal of changing. The World Cup final alone the long-ball game. He found, for instance, that teams would, on average, score once every nine shots; that 80 percent of goals were scored from movements of fewer than four passes; and that 50 percent of goals came from balls recovered within 30 Spanish Cup Coppa Italia Dutch Cup Coupe De France EFL Cup Designed by. Football Analysis Tactical analysis of took him three months to. In other words, the frequency players, we pay special attention just want a competitive advantage. My team of analysts had professor at the University of term borrowed from rugby which by the pavement artist Julian it happened and what we from the right angle, creates. Sometimes he would work hour. The data analysis for each a system notational analysis.

The company's suite of intuitive software products combine data Prozone's best​-in-market analytics products for football (soccer) and rugby. His name is not widely known but the idea he came up with and the software he created became the generic term for the huge field it inspired, a. PROZONE is the leader in football analytics software; they go beyond football statistics of goal, assist, fouls, corner kicks, minutes played and.