Premier league analytics

Premier league analytics

Foomni Analytics combines the love for football with the in-depth statistical approach of American fantasy games in the state-of-the-art projections algorithmwith the goal of improving your FPL score.

premier league analytics

Our advanced technology dives in the ocean of data and stats, and returns with projections that can help you make better transfer and selection decisions. In the artificial intelligence revolution, the best results are always achieved with the combination of human skill and machine data processing.

Foomni is your best FPL data processor. Salah, Henderson, van Aanholt, Sterling and Ings are picks in gameweek overshadowed by coronavirus concerns, with The meaning is simple - it's easier to kn Our algorithm analyses several years worth of statistical data to make projections about player performance for each gameweek.

We use many statistical indicators, including player's past form, strengths of the teams involved in the fixture, team forms and even some advanced matchups, for example how a player plays against a certain opponent.

We are continually increasing the number of factors in our algorithm, and improving its precision. We know how each player tends to perform on Saturday morning, Monday night, or when that particular referee is officiating. The devil can easily hide in such details. Each player is indexed in six categories : influence on team's scoring, points-per-minute, usage rate, consistency, current form and points-per-pound.

Based on that, the player is also given his overall index. We even prepared radar charts familiar from video games so that you can know everything about a player with just a glance! To get the full picture, we use three distinct player ranking systems: point ranking, point-per-game ranking and value ranking. Point rank is based merely on player's historical or projected points scored.

Points-per-game rank is there so injured, and new players in the league can be ranked appropriately. Value rank divides player's total points by his value, quickly showing you who gives the best value for money. Look out for those bargains that can make a difference! Based on our projections, we select a dream team of eleven players that are expected to perform the best in current gameweek.Even in early April, when Leicester City Football Club were riding high at the top of the Premier League, seasoned pundits expected them to falter as they approached the finish line.

But while rival contenders fell by the wayside, Leicester continued to pick up points by playing the same brand of quick counter-attacking football that had catapulted them to the top in the first place. When the club sealed the title earlier in Mayit was branded one of the biggest upsets in sports history. Leicester has been using a number of different sophisticated data and analytical toolscoupled with wearable technologyfor years.

According to Chris Mann, marketing executive at Prozone Sports — which currently supplies technology to 19 of the 20 Premier League teams — Leicester has been using its products for 10 of the past 11 seasons. By the end of AprilLeicester racked up fewer injuries than every club in the Premier League, according to Physioroom.

As a result, Leicester manager Claudio Ranieri — who was famously labelled the Tinkerman during his time at Chelsea FC — was able to pick the same starting 11 for the majority of the season. What this technology enables users to do is — among other things — establish the risk of a player getting injured at any given time based on benchmark data that automatically shows when they have exceeded their usual workload.

In addition to these toolsLeicester uses the latest sports science techniques, including cryotherapy to aid recovery programme of players. The club installed ice chambers that use liquid nitrogen to expose players to extremely cold temperatures for short periods of time.

Data is embedded into the culture of the club, with its performance team relying on analytical products from OptaPro. Bahia detailed how players are given pre- and post-match interactive reports to read from the performance team via iPads, which include statistics, subjective comments and match footage. The tools that Leicester uses are available to every Premier League club. There is also the growing array of GPS tracking tools that are seeing a lot of take up by Premier League teams.

For instance, StatSports Technologies produces a number of different player tracking tools. It currently works with 15 of the 20 Premier League clubs, in addition to the likes of European giants such as Barcelona and Juventus. Richard Byrne, head of business development at StatSports, says GPS player tracking technology offers teams huge advantages in terms of monitoring the load they are putting on players in training at the moment players are not allowed to wear wearable technology during competitive matches.

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Cloud optimization tools can help companies manage costs on a day-to-day basis, but only clear business goals and governance Mike Kelly dives into his role as CIO and the data literacy program he co-founded at Red Hat, as well as provides insight for Organizations can reap benefits from IoT technology but only if it is properly secured. Learn the components of IoT network Even during pandemics, hackers use malware such as ransomware and phishing to exploit an organization's vulnerabilities.

Make sure you're covering all the bases, from Organizations have long relied on VPNs to connect remote workers with company resources. Configuration management is essential to keep accurate network configuration records and to help organizations avoid potential The costs associated with cloud repatriation go beyond the migration itself. IT managers must account for any new hardware, Server hardware has consistently evolved since the s.

Premier League Fantasy

CPUs have evolved to meet ever-increasing technology demands. We look at the way performance and power characteristics have With organizations of all sizes coping with the impact of COVID, the importance of enterprise data governance and chief data On-site monitoring centers come under stress when it's necessary for most workers to telecommute.The new Premier League season is just around the corner.

premier league analytics

For myself, and many others, this does not just mean early mornings but a new fantasy football season! The league can get quite competitive and I am always looking for an advantage that can bring me bragging rights come the end of May.

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What better way to gain an advantage than by diving into the numbers and doing some analysis! There will be a series of posts on this topic. I will begin with exploratory analysis achieved through data visualization in RStudio with Tidyverse packages.

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So, onto visualization! The primary focus of my analysis is the points scored by players, as it is the most important aspect of fantasy football. My data set was pulled at the beginning of August.

It includes the price of players at the beginning of this season and some basic statistics from last season. After importing and inspecting the data, it is evident there was some quick cleaning to do.

Fortunately, this only consisted of dropping some variables. Now my data set is nice and clean and ready to visualize. As I mentioned, points are the primary focus of my analysis. Therefore, my initial visual was a simple histogram to see the distribution of points.

The first insight that jumped out to me is that there are a lot of zero values. Why are there so many? There are over players with no score from the previous season.

premier league analytics

I went back to the data set to look at these players with no score. This was easy to achieve with the dplyr package. Upon first glance I identified that these players with no score are all new to the league.

They were either promoted from the Championship or were new signings this summer. Having looked at the distribution of points across all players, I wanted to analyze the relationship between points and the other variables in my data set.

The first variable I analyzed was position. Specifically, I wanted to see if and how position impacted the points scored by players.

I visualized this relationship with a box plot or a box and whisker plot. This allowed me to quickly see variability of points for the four positions. This simple visualization reveals some quick insights. The position with the highest median points is goalkeeper.

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The other three positions have comparable medians. The box plots for both forwards and midfielders are positively skewed, meaning there is a wider range of higher scores above the median than below the median.

For example, when examining the box plot for forwards, the median points value is around Compare this to the negatively skewed box plot of the goalkeepers. The median points value is around TNW uses cookies to personalize content and ads to make our site easier for you to use. Boffins from soccer stats firm Opta produced their final English Premier League table by analyzing four years of historic results to predict the likely outcome of each match. And in a huge shock in the title race, Manchester City pipped Liverpool to the trophy by a single point!

Only joking! The Reds were comfortably crowned champions — just like everyone expected — with an estimated probability of At the other end of the table, there was heartbreak for Bournemouth fans, who finished level on points with Watford but were cruelly relegated on goal difference. To separate the winners from the losers, Opta studied the attacking and defensive qualities displayed by each team in the past, with more recent results given extra weight in the calculations.

Analysts then estimated the difficulty of each remaining match, and used goal predictions to guess the results. As officials debate whether to void the season, honor the current positions, or complete the campaign once the coronavirus is contained, the AI has offered an alternative that should at least please Liverpool fans.

Published April 8, — UTC. April 8, — UTC. Read our daily coverage on how the tech industry is responding to the coronavirus and subscribe to our weekly newsletter Coronavirus in Context. For tips and tricks on working remotely, check out our Growth Quarters articles here or follow us on Twitter. Human-centric AI news and analysis.

Jon Candy. Corona coverage Read our daily coverage on how the tech industry is responding to the coronavirus and subscribe to our weekly newsletter Coronavirus in Context.Is football so chaotic and random that it can only be enjoyed, but never understood? Or is it a problem that can be worked through and solved? Those are extreme positions but the debate itself is real, and is more contested than ever before. Other sports are increasingly understood, analysed and predicted through numbers.

And yet in football we are still told that only one statistic counts.

premier league analytics

Or that a statistic has never made a save, or scored a goal. But neither the difficulties of football analysis nor the cultural resistance to it have put off some people. Because those very barriers only make the potential benefits gained from insightful football analysis far greater. Anyone who can build a model that can even start to understand and predict football has something very valuable on their hands. There is a new generation of football analysts whose attempts to predict outcomes have been tested on betting markets and then sold to clubs.

Campbell was at the Sloan Sports Analytics Conference in Boston earlier this year, listening to a discussion of golf analytics. Football, with far more variables than golf, is the opposite.

In my mind, hard is good. Because there is the most opportunity to gain a competitive advantage if you use it right. This is why poker players make more money than chess players.

The challenge, then, for the next generation of analysts is to break the game down and measure it in such a way as to better predict what is likely to happen next.

But how do we get closer? The key is to know what to look for. Data are pieces of information — not necessarily numbers — about things that happened in the past. Analytics is using that information to help better predict the future.

The big problem for football used to be the data itself.Yes, one has to be a bit data enthusiast to precisely hit the cross-bars.

Since the game and the surroundings change every week, beginning with a high point is crucial. In the first week, one should look for players who are doing well in the pre-season friendlies. It is better to avoid statistics from the previous season since a lot of changes take place between two seasons. The pre-friendly statistics of a football gives a look at the present condition.

The statistics to precisely look at are not goals, but the amount of threat made, creating chances for the team, the coordination with others in the team and the rate of injury. A player can use a linear regression model or a basic neural network model to begin the first week by training it with datasets. The datasets should include historical data of the above-mentioned parameters to judge a players performance before the season begins.

GBMs or gradient boosting machines are often termed as the most effective one in prediction due to the unbalanced nature of available datasets from a number of different sources. Previously, historical data were combined with Naive-Bayes and other ensemble methods to reach predictions. The reported accuracy was recorded at 81 per cent when data was used to train a Gaussian naive Bayes algorithm.

However, the prediction result rose up to 86 per cent with the use of GBMs.

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While buying a player, the first thing that comes to mind is how many points a footballer will give back for the money spent since a player has only million euros to spend from which 15 players can be bought.

Using scatterplotsone can classify the players with keeping the cost on the Y-axis and the Fantasy points on the X-axis. This way, a player can determine which are the players yielding the highest number of points in contrast to the cost. The idea is to select the ones, which generate points between 80 to 90 and are priced within 4. However, to generate the maximum point for the team, it is essential to bring one or two players who generate more than points a match but are priced high up to 12 million euros as is the case of Mohammad Salah from Liverpool.

It is advisable to spend more than 10 million euros on two players, preferably one from the mid-field and another one as a defender. Keeping aside favouritism, the top five teams are the ideal place from where footballers should be selected.

Although, most of the footballers playing in the top five teams keeping aside the exception of the Leicester from might come with hefty fees. Remember a player needs a total of 15 footballers and it is advisable to pick nine footballers from the top five teams, and the rest six from the teams ranked between sixth to 15 on the table.

The changing nature of the game makes it hard to pinpoint on a particular team, and when it comes to 14 teams, the number of minimum footballers involved are 14 teams x 11 players.

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By running a simplex algorithmone can come to the decision of choosing the playing 11, which usually is the culmination of the players generating a maximum number of points. It must be kept in mind that other factors also bring in a difference to the performance such as Influence, Creativity and Threat ICT. Due to this reason, the score of these factors must be run through the algorithm. Once the ICT score is generated, an average score can be calculated between the ICT score and maximum points generated by players to select the 11 footballers.

Serious players who aspire to win the Fantasy Premier League can also look up to a number of tools such as Alteryxwhich consist of three different workflows and macros.

Foxy Leicester City FC won Premiership with data analytics

It also takes into account, which referee is supervising a match. Fantasy Premier League has created a buzz among football fans all over the world. However, winning the game also depends on luck since injuries, and certain fouls cannot be controlled by any. With different strategies, it is still difficult to say if one player will definitely win or not. To end with, Analytics India Magazine has another article lined up for sports enthusiasts who might want to know the different uses of data analytics in a variety of sports.

Click on the link to have a read. First Week Preparation In the first week, one should look for players who are doing well in the pre-season friendlies. Rohit Chatterjee.If you have not created an account yet, then please sign up first.

Legal Terms of Use. Whether you are offering feedback, looking for a career with us or just want to say hello. Predict22 is a live sports analytics website with a focus on cricket, football and basketball. It is all for sports fans! The website attempts to provide sports fans with a one-stop and integrated environment for all their sporting needs. Users can check scores, schedules and analytics related to their favourite teams and leagues in one simple interface.

Predict22 is a live sports analytics website. It is all about sports analytics!

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The website attempts to provide sports fans with a one-stop and integrated environment for all their sports analytics needs. Users can check scores, predictions, schedules and stats for their favourite teams and leagues in one simple interface. Predict22 currently covers matches over three sports - cricket, basketball, and football.

For cricket, we cover all international over and over games along with prominent domestic T20 leagues. We cover all matches in the leagues and competitions that we cover, irrespective of whether they are televised events or not. Predict22 provides a pre-match prediction for all games across all three sports. Specifically for cricket, we also have a live match prediction algorithm and we also provide fantasy picks.

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Using an Artificial Neural Network ANNa machine learning technique, we process all relevant matches that have been played so far and highlight the pre-match favourites for a particular game.

The pre-match favourites are assigned a numerical value to signify the confidence of the prediction. The team with the highest probability is the favourite for winning the match.

The factors considered in the algorithm vary as per the sports and format. However, in a nutshell, they are, but not limited to, the past performances of players and teams in recent matches and against the given opposition. Once our algorithm has taken into account the various parameters that contribute to the pre-match prediction, there is nothing that will change the pre-match prediction.

The three sports are very different from each other. Are there any differences in the pre-match predictions for the three sports? We have a common base algorithm for the pre-match predictions which is tweaked for each of the sports. Football - has 3 possibilities including a draw league matches and 1st leg of knockout matches. Cricket - we ignore the possibility of a tie, so have 2 possibilities. We started predicting basketball matches in with the NBA Season.

This means that if the match is played times within the same parameters, Team A would win the match 55 times. It does not reflect the margin of victory. We are looking at new additions to our algorithms to predict margins of victory. Predict22 does not support or endorse betting of any form.

Predict22 provides ball-by-ball updates and ball-by-ball analytics for cricket. This roughly turns out to be updated every 30 seconds during a live match for cricket.

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