Predicting the Premier League finish with extensive data modelling

Football has returned! This last weekend saw the Bundesliga resume play and strong television ratings – with double the average audience in Germany and FS1’s largest ever audience in the United States! Now eyes will turn to the remaining big European Football leagues (Premier League, Serie A and La Liga) to see when (or if) they will return.

Consider the case of the Premier League. They have been negotiating their return for several weeks with an eye on a June restart. This has received pushback from both players and several clubs. Even more interesting, the Scottish Premiership this week unanimously decided the season could not be concluded and simply awarded Celtic the title.

Part of the challenge to restarting the season is the resistance of six clubs to playing the remainder of the season based on the fairness of completing the season under different circumstances and the economic result of relegation on these teams. After the work DataRobot has done predicting the Champions League knock-out stage, the French Ligue 1 and Bundesliga conclusion based on how the teams were playing prior to the postponement of the season, I was curious if we could determine how the Premier League season was likely to conclude under normal circumstances.

We used the same models and approach we developed to predict the knockout stage of the Champions League, using the game data provided by Data Sports Group. We used DataRobot’s models to predict which team would win each match to simulate the final nine matchdays 10,000 times. For each team, we calculated the average number of wins, draws and losses over those 10,000 seasons to build an average final table.

In the remaining nine games, we see few changes of team standings. Liverpool continues its historic run to the title and adds to the historic nature of the season by tying the 2017-18 Manchester City club for most points in a Premier League season with 100. Liverpool will beat that club with a winning point margin of 23. Manchester City will hold onto second place, with the three additional Champions League Group Stage positions going to Leicester City, Chelsea and Manchester United.

Wolverhampton and Sheffield will hold onto the Europa League positions, while Tottenham and Arsenal will tie just three points from the Europa League slots. Brighton & Hove Albion, West Ham and Watford will cling to their Premier League spots for next year, with West Ham and Watford tying just one point above Aston Villa. While Aston Villa falls just one point from remaining in the Premier League it does pull ahead of Bournemouth as Bournemouth, Norwich City and Aston Villa all face relegation to the Championship.

It will be interesting to see, when/if the season resumes how things play out. Will we see a record tying or breaking year from Liverpool? How will teams perform in empty or neutral sites? What will be the impact of several months off on the injury situations for these teams and how will that impact the final standings. We are looking forward to finding all of this out.

Contact our Sales Team to learn how you can apply predictive analytics, machine learning and AI for your sport business.

Fan Engagement and The Second Screen

For the average sports fan, it’s no longer possible to passively sit on the couch and just watch a game. The ubiquity of internet-connected devices along with the multitude of media channels available at fans’ fingertips has led to a multi-screen, interactive experience.

Second screens are primarily social and consumed via smartphones, tablets and other connected devices. It’s estimated that over 80 percent of sports fans use second screens while watching a live sports TV broadcast.

The most popular engagement on second screens during live sports broadcasts are:

1. Use of Social Networks: A large majority of users, browse through social networks reviewing reactions as a game progress

2. Chat/Message with friends: Communicating via messaging apps is the next most common thing. Stats, quips, and “did-you-see-that?” moments discussed

3. Read news updates: During the game, fans browse through the news about the match or their team, whether its an injury update or a shocking incident on the pitch

4. Search for related information: Fans turn to their devices to search for information around stats for players and teams, upcoming fixtures, progress around other matches played simultaneously and so on.

For advertisers & brands becoming part of the social conversation with sports fans can be easier said than done, but there’s no doubt that marketers will have to innovate and create new activations between TV and social.

Some suggestions on how to engage fans in these moments:

1. Make visual, credible content that is easy to share.

2. Plan ahead, identify possible triggers for conversation and create content that anticipates these triggers

3. Ignite debate with questions and fan interaction.

4. Create snackable facts, stats and trivia.

At Data Sports Group, we are already helping media achieve great user experiences. For example, moment marketing services via social networks provides brands an opportunity to create content that anticipates triggers for conversation and that can be tweaked on the fly to be delivered quickly when needed, Being agile in the moment allows for capitalization on unfolding events that brands can relate to.

Data rich features can boost active sports consumption on OTT platforms

Over-the-top (OTT) for sports is becoming increasingly attractive. There are growing investments in content acquisition that show how more and more companies are getting into the OTT game. Typically, broadcasters have played a key role in sports rights acquisitions driving revenues for Leagues & Clubs, however, the entrance of OTT providers in the last few years has increased the competitive landscape.

OTT providers, like Amazon Prime have entered the sports arena, some of which are also pure-play sports operators like DAZN and Eleven Sports or digital channels such as the Tennis Channel, aim to deliver a more engaging sports-focused experience.

Increasingly the user experience is now a two-way channel. Sports OTT services are underpinned by three important criteria: interaction, gamification and personalization across the video services offered.

Data has become an even more prevalent part of interactive storytelling for publishers, media outlets and rights holders pre-game or during a live broadcast. New data points help broadcasters and content owners tell more engaging stories to tech-savvy audiences.

Gamification can be used to support programming on streaming sports platforms in the form of polls, quizzes and social gaming settings using sports data, offering a free and engaging content module. By bringing together data and video to engage the audience, gamification can unlock monetisation opportunities and provides possible secondary benefits for commercial partners.

With deep descriptive metadata, it enables intuitive content navigation and nuanced recommendations that connect viewers to the content they like, driving personalization features for each user.

As an example, Data Sports Group (DSG) created a sports data module for an OTT provider for a live match broadcast. Using DSG data as reference, the user could navigate away from the match into the game timeline, view player & match stats and other data metrics. Linked to the personalization is also the ability to buy merchandise related to the teams or players from the specific match in progress.

A snippet of this service is available to view here.

A data-led approach within OTT offering is an increasingly efficient way to keep users engaged, gather fan insights and thereby unlock the full commercial potential of an OTT platform’s offering.

https://datasportsgroup.com/news-article/30108/esports-popularity-surges-during-coronavirus-pandemic/

Esports is increasingly gaining great traction in these times. Boosted by a conspicuous lack of professional sporting events world over, stuck-at-home consumers have increasingly embraced the idea of following and watching other gamers play matches.

Video games that are the basis of official live events are still serving as digital battlegrounds. The professional esports league of video game Counter-Strike: Global Offensive (CSGO) called ESL Pro League is currently organising games. The league has games played daily attracting a large viewership.

League of Legends has tournaments running across the globe, with games being moved fully online from a venue organized event. Similarly, events across several other game titles like Rocket League, Rainbow Six, Overwatch and DOTA2 are being held.

In response to the lack of traditional sporting events, clubs, organizers & sports federations are looking at ways to give fans a chance to engage with the sport. Last week, saw the launch of inaugural eNASCAR iRacing Pro Invitational Series race, featuring 35 current and retired Nascar drivers which was the most viewed e-sports event in US television history.

English League Two football side Leyton Orient organized an eSports tournament, FIFA20 Ultimate QuaranTeam. Clubs like Manchester City, Ajax, Roma who have existing esports teams have pitted to play in the tournament consisting of 128 clubs. Similarly, the Bundesliga is organizing an online tournament called Bundesliga Home Challenge compromising clubs from the German Bundesliga club structure

We will continue to see several non-official virtual tournaments replacing traditional sporting events for football, motorsports, and other sports, whereas video game publishers will seek to capitalize on viewership by running their official tournaments during this period.

Data Sports Group has been offering live eSports data for several years tracking tournaments across all popular game titles. You can request free access to our eSports catalog for April 2020 by contacting our Sales Team.

DataRobot uses DSG’s data to predict UEFA Champions League winner

Ahead of the knockout stage which begins this Tuesday, DataRobot approached Data Sports Group to learn if they could predict the Champions League Knockout Stage and the eventual Champions League winner.

DSG’s rich coverage for the UCL includes detailed match data, advanced team & player statistics covering 40+ data points, Player Career Stats and more. Using our Premium archive data from past seasons across the top 5 domestic leagues in Europe, DataRobot was able to use previous performance data to generate player ratings and predictions.

The modelling approach & winner predictions is detailed in DataRobot’s blog post which can be read here.

Have an interesting use-case for advanced sports data? Get in touch with us at sales@datasportsgroup.com

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