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What are Oracle's Match Insights for the Premier League?

4 Jul 2021
FF_WinProbability_V2.2.1

Premier League's official cloud supplier Oracle offering fans around the world more insight than ever

From this season, Premier League fans around the world will enjoy greater insight when watching matches live on TV thanks to Oracle's Match Insights.

Oracle's data and analytics and machine-learning technologies will deliver in real-time in-match statistics, providing a deeper understanding of the live action to a global audience of billions.

Here is what you will see.

Average Position

The Average Position metric tracks the positions of all players when their team are in and out of possession.  

The model highlights differences in how teams organise themselves when attacking and defending.

During the match, fans will see how teams react to their opposition's tactics, helping them to understand the strategies behind different styles of play.

FF_AveragePosition_V1.4 - JH Edit
Win Probability

The Win Probability statistic tells the story of a team’s performance by calculating the chance of a team securing a win or draw by simulating the remainder of the match 100,000 times.

The model is based on four years of match data and takes into account if a team are home or away, the current score, penalties awarded, players on the pitch, red cards and time left in the match.

PLP-screengrab-Win-Probability-MUNEVE-edit-Zoom
Attacking Threat

The Attacking Threat metric measures the likelihood of the team in possession scoring a goal in the next 10 seconds.

The results are based on data from thousands of historical matches and the last five events in the current possession.

The model incorporates the outcomes of passes, dribbles in possession, tackles and the locations on the pitch where they take place. 

PLP-screengrab-Attacking-Threat-MUNEVE-edit Zoom

Additional Match Insights from Oracle will be introduced throughout the 2021/22 Premier League season, based on live data streams, real-time tracking data and facts collected on each of the League’s players and from thousands of past matches have been developed by machine learning models.

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