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Is eSport sport?
 
That is a good question to ask sports fans at a dinner party if you want to get a good discussion going.
 
Luckily the question we were asked was: “Can SportsML handle eSports”? And that seemed like a more straightforward question to answer.
 
Here is a short clip that shows how big eSport really is, and also touches on the question at the beginning of this article:
 
 
SportsML is an IPTC standard that covers all aspects of sports when it comes to scheduling, tournaments, results, live reporting, standings and statistics. And even if eSports is very different to traditional sports, on this level it is very similar. All eSports consist of games between teams or players, much like football, hockey, tennis or any other event where the competitors meet “head-to-head”. From those games we have results, standings and statistics, which are all supported in SportsML.
 
But there are some areas of difference to note.

Home and away teams

In traditional sports that meet in this way, the concept of home and away is often important. For example, the home team can have first choice in colour, starting side, familiar playing ground etc. And in some football tournaments, goals scored away from the team’s home location can be worth more if the game is tight. Plus, often the home team have a much bigger crowd to cheer them on.
 
In eSports, there is really no concept of home or away. Technically, players can be anywhere and play connected through the internet. Players of the same team do not have to sit together. In reality, though, for bigger tournaments the players will usually gather in an arena with big screens and a huge audience watching. If players are in separate locations, the quality of their internet connection will be a factor.
 
In SportsML we still have to handle one side as home and the other as away using the alignment attribute.

Pre-game actions

Another difference in eSports is that actions can take place before the official start of the game. For example, teams can choose or reject characters or maps from the game they are playing. This is an important part of the game, since each team’s aim is to get characters and/or maps that they are good at into the game, while rejecting the characters and/or maps that their opponent is best at.
 
It is as if Argentina and Portugal would meet in football and Portugal could reject Messi from the available players for Argentina while managing to have Ronaldo still in their own squad. Or if Arsenal and Tottenham were playing and they could “battle” over which field to play on.
 
In SportsML we have something called actions that can be used to represent pre-game actions:

<actions>
<action sequence-number="1" team-idref="team_9572" type="esacttype:remove" comment="Nuke"></action>
<action sequence-number="2" team-idref="team_6134" type="esacttype:remove" comment="Inferno"></action>
<action sequence-number="3" team-idref="team_9572" type="esacttype:choose" comment="Cache"></action>
<action sequence-number="4" team-idref="team_6134" type="esacttype:choose" comment="Train"></action>
<action sequence-number="5" team-idref="team_9572" type="esacttype:remove" comment="Overpass"></action>
<action sequence-number="6" team-idref="team_6134" type="esacttype:remove" comment="Dust2"></action>
<action sequence-number="7" type="esacttype:remaining" comment="Mirage"></action>
</actions>

Statistics for eSports teams, players and tournaments

There are many types of eSport games with possibly different sets of stats. We focused on Counter-Strike (CS) where teams play across three different maps. On each map the teams take turns in playing as “terrorists” or “counter-terrorists” and the first to reach 16 wins, wins that map. Then the results across maps are aggregated in a best of three format, so the end score will be 2-0 or 2-1. So it is a bit like games and sets in tennis.
 
We can represent this structure with a scoping-label on outcome-totals in SportsML:
 
<team-stats score="16" event-outcome="speventoutcome:win">
  <outcome-totals scoping-label="T" wins="4" />
  <outcome-totals scoping-label="CT" wins="12"/>
</team-stats>
 
Tournament structure is always interesting regardless of sport. There are many tournament models from straight round-robin where the top team wins to constructions of combinations of group play, qualification games, more group play and then finals of various levels.
 
The eSports tournaments we looked at were a construction of quarter finals, semi-finals and final. I’m not sure if there were more levels such as qualifying games before that. In the end we always have one winner of the final.
 
If we dig deeper, the stats for individual players will be very different from other sports. But that is more an issue of listing the terms for the types of statistics. To do this, we can make use of the “generic stats” construction in SportsML:
 
<player-stats>
  <rating rating-value="1.11"/>
  <stats>
    <stat stat-type="esstat:kills" value="15" />
    <stat stat-type="esstat:headshot" value="6" />
    <stat stat-type="esstat:assist" value="4" />
    <stat stat-type="esstat:flashassist" value="2" />
    <stat stat-type="esstat:deaths" value="11" />
    <stat stat-type="esstat:KAST" value="78.3" />
    <stat stat-type="esstat:ADR" value="68.4" />
    <stat stat-type="esstat:FKdiff" value="0" />
  </stats>
</player-stats>
 
There is no other sport that has kills and deaths as individual player stats! But with the SportsML stat construction with stat-type and value we can handle any type of statistic.
 
The eSports qcode prefixes of esstat: and esacttype: in these examples do not currently exist in the IPTC NewsCodes catalog but could easily be set up if needed. It might be necessary to have different prefixes for different type of eSports games. But that would require some more investigation.
 
If you are interested in using SportsML to represent results of eSports matches or if you would like copies of the complete SportsML example files that we created during this investigation, please get in touch – we would be happy to help.
 
Johan Lindgren, Lead of the IPTC Sports Content Group

Lúí Smyth, Shutterstock at IPTC Photo Metadata Conference 2019

Last week’s 2019 IPTC Photo Metadata Conference was again hosted in association with the CEPIC Congress. This year’s conference was held in a slightly rainy Paris but at least that meant that we didn’t mind staying indoors in late May.

The event kicked off with an introduction from event chair Stéphane Guérillot from AFP, who is also on the Board of IPTC and Chair of the IPTC Standards Committee. The theme of the afternoon was “putting IPTC metadata to work for your image collections” and the emphasis on practical outcomes was a constant refrain.

Isabelle Wirth, AFP at IPTC Photo Metadata Conference 2019

The first panel was around the question of “do we still need IPTC Photo Metadata?” Michael Steidl, lead of the IPTC Photo Metadata Working Group started off by presenting results from the IPTC Photo Metadata surveys that the Working Group has undertaken earlier this year. Lúí Smyth from Shutterstock  showed how metadata has helped them to organise millions of photos from thousands of sources. Isabelle Wirth, photo editor at AFP discussed how the agency uses IPTC Photo Metadata along with other IPTC standards such as News Codes and NewsML-G2 to make content searchable and shareable for their clients. And independent photographer and 3D photogrammetry expert with Deep3D, Simon Brown, explained how metadata was crucial for creating 3D views of sunken shipwrecks via tens of thousands of still photographs and some innovative software. In Simon’s words: “For more than one 3D project, projects with multiple contributors, or projects conducted over a longer period of time, IPTC entry becomes mandatory.”

Andrew Wiard, IPTC Photo Metadata Conference 2019

The next session examined how creating and editing IPTC Photo Metadata could be improved. Sarah Saunders representing CEPIC presented results from the IPTC Photo Metadata surveys of both image suppliers and software makers showing that metadata usage has grown in sophistication but still varies greatly between independent photographers and large companies.  Andrew Wiard, photographer and member of the British Press Photographers’ Association, spoke with passion about how we could improve the handling of photo metadata once it leaves the photographer’s desk, a constant goal of the Photo Metadata Working Group and which will form part of our work plan for the rest of 2019. Mayank Sagar from Image Data Systems showed some exciting tools with videos showing how their AI algorithms can detect objects from luggage and handbags for commuters to brands and logos on advertisements in sports footage, and talked about the current limits of AI classification and future issues such as how to handle artificially synthesised images. Andreas Gnutzmann of popular photo management software Fotoware showed how their system is moving to the cloud, putting metadata at its core even more than previously.

Anna Dickson, Google at IPTC Photo Metadata Conference 2019

The third session looked at the end-user side and how the industry can benefit from photo metadata. Brendan Quinn of IPTC presented the Photo Metadata Crawler project, examining how news publishers around the world are embedding photo metadata in the images used on their sites. Michael Steidl showed results of the Photo Metadata Working Group’s updated analysis of social media systems and sharing platforms, which will be shared through an IPTC news article in the coming months. And Anna Dickson of Google gave us an update on her history working with images as photo editor at Huffington Post and Dow Jones among others, and discussing how Google are working with metadata and the IPTC, including our shared challenges of encouraging more site owners to publish embedded metadata so that it can be picked up by Google Search and other services. At the event, Google also announced some very interesting features that are currently in the pipeline.

Michael Steidl and Stéphane Guérillot closed out the event talking about the work the the IPTC Photo Metadata Working Group would be undertaking this year as a result of the discussions and of the survey results.

All slides from the day are available in PDF format from the event page, both to IPTC members and non-members. 

Key findings from the Photo Metadata surveys will be shared in future news posts, so please watch this space for updates.

More information about the Google presentation and their proposed new features around image metadata is available to all IPTC members who have joined the Photo Metadata Working Group.

Thanks to all the speakers, to CEPIC for their assistance in hosting the conference, and to everyone who attended for making the event such a success!

At the IPTC Spring Meeting in Lisbon, the IPTC Standards Committee signed off on version 3.1 of SportsML.

Updates include:

  • round-number attribute added to baseEventMetadataComplexType
  • Added events-discarded to outcomeTotalsComplexType and result-status to base3StatsComplexType to support events where players or teams can discard some of their results.
  • Fixed examples to use the correct qcodes nprt:givennrol:short etc for names
  • Corrected description of  distance in actionAttributes

You can download the ZIP Package of SportsML 3.1 with XML Schemas and documentation included.

Development of SportsML is open to collaboration. Your feedback on the SportsML Users Forum is welcome!

We’re excited that the biggest week in the photo metadata calendar has arrived – the IPTC Photo Metadata Conference 2019 will be held in Paris this Thursday, 6 June.

We are looking forward to hearing from some IPTC members: Andreas Gnutzmann from Fotoware, Lúí Smyth from Shutterstock, Isabelle Wirth of Agence France Presse and Michael Steidl, Chair of the Photo Metadata Working Group and honourable member of IPTC. Stéphane Guerrilot, CEO of AFP Blue will be chairing the event.

We will also be hearing from independent photographer Andrew Wiard representing the British Press Photographer’s Association (BPPA), plus Anna Dickson, Visual Lead, Image Search at Google attend, bringing her expertise as one of Google’s experts on images but also with a history leading photography teams at Dow Jones and Huffington Post. Mayank Sagar from Image Data Systems will be speaking about the latest developments in automatic image tagging, and Simon Brown of Deep3D will look at the photographer’s view around embedding metadata.

Michael Steidl and Sarah Saunders will be presenting the results of the 2019 Photo Metadata Survey, where we have obtained the views of image creators, publishers and software makers regarding embedded image metadata.

Brendan Quinn, Managing Director of IPTC will be presenting the IPTC Photo Metadata Crawler which looks at usage of embedded photo metadata among news publishers.

We’re looking forward to analysing the world of photo metadata from the perspective of image creators and editors, software makers, publishers, search engines and end users.

There are still some tickets available, so please join us! Attendance is free for CEPIC Congress attendees, but if you just want to come for the IPTC event on Thursday afternoon you can register using this form for €100 + VAT.

See you there!