Image: Liuzishan

By Johan Lindgren

The Sports Content Working Group of IPTC started in the early 2000’s, initially to develop the XML standard SportsML. But the group has evolved to handle many aspects of reporting sports in the news.

The initial big question for news organisations handling sports is to decide if it should be handled as text or as data. The sports articles have, obviously, more in common with articles about other subjects. It is the results, schedules, statistics and standings that provide the dilemma. You can choose to provide the results ready for display on screen or on paper. Or you can provide the results as detailed marked up data and let the receiver handle the formatting, depending on purpose.

In fact, with using both NewsML-G2 and SportsML from IPTC you can provide both variants in parallel, if you wish so. In a NewsML-G2 news item as wrapper you provide one rendition of the content with the results as data in SportsML markup, and in another rendition you provide the same results, but in a displayable format like HTML5.

Vocabularies and Media Topics

Another big issue in handling sports data is knowing all the terms, what they mean and how they are used. The people in the sports group have spent a lot of time on this and provide very extensive vocabularies. Some are found in the Media Topics, maintained by the NewsCodes  Working Group of IPTC. The same is true for the new addition to this, called facets. Facets refine the semantics of a Media Topic.

Example: If you try to combine Nordic skiing, female, relay, freestyle, 4×5 km as constituting one combined Media Topic and think of all the variations resulting from alternates to those terms, and then expand that thought to all sports events, the number of Media Topics will be overwhelming. Instead, IPTC chose to minimize the number of Media Topics and instead create a system of facets that qualify these broader topics. So, for example, “male” and “female” can apply to many, many sport competition topics, eliminating the need to create separate Media Topic terms for all of them.

About SportsML

Apart from the topics and their facets there is a huge number of metadata property values maintained by the sports group. These values are listed in 113 vocabularies (they can be downloaded), 37 of them are used for the core of SportsML and the other 76 are used for sport-specific additions. In total there are 1,850 values defined and listed as concepts in 113 knowledge items. The list of metadata values and their explanations is fundamental know-how in the sports reporting. You can have names and definitions in several languages.

Example of a code saying the player started the game on the field:

<concept id=”spplayerstatusstarter”>

<conceptId qcode=”spplayerstatus:starter” />

<name xml:lang=”en-US”>starter</name><name xml:lang=”en-GB”>starter</name>

<definition xml:lang=”en-GB”>A member of the lineup that enters the field at the commencement of play.</definition></concept>

SportsML is used by news organisations around the world both for everyday sport reporting and big events. BBC, for example, built their handling of the Olympic results in London around SportsML. It is also used by organisers of so-called fantasy sports leagues. Even by just using the core you can handle most normal news reporting of all sports events and competitions. There are also plugins for eleven sports, when you want to handle very in-depth data of these sports. And more plugins can be added. There are also ways to extend the standard with your own values or constructs. When developing SportsML the aim has always been to handle things in the core if the things are applicable to more than one sport. But some things are very specific to one sport and will instead be placed in its own schema which is imported and linked in proper places.

To illustrate this we can use this snippet from a soccer game:

 <team-stats score=”0″ score-opposing=”2″ event-outcome=”speventoutcome:loss”>

 <team-stats-soccer line-formation=”433″>

 <stats-soccer-offensive corner-kicks=”2″/>

The first line is general with the score and outcome. But the two other lines are soccer-specific with a line-formation and the number of corner-kicks this team shot in this game.

SportsML for JSON

Up until now SportsML has mainly been serialized using XML. But with increasing interest in JSON the sports group is working on also providing a schema of SportsML for JSON usage. The work is close to being ready for the first public release. Some details of the schema need to be finalized and then the Working Group provide samples and some tools. We’re hoping to have this ready to release by early 2018.

The release of 3.0 of SportsML in XML also provided some tools (see our Github repository), mainly to transform between the earlier version, 2.2, and 3.0. One of the big developments in 3.0 was the possibility to handle statistics either in generic structures or in specific structures. So there are tools to transform between the two variants. To show this we can compare the above soccer example with the similar generic sample:

<stat stat-type=”spsocstat:line-formation” value=”433″/>

<stat class=”spct:offense”  stat-type=”spsocstat:corner-kicks” value=”2″/>

As you see the attribute names become type-values in the generic stat-construction.

The work in the Sports Content Group is completely done by volunteers. The members of the group work in the news business and contribute to the group as much as their work allows. We welcome all interested persons, e.g. by joining our public discussion forum. The more people who can contribute the better, and there seem to be a never-ending flow of interesting topics when you start talking about sports data.

Johan Lindgren is the Chair of the Sports Content Working Group and a developer at TT Nyhetsbyrån, Sweden.