Understanding human conversations:
Wit.ai is a Natural Language Processing (NLP) software that uses machine learning to let developers teach a system how to understand conversations, translating natural speech into a language that computers can better understand.
The software also provides the concept of synonyms, meaning that several different utterances (such as “passes to”, “plays to”, “gives the ball to”, “gives it to”, “lays it off for”, “threads it through towards” etc.) can all map to a single normalised action that Wit has been trained with (in this case, “pass”).
The video below provides a great example of how Wit.ai processes natural language and understands the intent behind what we’re saying:
Interpreting subjects and objects:
Once the API receives an initial analysis from Wit, further processing is performed to determine the subjects and objects of the micro-event. These concepts are relatively self-explanatory; a subject is the team (and optionally player) who is performing the action, and an object is the team (and optionally player) who is having the action performed to them.
This can either be explicitly defined in the initial utterance that is sent to Wit (in the form of “home team” or “away team”), otherwise, the subject/object data is extrapolated from the current team in possession.
Most actions (such as “pass” and “shot”) dictate that the team currently in possession is the micro-event’s subject. However, other actions, such as a “tackle”, would invert this principle. It’s unlikely that a player would make a tackle if their team were already in possession, therefore the team NOT currently in possession becomes the inferred subject of the micro-event.
Football data analytics in action:
For example, a voice command sequence such as “kick-off home 9, pass 7, pass 5, long pass 10, interception 4, pass 7” implies the following:
- The game begins with Home team player 9 assuming possession
- Home team player 9 passes to Home team player 7
- Home team player 7 passes to Home team player 5
- Home team player 5 performs a long pass to Home team player 10
- Away team player 4 intercepts this pass and assumes possession
- Away team player 4 passes to Away team player 7.
Giving local clubs tech that can compete in the big leagues
For years coaches and managers have sat around computer screens counting different statistics, from possession and passes, to the number of steps Ronaldo took in his last appearance. But what if you don’t play at Wembley with millions to invest in data analysis or the resources available to record every stat for every player in every game?
That’s where StatChat comes into play.
With the considerable cost savings for clubs looking to record stats for their games (only requiring an audio recording, rather than a number of videos), these clubs can avoid having to spend hours analysing footage and manually recording statistics. Which means that the StatChat app can be used by anyone with a passion to improve their team, not just those who have the budget and resources to do so.
Following on from the ideas around local clubs using our system, once I’ve had the time to work on the project further I hope to develop a data dashboard to visualise these statistics in real-time. Providing a more engaging way of presenting and displaying information on clubs, leagues and players, this could be displayed in clubhouses or at the end-of-season awards.
Cube FC: Our team of developers and designers at the Meetball beach football tournament in Bournemouth last year.
The future of football data analytics?
Artificial intelligence & machine learning.
Both artificial intelligence (AI) and machine learning (ML) are trends that have changed the way brands and organisations reach and engage with their audiences, from apps recognising objects to chatbots interacting with humans.
However, it’s important to note that the value in these tech trends arise when they’re both used simultaneously; machine learning being an application of artificial intelligence.
In fact, any potential application of artificial intelligence will need some degree of machine learning, the best use-cases for this technology do exactly that, adding value to brands who use AI and ML to support their processes and user-experience, rather than replace it entirely.
I plan on moving forward with the project by training the system to handle data analytics in sports other than football. If you have any ideas on how I can improve or implement the system, get in touch!