Most data analytics platforms and their temporal features make some assumptions about time and you might too. Time is what you see when you look at your watch and we use UTC timestamps. Not so in football. Everyone knows at what minute in the match their team scored the deciding goal in a legendary match. Nobody cares what UTC clock moment that was, to the extent that it's practically impossible to find out what UTC moment that was. Also, pretty much each football match has the 45th minute twice! And if you do analysis on the match, you probably want to ignore injury periods or other occasions when the ball was out of play. In motorsports and most other sports where it's a primary objective to "beat the clock", the clock is actually not a good x-axis for analytics since lap-to-lap times differ as a matter of principle and make data hard to compare. In this session, we'll look at the clocks in sports and some other fields and how to make sense of them in data analytics.