Finally, the timeline forms a dot-plot, an easy way to identify key moments in the match for analysis!

It's important though to have built a trusting team culture where they can be honest with their analysis. There are plenty of questions in their comments -having an open dialogue with coaches without the fear of a berating is essential.

No need to point out individual mistakes if they've already done so. Key moments that aren't tagged? That's a clear indication we need to review those concepts in training/film.

Is it sprint *distance* or *number* of sprints that has increased? If it is the latter then I could see a small increase in speed from a high intensity run to a sprint not necessarily changing the total distance covered in a match.

Alison this is a great infographic!

Another case of *future research is needed*! Perfect timing of your post as I was considering how I might implement an xT model in R. Lots of great insights I can steal. Appreciate it!

Do you know if anyone has looked at differences in xT grids between leagues (eg women v men, lower leagues, etc)?

Although I also said that Iowa was over-performing their xg and could be in for an upset.... so if later I predict Iowa to win I'll have covered all my bases.

USC v Sacramento St. | Mississippi St. v Southern U.
#woso #ncaawoso