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This week saw an historic performance from the England men's cricket team against Pakistan as England became the first team to give up over 500 runs in the first innings, and still go on to win by an innings; with Joe Root scoring 262 and Harry Brook 317 while setting a new all-time highest partnership score for England.
Along the way, Joe Root also became England's top test match run scorer. Ben Norland has taken a look at how Root's performance compares to the top 25 run scorers of all time. I love a good career line graph and cricket is a natural fit (it is the spiritual home of "the worm"). I appreciate Ben's decision to only colour Root and Cook (England's previous highest scorer) which brings some focus for the reader to start exploring. Looking at Root's trajectory, it's conceivable that he could become the second top scorer of all time, although I suspect that Tendulkar's record is safe for now.
Next is Sidharth Suresh's Euro 2024 match dashboard. Sid's done a really great job keeping his dashboard looking clean and elegant which allows the main two xG plots to shine. The design choices to remove axis and almost all the labels makes it easy to track the flow of the match and the key events, as well as using the xG to evaluate how big the chances were. I'm looking forward to Sid makes next!
Baseball has long been a home for sports analytics (I've heard it said that the original box scores in the late 1800s were the first) and Melissa Anino has done a great job of pulling out some key metrics to evaluate the offensive performance of the teams in the 2024 season. It's particularly interesting to compare the performance over the last three games with each team's season average and notable that the San Diego Padres - who Melissa references in her accompanying post too - were playing about as well in their last 3 games as they had across the season, so can't even claim to be in an end-of-season slump.
Adam Green has updated his Bauhaus running visualisation for September. It's looking really impressive now that there is so much data in there. I have to keep reminding myself that it's not spelling a word though as my brain is determined to find one.
Lastly for this week is Owen Phillips look at the 100 most common assisted and unassisted shot locations in the NBA last season. Although using hexagons has become the standard for this type of visualisation, Owen has managed to increase the value from his visualisation by focusing down to just the most common locations, and categorising each hexagon as one of only three options. This simplifies the message really nicely and gives the visualisation more power. I'm also a huge fan of using the title as a key too - that's something I find myself trying to do more and more.
The NFL #BigDataBowl has started for this year, where the community are encouraged to use an NFL dataset to create new insights and models. This year there is a massive new element in pre-snap data! I'm intending to have a look into this and see what I can visualise in terms of cause and effect for different teams.
And that's it for this week! Looking forward to seeing y'all next time.
Mo & the #SportsVizSunday team
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