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This exercise stages a data-analysis task within the world of US men's college basketball. The National Collegiate Athletics Association (NCAA) Division 1 Men's Basketball Championship Tournament, known as "March Madness," begins every March with 68 college teams and concludes in April with 1 champion. It is one of the biggest sporting events of the year in the United States—a multi-billion-dollar endeavor that is wildly popular with fans and that brings tremendous energy to the specific schools involved. The tournament, which is played across three weeks, begins with a "First Four" play-in round where 8 teams play four games to bring the field down to 64 teams. Two of the First Four games are played between the 4 lowest-ranked teams, and two are played between the four lowest-ranked "at-large" teams. Then a six-round single-elimination tournament is conducted with the 64 remaining teams, with two rounds played each weekend. The last weekend is known as the "Final Four." Each year, a selection committee comprising university athletic directors and conference commissioners chooses which teams will participate in the tournament and then seeds the teams—that is, ranks them from best to worst to assign matchups. But how the committee chooses to define "best" is imprecise. Students are tasked with examining the provided dataset, which contains information on prior selections and subsequent team performance, and then determining what past committees have prioritized in selecting at-large teams. Students must then take on the role of advising the committee on how to proceed with its next selection round. Does the committee get it right, or should it do things differently going forward?