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The authors looked at which measurements from the NFL combine were the most predictive of success for defensemen in the NFL.

Here, the authors sought to develop a new metric to evaluate the efficacy of baseball pitchers using machine learning models. They found that the frequency of balls, was the most predictive feature for their walks/hits allowed per inning (WHIP) metric. While their machine learning models did not identify a defining trait, such as high velocity, spin rate, or types of pitches, they found that consistently pitching within the strike zone resulted in significantly lower WHIPs.

Studying cut-resistant socks for hockey players

In this study, the authors assess the factors that allow some speedcubers to solve Rubik's Cubes faster than others.