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In this article, the authors use datasets of professional and youth soccer players' movements to map and statistically compare them. Analysis compared movements that led to goals or no-goals and differences between pros and youth.

The authors looked at the importance of different point breakdowns needed to win in a game of tennis.

Here the authors investigated the relationship between offensive play-calling styles and scoring success within the NFL's red zone by analyzing play-by-play data and expected points metrics. Their findings suggest that a conservative approach to play design and execution is more strongly associated with maximizing efficiency and point-value gains than aggressive strategies.

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.

Athletes with performance anxiety may struggle to play their best and enjoy the game. Various factors may impact how much anxiety an athlete feels, including how much experience they have in the sport. Concha-Ortiz and Navins survey teenage club volleyball players to look for relationships between years of experience and performance anxiety symptoms.

The authors looked at different ways to build computational resources that would analyze shooting form for basketball players.