Journal of Sports Analytics

In a close game of basketball, victory or defeat can depend on a single shot. Being able to identify the best player and play scenario for a given opponent’s defense can increase the likelihood of victory. Progress in technology has resulted in an increase in the popularity of sports analytics over the last two decades, where data can be used by teams and individuals to their advantage. A popular data analytic technique in sports is deep learning. Deep learning is a branch of machine learning that finds patterns within big data and can predict future decisions. The process relies on a raw dataset for training purposes. It can be utilized in sports by using deep learning to read the data and provide a better understanding of where players can be the most successful.

In this study the data used were on division I women’s basketball games of a private university in a conference featuring top 25 teams. Deep learning was applied to optimize the best offensive play in a game scenario for a given set of features. The system is used to predict the play that would lead to the highest probability of a made shot.

We present a new, simple knockout format for sports tournaments, that we call “Choose Your Opponent”, where the teams that have performed best during a preliminary group stage can choose their opponents during the subsequent knockout stage. The main benefit of this format is that it essentially solves a recently identified incentive compatibility problem when more than one teams from a group advance to the knockout stage, by effectively canceling the risk of tanking. This new design also makes the group stage more exciting, by giving teams a strong incentive to perform at their best level, and more fair, by limiting the risk of collusion and making sure that the best group winners are fairly rewarded in the knockout round. The choosing procedure would add a new, exciting strategic component to the competition. Advancing teams would choose their opponent during new, much anticipated TV shows which would attract a lot of media attention. We illustrate how this new format would work for the round of 16 of the UEFA Champions League, the most popular soccer club competition in the world.

There is growing on-going research into how footballer attributes, collected prior to, during and post-match, may address the demands of clubs, media pundits and gaming developers. Focusing upon individual player performance analysis and prediction, we examined the body of research which considers different player attributes. This resulted in the selection of 132 relevant papers published between 1999 and 2020. From these we have compiled a comprehensive list of player attributes, categorising them as static, such as age and height, or dynamic, such as pass completions and shots on target. To indicate their accuracy, we classified each attribute as objectively or subjectively derived, and finally by their implied accessibility and their likely personal and club sensitivity. We assigned these attributes to 25 logical groups such as passing, tackling and player demographics. We analysed the relative research focus on each group and noted the analytical methods deployed, identifying which statistical or machine learning techniques were used. We reviewed and considered the use of character trait attributes in the selected papers and discuss more formal approaches to their use. Based upon this we have made recommendations on how this work may be developed to support elite clubs in the consideration of transfer targets.

During the National Basketball Association (NBA) playoffs, teams are required to frequently travel to different venues to play opponents in series of up to seven games. Despite playoff schedules allowing for some rest between games, it is still possible for teams to face circadian misalignment when playing. Thus, the current study serves as a replication and extension of previous research, which has indicated that there is an advantage for teams playing closer to their circadian peak and when they are traveling east. This study specifically investigates the effects of travel, as well as time of game on various performance indicators in professional basketball. We examined a series of box-score statistics (e.g., game outcomes, points scored, shooting percentages, rebounds, assists, steals, blocks, turnovers, and personal fouls) from a total of 499 postseason games played between the 2013–14 and 2018–19 NBA seasons. Findings from our study indicate that teams traveling eastward scored more points than teams traveling within the same time zone. We also observed that teams playing evening games had higher three-point shooting percentages than teams playing in the afternoon. Our study demonstrates an extended impact of travel and time of day on more specific performance indicators in the NBA. Future directions and implications for professional basketball and other sports are discussed.

Major League Baseball (MLB) teams have 20 rounds to select players with projectable ability to compete at the MLB level. In this exploratory study, players were evaluated for differences in Wins Above Replacement (WAR) related to draft round, first round pick, educational designation, and by team. It was hypothesized WAR differences exist by round, pick number, educational designation and by team. From 2005–2015, 1,623 players were examined to determine population differences owed to draft selection. First round draftees had greater average career WAR compared to Rounds 2 to 20. Collectively, the first five picks had greater WAR versus picks grouped 16 through 30. High school (HS) draft picks were selected in earlier rounds versus collegiate athletes and HS hitters displayed more WAR in first round versus 4-year college pitchers. WAR outcomes in the first 15 picks offer more success with greater performance of HS hitters versus 4-year college pitchers. These trends may influence the current landscape of scouting and draft selection in the new draft format that has reduced player selection from 40 to 20 rounds.