Presenting Multiagent Challenges in Team Sports Analytics

This paper draws correlations between several challenges and opportunities within the area of team sports analytics and key research areas within multiagent systems (MAS). We specifically consider invasion games, defined as sports where players invade the opposing team's territory and can interact anywhere on a playing surface such as ice hockey, soccer, and basketball. We argue that MAS is well-equipped to study invasion games and will benefit both MAS and sports analytics fields. Our discussion highlights areas for MAS implementation and further development along two axes: short-term in-game strategy (coaching) and long-term team planning (management).Comment: 5 pages, 1 figure, In Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023

Improving managerial decision-making quality in the NBA draft: a closer look at the policy, behavioural economic dynamics, and cognitive biases

The thesis explores the intricacies of the NBA Draft. This mechanism is a policy a professional basketball league installed to foster competitive balance within their self-created market. The goals of this regulation are clear and noble. It provides great intentions and attractive potential benefits for most stakeholders involved. The entire league and its franchises would immensely profit from a perfectly functioning draft regulation. Nonetheless, historically it has failed to produce many of the positive outcomes it was intended to provide. This thesis explores this dissonance through the lens of behavioral economics. The application of the NBA Draft policy hinges on human decision-making. However, making choices in a complex environment like professional sports is incredibly difficult. It can be brutally unforgiving due uncontrollable factors. And yet, managerial decision-making quality within the mechanism can almost certainly be improved. The primary objective of this dissertation is to investigate the entire NBA Draft mechanism from a behavioral economic perspective. Using this approach, the overarching goal is to identify segments within the underlying managerial decision-making processes that offer room for decision-making quality improvement. These improvements in judgements and choices which ultimately could lead to a superior policy performance on a league-wide level, could either be achieved due to avoiding error-producing biases or enhancing the information subsequent draft decisions are based on. To reach this main objective, four academic papers were written to tackle important sub-issues. All articles provide sources for decision-making quality improvements within the NBA Draft setup. These are not only supposed to increase the performance of the individual managers and franchises, but also to enhance the results of the overarching league-wide policy with benefits for many stakeholders

Data-Driven Approaches to NBA Team Evaluation and Building

Gemstone Team PROCESSIn the National Basketball Association (NBA), it has historically been difficult to build and sustain a team that can consistently compete for championships. Given this challenge, we have developed a series of analyses to support NBA teams in making data-driven decisions. Relying on a variety of datasets, we examined several facets related to the construction of NBA rosters and their performance. In our analysis of on-court performance, we have used clustering algorithms to classify teams in terms of play style, and determined which play styles tend to lead to success. In our analysis of roster construction and transactions, we have investigated the relative value of draft picks and the impact of trades involving draft picks, as well as the effect of roster continuity (i.e. maintaining the same players across seasons) on team success. Additionally, we have developed a model for predicting player contract values and performance versus contract value, which will help teams in identifying the most cost-effective players to acquire. Ultimately, this assembly of analyses, in conjunction, can be used to inform any NBA team’s decisions in its pursuit of success

Drafting Defensemen's Effect on National Hockey League Outcomes

Any draft in the league’s “salary cap era” (2005– Present) would go differently with hindsight & with knowledge of the ultimate realized value of players. This study aimed to explore whether National Hockey League (NHL) teams that choose to draft defensemen in the first and second round of the draft more often will perform better as a team. The study investigated data from 12 seasons 2007-08—2018-19 in the National Hockey League with 30 teams per year. The study incorporated several independent control variables and conducted an OLS regression on defensive draft investments’ ability to predict regular season winning percentage. The OLS model was a generally good fit with an adjusted R 2 of .632 (F(15, 344) = 42.04; p<.001). The results indicate that defensemen selected in the draft’s first two rounds are responsible for a tangible effect on NHL team outcomes. It was found that the number of Draft Selections had a statistically significant relationship with regular-season winning percentage (p=.006) and each additional defenseman drafted in those rounds corresponded to a 0.7% improvement in a team’s season winning percentage (p<.001). Additionally, for every standard deviation increase in the use of non-drafted defensemen (free agents or transfer minutes), you would expect to see a reduction in team winning percentage of 4.6% (p=.003). A logit regression was also used to analyze the same variables’ ability to predict a playoff berth (Model fit: χ 2 (15,360) = 205.22, p<.001). While traditional performance factors of Offense Quality and Goalie Quality were significant predictors of playoff qualification (p<.001), the defensive draft variables were not significant. Offensively-minded defensemen also did not present a statistically significant effect on winning percentage or playoff berths for NHL clubs in either mode

Football By the Numbers: A Look Into Sports Analytics Currently Used in the National Football League

Sports analytics is a fast-growing field of analytics. In particular, sports analytics with a focus on National Football League (NFL). In this thesis, we will review many articles on football analytics to have an in-depth understanding of the current stat of football analytics. In addition, we can learn from past research to identify interesting research direction to advance sports analytics with a focus on football analytics. In this thesis, we have carefully examined all current analytical results in the following fields: current state of football analytics, analytics regarding the draft, analytics for wide receivers as well as offensive linemen, analytics on other offensive positions, and we have identified the following research direction: the need for a scale rating system that is equal of all positions but unique to expectations of that position especially when it comes to wide receivers and offensive linemen. Lastly, we lay the groundwork for future work, which will make use of the following statistical learning algorithms: logistic regression, XG Boost, decision trees, and time series, to analyze the NFL data, both tracking data from the first six weeks of the 2020 season as well as play by play data from 1999 to 2022 to introduce these new algorithms to sports analytics community

Undergraduate Bulletin, 2023-2024

https://red.mnstate.edu/bulletins/1107/thumbnail.jp

Undergraduate Bulletin, 2022-2023

https://red.mnstate.edu/bulletins/1106/thumbnail.jp

A Better Predictor of NFL Success: Collegiate Performance or the NFL Draft Combine?

NFL teams spend massive sums to ensure they are prepared for the future, but how should they determine whom that future includes? This study set out to find what predicts NFL success more accurately – collegiate in-game performance or the NFL Draft Combine. In the sample of 2007-2012 first-round picks, 191 athletes were measured in three NFL Draft Combine drills, two physical components, and a varying amount of ingame collegiate and NFL performance statistical categories, dependent on position. Secondarily, this work examined Power 5 and non-Power 5 players to determine if attending a more prolific program was predictive of NFL success. Findings included that 40-yard dash and vertical jump are predictive of offensive linemen and cornerback NFL success, that in-game collegiate statistics are most indicative of NFL success amongst defensive players, and that Power 5 prospects are no more prepared for NFL success than those coming from non-Power 5 schools