Oliver’s four-factor model: Validation through causality

While there has been a growing interest in sports analysis in recent years, much research first focused on a classical statistical approach and later on an artificial intelligence approach. This article aims instead to propose a causal inference approach to sports analysis. In particular, the present article intends to review the famous four-factor model proposed by Dean Oliver for assessing the winning ability of National Basketball Association (NBA) teams through a causal inference approach. A structural equation model is used to validate Oliver’s model. The present paper considers the winning percentage and the factors’ statistics over entire seasons from [Formula: see text] to [Formula: see text]. The statistics for the [Formula: see text] season are considered only on a subset of the games. This is because the games played in the Orlando bubble under the particular COVID-19 situation have been regarded as outliers compared to the games played in the other NBA seasons, hence they have not been taken into account. The second goal of the article is to analyse if the fitting ability of the four-factor model changes when it is fitted over the pre[Formula: see text] and post[Formula: see text] basketball eras datasets, considering the year [Formula: see text] as the turning point for the NBA playing style.

EXPRESS: Spotlight Personnel: How Hiring and Turnover Drive Service Performance versus Demand

In many sectors of the entertainment industry a few employees are in the public spotlight when performing the key service. For example, in professional team sports a team of players competes in games and in TV shows a cast of artists acts in different episodes. These employees, coined spotlight personnel, are an essential but expensive element of ongoing service delivery. Despite their importance and cost, very little is known about how changes in spotlight personnel affect service performance and demand. To address this gap, this paper uses unique data on professional German soccer teams tracking the quantity (number of players) and quality (average transfer price) of spotlight personnel hiring (incoming transfers) and turnover (outgoing transfers), objective service performance (winning percentage) and demand (ticket sales) across four decades, utilizing both traditional and novel time series methods. The results show that service performance and demand are primarily affected by spotlight personnel hiring rather than turnover. Hiring quantity decreases service performance yet increases demand whereas hiring quality benefits both service performance and demand. The analysis further uncovers that these effects are subject to dynamic interactions and nonlinearities. Investment scenarios showcase how understanding these effects can substantially improve managerial decision making.

A comparative study of the effectiveness of an osteopathic primary care sports medicine led intervention on performance in men’s collegiate lacrosse players

Context Comprehensive sports medicine care goes beyond the treatment of injuries resulting from athletic activities. Ultimately, it is a competence that includes knowledge in physical therapy, training, nutrition, coaching, motivation, competition, mentoring, psychology, and spirituality that allows the physician and patient to collaborate on promoting the patient’s health goals. The current literature demonstrates a lack of knowledge in the Osteopathic Primary Care Sports Medicine Model’s effectiveness in performance. Objectives To determine whether a comprehensive osteopathic primary care sports medicine approach can improve performance and health outcomes in collegiate athletes. Methods A randomized controlled trial commenced just prior to the start of the lacrosse season and concluded at the end of the season. All the New York Institute of Technology (NYIT) collegiate lacrosse players were educated first in a 1-day seminar of the core competencies, and all participants had access to ask questions on their own volition. Then they were randomized into two groups, either the experimental group receiving the direct osteopathic primary care sports medicine intervention (n=18) or the control group not having active intervention (n=19). Also, the overall team winning percentage for that season was computed and compared to that for the previous years and the following year. Participants were assessed before and after the intervention with the Patient Health Questionnaire (PHQ-9), the 36-Item Short Form Survey (SF-36), custom Osteopathic Primary Care Sports Medicine questionnaire, and body fat composition, and their changes were compared between the experimental group and the control group. Collected data were analyzed using the repeated-measures analysis of variance. Results Thirty-seven participants were enrolled in the study. After 14 participants were excluded due to being lost to follow-up, 23 athlete records were analyzed. The winning percentage of the team was highest during the year of the study period time than in the 3 previous years and the following year. The test group did not have any statistically significant change in the PHQ-9, SF-36, custom Osteopathic Primary Care Sports Medicine questionnaire, as well as in body fat composition. Conclusions When used during a collegiate lacrosse season, this Osteopathic Primary Care Sports Medicine intervention did not significantly improve health outcomes. This preliminary study, despite its limitations in compliance and study population size, did demonstrate improvement in overall team performance when comparing the intervention sport season to other seasons but was not statistically significantly. Therefore, further studies are warranted to improve the understanding in this approach to athlete health outcomes and performance.

What Constitutes Fairness in Games? A Case Study with Scrabble

The compensation system called komi has been used in scoring games such as Go. In Go, White (the second player) is at a disadvantage because Black gets to move first, giving that player an advantage; indeed, the winning percentage for Black is higher. The perceived value of komi has been re-evaluated over the years to maintain fairness. However, this implies that this static komi is not a sufficiently sophisticated solution. We leveraged existing komi methods in Go to study the evolution of fairness in board games and to generalize the concept of fairness in other contexts. This work revisits the notion of fairness and proposes the concept of dynamic komi Scrabble. We introduce two approaches, static and dynamic komi, in Scrabble to mitigate the advantage of initiative (AoI) issue and to improve fairness. We found that implementing the dynamic komi made the game attractive and provided direct real-time feedback, which is useful for the training of novice players and maintaining fairness for skilled players. A possible interpretation of physics-in-mind is also discussed for enhancing game refinement theory concerning fairness in games.

294 Impacts of travel and time zone differences in the National Hockey League (NHL)

Introduction Elite athletes are at risk of poor sleep which can be exacerbated by frequent travel. The present exploratory study investigated the impact of travel on the winning percentage, number of goals scored in the 3rd period and the number of penalties in the 3rd period over the 2013–2020 seasons in the National Hockey League (NHL). Methods Data from away and home games from the 2013–2020 seasons in the NHL were included in this study. The outcomes were based on winning percentage with additional covariates including home and away games; timing of the game (afternoon/17:30 or earlier; evening/18:00 or later; number of time zones travelled (one, two or three); direction of the travel (eastward or westward); length of the game (regular, overtime or shootout). Additionally, data exclusively from the 3rd period were assessed for the number of penalties received and the number of goals scored for and against. Data were analyzed with logistic regressions to evaluate the effects of the aforementioned variables on winning percentage for both eastern and western conference teams. Results Regardless of the length of the game, results indicated no difference between eastern and western teams on winning percentage. However, there was a significant impact of home-ice on winning percentage for both conferences (p<0.001). In addition, there was no difference on the winning percentage based on the travel direction and the number of time zones crossed (p = 0.747) or the time of the day (p=0.991). Moreover, visiting teams received significantly more 3rd period penalties than home teams (p<0.001), regardless of travel and while travelling within the same time zone compared to eastward travel (p<0.001) but not westward travel (p=0.078). Finally, there was an increased risk of being scored against when team travelled three time zones (p=0.03), regardless of the direction. Conclusion This 7-year investigation of data from the NHL demonstrates an unexplored aspect of the impact that travel and circadian factors may have on emotion regulation and performance. Translational application of this knowledge to enhance general public health and performance would be warranted. Support (if any):

295 Impacts of travel distance and travel direction on back-to-back games in the National Basketball Association (NBA)

Introduction Travel fatigue and circadian disruptions are known factors that can hinder performance in professional athletes. The present exploratory study focused on investigating the impact of travel distance and direction on back-to-back games over the 2013–2020 seasons in the National Basketball Association (NBA). Methods Data from away and home games of back-to-back sequences, in two different cities, from the 2013 to 2020 seasons in the National Basketball Association were included in this study. Information from every selected game was retrieved from the official website of the NBA (www.nba.com). The outcomes were based on winning percentage with additional covariates including the direction of travel (eastward or westward) and the distance travelled (0-500km – 501-1000km – 1001-1500km – 1501km and more). If a team played both games of a back-to-back sequence on the road, they were considered Away-Away; if a team played the first game of a back-to-back sequence at home they were considered Home-Away; if a team played the first game of a back-to-back sequence on the road they were considered Away-Home. Results The sequence Away-Home significantly increases the likelihood of winning compared to the Away-Away and Home-Away sequences 54.4% (95%CI: 54.4,54.5); 39.2% (95%CI: 37.2,41.2), and 36.8%, (95%CI: 36.7,36.8), respectively. Following a road game, when teams travel back home, every additional 500km reduces the likelihood of winning by approximately 4% (p = 0.038). Finally, after withdrawing the Away-Home sequence, travelling eastward significantly increases the chance of winning (p = 0.024) compared to westward travel but has no significant impact on the probability of winning compared to neutral time zone travel (p = 0.091). Conclusion The accumulation of travel fatigue and the chronic circadian desynchronization that occurs over the NBA season can acutely disturb sleep and recovery. It appears that tailored sleep and recovery strategies need to be dynamically developed throughout the season to overcome the different challenges of the NBA schedule. Support (if any):

Predicting the winning percentage of limited-overs cricket using the pythagorean formula

The Pythagorean Win-Loss formula can be effectively used to estimate winning percentages for sporting events. This formula was initially developed by baseball statistician Bill James and later was extended by other researchers to sports such as football, basketball, and ice hockey. Although one can calculate actual winning percentages based on the outcomes of played games, that approach does not take into account the margin of victory. The key benefit of the Pythagorean formula is its utilization of actual average runs scored and actual average runs allowed. This article presents the application of the Pythagorean Win-Loss formula to two different types of limited-overs cricket formats, namely One Day International cricket (ODI) and Twenty20 cricket. The data for the application was used from the matches played by the top 10 International Cricket Council (ICC) members who participated in the 2019 ICC Cricket World Cup. For matches for which the second batting team won, runs scored were estimated by considering the remaining amount of resources, based on the Duckworth–Lewis method.

Penerapan Decision tree dalam pengambilan keputusan untuk pemain Texas Holdem Poker

Texas holdem poker is a popular poker game. This game is played by millions of people every day, both to find additional income or just for fun. But in fact not everyone who plays poker with these goals gets according to what they have planned. Some people actually experience losses after playing. They unconsciously develop the logic of gambler's fallacy that causes them to play poker without using strategy. This research made a system that can prove the defect of gambler's fallacy and made a tool for playing poker. The processed dataset is a dataset containing information of cards used in poker and their probability of occurrence. The method used in this research are iterative deepening search tree and decision tree. The main results of this research is a tool that can provide insight as a basis for decision making. However, this tool has not been able to prove its capabilities in helping to increase the winning percentage, so that further study is needed. In addition, this study also shows that playing with gambler's fallacy logic only gives 48.13% wins of 6,000 trials. These results proved that using gambler's fallacy logic in playing poker is a mistake.Keywords – Tree, Poker, Probability, Gambler’s Fallacy Abstrak Texas holdem poker merupakan permainan poker yang populer. Permainan ini dimainkan oleh jutaan orang setiap harinya, baik untuk mencari penghasilan tambahan ataupun hanya untuk bersenang-senang. Namun pada kenyataanya tidak semua orang yang bermain poker dengan tujuan tersebut mendapatkan sesuai dengan apa yang mereka rencanakan. Sebagian orang justru mengalami kerugian pasca bermain. Mereka tanpa sadar mengembangkan logika berpikir gambler’s fallacy yang mengakibatkan bermain tanpa strategi. Makalah ini menyajikan hasil studi penerapan pohon keputusan dan pohon pencarian untuk membuktikan kecacatan gambler’s fallacy dan membantu dalam bermain poker. Dataset yang diolah adalah dataset yang berisi informasi kartu-kartu yang dipakai dalam permainan serta probabilitas kemunculannya. Metode yang digunakan pada penelitian ini adalah metode pencarian pada struktur tree dan metode pohon keputusan. Hasil utama dari penelitian ini adalah alat bantu yang mampu memberikan insight sebagai dasar pengambilan keputusan. Namun, alat bantu ini belum bisa dibuktikan kapabilitasnya dalam membantu menaikkan persentase kemenangan sehingga diperlukan studi lanjutan. Selain itu, penelitian ini juga menunjukkan bahwa bermain dengan logika gambler’s fallacy hanya memberikan 48.13% kemenangan dari 6000 percobaan. Hasil tersebut membuktikan bahwa menggunakan logika gambler’s fallacy dalam bermain poker merupakan suatu kesalahan.Kata kunci – Tree, Poker, Probabilitas, Gambler’s Fallacy

Indian Premier League Using Different Aspects of Machine Learning Algorithms

The purpose of the present article is to highlight the outcomes of Indian premier league cricket match utilizing a managed taking in come nearer from a team-based point of view. The methodology consists of prescriptive and descriptive models. Descriptive model focuses mainly on two aspects they are, it describes data and statistics of the previous information. i.e., batting, balling or allrounder and It predicts past matches of IPL. Predictive model predicts ranking and winning percentage of the team. The two models show the measurements of winning level of the group Winner that the user has selected. This paper predicts the result through which technique match has highest result. The dataset consists of two groups that is the toss outcome, venue date, which tells about of the counterpart for all matches. Since the nature impact can't be expected in the game, 109 matches which were either finished by downpour or draw/tie, have been taken out from the dataset. The dataset is partitioned into two sections to be specific the test information and the train information.The readiness dataset contains the 70% of the information from our dataset and the test dataset contains 30% of the information from our dataset. There were all out of 3500 coordinates in getting ready dataset and 1500 matches. This paper has been researched earlier by different scholars like Pathak and Wadwa, Munir etl ,and many other scholars. This viewpoint discusses the application of INDIAN PREMIER LEAGUE Matches held in different states. Gives the score of batsman and bowler with the help of machine learning techniques. Focuses on predicted analysis which is predicted by applying with various AI strategies to the real outcome actual result and gives the percentage of predicted result.

Utilizing the National Basketball Association’s COVID-19 restart “bubble” to uncover the impact of travel and circadian disruption on athletic performance

On March 11th, 2020, the National Basketball Association (NBA) paused its season after ~ 64 games due to the Coronavirus 2019 (COVID-19) outbreak, only to resume ~ 5 months later with the top 22 teams isolated together (known as the “bubble”) in Orlando, Florida to play eight games each as an end to the regular season. This restart, with no new travel by teams, provided a natural experiment whereby the impact of travel and home-court advantage could be systematically examined. We show here that in the pre-COVID-19 regular season, traveling across time zones reduces winning percentage, team shooting accuracy, and turnover percentage, whereas traveling in general reduces offensive rebounding and increases the number of points the opposing (home) team scores. Moreover, we demonstrate that competition in a scenario where no teams travel (restart bubble) reduces the typical effects of travel and home-court advantage on winning percentage, shooting accuracy, and rebounding. Thus, home-court advantage in professional basketball appears to be linked with the away team’s impaired shooting accuracy (i.e., movement precision) and rebounding, which may be separately influenced by either circadian disruption or the general effect of travel, as these differences manifest differently when teams travel within or across multiple time zones.