The Metropolitan Lagos dataset consists of the files (i) tsetimi_lagos_dataset.sav and (ii) tsetimi_lagos_dataset.xlxs. The two files contain the same number of records (377) and same information. The first file is in IBM SPSS database format while the second is in Microsoft Excel spreadsheet format. The SPSS database format can be accessed in the data view of SPSS. The fieldnames, field descriptions and field types are self-contained in the SPSS database file.

The dataset is part of a nationwide survey on the problems associated with electricity distribution and generation in Nigeria. A pilot survey [1] of this research was conducted in Delta State South-South, Nigeria. The files for the pilot survey are available in [2]. The survey for the Lagos data set was conducted by means of a well-structured questionnaire administered by trained interviewers. The questionnaire for the research collected information on respondents’ bio-data, experience with the services of their distribution companies and observed problems on electricity distribution from the fieldwork. The perception ratings on the services of distributions companies from the electricity customers was on a five-point scale based on the following metrics adapted from [3]: i. Overall satisfaction with services of distribution company; ii. Quality and reliability of power from distribution company; iii. Reasonableness of bills from distribution company; iv. Billing system of distribution company; v. Corporate image of distribution company; vi. Effectiveness of Communication of distribution company with stakeholders; vii. Customers service of the distribution company. The respondents scored the metrics between 0 and 5 inclusive depending on their perception on the above metrics. The scores of the respondents on the observed problems were based on the following items listed below: i. Low voltage; ii. Incessant power outages; iii. Load Shedding; iv. Inadequate number of meters; v. Inadequate distribution lines; vi. Unreasonable price of power; vii. Illegal connections; viii. Inadequate number of transformers; ix. Stealing of Distribution facilities; The respondents assign a score between 0 and 10 inclusive depending on their perception on the level of severity of the observed problems.

References [1] J. Tsetimi, A. O. Atonuje and E. J. Mamadu. An Analysis of a Pilot Survey of the Problems of Electricity Distribution in Delta State, Nigeria. Transactions of Nigerian Institution of Mathematical Physics. 2020; 12(7): 109-116 [2] J. Tsetimi. Customers' Problems with Electricity Distribution in Delta State Nigeria, [dataset], Mendeley Data, V1, doi: 10.17632/msrhyv489k.1. 2020. Accessed 16th February, 2021. Available: http://dx.doi.org/10.17632/msrhyv489k.1 [3] D. Smith, S. Nayak, M. Karig, I. Kosnik, M. Konya, K. Lovett, Z. Liu, and H.Luvai. Assessing Residential Customer Satisfaction for Large Electric Utilities. UMSL, Department of Economics Working Papers. (2011).

The optimal numbers of clusters (for three data-sets) for a clustering solution is represented by the set , where is the optimal number of clusters in the data.

The Sports Analytics Market size was valued at USD 3.78 USD billion in 2023 and is projected to reach USD 9.00 USD billion by 2032, exhibiting a CAGR of 13.2 % during the forecast period. The rising demand for performance enhancement, player evaluation, and fan engagement is driving the growth of the sports analytics market. Sports analytics is the process of plugging statistics into mathematical models to predict the outcome of a given play or game. Coaches rely on analytics to scout opponents and optimize play calls in games, while front offices use it to prioritize player development. Analytics also play a major role off the field, providing fans with both sports betting and fantasy sports insights. Tracking software and machine learning have taken sports analytics to the next level. Companies are able to generate statistical breakdowns from video footage to help coaches optimize their play calling during games or generate post-game takeaways. Recent developments include: October 2023 – Zelus Analytics, a sports analytics company that is revolutionizing player evaluations and in-game decision-making platform, announced its completion of the first tranche of its Series A. This funding would be used for their business expansion., March 2023 – Alteryx, Inc. launched Alteryx Fanalytics, which showcases how analytics impact decisions in professional sports, from players using data to enhance their game to enthusiasts exploring insights on their favored teams. The company has also partnered with professional sports organizations such as NBA, F1, Premier League, NFL, and the PGA Tour., March 2023 – Relo Metrics, a sponsorship analytics platform offering free social media intelligence services to help European soccer clubs capitalize on the momentum of the women’s game., March 2023 – Memryx Inc. partnered with Cachengo to provide their modular computing and storage services with AI processors. Their collaboration offers a unique solution for edge-based data-intensive applications that support various sectors, including sports analytics, retail analytics, and healthcare monitoring., March 2022 – nVenue raised USD 3.5 million in seed funding to expand its real-time B2B sports prediction platform to broadcasters and sportsbooks.. Key drivers for this market are: Rising Adoption of Big Data Analytics to Boost Market Growth. Potential restraints include: Lack of Awareness about the Benefits and Integration of Data from Data Silos May Impede Market Growth. Notable trends are: Changing Sports Dynamics and Technology Adoption to Drive Sports Analytics Growth in the Forecast Period.

The aerial photographs, taken on the 6th of February 1975 at a scale 1: 50 000, were obtained from the Survey of Kenya and were used to generate my original data.