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272 results for “Football”
Real-life instances of a non-commercial indoor football league
<p>This repository accompanies the paper 'Scheduling a Non-Commercial Indoor Football League: a Tabu Search Based Approach' (Van Bulck, Goossens, Spieksma (2017)). More specifically, it stores all input instances and the generated schedules.</p>
"Determinants of football players' valuation: a systematic review" datasets
<p>3 interdependent tables are available and are the materials used for a systematic review of the determinants of football players' valuation. </p> <p>- model_specifications is a table where each row represents one of the 111 model specifications included in the systematic review. Characteristics of the article from which specification was retrieved (title, year, authors, journal), attributes of the specification (sample size, population, econometric modeling, etc.), the significance levels of included variables, and the associated coefficients for significant variables. </p> <p>- model_specifications_dictionnary precise the names of the columns of the table model_specifications.</p> <p>- variables_definitions_and_classification is a table that details all the 471 variables used in the 29 articles analysed with definitions quoted from articles when possible and presents a classification of these variables into 6 categories and several subcategories. </p>
Champions League 2018-2019. Juventus Football Club
<p>Adjacency matrices of Juventus and its rivals in the 2018-2019 Champions League.</p> <p> </p>
IUPUI Football - Hoodie - Grey
To celebrate IUPUI's 50th anniversary, we're 3D scanning some of the apparel the University has released throughout the years. This shirt was 3D scanned using a Creaform Go Scan 50. Source: Objaverse 1.0 / Sketchfab
Data for Manuscript "Changes in P300 Latency and Amplitude Reflect Expertise Acquisition in a Football Visuomotor Learning Task"
<p>Open-access data for the manuscript "Changes in P300 Latency and Amplitude Reflect Expertise Acquisition in a Football Visuomotor Learning Task". Includes:</p> <p>Grand Average dEEG file containing data from all days and all conditions. .csv file outputs used for all analyses. .rmd file detailing the statistical workflow used to obtain the stats presented in the manuscript.</p>
Exploring Sleep Patterns and Influencing Factors in Elite Female Football Athletes
<p>This dataset contains a novel collection of data from 21 elite female footballers who were continuously monitored for 17 days. The dataset includes measures of actigraphy, well-being, caffeine consumption, screen time and daily hand strength tests. The main objective is to gain a deeper understanding of the interactions between lifestyle, sleep and athletic performance.</p> <p>Sleep is essential for physical and mental recovery, memory performance and brain development. Athletes' sleep quality can be significantly affected by various factors, such as rigorous training schedules, stress, light exposure and caffeine consumption. By closely examining these factors, this dataset supports the creation of personalised training models that take into account the individual sleep patterns and recovery needs of each athlete. Such personalised approaches aim to optimise training and recovery strategies to ultimately improve the overall performance and well-being of athletes.</p>
Chinese Football Association Champions Cup
Chinese Football Association Champions Cup scanned by Thunk3D Fisher S Whatsapp/phone/wechat: +86 18518781107 Email:daicy@thunk3d.com Facebook:www.facebook.com/qinqin.li.77 Linkedin: https://www.linkedin.com/in/daicy-li-399b82119/ Titter: https://twitter.com/DaicyLi Source: Objaverse 1.0 / Sketchfab
Football from the 1930's
This leather ball was used in the friendly match between Finland and Italy played in the Olympic Stadium of Helsinki on 20 July 1939. Silvio Piola scored a hat trick for the visitors, but Finland lost by only one goal, 2–3. That was considered a very good result, as the Italians had won the World Cup in 1934 and 1938 as well as Olympic gold in 1936. Source: Objaverse 1.0 / Sketchfab
Most-valuable-football-player
<p>Dataset of Most Valuable players</p>
Most-valuable-football-player
<p>Dataset of Most Valuable players</p>
Football Cairn D (Dec 2007)
Boulder near Glitteringstone, 1200 metres NW of Rothbury, Northumberland. This boulder can be found approx. 175 metres NE of the Football Cairn. Referenced on the Beckensall Archive (BA) as 'Football Cairn D', the record was added to ERA by NADRAP in 2008. Team 3 describe: "Small panel sitting among larger blocks on the hillside, well-covered with cups in variety of size and depth. An upper ridge is formed by cups and joined cups, but no evidence of cups on other side and no distinguishable pattern to random cup marks. All cups are well-rounded." ERA & BA info: https://archaeologydataservice.ac.uk/era/section/panel/overview.jsf?eraId=1158 Model created from 6 stereo pairs captured by Joe Gibson of NADRAP Team 3 in December 2007. The imagery forms part of the full NADRAP archive deposited with Historic England & Northumberland County Council. Source: Objaverse 1.0 / Sketchfab
A Database of In-Game Player Movements (Actions and Events) in Gaelic Football
<p>In this study, data was acquired by fitting GPS sensors to Gaelic football players which are worn during game time. These wearable devices collect internal (heart rate) and external (gps positioning) data related to the movement of players. However, this form of raw data is not well suited to machine learning algorithms as it lacks the necessary semantics which can identify the type and duration of movements. The dataset presented here is created by a data engineering exercise, driven by domain experts, to transform the GPS coordinates into a series of (player) actions. The end result is a database comprising 12 variables and almost 160k actions. It’s reuse potential is targeted at machine learning researchers, sport scientists and coaches who are seeking to understanding the effort and load of players during game time. Analysis is enables across five dimensions: games, players, actions, duration and speed. In addition, the concept of an event groups together actions that belong to the same sequence and enables analysis at a different level of abstraction. </p> <p> </p>
Forecasting extremes of football players' performance in matches
<p>This repository contains material auxiliary to paper titled "Forecasting extremes of football players' performance in matches", divided into the following parts.</p> <p>A. Athlete performance parameters, as generated from the Apex Pro Series, STATSports, Premium System607<br>2023, Sonra 4.0</p> <ul> <li>"Appendix A.pdf"</li> </ul> <p>B. Sample datasets used in modeling</p> <ul> <li>"Appendix B.xlsx" - an athelete's performance log as produced by STATSports system</li> <li>match-day-gps.csv - an athlete's GPS trace on a match day (MD)</li> <li>md-5-training-gps.csv - the athlete's GPS trace on a training session 5 days before MD</li> </ul> <p>C. Sample models and modeling results</p> <ul> <li>Z_cnt.py - sample pre-processed and aggregated GPS data, used further in modeling</li> <li>zenodo_gps_demo.py - a scipt examining a range of time vs. speed definitions of an interval that correlate training and match performace best</li> <li>zenodo_apx_demo.py - a script generating sample models from APX-Data</li> </ul>
Wikidata Dump Wikidata上所有occupation为association football player (Q937857)的人
<p> RDF dump of wikidata produced with <a href="https://tools.wmflabs.org/wdumps/">wdumps</a>. </p> <p> 检索到Wikidata上所有occupation为association football player (Q937857)的人<br> <a href="https://tools.wmflabs.org/wdumps/dump/1601">View on wdumper</a> </p> <p> <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0 </p>
21st Century Spanish Football League Dataset
<p>This dataset consists in 22 JSON files representing a season of the Spanish Football League ("La Liga").</p> <p>The dataset represents several hierarchically related elements, however, only the <strong>Match</strong>, <strong>Event</strong> and <strong>Player</strong> elements contain relevant information for analysis. The rest of the elements simply serve to keep the data structured, by seasons and matchdays. The dataset collects information from several seasons between the years 2000 and 2022. The attributes of each of the elements that make up the dataset are described below:</p> <p><strong>Season</strong>: JSON documents represent a season, their root contains the following information:</p> <ul> <li><strong>competition</strong>: Name by which the competition is known</li> <li><strong>country</strong>: Country where the competition is held</li> <li><strong>season_id</strong>: Identifier of the season, example: Season 2021/22</li> <li><strong>season_url</strong>: Relative URL of the season's web page</li> <li><strong>rounds</strong>: List of Round elements, the days into which the championship is divided</li> </ul> <p><strong>Rounds</strong>: (or matchdays) Collection of matches:</p> <ul> <li><strong>number</strong>: Name of the matchday, e.g.: Matchday 1.</li> <li><strong>matches</strong>: List of Match elements, matches that are played on the same day/s of the championship.</li> </ul> <p><strong>Match</strong>: contains relevant match information.</p> <ul> <li><strong>id</strong>: Match identifier used at BeSoccer.com</li> <li><strong>status</strong>: Code representing the status of the match: Played (1), Not Played (0)</li> <li><strong>home_team</strong>: Name of the home team</li> <li><strong>away_team</strong>: Name of the away team</li> <li><strong>result</strong>: List of two integers representing the match score</li> <li><strong>date_time</strong>: Date and time at which the match started</li> <li><strong>referee</strong>: First and last name of the referee of the match</li> <li><strong>href</strong>: URL relative to the match page</li> <li><strong>home_tactic</strong>: Tactical arrangement of the home team, e.g.: 4-3-3</li> <li><strong>home_lineup</strong>: List of players in the starting lineup of the home team</li> <li><strong>home_bench</strong>: List of the home team's substitute players</li> <li><strong>away_tactic</strong>: Tactical arrangement of the away team, e.g. 4-3-3</li> <li><strong>away_lineup</strong>: List of players in the home team's starting lineup</li> <li><strong>away_bench</strong>: List of substitute players of the away team</li> </ul> <p><strong>Event</strong>: contains information that defines each of the relevant actions that occur during a soccer match. Events can be described by the following attributes:</p> <ul> <li><strong>player</strong>: Player identifier. Relative URL</li> <li><strong>team</strong>: Team of the player who participates in the event</li> <li><strong>minute</strong>: Minute of the match in which the event occurs</li> <li><strong>type</strong>: Event type (Enumeration)</li> </ul> <p><strong>Players</strong>: Player information:</p> <ul> <li><strong>name</strong>: First name</li> <li><strong>fullname</strong>: Player's full name</li> <li><strong>dob</strong>: Date of birth</li> <li><strong>country</strong>: Nationality</li> <li><strong>position</strong>: Position the player usually occupies: GOA (GoalKeeper), DF (Defender), MID (Midfielder), STR (Striker)</li> <li><strong>foot</strong>: Dominant Foot: Right-footed, Left-footed, Two-footed, Unknown</li> <li><strong>weight</strong>: Weight of player in kilograms</li> <li><strong>height</strong>: Player height in centimeters</li> <li><strong>elo</strong>: Measurement of the player's skills on a scale of 1 to 100</li> <li><strong>potential</strong>: Estimate of the maximum ELO that a player can reach on a scale of 1 to 100.</li> <li><strong>href</strong>: Relative URL of the player's record</li> </ul>
Reflection Of Video Assistant Referee Technology on Professional Football Discipline Board Decisions
<p>The aim of this study is to understand how Video Assistant Referee (VAR) technology is reflected in the decisions of Professional Football Disciplinary Board (PFDK) and to examine whether this technology creates any change. In accordance with this purpose, comparison of disciplinary violations detected in Super League competitions in general and direct send-off decisions to PFDK decisions was carried out within the scope of 2018-2019 and 2019-2020 seasons, the first two seasons where VAR technology was used in Super League, and 2016-2017 and 2017-2018 seasons, the last two seasons where VAR technology was not used. PFDK decisions for four seasons between 2016-2020 were collected by document analysis.</p>
footballing-nations
footballing-nations <blockquote> <p>relies on <a href="https://github.com/sscu-budapest/datazimmer">datazimmer</a> and the <a href="https://sscu-budapest.github.io/tooling">tooling</a> of sscub</p> </blockquote>
Dataset for Project T2EDK-04581: DFVA (Deep Football Video Analytics)
<p>This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call "RESEARCH-CREATE-INNOVATE", project DFVA (Deep Football Video Analytics, project code: T2EDK-04581)</p>
Football boot made by Karhu
Football boots in the 1940's had normally six studs. In Karhu's boots each stud was usually attached with four nails, but in this pair only three nails were used. Finnish national team players used this type of boots in the 1930's and 40's. Source: Objaverse 1.0 / Sketchfab
Muscle Injury RTP Design in Football; Effects of Backward Design vs Forward Design.
ClinicalTrials.gov study NCT07012408. IPD Sharing: YES. Countries: 1. Publications: 26.
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.