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10 results for “football leagues”
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>
Champions League 2018-2019. Juventus Football Club
<p>Adjacency matrices of Juventus and its rivals in the 2018-2019 Champions League.</p> <p> </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>
Football is becoming more predictable: Network analysis of 88 thousand matches in 11 major leagues
<p>In recent years excessive monetization of football and professionalism among the players has been argued to have affected the quality of the match in different ways. On the one hand, playing football has become a high-income profession and the players are highly motivated; on the other hand, stronger teams have higher incomes and therefore afford better players leading to an even stronger appearance in tournaments that can make the game more imbalanced and hence predictable. To quantify and document this observation, in this work we take a minimalist network science approach to measure the predictability of football over 26 years in major European leagues. We show that over time, the games in major leagues have indeed become more predictable. We provide further support for this observation by showing that inequality between teams has increased and the home-field advantage has been vanishing ubiquitously. We do not include any direct analysis on the effects of monetization on football's predictability or therefore, lack of excitement, however, we propose several hypotheses which could be tested in future analyses.</p>
Football is becoming more predictable: Network analysis of 88 thousand matches in 11 major leagues
Open the record for dataset details and reuse information.
Validity and Reliability of the Upper Extremity Star Balance Test in Amateur League American Football Players
ClinicalTrials.gov study NCT05903248. IPD Sharing: NO. Countries: 0. Publications: 1.
Big 5 European football leagues: team and player stats
<p>Extracció sobre jugadors i equips de futbol de la pàgina fbref.com. La selecció de dades s'ha basat en 11 temporades (de la 2010-2011 fins la 2020-2021 incloses) de les 5 grans lligues d'aquest esport: espanyola, anglesa, francesa, italiana i alemanya.</p> <p>Publicat sota la llicència CC BY-NC-SA 4.0 License.</p>
Sleep Bootcamp: A Pilot Tele-Sleep Program for Former National Football League (NFL) Players
ClinicalTrials.gov study NCT04159233. IPD Sharing: NO. Countries: 1. Publications: 0.
Smart Textile Solutions as Biofeedback Method for Injury Prevention for Latvian Football Youth League Players
ClinicalTrials.gov study NCT06551454. IPD Sharing: NO. Countries: 1. Publications: 0.
Environmental temperatures in the Australian and German professional football leagues.
<p>This is environmental data for each match of the German Bundesliga (seasons 2014-21) and Australian A-League (seasons 2016-20). </p> <p>Environmental conditions in the form of temperature and WBGT were collated retrospectively for each match. Whereas temperature refers to the commonly known and easily accessible ambient air temperature, WBGT is a feels-like temperature adding the influence of relative humidity, wind, and solar radiation, for a more detailed interpretation of the observed heat stress. The use, advantages, and disadvantages of WBGT have been described extensively in previous research.<sup>1-3</sup> Despite its widespread use, the black globe temperature (radiative heat gain) and natural wet-bulb temperature (evaporative heat loss) measurements are criticized as not representing human thermoregulation, thereby underestimating heat stress in many settings.<sup>1,4</sup> It should also be mentioned, that WBGT is a heat stress index and is not validated for colder conditions. Therefore, to interpret the effects of colder environments on injury occurrence temperature was also used in our analyses. Although more modern and sophisticated thermal indexes exist <sup>4,5</sup>, WBGT remains widely used, especially in sports federation heat policies. Specifically, this index is also used in the heat policy introduced by FIFA, which recommends the use of drinking breaks at 32 °C WBGT<sup>6</sup>.</p> <p>For Bundesliga matches, weather data was obtained from Meteostat.net.<sup>7</sup> This is an open-source service, providing hourly meteorological data for any given coordinates. Data is obtained as a weighted interpolation depending on the distance and elevation difference from the four closest weather stations to a geological location. They provide the following data: temperature, relative humidity, dew point, wind speed, air pressure, total precipitation, and the current weather condition. Based on this, WBGT can be estimated in a variety of ways according to previous research.<sup>2</sup> We used the estimation developed by Liljegren et al. (2008).<sup>3</sup> This is validated and reliable in different environmental settings and is described as the best estimate for WBGT from different methods.<sup>8</sup> The R code needed to implement these calculations has been provided and used in previous research.<sup>9</sup> Wind speed was assumed to be a minimum of 1 m/s, as moving players generate airflow of at least equivalent to that. Solar radiation was estimated using the solar angle at the time and location of the match<sup>10</sup>. As Meteostat.net provides hourly data, two time points (the kick-off time and one hour later) were used per match and averaged. If the match did not start at a full hour, but at 15 or 30 minutes past the hour, the previous full hour was used as a starting point and the following hour as a second time point. For A-League matches, environmental conditions were provided by UBIMET.com.<sup>11</sup> This commercial provider uses artificial intelligence and data input from multiple weather stations, radar, and satellite data, to estimate meteorological data at given ground locations. They provide temperature, relative humidity, solar radiation, and WBGT measurements for the starting times of the first and second half, which were then averaged to create one value per match. To validate the WBGT data based on Meteostat.net data, the WBGT estimation method used for the Bundesliga data was also performed with the A-League data. As internal validation, results were then compared to the WBGT reported from UBIMET.com. There was a very good linear association (correlation coefficient r = 0.93).</p> <p>1. Brocherie F, Millet G. Is the Wet-Bulb Globe Temperature (WBGT) Index Relevant for Exercise in the Heat? . <em>Sports Med</em>. 2015;45:1619-1621.</p> <p>2. Lemke B, Kjellstrom T. Calculating Workplace WBGT from Meteorological Data: A Tool for Climate Change Assessment. <em>Ind Health</em>. 2012;50:267-278.</p> <p>3. Liljegren J, Carhart RA, Lawday P, Tschopp S, Sharp R. Modelling the Wet Bulb Globe Temperature Using Standard Meteorological Measurements. <em>J Occup Environ Hyg</em>. 2008;5(10):645-655.</p> <p>4. Blazejczyk K, Epstein Y, Jendritzky G, Staiger H, Tinz B. Comparison of UTCI to selected thermal indicies. <em>Int J Biometeorol</em>. 2012;56:515-535. doi:<a href="https://doi.org/10.1007/s00484-011-0453-2">https://doi.org/10.1007/s00484-011-0453-2</a></p> <p>5. Jendritzky G, de Dear R, Havenith G. UTCI - Why another thermal index? <em>Int J Biometeorol</em>. 2012;56:421-428. doi:<a href="https://doi.org/10.1007/s00484-011-0513-7">https://doi.org/10.1007/s00484-011-0513-7</a></p> <p>6. Brown H, Chalmers S, Topham T, et al. Efficacy of the FIFA cooling break heat policy during an intermittent treadmill football simulation in hot conditions in trained males. <em>Br J Sports Med</em>. 2024;doi:10.1136/bjsports-2024-108131</p> <p>7. Meteostat.net. The Weather’s Record Keeper. <a href="https://meteostat.net/en/">https://meteostat.net/en/</a></p> <p>8. Patel T, Mullen SP, Santee WR. Comparison of Methods for Estimating Wet-Bulb Globe Temperature Index From Standard Meteorological Measurements. <em>Military Medicine</em>. 2013;178(8):926-933.</p> <p>9. <em>HeatStress</em>. Casanueva, A; 2019. <a href="https://zenodo.org/records/3264930">https://zenodo.org/records/3264930</a></p> <p>10. Duffie J, Beckman W. <em>Solar Engineering of Thermal Processes</em>. 4th ed. John Wiley & Sons, Inc.; 2013.</p> <p>11. UBIMET GmbH. UBIMET WEATHER MATTERS. <a href="https://www.ubimet.com/en/">https://www.ubimet.com/en/</a> </p>
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