DEVELOPMENT... { "data_id": "43464", "name": "UFC-Fights-(2010---2020)-with-Betting-Odds", "exact_name": "UFC-Fights-(2010---2020)-with-Betting-Odds", "version": 1, "version_label": "v1.0", "description": "Context\nThere are some great UFC datasets out there, but I could not find one that included gambling odds. So I went and made one myself. This dataset focuses very generally on the fights and hopes to be able to draw very broad conclusions. More a more in depth statistical fight analysis I would recommend Rajeev Warrier's excellent datasetwhich was the inspiration for my work. \nContent\nThis dataset consists of 11 columns of data with basic information about every match that took place between March 21, 2010 and March 14, 2020.\nColumn Definitions:\nR_fighter and B_fighter: The names of the fighter in the red corner and the fighter in the blue corner\nR_odds and B_odds: The American odds of the fighter winning. \ndate: The date of the fight\nlocation: The location of the fight\ncountry: The country the fight occurred in\nWinner: The winner of the fight ('Red' or 'Blue')\ntitle_bout: Was this fight a title bout? ('True' or 'False')\nweight_class: What weight class did this fight occur at?\ngender: Male or Female\nAcknowledgements\nI was inspired by the work of Rajeev Warrier\nWant More?\nMy work, including a scraper to help gather data for upcoming events, can be found on my GitHub. I promise I'll add more documentation soon.", "format": "arff", "uploader": " Lowe", "uploader_id": 30126, "visibility": "public", "creator": null, "contributor": null, "date": "2022-03-23 13:23:15", "update_comment": null, "last_update": "2022-03-23 13:23:15", "licence": "Attribution 4.0 International (CC BY 4.0)", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/22102289\/dataset", "default_target_attribute": null, "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "UFC-Fights-(2010---2020)-with-Betting-Odds", "Context There are some great UFC datasets out there, but I could not find one that included gambling odds. So I went and made one myself. This dataset focuses very generally on the fights and hopes to be able to draw very broad conclusions. More a more in depth statistical fight analysis I would recommend Rajeev Warrier's excellent datasetwhich was the inspiration for my work. Content This dataset consists of 11 columns of data with basic information about every match that took place between Mar " ], "weight": 5 }, "qualities": { "NumberOfInstances": 5528, "NumberOfFeatures": 11, "NumberOfClasses": null, "NumberOfMissingValues": 14168, "NumberOfInstancesWithMissingValues": 1288, "NumberOfNumericFeatures": 2, "NumberOfSymbolicFeatures": 1, "PercentageOfSymbolicFeatures": 9.090909090909092, "AutoCorrelation": null, "PercentageOfNumericFeatures": 18.181818181818183, "PercentageOfMissingValues": 23.299565846599133, "PercentageOfInstancesWithMissingValues": 23.299565846599133, "PercentageOfBinaryFeatures": 9.090909090909092, "NumberOfBinaryFeatures": 1, "MinorityClassSize": null, "MinorityClassPercentage": null, "MajorityClassSize": null, "MajorityClassPercentage": null, "Dimensionality": 0.0019898697539797393 }, "tags": [], "features": [ { "name": "R_fighter", "index": "0", "type": "string", "distinct": "1217", "missing": "1288" }, { "name": "B_fighter", "index": "1", "type": "string", "distinct": "1400", "missing": "1288" }, { "name": "R_odds", "index": "2", "type": "numeric", "distinct": "289", "missing": "1288", "min": "-1700", "max": "775", "mean": "-118", "stdev": "273" }, { "name": "B_odds", "index": "3", "type": "numeric", "distinct": "295", "missing": "1288", "min": "-1200", "max": "1300", "mean": "69", "stdev": "252" }, { "name": "date", "index": "4", "type": "string", "distinct": "362", "missing": "1288" }, { "name": "location", "index": "5", "type": "string", "distinct": "144", "missing": "1288" }, { "name": "country", "index": "6", "type": "string", "distinct": "27", "missing": "1288" }, { "name": "Winner", "index": "7", "type": "string", "distinct": "3", "missing": "1288" }, { "name": "title_bout", "index": "8", "type": "nominal", "distinct": "2", "missing": "1288", "distr": [] }, { "name": "weight_class", "index": "9", "type": "string", "distinct": "13", "missing": "1288" }, { "name": "gender", "index": "10", "type": "string", "distinct": "2", "missing": "1288" } ], "nr_of_issues": 0, "nr_of_downvotes": 0, "nr_of_likes": 0, "nr_of_downloads": 0, "total_downloads": 0, "reach": 0, "reuse": 0, "impact_of_reuse": 0, "reach_of_reuse": 0, "impact": 0 }