Kaggle’s [March Machine Learning Mania](https://www.kaggle.com/c/march-machine-learning-mania-2016) competition challenged data scientists to predict winners and losers of the men's 2016 NCAA basketball tournament. This dataset contains the 1070 selected predictions of all Kaggle participants. These predictions were collected and locked in prior to the start of the tournament. How can this data be used? You can pivot it to look at both Kaggle and NCAA teams alike. You can look at who will win games, which games will be close, which games are hardest to forecast, or which Kaggle teams are gambling vs. sticking to the data. [![First round predictions][1]](https://www.kaggle.com/wcukierski/d/wcukierski/2016-march-ml-mania/official-first-round-predictions) *The NCAA tournament is a single-elimination tournament that begins with 68 teams. There are four games, usually called the “play-in round,” before the traditional bracket action starts. Due to competition timing, these games are included in the prediction files but should not be used in analysis, as it’s possible that the prediction was submitted after the play-in round games were over.* ## Data Description Each Kaggle team could submit up to two prediction files. The prediction files in the dataset are in the 'predictions' folder and named according to: > TeamName\_TeamId\_SubmissionId.csv The file format contains a probability prediction for every possible game between the 68 teams. This is necessary to cover every possible tournament outcome. Each team has a unique numerical Id (given in Teams.csv). Each game has a unique Id column created by concatenating the year and the two team Ids. The format is the following: > Id,Pred > 2016\_1112\_1114,0.6 > 2016\_1112\_1122,0 > ... The team with the lower numerical Id is always listed first. “Pred” represents the probability that the team with the lower Id beats the team with the higher Id. For example, "2016\_1112\_1114,0.6" indicates team 1112 has a 0.6 probability of beating team 1114. For convenience, we have included the data files from the 2016 March Mania competition dataset in the Scripts environment (you may find TourneySlots.csv and TourneySeeds.csv useful for determining matchups, see [the documentation][2]). However, the focus of this dataset is on Kagglers' predictions. [1]: https://www.kaggle.io/svf/183298/a13d9a2275f63f167b9c1d4b25330646/kaggle_first_round_2016.png [2]: https://www.kaggle.com/c/march-machine-learning-mania-2016/data