I'm creating a recommendation system of football players based on stats of each player (number of passes, crosses, shots, tackles, etc ...) and I have already tried with a Content based recommender. Even though the results are ok, I want to try to apply a collaborative filtering. What I want to try is to recommend a player to a specific team and so far what i have thought is to create an implicit feedback system where i create an abstract …
In 2018 the NCAA allowed players to transfer school more freely, so now teams want an advantage by getting transfer players. I want to create a model that can predict which transfer players can be good if they transfer to my favorite team. Unfortunately, due to various constraints like Covid kicking out the 2020 season, and NCAA rules, the number of samples where a player has had play time for an old team and then transferred to a Power 5 …
I am trying to create a machine learning model to predict the winner of an upcoming cricket tournament (winners of all matches in the tournament). I have couple of questions here: What kind of input data can I use for training? I can't use information like who won the toss, how much did the teams score in each innings, etc because I wouldn't be having those data for the final prediction dataset What kind of algorithms should I be looking …
I am trying to use LSTMs to predict player positions in a field game. I try to overfit 8 slightly different time series. For this overfitting task I just use the positions of the players. A data sample looks like this: [Blue are the player trajectories corresponding to the green targets I want to predict, while yellow are the enemies and red is the ball] When trying to overfit 8 future positions for a single player ( one single player …
I have a variety of NFL datasets that I think might make a good side-project, but I haven't done anything with them just yet. Coming to this site made me think of machine learning algorithms and I wondering how good they might be at either predicting the outcome of football games or even the next play. It seems to me that there would be some trends that could be identified - on 3rd down and 1, a team with a …
I want to compare ratings of players from different leagues and predict rating of player in a league he/she didnt participate in. Rating of a player is estimated within a league where he was playing. There are some cross-observations which are players that have ratings estimated for more then one league. For example there is a player P1 who has a rating 40 in league L1 and rating 55 in league L2. As you can see at the picture. There …
I am currently playing around with Keras and try to use it with various datasets. Now I have a small datasets of football game results. date, home_team, away_team, goals_home_team, goals_away_team Predicting the goals is probably too hard so I combined them into a single feature outcome (win, draw, loss). date, home_team, away_team, outcome Using random forest or decision trees, I could simply leave the teams as they are but for a NN I need some encoding. Here is my problem. …
I've recently become interested in possibly of developing some sort of method for ranking athletes of sports such as American football and determining which players are better than others in terms of specific statistics. My thoughts are that there are two ways to go about doing this. The first would be some sort of mathematical formula which would take in the statistics of a given player and provide some sort of standardized score which could be compared with other players …