(WIP) Kaggle competition: 1st and Future - Player Contact Detection

Detect Player Contacts from Sensor and Video Data

First step: Using sensor data

Each player can be, at any time, in contact with a variable number of player, the ground or nothing.

The simplest way to get started is to use only the sensor data, since they are tabular. Also it is apparently the solution currently used so it must be possible.

To predict a contact between players, we clearly need to use the sensor data for both of them: for instance a sustained contact probably implies a low relative speed. It is also likely that we want to predict the entire sequence of contact between two players at once, as the presence (or absence) of a contact at a given time is likely to affect the probability of a contact right after.

Submission 0

Features: No feature

Model: Dummy classifier: the contact is 1 with probability the mean value of the column contact in the train_labels.csv file

Score: 0.0

Submission 1

Feature:

• distance between players

Model:

• Decision tree or SVM with gridsearch

Cross validation strategy

• Contacts are likely to have some have amount of auto-correlation, meaning that if a player is in contact (resp. not in contact) with another player. If that is true, this suggests we should use K-fold cross validation with game_play as a group
• contacts are overall relatively rare: ~1.4% of all allowable contacts in the training set. If we use grouped K-fold cross-validation, we should probably consider insuring that contacts are not completely absent of some of the folds. If we train a binary classifier, it seems like a Stratified K-Folds with non-overlapping groups strategy is the most appropriate (StratifiedGroupKFold)

Submission 1

Features:

• distance between players
• relative speed between players

Model: Decision Tree classifier, no hyperparameter tuning

Score: 0.373

Submission 2

Features:

• distance between players
• relative speed between players
• are the players in the same team (boolean)
• is contact with ground (boolean)

Model: Decision Tree classifier, no hyperparameter tuning

Score: 0.442

Submission 3

Features:

• distance between players
• relative speed between players
• are the players in the same team (boolean)
• is contact with ground (boolean)
• raw features: x_position, y_position, speed, direction, orientation, acceleration, sa

Model: XGBoost, Optuna

Score: 0.594

Submission 4

Features:

• distance between players
• relative speed between players
• are the players in the same team (boolean)
• is contact with ground (boolean)
• raw features: x_position, y_position, speed, direction,
  sa
• made cyclical features out of: orientation, acceleration
• cos(orientation_<n> - directio_<n>_) for single players
• cos(orientation_1 - orientation_2) for two players contacts

Model: XGBoost, Optuna

Score: 0.594

Submission 5

same as #4 but using matthews_corrcoeff for scoring

Score: 0.599

Submission 6

Increasing number of cross validation splits from 3 to 5

Score: 0.597 not helping

Submission 7

using scale_pos_weight parameters to handle class imbalance surprisingly causes huge degradation in performance... 🤔

Score: 0.465

Submission 8

we try again but with 'roc_auc' scoring

Score: ....

Submission xxx

Optuna search with small number of trials seem to show that:

• creating an independent feature pipeline for ground contact and one for player contact doesn't improve performance
• creating two models, one for player contact and one for ground contact seems to help

Score: