The data is taken from kaggle's Kobe Bryant Shot Selection competition :
https://www.kaggle.com/c/kobe-bryant-shot-selection
I would like to credit the following kernels that helped me alot with my analysis:
- https://www.kaggle.com/khozzy/kobe-bryant-shot-selection/kobe-shots-show-me-your-best-model
- https://www.kaggle.com/selfishgene/kobe-bryant-shot-selection/psychology-of-a-professional-athlete
- https://www.kaggle.com/apapiu/kobe-bryant-shot-selection/exploring-kobe-s-shots
Kobe Bryant marked his retirement from the NBA by scoring 60 points in his final game as a Los Angeles Laker on Wednesday, April 12, 2016. Drafted into the NBA at the age of 17, Kobe earned the sport’s highest accolades throughout his long career.
Using 20 years of data on Kobe's swishes and misses, can you predict which shots will find the bottom of the net? This competition is well suited for practicing classification basics, feature engineering, and time series analysis. Practice got Kobe an eight-figure contract and 5 championship rings. What will it get you?
This data contains the location and circumstances of every field goal attempted by Kobe Bryant took during his 20-year career.The task is to predict whether the basket went in (shot_made_flag).
To avoid leakage, your method should only train on events that occurred prior to the shot for which you are predicting!
The field names are self explanatory and contain the following attributes:
action_type
combined_shot_type
game_event_id
game_id
lat
loc_x
loc_y
lon
minutes_remaining
period
playoffs
season
seconds_remaining
shot_distance
shot_made_flag (this is what I want to predict)
shot_type
shot_zone_area
shot_zone_basic
shot_zone_range
team_id
team_name
game_date
matchup
opponent
shot_id
1)Data Preprocessing
2)Feature Selection
3)Model Evaluation using KFold cross-validation
4)Trained models and evaluated them( Logistic Regression, KNN Classifier, Decision Trees, Random Forests, ensembles ... )
5)Tuned the hyperparameters of the most promising models to improve my results
6)Exploratory Data Analysis answering the questions:
- What is Kobe's FG% and how many shots did he take?
- Can we plot Kobe's shot attempts and accuracy as the game progresses?
- Can we visualize based on location if distance from the basket, court zone, shot type etc. makes a difference in shot accuracy?
- Can we plot out Kobe's accuracy based on the different features in our dataset?