1. NBA Analytics
Zyrus Johnson

2. Problem Inspiration

3. Problem Description
Create an algorithm that predicts NBA player stats for an upcoming game
Factor things such as pace and defensive statistics in order to get as good of an estimate as possible

4. Formal description defStats(defTeam, offTeam, offPlayer)
Defensive and offensive teams are lists including defensive rating, pace, steals, and defensive rebounding percentage
Offensive player is a list of points, rebounds, assists, steals, and turnovers

5. Challenges This is a calculation that does not happen in real time
Statistics vary and there is no way to calculate things such as motivation
Accuracy is difficult like predicting NCAA tournament games

6. Algorithm types involved
Brute Force - For each new player there were two lists from different websites to synthesize. This was done by going alphabetically through players and analyzing them
Dynamic/Constrained-This problem was constrained by using the first list (all the players in the NBA) and returning only those who played 20 or more minutes a game. Dynamically limiting the sample made the problem easier and more accurate

7. Results
This is like a limit problem since it’s impossible to get to the exact correct answer
This program gives the statistics within one standard deviation

8. Question 1
Simply because a problem isn’t complex, doesn’t mean it’s accurate. In a problem such as this, what specific ways would success be measured?
This type of problem would be successful based on accuracy of predicitons.

9. Question 2
A program can either be judged based on accuracy (predictions like the weather) or efficiency (traveling salesman). Give an example of when constraining the problem would make it more accurate or more efficient.
In the TSP problem shortest path gives you less operations than O(N!) which is more efficient.