NBA Monte Carlo Simulation - Revision history

Prin03vse: Prin03vse moved page Simulace.info:NBA Monte Carlo Simulation to NBA Monte Carlo Simulation

2023-06-21T00:03:38Z

Prin03vse moved page Simulace.info:NBA Monte Carlo Simulation to NBA Monte Carlo Simulation

Prin03vse: Prin03vse moved page User:Prin03vse to Simulace.info:NBA Monte Carlo Simulation

2023-06-21T00:03:24Z

Prin03vse moved page User:Prin03vse to Simulace.info:NBA Monte Carlo Simulation

Prin03vse at 00:00, 21 June 2023

2023-06-21T00:00:57Z

Prin03vse at 00:00, 21 June 2023

2023-06-21T00:00:41Z

Prin03vse at 00:00, 21 June 2023

2023-06-21T00:00:15Z

Prin03vse at 23:57, 20 June 2023

2023-06-20T23:57:26Z

Prin03vse at 23:56, 20 June 2023

2023-06-20T23:56:37Z

Prin03vse at 23:55, 20 June 2023

2023-06-20T23:55:40Z

Prin03vse at 23:54, 20 June 2023

2023-06-20T23:54:46Z

Prin03vse at 23:53, 20 June 2023

2023-06-20T23:53:04Z

@@ Line 21: / Line 21: @@
 ==Running the application==
 '''Application description
-'''*
+'''
 After opening .ipynb file, click "Run" on the menu above.
 Under the cell, you will see 2 input fields. Please, provide the path of Excel file (from this page) and sheet name (default is Data_final)

@@ Line 21: / Line 21: @@
 ==Running the application==
 '''Application description
-'''
+'''*
 After opening .ipynb file, click "Run" on the menu above.
 Under the cell, you will see 2 input fields. Please, provide the path of Excel file (from this page) and sheet name (default is Data_final)

@@ Line 72: / Line 72: @@
 Requirements (txt):
 ==Data sources==
 https://edraft.com/nba/fantasy-basketball/tools/player-consistency/?season=2022-2023&col1=0&col2=0&con=0
 https://www.basketball-reference.com/leagues/NBA_2023_per_game.html

@@ Line 64: / Line 64: @@
 All the documents can be downloaded here.
 Simulation (ipynd):
 Data source (xlsx):
 [[File:NBA PO Simulation Data.xlsx]]
 Requirements (txt):

@@ Line 66: / Line 66: @@
 Data source (xlsx):
+[[File:NBA PO Simulation Data.xlsx]]
 Requirements (txt):

← Older revision	Revision as of 00:03, 21 June 2023
(No difference)

@@ Line 59: / Line 59: @@
 ==Conclusion==
-To sum up, I would like to say that the simulation is reliable and relates to reality. There can be some improvements from both UI and backend sides, but otherwise the aim of the simulation is completed.
+To sum up, I would like to say that the simulation is reliable and relates to reality. There can be some improvements from both UI and backend sides, but otherwise the aim of the simulation is completed, and the application can be used for real life predictions.
 ==Files==

@@ Line 25: / Line 25: @@
 Under the cell, you will see 2 input fields. Please, provide the path of Excel file (from this page) and sheet name (default is Data_final)
-[[File:Main_menu.jpg]]
+[[File:Main menu.JPG]]
 After clicking "Confirm", user will be offered with 3 variants: get data analysis, simulate one game or simulate playoffs.

@@ Line 10: / Line 10: @@
 . run jupyter notebook
 . on opened window, click new -> terminal
-[[File:Tree.jpg]]
 . in terminal, paste in order:
        - pip install pandas
@@ Line 18: / Line 20: @@
 ==Running the application==
-'''Application description'''
+'''Application description
 After opening .ipynb file, click "Run" on the menu above.
 Under the cell, you will see 2 input fields. Please, provide the path of Excel file (from this page) and sheet name (default is Data_final)
 [[File:Main_menu.jpg]]
 After clicking "Confirm", user will be offered with 3 variants: get data analysis, simulate one game or simulate playoffs.
 If user chooses analysis, some graphs with tables will be generated. In case one game simulation, user can choose 2 teams and simulate the result for them. In case simulating the Playoffs, user will be able to select bracket by seeds and simulate till NBA Finals.
-'''Pre-Analysis'''
+'''Pre-Analysis
 Based on the final data, we can say that there is a direct relationship between PPG and MPG. This simulation is based on 2 random variables: scoring rate and missed game. The first is calculated using the PPG / MPG formula with a standard variation, and the second as a percentage. Therefore, we can say that the team's success directly depends on 2 main aspects: the higher the scoring rate and MPG and the more games a player played in a season, the more likely it is to win. The essence of the second aspect concludes in the fact that in modern basketball, player injuries, as well as the composition of the rotation, play a significant role. And in this case, I see the possibility of improving the program in that if a rotation player is injured, the substitute player gets more chances to enter the floor and minutes per game.
-'''Simulation'''
+'''Simulation
 As already mentioned, there are 2 random variables: scoring rate (with standard deviation) and game missing chance (to make games more chaotic, as real ones).
 Firstly, it calculates the percentage of player to miss the game - random between 0 and 100. If players game participation statistic is more or equal to this variable, then a player will be in rotation. Then, for those, who are active, the program calculates scoring rate - draws random samples from Gaussian distribution. In the application, you should also input parameter n - how many times the simulation should be repeated. And final result is got by averaging team scores (e.g. 2 times - 110, 120, final score 115).
 ==Results==
-'''Post-Analysis'''
+'''Post-Analysis
 After many combinations and many tries, I can prove my words from pre-analysis, that simulation highly depends on scoring rate and possibility of loosing key players.
 The top 5 teams I got as NBA champions are:
@@ Line 44: / Line 52: @@
 This year (2022/2023) Playoffs were quite surprise, because number 8 seed Miami Heat went to NBA Finals, but they never appeared during my simulation testing as NBA champions. Also, Milwaukee Bucks, who were belived as highly possible NBA champions, lost in the first round. Otherwise, Denver Nuggets came as NBA champions much more times then others in the simulation, which can mean that the model can really represent the reality.
-'''Improvements'''
+'''Improvements
 From UI/UX point of view, I tried to divide the logical parts of running the app. Unfortunately, due to lack of times and jupyter notebook abilities, there were no chance to make truly user friendly application for analysis. Of course, users see all the details from output, but I believe it would be more convenient in e.g. Flask Web Application.