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KNICKS NIX LOSING

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SUMMARY

With the current Knicks, I have hope that that can be a contender for the NBA championship. 
How can they improve their chances even more ?
With the use of data aggregation techniques, I looked at data from different angles and sources, to find attributes that contribute to how well teams perform.
Using these attributes, we can find the ones that contribute most to winning games and playoff chances.
I created models to find the attribute that leads to greater playoff chances.
Using this data, we can also look at the current roster of Knicks players and find trades with players that have higher performance with equal or lower salaries.

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DATA

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TECHNIQUES AND LIBRARIES

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This project was completed in Python and uses many of its libraries.
Pandas was used for aggregating and manipulating data. 
Sklearn was used to scale dataframes for visualization, as well as to create logistic regression models.
Matplotlib and seaborn was used for data visualization.

RELATIVE STATISTICS OF EACH TEAM IN THE 2020-2021 SEASON

Data is scaled from 0 to 1, with 1 meaning the team had the highest rating for that statistic last season.

Relative Team Statistics.jpg
Team Stats Correlations.png

CORRELATIONS BETWEEN SELECT STATS AND WINS

Results of Calculating the Pearson Correlation Coefficient

REGRESSION LINE OF WINS RELATIVE TO OFFENSIVE RATING

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Regression Line.png
Team Efficiency Score.png

TEAM EFFICIENCY RATINGS

Calculated by summing the PER's of players on each time, multiplied by their respective average minutes per game.

KNICK PLAYERS PER

A visualization of the PER's of Knicks players

Knicks PER.png
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TRADES

Using the data acquired from above and the salaries of NBA players around the league, trades with underperforming Knicks for players that increase the overall Offensive Rating of the Knicks will improve their chances of making the playoffs. You have to make the playoffs before you even consider winning the championship!

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